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\n \n\n \n \n \n \n \n \n Biomimetic model of corticostriatal micro-assemblies discovers new neural code.\n \n \n \n \n\n\n \n Pathak, A.; Brincat, S. L.; Organtzidis, H.; Strey, H. H.; Senneff, S.; Antzoulatos, E. G.; Mujica-Parodi, L. R.; Miller, E. K.; and Granger, R.\n\n\n \n\n\n\n July 2024.\n Pages: 2023.11.06.565902 Section: New Results\n\n\n\n
\n\n\n\n \n \n $\"Biomimetic$ Paper\n \n \n \n $\"Biomimetic$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@misc{pathak_biomimetic_2024,\n\ttitle = {Biomimetic model of corticostriatal micro-assemblies discovers new neural code},\n\tcopyright = {© 2024, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},\n\turl = {https://www.biorxiv.org/content/10.1101/2023.11.06.565902v3},\n\tdoi = {10.1101/2023.11.06.565902},\n\tabstract = {Although computational models have deepened our understanding of neuroscience, it is still highly challenging to link actual low-level physiological activity (spiking, field potentials) and biochemistry (transmitters and receptors) with high-level cognitive abilities (decision-making, working memory) nor with corresponding disorders. We introduce an anatomically-organized multi-scale model directly generating simulated physiology from which extended neural and cognitive phenomena emerge. The model produces spiking, fields, phase synchronies, and synaptic change, directly generating working memory, decisions, and categorization, all of which were then validated on extensive experimental macaque data from which the model received zero prior training of any kind. Moreover, the simulation uncovered a previously unknown neural code specifically predicting upcoming erroneous (“incongruous”) behaviors, also subsequently confirmed in empirical data. The biomimetic model thus directly and predictively links novel decision and reinforcement signals, of computational interest, with novel spiking and field codes, of potential behavioral and clinical relevance.},\n\tlanguage = {en},\n\turldate = {2024-08-05},\n\tpublisher = {bioRxiv},\n\tauthor = {Pathak, Anand and Brincat, Scott L. and Organtzidis, Haris and Strey, Helmut H. and Senneff, Sageanne and Antzoulatos, Evan G. and Mujica-Parodi, Lilianne R. and Miller, Earl K. and Granger, Richard},\n\tmonth = jul,\n\tyear = {2024},\n\tnote = {Pages: 2023.11.06.565902\nSection: New Results},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/SB26GINP/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Brain signaling becomes less integrated and more segregated with age.\n \n \n \n \n\n\n \n Razban, R. M.; Antal, B. B.; Dill, K. A.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n Network Neuroscience,1–36. May 2024.\n \n\n\n\n
\n\n\n\n \n \n $\"Brain$ Paper\n \n \n \n $\"Brain$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{razban_brain_2024,\n\ttitle = {Brain signaling becomes less integrated and more segregated with age},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {2472-1751},\n\turl = {https://doi.org/10.1162/netn_a_00389},\n\tdoi = {10.1162/netn_a_00389},\n\tabstract = {The integration-segregation framework is a popular first step to understand brain dynamics because it simplifies brain dynamics into two states based on global vs. local signaling patterns. However, there is no consensus for how to best define what the two states look like. Here, we map integration and segregation to order and disorder states from the Ising model in physics to calculate state probabilities, Pint and Pseg, from functional MRI data. We find that integration/segregation decreases/increases with age across three databases, and changes are consistent with weakened connection strength among regions rather than topological connectivity based on structural and diffusion MRI data.The integration-segregation framework succinctly captures the tradeoff brains face between seamless function (more integration) in light of energetic constrains (more segregation). Despite its ubiquitous use in the field, there is no consensus on its definition with various graph theoretical properties being proposed. Here, we define the two states based on the underlying mechanism of neuronal coupling strength to provide a physical foundation for the framework. We find that younger adults’ brains are close to perfectly balancing between integration and segregation, while older adults’ brains veer off towards random signaling.},\n\turldate = {2024-06-24},\n\tjournal = {Network Neuroscience},\n\tauthor = {Razban, Rostam M. and Antal, Botond B. and Dill, Ken A. and Mujica-Parodi, Lilianne R.},\n\tmonth = may,\n\tyear = {2024},\n\tpages = {1--36},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/NGHTQGZD/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Achieving Occam’s razor: Deep learning for optimal model reduction.\n \n \n \n \n\n\n \n Antal, B. B.; Chesebro, A. G.; Strey, H. H.; Mujica-Parodi, L. R.; and Weistuch, C.\n\n\n \n\n\n\n PLOS Computational Biology, 20(7): e1012283. July 2024.\n Publisher: Public Library of Science\n\n\n\n
\n\n\n\n \n \n $\"Achieving$ Paper\n \n \n \n $\"Achieving$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{antal_achieving_2024,\n\ttitle = {Achieving {Occam}’s razor: {Deep} learning for optimal model reduction},\n\tvolume = {20},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1553-7358},\n\tshorttitle = {Achieving {Occam}’s razor},\n\turl = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012283},\n\tdoi = {10.1371/journal.pcbi.1012283},\n\tabstract = {All fields of science depend on mathematical models. Occam’s razor refers to the principle that good models should exclude parameters beyond those minimally required to describe the systems they represent. This is because redundancy can lead to incorrect estimates of model parameters from data, and thus inaccurate or ambiguous conclusions. Here, we show how deep learning can be powerfully leveraged to apply Occam’s razor to model parameters. Our method, FixFit, uses a feedforward deep neural network with a bottleneck layer to characterize and predict the behavior of a given model from its input parameters. FixFit has three major benefits. First, it provides a metric to quantify the original model’s degree of complexity. Second, it allows for the unique fitting of data. Third, it provides an unbiased way to discriminate between experimental hypotheses that add value versus those that do not. In three use cases, we demonstrate the broad applicability of this method across scientific domains. To validate the method using a known system, we apply FixFit to recover known composite parameters for the Kepler orbit model and a dynamic model of blood glucose regulation. In the latter, we demonstrate the ability to fit the latent parameters to real data. To illustrate how the method can be applied to less well-established fields, we use it to identify parameters for a multi-scale brain model and reduce the search space for viable candidate mechanisms.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2024-08-05},\n\tjournal = {PLOS Computational Biology},\n\tauthor = {Antal, Botond B. and Chesebro, Anthony G. and Strey, Helmut H. and Mujica-Parodi, Lilianne R. and Weistuch, Corey},\n\tmonth = jul,\n\tyear = {2024},\n\tnote = {Publisher: Public Library of Science},\n\tpages = {e1012283},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/MDD5ZAMA/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Parameter estimation from an Ornstein-Uhlenbeck process with measurement noise.\n \n \n \n \n\n\n \n Carter, S.; Mujica-Parodi, L.; and Strey, H. H.\n\n\n \n\n\n\n July 2024.\n arXiv:2305.13498 [cs, q-bio, stat]\n\n\n\n
\n\n\n\n \n \n $\"Parameter$ Paper\n \n \n \n $\"Parameter$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@misc{carter_parameter_2024,\n\ttitle = {Parameter estimation from an {Ornstein}-{Uhlenbeck} process with measurement noise},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\turl = {http://arxiv.org/abs/2305.13498},\n\tdoi = {10.48550/arXiv.2305.13498},\n\tabstract = {This article aims to investigate the impact of noise on parameter fitting for an Ornstein-Uhlenbeck process, focusing on the effects of multiplicative and thermal noise on the accuracy of signal separation. To address these issues, we propose algorithms and methods that can effectively distinguish between thermal and multiplicative noise and improve the precision of parameter estimation for optimal data analysis. Specifically, we explore the impact of both multiplicative and thermal noise on the obfuscation of the actual signal and propose methods to resolve them. First, we present an algorithm that can effectively separate thermal noise with comparable performance to Hamilton Monte Carlo (HMC) but with significantly improved speed. We then analyze multiplicative noise and demonstrate that HMC is insufficient for isolating thermal and multiplicative noise. However, we show that, with additional knowledge of the ratio between thermal and multiplicative noise, we can accurately distinguish between the two types of noise when provided with a sufficiently large sampling rate or an amplitude of multiplicative noise smaller than thermal noise. Thus, we demonstrate the mechanism underlying an otherwise counterintuitive phenomenon: when multiplicative noise dominates the noise spectrum, one can successfully estimate the parameters for such systems after adding additional white noise to shift the noise balance.},\n\turldate = {2024-08-26},\n\tpublisher = {arXiv (Physical Review E in press)},\n\tauthor = {Carter, Simon and Mujica-Parodi, Lilianne and Strey, Helmut H.},\n\tmonth = jul,\n\tyear = {2024},\n\tnote = {arXiv:2305.13498 [cs, q-bio, stat]},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/EL2RINIA/file/view}\n}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

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\n \n\n \n \n \n \n \n \n D-ꞵ-hydroxybutyrate stabilizes hippocampal CA3-CA1 circuit during acute insulin resistance.\n \n \n \n \n\n\n \n Kula, B.; Antal, B.; Weistuch, C.; Gackière, F.; Barre, A.; Velado, V.; Hubbard, J. M; Kukley, M.; Mujica-Parodi, L. R; and Smith, N. A\n\n\n \n\n\n\n PNAS Nexus, 3(5): 196. 2024.\n \n\n\n\n
\n\n\n\n \n \n $\"D-ꞵ-hydroxybutyrate$ Paper\n \n \n \n $\"D-ꞵ-hydroxybutyrate$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{kula_d--hydroxybutyrate_2024,\n\ttitle = {D-ꞵ-hydroxybutyrate stabilizes hippocampal {CA3}-{CA1} circuit during acute insulin resistance},\n\tvolume = {3},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {2752-6542},\n\turl = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11138115/},\n\tdoi = {10.1093/pnasnexus/pgae196},\n\tabstract = {The brain primarily relies on glycolysis for mitochondrial respiration but switches to alternative fuels such as ketone bodies (KBs) when less glucose is available. Neuronal KB uptake, which does not rely on glucose transporter 4 (GLUT4) or insulin, has shown promising clinical applicability in alleviating the neurological and cognitive effects of disorders with hypometabolic components. However, the specific mechanisms by which such interventions affect neuronal functions are poorly understood. In this study, we pharmacologically blocked GLUT4 to investigate the effects of exogenous KB D-ꞵ-hydroxybutyrate (D-ꞵHb) on mouse brain metabolism during acute insulin resistance (AIR). We found that both AIR and D-ꞵHb had distinct impacts across neuronal compartments: AIR decreased synaptic activity and long-term potentiation (LTP) and impaired axonal conduction, synchronization, and action potential properties, while D-ꞵHb rescued neuronal functions associated with axonal conduction, synchronization, and LTP.},\n\tnumber = {5},\n\turldate = {2024-06-19},\n\tjournal = {PNAS Nexus},\n\tauthor = {Kula, Bartosz and Antal, Botond and Weistuch, Corey and Gackière, Florian and Barre, Alexander and Velado, Victor and Hubbard, Jeffrey M and Kukley, Maria and Mujica-Parodi, Lilianne R and Smith, Nathan A},\n\tyear = {2024},\n\tpmid = {38818236},\n\tpmcid = {PMC11138115},\n\tpages = {196},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/NE2MXVTU/file/view}\n}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

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\n \n\n \n \n \n \n \n \n Ketosis regulates K+ ion channels, strengthening brain-wide signaling disrupted by age.\n \n \n \n \n\n\n \n van Nieuwenhuizen, H.; Chesebro, A. G.; Polizu, C.; Clarke, K.; Strey, H. H.; Weistuch, C.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n Imaging Neuroscience, 2: 1–14. 2024.\n Publisher: MIT Press 255 Main Street, 9th Floor, Cambridge, Massachusetts 02142, USA …\n\n\n\n
\n\n\n\n \n \n $\"Ketosis$ Paper\n \n \n \n $\"Ketosis$ paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{van_nieuwenhuizen_ketosis_2024,\n\ttitle = {Ketosis regulates {K}+ ion channels, strengthening brain-wide signaling disrupted by age},\n\tvolume = {2},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\turl = {https://direct.mit.edu/imag/article/doi/10.1162/imag_a_00163/120749},\n\turldate = {2024-06-28},\n\tjournal = {Imaging Neuroscience},\n\tauthor = {van Nieuwenhuizen, Helena and Chesebro, Anthony G. and Polizu, Claire and Clarke, Kieran and Strey, Helmut H. and Weistuch, Corey and Mujica-Parodi, Lilianne R.},\n\tyear = {2024},\n\tnote = {Publisher: MIT Press 255 Main Street, 9th Floor, Cambridge, Massachusetts 02142, USA …},\n\tpages = {1--14},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/ASMAIMGZ/file/view}\n}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

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\n \n\n \n \n \n \n \n \n Ion gradient-driven bifurcations of a multi-scale neuronal model.\n \n \n \n \n\n\n \n Chesebro, A. G.; Mujica-Parodi, L. R.; and Weistuch, C.\n\n\n \n\n\n\n Chaos, Solitons & Fractals, 167: 113120. 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Ion$ Paper\n \n \n \n $\"Ion$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{chesebro2023,\n\ttitle = {Ion gradient-driven bifurcations of a multi-scale neuronal model},\n\tvolume = {167},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {0960-0779},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0960077923000218},\n\tdoi = {10.1016/j.chaos.2023.113120},\n\tabstract = {Metabolic limitations within the brain frequently arise in the context of aging and disease. As the largest consumers of energy within the brain, ion pumps that maintain the neuronal membrane potential are the most affected when energy supply becomes limited. To characterize the effects of such limitations, we analyze the ion gradients present in a conductance-based (Morris–Lecar) neural mass model. We show the existence and locations of Neimark–Sacker and period-doubling bifurcations in the sodium, calcium, and potassium reversal potentials and demonstrate that these bifurcations form physiologically relevant bounds of ion gradient variability. Within these bounds, we show how depolarization of the gradients causes decreased neural activity. We also show that the depolarization of ion gradients decreases inter-regional coherence, causing a shift in the critical point at which the coupling occurs and thereby inducing loss of synchrony between regions. In this way, we show that the Larter-Breakspear model captures ion gradient variability present at the microscale level and propagates these changes to the macroscale effects such as those observed in human neuroimaging studies.},\n\turldate = {2023-11-28},\n\tjournal = {Chaos, Solitons \\& Fractals},\n\tauthor = {Chesebro, Anthony G. and Mujica-Parodi, Lilianne R. and Weistuch, Corey},\n\tyear = {2023},\n\tpages = {113120},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/BAU89TNH/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Acute administration of ketone beta-hydroxybutyrate downregulates 7T proton magnetic resonance spectroscopy-derived levels of anterior and posterior cingulate GABA and glutamate in healthy adults.\n \n \n \n \n\n\n \n Hone-Blanchet, A.; Antal, B.; McMahon, L.; Lithen, A.; Smith, N. A.; Stufflebeam, S.; Yen, Y.; Lin, A.; Jenkins, B. G.; Mujica-Parodi, L. R.; and Ratai, E.\n\n\n \n\n\n\n Neuropsychopharmacology, 48(5): 797–805. 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Acute$ Paper\n \n \n \n $\"Acute$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 9 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{hone-blanchet2023,\n\ttitle = {Acute administration of ketone beta-hydroxybutyrate downregulates {7T} proton magnetic resonance spectroscopy-derived levels of anterior and posterior cingulate {GABA} and glutamate in healthy adults},\n\tvolume = {48},\n\tcopyright = {2022 The Author(s), under exclusive licence to American College of Neuropsychopharmacology},\n\tissn = {1740-634X},\n\turl = {https://www.nature.com/articles/s41386-022-01364-8},\n\tdoi = {10.1038/s41386-022-01364-8},\n\tabstract = {Glucose metabolism is impaired in brain aging and several neurological conditions. Beneficial effects of ketones have been reported in the context of protecting the aging brain, however, their neurophysiological effect is still largely uncharacterized, hurdling their development as a valid therapeutic option. In this report, we investigate the neurochemical effect of the acute administration of a ketone d-beta-hydroxybutyrate (d-βHB) monoester in fasting healthy participants with ultrahigh-field proton magnetic resonance spectroscopy (MRS). In two within-subject metabolic intervention experiments, 7 T MRS data were obtained in fasting healthy participants (1) in the anterior cingulate cortex pre- and post-administration of d-βHB (N = 16), and (2) in the posterior cingulate cortex pre- and post-administration of d-βHB compared to active control glucose (N = 26). Effect of age and blood levels of d-βHB and glucose were used to further explore the effect of d-βHB and glucose on MRS metabolites. Results show that levels of GABA and Glu were significantly reduced in the anterior and posterior cortices after administration of d-βHB. Importantly, the effect was specific to d-βHB and not observed after administration of glucose. The magnitude of the effect on GABA and Glu was significantly predicted by older age and by elevation of blood levels of d-βHB. Together, our results show that administration of ketones acutely impacts main inhibitory and excitatory transmitters in the whole fasting cortex, compared to normal energy substrate glucose. Critically, such effects have an increased magnitude in older age, suggesting an increased sensitivity to ketones with brain aging.},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2023-11-28},\n\tjournal = {Neuropsychopharmacology},\n\tauthor = {Hone-Blanchet, Antoine and Antal, Botond and McMahon, Liam and Lithen, Andrew and Smith, Nathan A. and Stufflebeam, Steven and Yen, Yi-Fen and Lin, Alexander and Jenkins, Bruce G. and Mujica-Parodi, Lilianne R. and Ratai, Eva-Maria},\n\tyear = {2023},\n\tpages = {797--805},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/JA7G8M5U/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Quantifying Individual Variability in Neural Control Circuit Regulation Using Single-Subject fMRI.\n \n \n \n \n\n\n \n Kumar, R.; Strey, H. H.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n Computational Brain & Behavior. 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Quantifying$ Paper\n \n \n \n $\"Quantifying$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 26 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{kumar2023,\n\ttitle = {Quantifying {Individual} {Variability} in {Neural} {Control} {Circuit} {Regulation} {Using} {Single}-{Subject} {fMRI}},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {2522-087X},\n\turl = {https://doi.org/10.1007/s42113-023-00185-2},\n\tdoi = {10.1007/s42113-023-00185-2},\n\tabstract = {As a field, control systems engineering has developed quantitative methods to characterize the regulation of systems or processes, whose functioning is ubiquitous within synthetic systems. In this context, a control circuit is objectively “well regulated” when discrepancy between desired and achieved output trajectories is minimized and “robust” to the degree that it can regulate well in response to a wide range of stimuli. Most psychiatric disorders are assumed to reflect dysregulation of brain circuits. Yet, probing circuit regulation requires fundamentally different analytic strategies than the correlations relied upon for analyses of connectivity and their resultant networks. Here, we demonstrate how well-established methods for system identification in control systems engineering may be applied to functional magnetic resonance imaging (fMRI) data to extract generative computational models of human brain circuits. As required for clinical neurodiagnostics, we show these models to be extractable even at the level of the single subject. Control parameters provide two quantitative measures of direct relevance for psychiatric disorders: a circuit’s sensitivity to external perturbation and its dysregulation.},\n\tlanguage = {en},\n\turldate = {2023-11-28},\n\tjournal = {Computational Brain \\& Behavior},\n\tauthor = {Kumar, Rajat and Strey, Helmut H. and Mujica-Parodi, Lilianne R.},\n\tyear = {2023},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/Z8JRQ8V6/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Early path dominance as a principle for neurodevelopment.\n \n \n \n \n\n\n \n Razban, R. M.; Pachter, J. A.; Dill, K. A.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n Proceedings of the National Academy of Sciences, 120(16): e2218007120. 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Early$ Paper\n \n \n \n $\"Early$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 9 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{razban2023,\n\ttitle = {Early path dominance as a principle for neurodevelopment},\n\tvolume = {120},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\turl = {https://www.pnas.org/doi/10.1073/pnas.2218007120},\n\tdoi = {10.1073/pnas.2218007120},\n\tabstract = {We perform targeted attack, a systematic computational unlinking of the network, to analyze its effects on global communication across the brain network through its giant cluster. Across diffusion magnetic resonance images from individuals in the UK Biobank, Adolescent Brain Cognitive Development Study and Developing Human Connectome Project, we find that targeted attack procedures on increasing white matter tract lengths and densities are remarkably invariant to aging and disease. Time-reversing the attack computation suggests a mechanism for how brains develop, for which we derive an analytical equation using percolation theory. Based on a close match between theory and experiment, our results demonstrate that tracts are limited to emanate from regions already in the giant cluster and tracts that appear earliest in neurodevelopment are those that become the longest and densest.},\n\tnumber = {16},\n\turldate = {2023-11-28},\n\tjournal = {Proceedings of the National Academy of Sciences},\n\tauthor = {Razban, Rostam M. and Pachter, Jonathan Asher and Dill, Ken A. and Mujica-Parodi, Lilianne R.},\n\tyear = {2023},\n\tpages = {e2218007120},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/VCE2VZUF/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Leveraging Julia's automated differentiation and symbolic computation to increase spectral DCM flexibility and speed.\n \n \n \n \n\n\n \n Hofmann, D.; Chesebro, A. G.; Rackauckas, C.; Mujica-Parodi, L. R.; Friston, K. J.; Edelman, A.; and Strey, H. H.\n\n\n \n\n\n\n bioRxiv: The Preprint Server for Biology. 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Leveraging$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{hofmann2023,\n\ttitle = {Leveraging {Julia}'s automated differentiation and symbolic computation to increase spectral {DCM} flexibility and speed},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tdoi = {10.1101/2023.10.27.564407},\n\tabstract = {Using neuroimaging and electrophysiological data to infer neural parameter estimations from theoretical circuits requires solving the inverse problem. Here, we provide a new Julia language package designed to i) compose complex dynamical models in a simple and modular way with ModelingToolkit.jl, ii) implement parameter fitting based on spectral dynamic causal modeling (sDCM) using the Laplace approximation, analogous to MATLAB implementation in SPM12, and iii) leverage Julia's unique strengths to increase accuracy and speed by employing Automatic Differentiation during the fitting procedure. To illustrate the utility of our flexible modular approach, we provide a method to improve correction for fMRI scanner field strengths (1.5T, 3T, 7T) when fitting models to real data.},\n\tlanguage = {eng},\n\tjournal = {bioRxiv: The Preprint Server for Biology},\n\tauthor = {Hofmann, David and Chesebro, Anthony G. and Rackauckas, Chris and Mujica-Parodi, Lilianne R. and Friston, Karl J. and Edelman, Alan and Strey, Helmut H.},\n\tyear = {2023},\n\tpmid = {37961652},\n\tpmcid = {PMC10634910},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/JHJBBD2W/file/view}\n}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

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\n \n\n \n \n \n \n \n \n Mega-analysis methods in ENIGMA: The experience of the generalized anxiety disorder working group.\n \n \n \n \n\n\n \n Zugman, A.; Harrewijn, A.; Cardinale, E. M.; Zwiebel, H.; Freitag, G. F.; Werwath, K. E.; Bas-Hoogendam, J. M.; Groenewold, N. A.; Aghajani, M.; Hilbert, K.; Cardoner, N.; Porta-Casteràs, D.; Gosnell, S.; Salas, R.; Blair, K. S.; Blair, J. R.; Hammoud, M. Z.; Milad, M.; Burkhouse, K.; Phan, K. L.; Schroeder, H. K.; Strawn, J. R.; Beesdo-Baum, K.; Thomopoulos, S. I.; Grabe, H. J.; Van der Auwera, S.; Wittfeld, K.; Nielsen, J. A.; Buckner, R.; Smoller, J. W.; Mwangi, B.; Soares, J. C.; Wu, M.; Zunta-Soares, G. B.; Jackowski, A. P.; Pan, P. M.; Salum, G. A.; Assaf, M.; Diefenbach, G. J.; Brambilla, P.; Maggioni, E.; Hofmann, D.; Straube, T.; Andreescu, C.; Berta, R.; Tamburo, E.; Price, R.; Manfro, G. G.; Critchley, H. D.; Makovac, E.; Mancini, M.; Meeten, F.; Ottaviani, C.; Agosta, F.; Canu, E.; Cividini, C.; Filippi, M.; Kostić, M.; Munjiza, A.; Filippi, C. A.; Leibenluft, E.; Alberton, B. A. V.; Balderston, N. L.; Ernst, M.; Grillon, C.; Mujica-Parodi, L. R.; van Nieuwenhuizen, H.; Fonzo, G. A.; Paulus, M. P.; Stein, M. B.; Gur, R. E.; Gur, R. C.; Kaczkurkin, A. N.; Larsen, B.; Satterthwaite, T. D.; Harper, J.; Myers, M.; Perino, M. T.; Yu, Q.; Sylvester, C. M.; Veltman, D. J.; Lueken, U.; Van der Wee, N. J. A.; Stein, D. J.; Jahanshad, N.; Thompson, P. M.; Pine, D. S.; and Winkler, A. M.\n\n\n \n\n\n\n Human Brain Mapping, 43(1): 255–277. 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Mega-analysis$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{zugman2022,\n\ttitle = {Mega-analysis methods in {ENIGMA}: {The} experience of the generalized anxiety disorder working group},\n\tvolume = {43},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1097-0193},\n\tshorttitle = {Mega-analysis methods in {ENIGMA}},\n\tdoi = {10.1002/hbm.25096},\n\tabstract = {The ENIGMA group on Generalized Anxiety Disorder (ENIGMA-Anxiety/GAD) is part of a broader effort to investigate anxiety disorders using imaging and genetic data across multiple sites worldwide. The group is actively conducting a mega-analysis of a large number of brain structural scans. In this process, the group was confronted with many methodological challenges related to study planning and implementation, between-country transfer of subject-level data, quality control of a considerable amount of imaging data, and choices related to statistical methods and efficient use of resources. This report summarizes the background information and rationale for the various methodological decisions, as well as the approach taken to implement them. The goal is to document the approach and help guide other research groups working with large brain imaging data sets as they develop their own analytic pipelines for mega-analyses.},\n\tlanguage = {eng},\n\tnumber = {1},\n\tjournal = {Human Brain Mapping},\n\tauthor = {Zugman, André and Harrewijn, Anita and Cardinale, Elise M. and Zwiebel, Hannah and Freitag, Gabrielle F. and Werwath, Katy E. and Bas-Hoogendam, Janna M. and Groenewold, Nynke A. and Aghajani, Moji and Hilbert, Kevin and Cardoner, Narcis and Porta-Casteràs, Daniel and Gosnell, Savannah and Salas, Ramiro and Blair, Karina S. and Blair, James R. and Hammoud, Mira Z. and Milad, Mohammed and Burkhouse, Katie and Phan, K. Luan and Schroeder, Heidi K. and Strawn, Jeffrey R. and Beesdo-Baum, Katja and Thomopoulos, Sophia I. and Grabe, Hans J. and Van der Auwera, Sandra and Wittfeld, Katharina and Nielsen, Jared A. and Buckner, Randy and Smoller, Jordan W. and Mwangi, Benson and Soares, Jair C. and Wu, Mon-Ju and Zunta-Soares, Giovana B. and Jackowski, Andrea P. and Pan, Pedro M. and Salum, Giovanni A. and Assaf, Michal and Diefenbach, Gretchen J. and Brambilla, Paolo and Maggioni, Eleonora and Hofmann, David and Straube, Thomas and Andreescu, Carmen and Berta, Rachel and Tamburo, Erica and Price, Rebecca and Manfro, Gisele G. and Critchley, Hugo D. and Makovac, Elena and Mancini, Matteo and Meeten, Frances and Ottaviani, Cristina and Agosta, Federica and Canu, Elisa and Cividini, Camilla and Filippi, Massimo and Kostić, Milutin and Munjiza, Ana and Filippi, Courtney A. and Leibenluft, Ellen and Alberton, Bianca A. V. and Balderston, Nicholas L. and Ernst, Monique and Grillon, Christian and Mujica-Parodi, Lilianne R. and van Nieuwenhuizen, Helena and Fonzo, Gregory A. and Paulus, Martin P. and Stein, Murray B. and Gur, Raquel E. and Gur, Ruben C. and Kaczkurkin, Antonia N. and Larsen, Bart and Satterthwaite, Theodore D. and Harper, Jennifer and Myers, Michael and Perino, Michael T. and Yu, Qiongru and Sylvester, Chad M. and Veltman, Dick J. and Lueken, Ulrike and Van der Wee, Nic J. A. and Stein, Dan J. and Jahanshad, Neda and Thompson, Paul M. and Pine, Daniel S. and Winkler, Anderson M.},\n\tyear = {2022},\n\tpmid = {32596977},\n\tpmcid = {PMC8675407},\n\tpages = {255--277},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/EGAFSCT4/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Parameter estimation for correlated Ornstein-Uhlenbeck time-series.\n \n \n \n \n\n\n \n Strey, H. H.; Kumar, R.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n Technical Report 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Parameter$ Paper\n \n \n \n $\"Parameter$ paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@techreport{strey2021,\n\ttitle = {Parameter estimation for correlated {Ornstein}-{Uhlenbeck} time-series},\n\tcopyright = {© 2021, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NoDerivs 4.0 International), CC BY-ND 4.0, as described at http://creativecommons.org/licenses/by-nd/4.0/},\n\turl = {https://www.biorxiv.org/content/10.1101/2021.02.12.430978v1},\n\tabstract = {In this article, we develop a Maximum likelihood (ML) approach to estimate parameters from correlated time traces that originate from coupled Ornstein-Uhlenbeck processes. The most common technique to characterize the correlation between time-series is to calculate the Pearson correlation coefficient. Here we show that for time series with memory (or a characteristic relaxation time), our method gives more reliable results, but also results in coupling coefficients and their uncertainties given the data. We investigate how these uncertainties depend on the number of samples, the relaxation times and sampling time. To validate our analytic results, we performed simulations over a wide range of correlation coefficients both using our maximum likelihood solutions and Markov-Chain Monte-Carlo (MCMC) simulations. We found that both ML and MCMC result in the same parameter estimations. We also found that when analyzing the same data, the ML and MCMC uncertainties are strongly correlated, while ML underestimates the uncertainties by a factor of 1.5 to 3 over a large range of parameters. For large datasets, we can therfore use the less computationally expensive maximum likelihood method to run over the whole dataset, and then we can use MCMC on a few samples to determine the factor by which the ML method underestimates the uncertainties. To illustrate the application of our method, we apply it to time series of brain activation using fMRI measurements of the human default mode network. We show that our method significantly improves the interpretation of multi-subject measurements of correlations between brain regions by providing parameter confidence intervals for individual measurements, which allows for distinguishing between the variance from differences between subjects from variance due to measurement error.},\n\tlanguage = {en},\n\turldate = {2021-11-30},\n\tauthor = {Strey, Helmut H. and Kumar, Rajat and Mujica-Parodi, Lilianne R.},\n\tyear = {2021},\n\tpages = {2021.02.12.430978},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/JCG2A84I/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Neuropredictome: a data-driven predictome for cognitive, psychiatric, medical, and lifestyle factors on the brain.\n \n \n \n \n\n\n \n Sultan, S. F.; Mujica-Parodi, L. R.; and Skiena, S.\n\n\n \n\n\n\n Technical Report 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Neuropredictome:$ Paper\n \n \n \n $\"Neuropredictome:$ paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@techreport{sultan2021,\n\ttitle = {Neuropredictome: a data-driven predictome for cognitive, psychiatric, medical, and lifestyle factors on the brain},\n\tcopyright = {© 2021, Posted by Cold Spring Harbor Laboratory. The copyright holder for this pre-print is the author. All rights reserved. The material may not be redistributed, re-used or adapted without the author's permission.},\n\tshorttitle = {Neuropredictome},\n\turl = {https://www.biorxiv.org/content/10.1101/2020.12.07.415091v2},\n\tabstract = {Most neuroimaging studies individually provide evidence on a narrow aspect of the human brain function, on distinct data sets that often suffer from small sample sizes. More generally, the high technical and cost demands of neuroimaging studies (combined with the statistical unreliability of neuroimaging pilot studies) may lead to observational bias, discouraging discovery of less obvious associations that nonetheless have important neurological implications. To address these problems, we built a machine-learning based classification framework, NeuroPredictome, optimized for the reliability and robustness of its associations. NeuroPredictome is grounded in a large-scale dataset, UK-Biobank (N=19,831), which includes resting and task functional MRI as well as structural T1-weighted and diffusion tensor imaging. Participants were assessed with respect to a comprehensive set of 5,034 phenotypes, including the physical and lifestyle factors most relevant to general medicine. Results generated by data-driven classifiers were then cross-validated, using deep-learning textual analyses, against 14,371 peer-reviewed research articles, providing an unbiased hypothesis-generator of linkages between diverse phenotypes and the brain. Our results show that neuroimaging reveals as many neurological links to physical and lifestyle factors as to cognitive factors, supporting a more integrative approach to medicine that considers disease interactions between multiple organs and systems.},\n\tlanguage = {en},\n\turldate = {2021-11-30},\n\tauthor = {Sultan, Syed Fahad and Mujica-Parodi, Lilianne R. and Skiena, Steven},\n\tyear = {2021},\n\tpages = {2020.12.07.415091},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/IS9Z86BE/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Type 2 diabetes mellitus accelerates brain aging and cognitive decline: complementary findings from UK Biobank and meta-analyses.\n \n \n \n \n\n\n \n Antal, B.; McMahon, L. P.; Sultan, S. F.; Lithen, A.; Wexler, D. J.; Dickerson, B.; Ratai, E.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n Technical Report 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Type$ Paper\n \n \n \n $\"Type$ paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@techreport{antal2021,\n\ttitle = {Type 2 diabetes mellitus accelerates brain aging and cognitive decline: complementary findings from {UK} {Biobank} and meta-analyses},\n\tcopyright = {© 2021, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},\n\tshorttitle = {Type 2 diabetes mellitus accelerates brain aging and cognitive decline},\n\turl = {https://www.medrxiv.org/content/10.1101/2021.05.23.21257682v2},\n\tabstract = {Background Type 2 diabetes mellitus is known to be associated with neurobiological and cognitive deficits; however, their extent, overlap with aging effects, and the effectiveness of existing treatments in the context of the brain are currently unknown.\nMethods We characterized neurocognitive effects independently associated with T2DM and age in a large cohort of human subjects from the UK Biobank with cross-sectional neuroimaging and cognitive data. We then proceeded to evaluate the extent of overlap between the effects related to T2DM and age by applying correlation measures to the independently characterized neurocognitive changes. Our findings were complemented by meta-analyses of published reports with cognitive or neuroimaging measures for T2DM and healthy controls (HC). We also evaluated in a cohort of T2DM diagnosed individuals using UK Biobank how disease chronicity and metformin treatment interact with respect to the identified neurocognitive effects.\nFindings The UK Biobank dataset included cognitive and neuroimaging data (N=26,125) including 1,270 T2DM and 24,855 HC. Duration of T2DM ranged from 0–45 years (mean 9.7±7.9 years); 559 were treated with metformin alone, while 473 were unmedicated. Our meta-analysis evaluated 34 cognitive studies (N=22,231) and 60 neuroimaging studies: 30 of T2DM (N=866) and 30 of aging (N=1088). As compared to age, sex, and education-matched HC, T2DM was associated with marked cognitive deficits, particularly in executive functioning and processing speed. Likewise, we found that the diagnosis of T2DM was significantly associated with gray matter atrophy, primarily within the ventral striatum, cerebellum, and putamen, with reorganization of brain activity (decreased in the caudate, frontal eye fields, and premotor cortex and increased in the subgenual area, thalamus, brainstem and posterior cingulate cortex). The structural and functional changes associated with T2DM show marked overlap with the effects correlating with age but appear earlier, with disease duration linked to more severe neurodegeneration. Metformin treatment status was not associated with improved neurocognitive outcomes.\nInterpretation The neurocognitive impact of T2DM suggests marked acceleration of normal brain aging, by approximately 24\\% ± 10\\%; T2DM chronicity was associated with increased atrophy. As such, neuroimaging-based biomarkers may provide a valuable adjunctive measure of T2DM progression and treatment efficacy based on neurological effects.},\n\tlanguage = {en},\n\turldate = {2021-11-30},\n\tauthor = {Antal, Botond and McMahon, Liam P. and Sultan, Syed Fahad and Lithen, Andrew and Wexler, Deborah J. and Dickerson, Bradford and Ratai, Eva-Maria and Mujica-Parodi, Lilianne R.},\n\tyear = {2021},\n\tpages = {2021.05.23.21257682},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/SJQ5T8X3/file/view}\n}\n\n\n\n

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\n Background Type 2 diabetes mellitus is known to be associated with neurobiological and cognitive deficits; however, their extent, overlap with aging effects, and the effectiveness of existing treatments in the context of the brain are currently unknown. Methods We characterized neurocognitive effects independently associated with T2DM and age in a large cohort of human subjects from the UK Biobank with cross-sectional neuroimaging and cognitive data. We then proceeded to evaluate the extent of overlap between the effects related to T2DM and age by applying correlation measures to the independently characterized neurocognitive changes. Our findings were complemented by meta-analyses of published reports with cognitive or neuroimaging measures for T2DM and healthy controls (HC). We also evaluated in a cohort of T2DM diagnosed individuals using UK Biobank how disease chronicity and metformin treatment interact with respect to the identified neurocognitive effects. Findings The UK Biobank dataset included cognitive and neuroimaging data (N=26,125) including 1,270 T2DM and 24,855 HC. Duration of T2DM ranged from 0–45 years (mean 9.7±7.9 years); 559 were treated with metformin alone, while 473 were unmedicated. Our meta-analysis evaluated 34 cognitive studies (N=22,231) and 60 neuroimaging studies: 30 of T2DM (N=866) and 30 of aging (N=1088). As compared to age, sex, and education-matched HC, T2DM was associated with marked cognitive deficits, particularly in executive functioning and processing speed. Likewise, we found that the diagnosis of T2DM was significantly associated with gray matter atrophy, primarily within the ventral striatum, cerebellum, and putamen, with reorganization of brain activity (decreased in the caudate, frontal eye fields, and premotor cortex and increased in the subgenual area, thalamus, brainstem and posterior cingulate cortex). The structural and functional changes associated with T2DM show marked overlap with the effects correlating with age but appear earlier, with disease duration linked to more severe neurodegeneration. Metformin treatment status was not associated with improved neurocognitive outcomes. Interpretation The neurocognitive impact of T2DM suggests marked acceleration of normal brain aging, by approximately 24% ± 10%; T2DM chronicity was associated with increased atrophy. As such, neuroimaging-based biomarkers may provide a valuable adjunctive measure of T2DM progression and treatment efficacy based on neurological effects.\n

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\n \n\n \n \n \n \n \n \n Machine Learning Predicts Outcomes of Phase III Clinical Trials for Prostate Cancer.\n \n \n \n \n\n\n \n Beacher, F. D.; Mujica-Parodi, L. R.; Gupta, S.; and Ancora, L. A.\n\n\n \n\n\n\n Algorithms, 14(5): 147. 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Machine$ Paper\n \n \n \n $\"Machine$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{beacher2021,\n\ttitle = {Machine {Learning} {Predicts} {Outcomes} of {Phase} {III} {Clinical} {Trials} for {Prostate} {Cancer}},\n\tvolume = {14},\n\tcopyright = {http://creativecommons.org/licenses/by/3.0/},\n\tissn = {1999-4893},\n\turl = {https://www.mdpi.com/1999-4893/14/5/147},\n\tdoi = {10.3390/a14050147},\n\tabstract = {The ability to predict the individual outcomes of clinical trials could support the development of tools for precision medicine and improve the efficiency of clinical-stage drug development. However, there are no published attempts to predict individual outcomes of clinical trials for cancer. We used machine learning (ML) to predict individual responses to a two-year course of bicalutamide, a standard treatment for prostate cancer, based on data from three Phase III clinical trials (n = 3653). We developed models that used a merged dataset from all three studies. The best performing models using merged data from all three studies had an accuracy of 76\\%. The performance of these models was confirmed by further modeling using a merged dataset from two of the three studies, and a separate study for testing. Together, our results indicate the feasibility of ML-based tools for predicting cancer treatment outcomes, with implications for precision oncology and improving the efficiency of clinical-stage drug development.},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2023-11-28},\n\tjournal = {Algorithms},\n\tauthor = {Beacher, Felix D. and Mujica-Parodi, Lilianne R. and Gupta, Shreyash and Ancora, Leonardo A.},\n\tyear = {2021},\n\tpages = {147},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/TI8QZQV7/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Metabolism modulates network synchrony in the aging brain.\n \n \n \n \n\n\n \n Weistuch, C.; Mujica-Parodi, L. R.; Razban, R. M.; Antal, B.; Nieuwenhuizen, H. v.; Amgalan, A.; and Dill, K. A.\n\n\n \n\n\n\n Proceedings of the National Academy of Sciences, 118(40). 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Metabolism$ Paper\n \n \n \n $\"Metabolism$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{weistuch2021,\n\ttitle = {Metabolism modulates network synchrony in the aging brain},\n\tvolume = {118},\n\tcopyright = {© 2021 . https://www.pnas.org/site/aboutpnas/licenses.xhtmlPublished under the PNAS license.},\n\tissn = {0027-8424, 1091-6490},\n\turl = {https://www.pnas.org/content/118/40/e2025727118},\n\tdoi = {10.1073/pnas.2025727118},\n\tabstract = {Brain aging is associated with hypometabolism and global changes in functional connectivity. Using functional MRI (fMRI), we show that network synchrony, a collective property of brain activity, decreases with age. Applying quantitative methods from statistical physics, we provide a generative (Ising) model for these changes as a function of the average communication strength between brain regions. We find that older brains are closer to a critical point of this communication strength, in which even small changes in metabolism lead to abrupt changes in network synchrony. Finally, by experimentally modulating metabolic activity in younger adults, we show how metabolism alone—independent of other changes associated with aging—can provide a plausible candidate mechanism for marked reorganization of brain network topology.},\n\tlanguage = {en},\n\tnumber = {40},\n\turldate = {2021-11-30},\n\tjournal = {Proceedings of the National Academy of Sciences},\n\tauthor = {Weistuch, Corey and Mujica-Parodi, Lilianne R. and Razban, Rostam M. and Antal, Botond and Nieuwenhuizen, Helena van and Amgalan, Anar and Dill, Ken A.},\n\tyear = {2021},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/JSPEAKAA/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Cortical and subcortical brain structure in generalized anxiety disorder: findings from 28 research sites in the ENIGMA-Anxiety Working Group.\n \n \n \n \n\n\n \n Harrewijn, A.; Cardinale, E. M.; Groenewold, N. A.; Bas-Hoogendam, J. M.; Aghajani, M.; Hilbert, K.; Cardoner, N.; Porta-Casteràs, D.; Gosnell, S.; Salas, R.; Jackowski, A. P.; Pan, P. M.; Salum, G. A.; Blair, K. S.; Blair, J. R.; Hammoud, M. Z.; Milad, M. R.; Burkhouse, K. L.; Phan, K. L.; Schroeder, H. K.; Strawn, J. R.; Beesdo-Baum, K.; Jahanshad, N.; Thomopoulos, S. I.; Buckner, R.; Nielsen, J. A.; Smoller, J. W.; Soares, J. C.; Mwangi, B.; Wu, M.; Zunta-Soares, G. B.; Assaf, M.; Diefenbach, G. J.; Brambilla, P.; Maggioni, E.; Hofmann, D.; Straube, T.; Andreescu, C.; Berta, R.; Tamburo, E.; Price, R. B.; Manfro, G. G.; Agosta, F.; Canu, E.; Cividini, C.; Filippi, M.; Kostić, M.; Munjiza Jovanovic, A.; Alberton, B. A. V.; Benson, B.; Freitag, G. F.; Filippi, C. A.; Gold, A. L.; Leibenluft, E.; Ringlein, G. V.; Werwath, K. E.; Zwiebel, H.; Zugman, A.; Grabe, H. J.; Van der Auwera, S.; Wittfeld, K.; Völzke, H.; Bülow, R.; Balderston, N. L.; Ernst, M.; Grillon, C.; Mujica-Parodi, L. R.; van Nieuwenhuizen, H.; Critchley, H. D.; Makovac, E.; Mancini, M.; Meeten, F.; Ottaviani, C.; Ball, T. M.; Fonzo, G. A.; Paulus, M. P.; Stein, M. B.; Gur, R. E.; Gur, R. C.; Kaczkurkin, A. N.; Larsen, B.; Satterthwaite, T. D.; Harper, J.; Myers, M.; Perino, M. T.; Sylvester, C. M.; Yu, Q.; Lueken, U.; Veltman, D. J.; Thompson, P. M.; Stein, D. J.; Van der Wee, N. J. A.; Winkler, A. M.; and Pine, D. S.\n\n\n \n\n\n\n Translational Psychiatry, 11(1): 502. 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Cortical$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{harrewijn2021,\n\ttitle = {Cortical and subcortical brain structure in generalized anxiety disorder: findings from 28 research sites in the {ENIGMA}-{Anxiety} {Working} {Group}},\n\tvolume = {11},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {2158-3188},\n\tshorttitle = {Cortical and subcortical brain structure in generalized anxiety disorder},\n\tdoi = {10.1038/s41398-021-01622-1},\n\tabstract = {The goal of this study was to compare brain structure between individuals with generalized anxiety disorder (GAD) and healthy controls. Previous studies have generated inconsistent findings, possibly due to small sample sizes, or clinical/analytic heterogeneity. To address these concerns, we combined data from 28 research sites worldwide through the ENIGMA-Anxiety Working Group, using a single, pre-registered mega-analysis. Structural magnetic resonance imaging data from children and adults (5-90 years) were processed using FreeSurfer. The main analysis included the regional and vertex-wise cortical thickness, cortical surface area, and subcortical volume as dependent variables, and GAD, age, age-squared, sex, and their interactions as independent variables. Nuisance variables included IQ, years of education, medication use, comorbidities, and global brain measures. The main analysis (1020 individuals with GAD and 2999 healthy controls) included random slopes per site and random intercepts per scanner. A secondary analysis (1112 individuals with GAD and 3282 healthy controls) included fixed slopes and random intercepts per scanner with the same variables. The main analysis showed no effect of GAD on brain structure, nor interactions involving GAD, age, or sex. The secondary analysis showed increased volume in the right ventral diencephalon in male individuals with GAD compared to male healthy controls, whereas female individuals with GAD did not differ from female healthy controls. This mega-analysis combining worldwide data showed that differences in brain structure related to GAD are small, possibly reflecting heterogeneity or those structural alterations are not a major component of its pathophysiology.},\n\tlanguage = {eng},\n\tnumber = {1},\n\tjournal = {Translational Psychiatry},\n\tauthor = {Harrewijn, Anita and Cardinale, Elise M. and Groenewold, Nynke A. and Bas-Hoogendam, Janna Marie and Aghajani, Moji and Hilbert, Kevin and Cardoner, Narcis and Porta-Casteràs, Daniel and Gosnell, Savannah and Salas, Ramiro and Jackowski, Andrea P. and Pan, Pedro M. and Salum, Giovanni A. and Blair, Karina S. and Blair, James R. and Hammoud, Mira Z. and Milad, Mohammed R. and Burkhouse, Katie L. and Phan, K. Luan and Schroeder, Heidi K. and Strawn, Jeffrey R. and Beesdo-Baum, Katja and Jahanshad, Neda and Thomopoulos, Sophia I. and Buckner, Randy and Nielsen, Jared A. and Smoller, Jordan W. and Soares, Jair C. and Mwangi, Benson and Wu, Mon-Ju and Zunta-Soares, Giovana B. and Assaf, Michal and Diefenbach, Gretchen J. and Brambilla, Paolo and Maggioni, Eleonora and Hofmann, David and Straube, Thomas and Andreescu, Carmen and Berta, Rachel and Tamburo, Erica and Price, Rebecca B. and Manfro, Gisele G. and Agosta, Federica and Canu, Elisa and Cividini, Camilla and Filippi, Massimo and Kostić, Milutin and Munjiza Jovanovic, Ana and Alberton, Bianca A. V. and Benson, Brenda and Freitag, Gabrielle F. and Filippi, Courtney A. and Gold, Andrea L. and Leibenluft, Ellen and Ringlein, Grace V. and Werwath, Kathryn E. and Zwiebel, Hannah and Zugman, André and Grabe, Hans J. and Van der Auwera, Sandra and Wittfeld, Katharina and Völzke, Henry and Bülow, Robin and Balderston, Nicholas L. and Ernst, Monique and Grillon, Christian and Mujica-Parodi, Lilianne R. and van Nieuwenhuizen, Helena and Critchley, Hugo D. and Makovac, Elena and Mancini, Matteo and Meeten, Frances and Ottaviani, Cristina and Ball, Tali M. and Fonzo, Gregory A. and Paulus, Martin P. and Stein, Murray B. and Gur, Raquel E. and Gur, Ruben C. and Kaczkurkin, Antonia N. and Larsen, Bart and Satterthwaite, Theodore D. and Harper, Jennifer and Myers, Michael and Perino, Michael T. and Sylvester, Chad M. and Yu, Qiongru and Lueken, Ulrike and Veltman, Dick J. and Thompson, Paul M. and Stein, Dan J. and Van der Wee, Nic J. A. and Winkler, Anderson M. and Pine, Daniel S.},\n\tyear = {2021},\n\tpmid = {34599145},\n\tpmcid = {PMC8486763},\n\tpages = {502},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/ACSN2FWA/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n The Refractory Period Matters: Unifying Mechanisms of Macroscopic Brain Waves.\n \n \n \n \n\n\n \n Weistuch, C.; Mujica-Parodi, L. R.; and Dill, K.\n\n\n \n\n\n\n Neural Computation, 33(5): 1145–1163. 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n \n $\"The$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{weistuch2021a,\n\ttitle = {The {Refractory} {Period} {Matters}: {Unifying} {Mechanisms} of {Macroscopic} {Brain} {Waves}},\n\tvolume = {33},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {0899-7667},\n\tshorttitle = {The {Refractory} {Period} {Matters}},\n\turl = {https://doi.org/10.1162/neco_a_01371},\n\tdoi = {10.1162/neco_a_01371},\n\tabstract = {The relationship between complex brain oscillations and the dynamics of individual neurons is poorly understood. Here we utilize maximum caliber, a dynamical inference principle, to build a minimal yet general model of the collective (mean field) dynamics of large populations of neurons. In agreement with previous experimental observations, we describe a simple, testable mechanism, involving only a single type of neuron, by which many of these complex oscillatory patterns may emerge. Our model predicts that the refractory period of neurons, which has often been neglected, is essential for these behaviors.},\n\tnumber = {5},\n\turldate = {2023-11-28},\n\tjournal = {Neural Computation},\n\tauthor = {Weistuch, Corey and Mujica-Parodi, Lilianne R. and Dill, Ken},\n\tyear = {2021},\n\tpages = {1145--1163},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/UPQV4B7J/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Ground-truth “resting-state” signal provides data-driven estimation and correction for scanner distortion of fMRI time-series dynamics.\n \n \n \n \n\n\n \n Kumar, R.; Tan, L.; Kriegstein, A.; Lithen, A.; Polimeni, J. R.; Mujica-Parodi, L. R.; and Strey, H. H.\n\n\n \n\n\n\n NeuroImage, 227: 117584. 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Ground-truth$ Paper\n \n \n \n $\"Ground-truth$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{kumar2021,\n\ttitle = {Ground-truth “resting-state” signal provides data-driven estimation and correction for scanner distortion of {fMRI} time-series dynamics},\n\tvolume = {227},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1053-8119},\n\turl = {https://www.sciencedirect.com/science/article/pii/S1053811920310697},\n\tdoi = {10.1016/j.neuroimage.2020.117584},\n\tabstract = {The fMRI community has made great strides in decoupling neuronal activity from other physiologically induced T2* changes, using sensors that provide a ground-truth with respect to cardiac, respiratory, and head movement dynamics. However, blood oxygenation level-dependent (BOLD) time-series dynamics are also confounded by scanner artifacts, in complex ways that can vary not only between scanners but even, for the same scanner, between sessions. Unfortunately, the lack of an equivalent ground truth for BOLD time-series has thus far stymied the development of reliable methods for identification and removal of scanner-induced noise, a problem that we have previously shown to severely impact detection sensitivity of resting-state brain networks. To address this problem, we first designed and built a phantom capable of providing dynamic signals equivalent to that of the resting-state brain. Using the dynamic phantom, we then compared the ground-truth time-series with its measured fMRI data. Using these, we introduce data-quality metrics: Standardized Signal-to-Noise Ratio (ST-SNR) and Dynamic Fidelity that, unlike currently used measures such as temporal SNR (tSNR), can be directly compared across scanners. Dynamic phantom data acquired from four “best-case” scenarios: high-performance scanners with MR-physicist-optimized acquisition protocols, still showed scanner instability/multiplicative noise contributions of about 6–18\\% of the total noise. We further measured strong non-linearity in the fMRI response for all scanners, ranging between 8–19\\% of total voxels. To correct scanner distortion of fMRI time-series dynamics at a single-subject level, we trained a convolutional neural network (CNN) on paired sets of measured vs. ground-truth data. The CNN learned the unique features of each session's noise, providing a customized temporal filter. Tests on dynamic phantom time-series showed a 4- to 7-fold increase in ST-SNR and about 40–70\\% increase in Dynamic Fidelity after denoising, with CNN denoising outperforming both the temporal bandpass filtering and denoising using Marchenko-Pastur principal component analysis. Critically, we observed that the CNN temporal denoising pushes ST-SNR to a regime where signal power is higher than that of noise (ST-SNR {\\textgreater} 1). Denoising human-data with ground-truth-trained CNN, in turn, showed markedly increased detection sensitivity of resting-state networks. These were visible even at the level of the single-subject, as required for clinical applications of fMRI.},\n\tlanguage = {en},\n\turldate = {2021-11-30},\n\tjournal = {NeuroImage},\n\tauthor = {Kumar, Rajat and Tan, Liang and Kriegstein, Alan and Lithen, Andrew and Polimeni, Jonathan R. and Mujica-Parodi, Lilianne R. and Strey, Helmut H.},\n\tyear = {2021},\n\tpages = {117584},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/7ETVMQ58/file/view}\n}\n\n\n\n

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\n The fMRI community has made great strides in decoupling neuronal activity from other physiologically induced T2* changes, using sensors that provide a ground-truth with respect to cardiac, respiratory, and head movement dynamics. However, blood oxygenation level-dependent (BOLD) time-series dynamics are also confounded by scanner artifacts, in complex ways that can vary not only between scanners but even, for the same scanner, between sessions. Unfortunately, the lack of an equivalent ground truth for BOLD time-series has thus far stymied the development of reliable methods for identification and removal of scanner-induced noise, a problem that we have previously shown to severely impact detection sensitivity of resting-state brain networks. To address this problem, we first designed and built a phantom capable of providing dynamic signals equivalent to that of the resting-state brain. Using the dynamic phantom, we then compared the ground-truth time-series with its measured fMRI data. Using these, we introduce data-quality metrics: Standardized Signal-to-Noise Ratio (ST-SNR) and Dynamic Fidelity that, unlike currently used measures such as temporal SNR (tSNR), can be directly compared across scanners. Dynamic phantom data acquired from four “best-case” scenarios: high-performance scanners with MR-physicist-optimized acquisition protocols, still showed scanner instability/multiplicative noise contributions of about 6–18% of the total noise. We further measured strong non-linearity in the fMRI response for all scanners, ranging between 8–19% of total voxels. To correct scanner distortion of fMRI time-series dynamics at a single-subject level, we trained a convolutional neural network (CNN) on paired sets of measured vs. ground-truth data. The CNN learned the unique features of each session's noise, providing a customized temporal filter. Tests on dynamic phantom time-series showed a 4- to 7-fold increase in ST-SNR and about 40–70% increase in Dynamic Fidelity after denoising, with CNN denoising outperforming both the temporal bandpass filtering and denoising using Marchenko-Pastur principal component analysis. Critically, we observed that the CNN temporal denoising pushes ST-SNR to a regime where signal power is higher than that of noise (ST-SNR \\textgreater 1). Denoising human-data with ground-truth-trained CNN, in turn, showed markedly increased detection sensitivity of resting-state networks. These were visible even at the level of the single-subject, as required for clinical applications of fMRI.\n

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\n \n\n \n \n \n \n \n \n Unique scales preserve self-similar integrate-and-fire functionality of neuronal clusters.\n \n \n \n \n\n\n \n Amgalan, A.; Taylor, P.; Mujica-Parodi, L. R.; and Siegelmann, H. T.\n\n\n \n\n\n\n Scientific Reports, 11(1): 5331. 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Unique$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{amgalan2021,\n\ttitle = {Unique scales preserve self-similar integrate-and-fire functionality of neuronal clusters},\n\tvolume = {11},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {2045-2322},\n\tdoi = {10.1038/s41598-021-82461-4},\n\tabstract = {Brains demonstrate varying spatial scales of nested hierarchical clustering. Identifying the brain's neuronal cluster size to be presented as nodes in a network computation is critical to both neuroscience and artificial intelligence, as these define the cognitive blocks capable of building intelligent computation. Experiments support various forms and sizes of neural clustering, from handfuls of dendrites to thousands of neurons, and hint at their behavior. Here, we use computational simulations with a brain-derived fMRI network to show that not only do brain networks remain structurally self-similar across scales but also neuron-like signal integration functionality ("integrate and fire") is preserved at particular clustering scales. As such, we propose a coarse-graining of neuronal networks to ensemble-nodes, with multiple spikes making up its ensemble-spike and time re-scaling factor defining its ensemble-time step. This fractal-like spatiotemporal property, observed in both structure and function, permits strategic choice in bridging across experimental scales for computational modeling while also suggesting regulatory constraints on developmental and evolutionary "growth spurts" in brain size, as per punctuated equilibrium theories in evolutionary biology.},\n\tlanguage = {eng},\n\tnumber = {1},\n\tjournal = {Scientific Reports},\n\tauthor = {Amgalan, Anar and Taylor, Patrick and Mujica-Parodi, Lilianne R. and Siegelmann, Hava T.},\n\tyear = {2021},\n\tpmid = {33674620},\n\tpmcid = {PMC7936002},\n\tpages = {5331},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/VXZQPAK3/file/view}\n}\n\n\n\n

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\n \n 2020\n \n \n (4)\n \n \n

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\n \n\n \n \n \n \n \n \n Making Sense of Computational Psychiatry.\n \n \n \n \n\n\n \n Mujica-Parodi, L. R.; and Strey, H. H.\n\n\n \n\n\n\n The International Journal of Neuropsychopharmacology, 23(5): 339–347. 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Making$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{mujica-parodi2020,\n\ttitle = {Making {Sense} of {Computational} {Psychiatry}},\n\tvolume = {23},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1469-5111},\n\tdoi = {10.1093/ijnp/pyaa013},\n\tabstract = {In psychiatry we often speak of constructing "models." Here we try to make sense of what such a claim might mean, starting with the most fundamental question: "What is (and isn't) a model?" We then discuss, in a concrete measurable sense, what it means for a model to be useful. In so doing, we first identify the added value that a computational model can provide in the context of accuracy and power. We then present limitations of standard statistical methods and provide suggestions for how we can expand the explanatory power of our analyses by reconceptualizing statistical models as dynamical systems. Finally, we address the problem of model building-suggesting ways in which computational psychiatry can escape the potential for cognitive biases imposed by classical hypothesis-driven research, exploiting deep systems-level information contained within neuroimaging data to advance our understanding of psychiatric neuroscience.},\n\tlanguage = {eng},\n\tnumber = {5},\n\tjournal = {The International Journal of Neuropsychopharmacology},\n\tauthor = {Mujica-Parodi, Lilianne R. and Strey, Helmut H.},\n\tyear = {2020},\n\tpmid = {32219396},\n\tpmcid = {PMC7251632},\n\tpages = {339--347},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/AXCFT56T/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Inferring a network from dynamical signals at its nodes.\n \n \n \n \n\n\n \n Weistuch, C.; Agozzino, L.; Mujica-Parodi, L. R.; and Dill, K. A.\n\n\n \n\n\n\n PLOS Computational Biology, 16(11): e1008435. 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Inferring$ Paper\n \n \n \n $\"Inferring$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{weistuch2020,\n\ttitle = {Inferring a network from dynamical signals at its nodes},\n\tvolume = {16},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1553-7358},\n\turl = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008435},\n\tdoi = {10.1371/journal.pcbi.1008435},\n\tabstract = {We give an approximate solution to the difficult inverse problem of inferring the topology of an unknown network from given time-dependent signals at the nodes. For example, we measure signals from individual neurons in the brain, and infer how they are inter-connected. We use Maximum Caliber as an inference principle. The combinatorial challenge of high-dimensional data is handled using two different approximations to the pairwise couplings. We show two proofs of principle: in a nonlinear genetic toggle switch circuit, and in a toy neural network.},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2021-11-30},\n\tjournal = {PLOS Computational Biology},\n\tauthor = {Weistuch, Corey and Agozzino, Luca and Mujica-Parodi, Lilianne R. and Dill, Ken A.},\n\tyear = {2020},\n\tpages = {e1008435},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/E347AKZZ/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Fast Spatial Autocorrelation.\n \n \n \n \n\n\n \n Amgalan, A.; Mujica-Parodi, L. R.; and Skiena, S. S.\n\n\n \n\n\n\n In 2020 IEEE International Conference on Data Mining (ICDM), pages 12–21, 2020. \n \n\n\n\n
\n\n\n\n \n \n $\"Fast$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{amgalan2020,\n\ttitle = {Fast {Spatial} {Autocorrelation}},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tdoi = {10.1109/ICDM50108.2020.00010},\n\tabstract = {Physical or geographic location proves to be an important feature in many data science models, because many diverse natural and social phenomenon have a spatial component. Spatial autocorrelation measures the extent to which locally adjacent observations of the same phenomenon are correlated. Although statistics like Moran's I and Geary's C are widely used to measure spatial autocorrelation, they are slow: all popular methods run in Ω(n2) time, rendering them unusable for large data sets, or long time-courses with moderate numbers of points. We propose a new SA statistic based on the notion that the variance observed when merging pairs of nearby clusters should increase slowly for spatially autocorrelated variables. We give a linear-time algorithm to calculate SA for a variable with an input agglomeration order (available at https://github.com/aamgalan/spatial\\_autocorrelation). For a typical dataset of n ≈ 63,000 points, our SA autocorrelation measure can be computed in 1 second, versus 2 hours or more for Moran's I and Geary's C. Through simulation studies, we demonstrate that SA identifies spatial correlations in variables generated with spatially-dependent model half an order of magnitude earlier than either Moran's I or Geary's C. Finally, we prove several theoretical properties of SA: namely that it behaves as a true correlation statistic, and is invariant under addition or multiplication by a constant.},\n\tbooktitle = {2020 {IEEE} {International} {Conference} on {Data} {Mining} ({ICDM})},\n\tauthor = {Amgalan, Anar and Mujica-Parodi, Lilianne R. and Skiena, Steven S.},\n\tyear = {2020},\n\tpages = {12--21},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/MD7X2BTU/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Diet modulates brain network stability, a biomarker for brain aging, in young adults.\n \n \n \n \n\n\n \n Mujica-Parodi, L. R.; Amgalan, A.; Sultan, S. F.; Antal, B.; Sun, X.; Skiena, S.; Lithen, A.; Adra, N.; Ratai, E.; Weistuch, C.; Govindarajan, S. T.; Strey, H. H.; Dill, K. A.; Stufflebeam, S. M.; Veech, R. L.; and Clarke, K.\n\n\n \n\n\n\n Proceedings of the National Academy of Sciences, 117(11): 6170–6177. 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Diet$ Paper\n \n \n \n $\"Diet$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{mujica-parodi2020a,\n\ttitle = {Diet modulates brain network stability, a biomarker for brain aging, in young adults},\n\tvolume = {117},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\turl = {https://www.pnas.org/doi/10.1073/pnas.1913042117},\n\tdoi = {10.1073/pnas.1913042117},\n\tabstract = {Epidemiological studies suggest that insulin resistance accelerates progression of age-based cognitive impairment, which neuroimaging has linked to brain glucose hypometabolism. As cellular inputs, ketones increase Gibbs free energy change for ATP by 27\\% compared to glucose. Here we test whether dietary changes are capable of modulating sustained functional communication between brain regions (network stability) by changing their predominant dietary fuel from glucose to ketones. We first established network stability as a biomarker for brain aging using two large-scale (n = 292, ages 20 to 85 y; n = 636, ages 18 to 88 y) 3 T functional MRI (fMRI) datasets. To determine whether diet can influence brain network stability, we additionally scanned 42 adults, age {\\textless} 50 y, using ultrahigh-field (7 T) ultrafast (802 ms) fMRI optimized for single-participant-level detection sensitivity. One cohort was scanned under standard diet, overnight fasting, and ketogenic diet conditions. To isolate the impact of fuel type, an independent overnight fasted cohort was scanned before and after administration of a calorie-matched glucose and exogenous ketone ester (d-β-hydroxybutyrate) bolus. Across the life span, brain network destabilization correlated with decreased brain activity and cognitive acuity. Effects emerged at 47 y, with the most rapid degeneration occurring at 60 y. Networks were destabilized by glucose and stabilized by ketones, irrespective of whether ketosis was achieved with a ketogenic diet or exogenous ketone ester. Together, our results suggest that brain network destabilization may reflect early signs of hypometabolism, associated with dementia. Dietary interventions resulting in ketone utilization increase available energy and thus may show potential in protecting the aging brain.},\n\tnumber = {11},\n\turldate = {2023-11-28},\n\tjournal = {Proceedings of the National Academy of Sciences},\n\tauthor = {Mujica-Parodi, Lilianne R. and Amgalan, Anar and Sultan, Syed Fahad and Antal, Botond and Sun, Xiaofei and Skiena, Steven and Lithen, Andrew and Adra, Noor and Ratai, Eva-Maria and Weistuch, Corey and Govindarajan, Sindhuja Tirumalai and Strey, Helmut H. and Dill, Ken A. and Stufflebeam, Steven M. and Veech, Richard L. and Clarke, Kieran},\n\tyear = {2020},\n\tpages = {6170--6177},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/5DWGA67F/file/view}\n}\n\n\n\n

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\n \n 2018\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n \n Oxytocin attenuates trust as a subset of more general reinforcement learning, with altered reward circuit functional connectivity in males.\n \n \n \n \n\n\n \n Ide, J. S.; Nedic, S.; Wong, K. F.; Strey, S. L.; Lawson, E. A.; Dickerson, B. C.; Wald, L. L.; La Camera, G.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n NeuroImage, 174: 35–43. 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Oxytocin$ Paper\n \n \n \n $\"Oxytocin$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{ide2018,\n\ttitle = {Oxytocin attenuates trust as a subset of more general reinforcement learning, with altered reward circuit functional connectivity in males},\n\tvolume = {174},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1053-8119},\n\turl = {https://www.sciencedirect.com/science/article/pii/S1053811918301320},\n\tdoi = {10.1016/j.neuroimage.2018.02.035},\n\tabstract = {Oxytocin (OT) is an endogenous neuropeptide that, while originally thought to promote trust, has more recently been found to be context-dependent. Here we extend experimental paradigms previously restricted to de novo decision-to-trust, to a more realistic environment in which social relationships evolve in response to iterative feedback over twenty interactions. In a randomized, double blind, placebo-controlled within-subject/crossover experiment of human adult males, we investigated the effects of a single dose of intranasal OT (40 IU) on Bayesian expectation updating and reinforcement learning within a social context, with associated brain circuit dynamics. Subjects participated in a neuroeconomic task (Iterative Trust Game) designed to probe iterative social learning while their brains were scanned using ultra-high field (7T) fMRI. We modeled each subject's behavior using Bayesian updating of belief-states (“willingness to trust”) as well as canonical measures of reinforcement learning (learning rate, inverse temperature). Behavioral trajectories were then used as regressors within fMRI activation and connectivity analyses to identify corresponding brain network functionality affected by OT. Behaviorally, OT reduced feedback learning, without bias with respect to positive versus negative reward. Neurobiologically, reduced learning under OT was associated with muted communication between three key nodes within the reward circuit: the orbitofrontal cortex, amygdala, and lateral (limbic) habenula. Our data suggest that OT, rather than inspiring feelings of generosity, instead attenuates the brain's encoding of prediction error and therefore its ability to modulate pre-existing beliefs. This effect may underlie OT's putative role in promoting what has typically been reported as ‘unjustified trust’ in the face of information that suggests likely betrayal, while also resolving apparent contradictions with regard to OT's context-dependent behavioral effects.},\n\tlanguage = {en},\n\turldate = {2021-11-30},\n\tjournal = {NeuroImage},\n\tauthor = {Ide, Jaime S. and Nedic, Sanja and Wong, Kin F. and Strey, Shmuel L. and Lawson, Elizabeth A. and Dickerson, Bradford C. and Wald, Lawrence L. and La Camera, Giancarlo and Mujica-Parodi, Lilianne R.},\n\tyear = {2018},\n\tpages = {35--43},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/QGN66WEQ/file/view}\n}\n\n\n\n

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\n Oxytocin (OT) is an endogenous neuropeptide that, while originally thought to promote trust, has more recently been found to be context-dependent. Here we extend experimental paradigms previously restricted to de novo decision-to-trust, to a more realistic environment in which social relationships evolve in response to iterative feedback over twenty interactions. In a randomized, double blind, placebo-controlled within-subject/crossover experiment of human adult males, we investigated the effects of a single dose of intranasal OT (40 IU) on Bayesian expectation updating and reinforcement learning within a social context, with associated brain circuit dynamics. Subjects participated in a neuroeconomic task (Iterative Trust Game) designed to probe iterative social learning while their brains were scanned using ultra-high field (7T) fMRI. We modeled each subject's behavior using Bayesian updating of belief-states (“willingness to trust”) as well as canonical measures of reinforcement learning (learning rate, inverse temperature). Behavioral trajectories were then used as regressors within fMRI activation and connectivity analyses to identify corresponding brain network functionality affected by OT. Behaviorally, OT reduced feedback learning, without bias with respect to positive versus negative reward. Neurobiologically, reduced learning under OT was associated with muted communication between three key nodes within the reward circuit: the orbitofrontal cortex, amygdala, and lateral (limbic) habenula. Our data suggest that OT, rather than inspiring feelings of generosity, instead attenuates the brain's encoding of prediction error and therefore its ability to modulate pre-existing beliefs. This effect may underlie OT's putative role in promoting what has typically been reported as ‘unjustified trust’ in the face of information that suggests likely betrayal, while also resolving apparent contradictions with regard to OT's context-dependent behavioral effects.\n

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\n \n 2017\n \n \n (3)\n \n \n

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\n \n\n \n \n \n \n \n \n From Anxious to Reckless: A Control Systems Approach Unifies Prefrontal-Limbic Regulation Across the Spectrum of Threat Detection.\n \n \n \n \n\n\n \n Mujica-Parodi, L. R.; Cha, J.; and Gao, J.\n\n\n \n\n\n\n Frontiers in Systems Neuroscience, 11: 18. 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"From$ Paper\n \n \n \n $\"From$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{mujica-parodi2017,\n\ttitle = {From {Anxious} to {Reckless}: {A} {Control} {Systems} {Approach} {Unifies} {Prefrontal}-{Limbic} {Regulation} {Across} the {Spectrum} of {Threat} {Detection}},\n\tvolume = {11},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1662-5137},\n\tshorttitle = {From {Anxious} to {Reckless}},\n\turl = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5383661/},\n\tdoi = {10.3389/fnsys.2017.00018},\n\tabstract = {Here we provide an integrative review of basic control circuits, and introduce techniques by which their regulation can be quantitatively measured using human neuroimaging. We illustrate the utility of the control systems approach using four human neuroimaging threat detection studies (N = 226), to which we applied circuit-wide analyses in order to identify the key mechanism underlying individual variation. In so doing, we build upon the canonical prefrontal-limbic control system to integrate circuit-wide influence from the inferior frontal gyrus (IFG). These were incorporated into a computational control systems model constrained by neuroanatomy and designed to replicate our experimental data. In this model, the IFG acts as an informational set point, gating signals between the primary prefrontal-limbic negative feedback loop and its cortical information-gathering loop. Along the cortical route, if the sensory cortex provides sufficient information to make a threat assessment, the signal passes to the ventromedial prefrontal cortex (vmPFC), whose threat-detection threshold subsequently modulates amygdala outputs. However, if signal outputs from the sensory cortex do not provide sufficient information during the first pass, the signal loops back to the sensory cortex, with each cycle providing increasingly fine-grained processing of sensory data. Simulations replicate IFG (chaotic) dynamics experimentally observed at both ends at the threat-detection spectrum. As such, they identify distinct types of IFG disconnection from the circuit, with associated clinical outcomes. If IFG thresholds are too high, the IFG and sensory cortex cycle for too long; in the meantime the coarse-grained (excitatory) pathway will dominate, biasing ambiguous stimuli as false positives. On the other hand, if cortical IFG thresholds are too low, the inhibitory pathway will suppress the amygdala without cycling back to the sensory cortex for much-needed fine-grained sensory cortical data, biasing ambiguous stimuli as false negatives. Thus, the control systems model provides a consistent mechanism for IFG regulation, capable of producing results consistent with our data for the full spectrum of threat-detection: from fearful to optimal to reckless. More generally, it illustrates how quantitative characterization of circuit dynamics can be used to unify a fundamental dimension across psychiatric affective symptoms, with implications for populations that range from anxiety disorders to addiction.},\n\turldate = {2023-11-28},\n\tjournal = {Frontiers in Systems Neuroscience},\n\tauthor = {Mujica-Parodi, Lilianne R. and Cha, Jiook and Gao, Jonathan},\n\tyear = {2017},\n\tpmid = {28439230},\n\tpmcid = {PMC5383661},\n\tpages = {18},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/VCGBF456/file/view}\n}\n\n\n\n

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\n Here we provide an integrative review of basic control circuits, and introduce techniques by which their regulation can be quantitatively measured using human neuroimaging. We illustrate the utility of the control systems approach using four human neuroimaging threat detection studies (N = 226), to which we applied circuit-wide analyses in order to identify the key mechanism underlying individual variation. In so doing, we build upon the canonical prefrontal-limbic control system to integrate circuit-wide influence from the inferior frontal gyrus (IFG). These were incorporated into a computational control systems model constrained by neuroanatomy and designed to replicate our experimental data. In this model, the IFG acts as an informational set point, gating signals between the primary prefrontal-limbic negative feedback loop and its cortical information-gathering loop. Along the cortical route, if the sensory cortex provides sufficient information to make a threat assessment, the signal passes to the ventromedial prefrontal cortex (vmPFC), whose threat-detection threshold subsequently modulates amygdala outputs. However, if signal outputs from the sensory cortex do not provide sufficient information during the first pass, the signal loops back to the sensory cortex, with each cycle providing increasingly fine-grained processing of sensory data. Simulations replicate IFG (chaotic) dynamics experimentally observed at both ends at the threat-detection spectrum. As such, they identify distinct types of IFG disconnection from the circuit, with associated clinical outcomes. If IFG thresholds are too high, the IFG and sensory cortex cycle for too long; in the meantime the coarse-grained (excitatory) pathway will dominate, biasing ambiguous stimuli as false positives. On the other hand, if cortical IFG thresholds are too low, the inhibitory pathway will suppress the amygdala without cycling back to the sensory cortex for much-needed fine-grained sensory cortical data, biasing ambiguous stimuli as false negatives. Thus, the control systems model provides a consistent mechanism for IFG regulation, capable of producing results consistent with our data for the full spectrum of threat-detection: from fearful to optimal to reckless. More generally, it illustrates how quantitative characterization of circuit dynamics can be used to unify a fundamental dimension across psychiatric affective symptoms, with implications for populations that range from anxiety disorders to addiction.\n

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\n \n\n \n \n \n \n \n \n Lost emotion: Disrupted brain-based tracking of dynamic affective episodes in anxiety and depression.\n \n \n \n \n\n\n \n Carlson, J. M.; Rubin, D.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n Psychiatry Research: Neuroimaging, 260: 37–48. 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Lost$ Paper\n \n \n \n $\"Lost$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{carlson2017,\n\ttitle = {Lost emotion: {Disrupted} brain-based tracking of dynamic affective episodes in anxiety and depression},\n\tvolume = {260},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {0925-4927},\n\tshorttitle = {Lost emotion},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0925492716301160},\n\tdoi = {10.1016/j.pscychresns.2016.12.002},\n\tabstract = {In our day-to-day lives we are confronted with dynamic sensory inputs that elicit a continuously evolving emotional response. Insight into the brain basis of the dynamic nature of emotional reactivity may be critical for understanding chronic symptoms of anxiety and depression. Here, individuals with generalized anxiety disorder, major depressive disorder, and healthy controls watched a video with dynamic affective content while fMRI activity was recorded. Across all participants there was a large-scale tracking of affective content in emotion processing regions and the default mode network. Anxious and depressed individuals displayed less brain-based coupling within these regions and the extent of this uncoupling correlated with variability in emotional numbing. Thus, abnormal neural tracking of affective information during dynamic emotional episodes appears to represent a disconnection between affective cues in the environment and an individual’s response to these cues—providing a putative neural basis for context insensitive affective reactivity and emotional numbing.},\n\tlanguage = {en},\n\turldate = {2021-11-30},\n\tjournal = {Psychiatry Research: Neuroimaging},\n\tauthor = {Carlson, Joshua M. and Rubin, Denis and Mujica-Parodi, Lilianne R.},\n\tyear = {2017},\n\tpages = {37--48},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/5F32PXVN/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Edge Detection Robust to Intensity Inhomogeneity: A 7T MRI Case Study.\n \n \n \n \n\n\n \n Cappabianco, F. A. M.; Lellis, L. S.; Miranda, P.; Ide, J. S.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n In Beltrán-Castañón, C.; Nyström, I.; and Famili, F., editor(s), Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, of Lecture Notes in Computer Science, pages 459–466, Cham, 2017. Springer International Publishing\n \n\n\n\n
\n\n\n\n \n \n $\"Edge$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{cappabianco2017,\n\taddress = {Cham},\n\tseries = {Lecture {Notes} in {Computer} {Science}},\n\ttitle = {Edge {Detection} {Robust} to {Intensity} {Inhomogeneity}: {A} {7T} {MRI} {Case} {Study}},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tisbn = {978-3-319-52277-7},\n\tshorttitle = {Edge {Detection} {Robust} to {Intensity} {Inhomogeneity}},\n\tdoi = {10.1007/978-3-319-52277-7_56},\n\tabstract = {Edge detection is a fundamental operation for computer vision and image processing applications. As of 1986, John Canny proposed a methodology that became known due to its simplicity, small number of parameters, and high accuracy. The method was designed to optimally detect, locate, and trace single edges over each local gradient maximum. Since then, a number of works were proposed but none of these improvements were capable of dealing with non-uniform intensity, which are notably present in ultra high field magnetic resonance imaging (MRI). In this paper, we evaluate the effects of inhomogeneity correction over automatic edge detection methods over 7T MRI. Importantly, we propose a non-supervised edge detection method which improves the accuracy of state of the art in 28.0\\% as detecting head and brain edges.},\n\tlanguage = {en},\n\tbooktitle = {Progress in {Pattern} {Recognition}, {Image} {Analysis}, {Computer} {Vision}, and {Applications}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Cappabianco, Fábio A. M. and Lellis, Lucas Santana and Miranda, Paulo and Ide, Jaime S. and Mujica-Parodi, Lilianne R.},\n\teditor = {Beltrán-Castañón, César and Nyström, Ingela and Famili, Fazel},\n\tyear = {2017},\n\tpages = {459--466},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/HSDRKVV2/file/view}\n}\n\n\n\n

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\n \n 2016\n \n \n (6)\n \n \n

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\n \n\n \n \n \n \n \n \n Signal Fluctuation Sensitivity: An Improved Metric for Optimizing Detection of Resting-State fMRI Networks.\n \n \n \n \n\n\n \n DeDora, D. J.; Nedic, S.; Katti, P.; Arnab, S.; Wald, L. L.; Takahashi, A.; Van Dijk, K. R. A.; Strey, H. H.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n Frontiers in Neuroscience, 10: 180. 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Signal$ Paper\n \n \n \n $\"Signal$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{dedora2016a,\n\ttitle = {Signal {Fluctuation} {Sensitivity}: {An} {Improved} {Metric} for {Optimizing} {Detection} of {Resting}-{State} {fMRI} {Networks}},\n\tvolume = {10},\n\tcopyright = {All rights reserved},\n\tissn = {1662-453X},\n\tshorttitle = {Signal {Fluctuation} {Sensitivity}},\n\turl = {https://www.frontiersin.org/article/10.3389/fnins.2016.00180},\n\tdoi = {10.3389/fnins.2016.00180},\n\tabstract = {Task-free connectivity analyses have emerged as a powerful tool in functional neuroimaging. Because the cross-correlations that underlie connectivity measures are sensitive to distortion of time-series, here we used a novel dynamic phantom to provide a ground truth for dynamic fidelity between blood oxygen level dependent (BOLD)-like inputs and fMRI outputs. We found that the de facto quality-metric for task-free fMRI, temporal signal to noise ratio (tSNR), correlated inversely with dynamic fidelity; thus, studies optimized for tSNR actually produced time-series that showed the greatest distortion of signal dynamics. Instead, the phantom showed that dynamic fidelity is reasonably approximated by a measure that, unlike tSNR, dissociates signal dynamics from scanner artifact. We then tested this measure, signal fluctuation sensitivity (SFS), against human resting-state data. As predicted by the phantom, SFS—and not tSNR—is associated with enhanced sensitivity to both local and long-range connectivity within the brain's default mode network.},\n\turldate = {2021-11-30},\n\tjournal = {Frontiers in Neuroscience},\n\tauthor = {DeDora, Daniel J. and Nedic, Sanja and Katti, Pratha and Arnab, Shafique and Wald, Lawrence L. and Takahashi, Atsushi and Van Dijk, Koene R. A. and Strey, Helmut H. and Mujica-Parodi, Lilianne R.},\n\tyear = {2016},\n\tpages = {180},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/WE7P4PK7/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Abnormal hippocampal structure and function in clinical anxiety and comorbid depression.\n \n \n \n \n\n\n \n Cha, J.; Greenberg, T.; Song, I.; Blair Simpson, H.; Posner, J.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n Hippocampus, 26(5): 545–553. 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Abnormal$ Paper\n \n \n \n $\"Abnormal$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{cha2016,\n\ttitle = {Abnormal hippocampal structure and function in clinical anxiety and comorbid depression},\n\tvolume = {26},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1098-1063},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1002/hipo.22566},\n\tdoi = {10.1002/hipo.22566},\n\tabstract = {Given the high prevalence rates of comorbidity of anxiety and depressive disorders, identifying a common neural pathway to both disorders is important not only for better diagnosis and treatment, but also for a more complete conceptualization of each disease. Hippocampal abnormalities have been implicated in anxiety and depression, separately; however, it remains unknown whether these abnormalities are also implicated in their comorbidity. Here we address this question by testing 32 adults with generalized anxiety disorder (15 GAD only and 17 comorbid MDD) and 25 healthy controls (HC) using multimodal MRI (structure, diffusion and functional) and automated hippocampal segmentation. We demonstrate that (i) abnormal microstructure of the CA1 and CA2-3 is associated with GAD/MDD comorbidity and (ii) decreased anterior hippocampal reactivity in response to repetition of the threat cue is associated with GAD (with or without MDD comorbidity). In addition, mediation-structural equation modeling (SEM) reveals that our hippocampal and dimensional symptom data are best explained by a model describing a significant influence of abnormal hippocampal microstructure on both anxiety and depression—mediated through its impact on abnormal hippocampal threat processing. Collectively, our findings show a strong association between changes in hippocampal microstructure and threat processing, which together may present a common neural pathway to comorbidity of anxiety and depression. © 2016 Wiley Periodicals, Inc.},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2021-11-30},\n\tjournal = {Hippocampus},\n\tauthor = {Cha, Jiook and Greenberg, Tsafrir and Song, Inkyung and Blair Simpson, Helen and Posner, Jonathan and Mujica-Parodi, Lilianne R.},\n\tyear = {2016},\n\tpages = {545--553},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/DLPKP8DY/file/view}\n}\n\n\n\n\n\n\n\n\n\n\n\n

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\n \n\n \n \n \n \n \n \n Power spectrum scale invariance as a neural marker of cocaine misuse and altered cognitive control.\n \n \n \n \n\n\n \n Ide, J. S.; Hu, S.; Zhang, S.; Mujica-Parodi, L. R.; and Li, C. R.\n\n\n \n\n\n\n NeuroImage: Clinical, 11: 349–356. 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Power$ Paper\n \n \n \n $\"Power$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{ide2016,\n\ttitle = {Power spectrum scale invariance as a neural marker of cocaine misuse and altered cognitive control},\n\tvolume = {11},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {2213-1582},\n\turl = {https://www.sciencedirect.com/science/article/pii/S2213158216300456},\n\tdoi = {10.1016/j.nicl.2016.03.004},\n\tabstract = {Background\nMagnetic resonance imaging (MRI) has highlighted the effects of chronic cocaine exposure on cerebral structures and functions, and implicated the prefrontal cortices in deficits of cognitive control. Recent investigations suggest power spectrum scale invariance (PSSI) of cerebral blood oxygenation level dependent (BOLD) signals as a neural marker of cerebral activity. We examined here how PSSI is altered in association with cocaine misuse and impaired cognitive control.\nMethods\nEighty-eight healthy (HC) and seventy-five age and gender matched cocaine dependent (CD) adults participated in functional MRI of a stop signal task (SST). BOLD images were preprocessed using standard procedures in SPM, including detrending, band-pass filtering (0.01–0.25Hz), and correction for head motions. Voxel-wise PSSI measures were estimated by a linear fit of the power spectrum with a log-log scale. In group analyses, we examined differences in PSSI between HC and CD, and its association with clinical and behavioral variables using a multiple regression. A critical component of cognitive control is post-signal behavioral adjustment, which is compromised in cocaine dependence. Therefore, we examined the PSSI changes in association with post-signal slowing (PSS) in the SST.\nResults\nCompared to HC, CD showed decreased PSS and PSSI in multiple frontoparietal regions. PSSI was positively correlated with PSS in HC in multiple regions, including the left inferior frontal gyrus (IFG) and right supramarginal gyrus (SMG), which showed reduced PSSI in CD.\nConclusions\nThese findings suggest disrupted connectivity dynamics in the fronto-parietal areas in association with post-signal behavioral adjustment in cocaine addicts. These new findings support PSSI as a neural marker of impaired cognitive control in cocaine addiction.},\n\tlanguage = {en},\n\turldate = {2021-11-30},\n\tjournal = {NeuroImage: Clinical},\n\tauthor = {Ide, Jaime S. and Hu, Sien and Zhang, Sheng and Mujica-Parodi, Lilianne R. and Li, Chiang-shan R.},\n\tyear = {2016},\n\tpages = {349--356},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/W34KR88E/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Clinically Anxious Individuals Show Disrupted Feedback between Inferior Frontal Gyrus and Prefrontal-Limbic Control Circuit.\n \n \n \n \n\n\n \n Cha, J.; DeDora, D.; Nedic, S.; Ide, J.; Greenberg, T.; Hajcak, G.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n The Journal of Neuroscience, 36(17): 4708–4718. 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Clinically$ Paper\n \n \n \n $\"Clinically$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{cha2016a,\n\ttitle = {Clinically {Anxious} {Individuals} {Show} {Disrupted} {Feedback} between {Inferior} {Frontal} {Gyrus} and {Prefrontal}-{Limbic} {Control} {Circuit}},\n\tvolume = {36},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {0270-6474},\n\turl = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6601720/},\n\tdoi = {10.1523/JNEUROSCI.1092-15.2016},\n\tabstract = {Clinical anxiety is associated with generalization of conditioned fear, in which innocuous stimuli elicit alarm. Using Pavlovian fear conditioning (electric shock), we quantify generalization as the degree to which subjects' neurobiological responses track perceptual similarity gradients to a conditioned stimulus. Previous studies show that the ventromedial prefrontal cortex (vmPFC) inversely and ventral tegmental area directly track the gradient of perceptual similarity to the conditioned stimulus in healthy individuals, whereas clinically anxious individuals fail to discriminate. Here, we extend this work by identifying specific functional roles within the prefrontal-limbic circuit. We analyzed fMRI time-series acquired from 57 human subjects during a fear generalization task using entropic measures of circuit-wide regulation and feedback (power spectrum scale invariance/autocorrelation), in combination with structural (diffusion MRI-probabilistic tractography) and functional (stochastic dynamic causal modeling) measures of prefrontal-limbic connectivity within the circuit. Group comparison and correlations with anxiety severity across 57 subjects revealed dysregulatory dynamic signatures within the inferior frontal gyrus (IFG), which our prior work has linked to impaired feedback within the circuit. Bayesian model selection then identified a fully connected prefrontal-limbic model comprising the IFG, vmPFC, and amygdala. Dysregulatory IFG dynamics were associated with weaker reciprocal excitatory connectivity between the IFG and the vmPFC. The vmPFC exhibited inhibitory influence on the amygdala. Our current results, combined with our previous work across a threat-perception spectrum of 137 subjects and a meta-analysis of 366 fMRI studies, dissociate distinct roles for three prefrontal-limbic regions, wherein the IFG provides evaluation of stimulus meaning, which then informs the vmPFC in inhibiting the amygdala., SIGNIFICANCE STATEMENT Affective neuroscience has generally treated prefrontal regions (orbitofrontal cortex, dorsolateral prefrontal cortex, inferior frontal gyrus, ventromedial prefrontal cortex) equivalently as inhibitory components of the prefrontal-limbic system. Yet research across the anxiety spectrum suggests that the inferior frontal gyrus may have a more complex role in emotion regulation, as this region shows abnormal function in disorders of both hyperarousal and hypoarousal. Using entropic measures of circuit-wide regulation and feedback, in combination with measures of structural and functional connectivity, we dissociate distinct roles for three prefrontal-limbic regions, wherein the inferior frontal gyrus provides evaluation of stimulus meaning, which then informs the ventromedial prefrontal cortex in inhibiting the amygdala. This reconfiguration coheres with studies of conceptual disambiguation also implicating the inferior frontal gyrus.},\n\tnumber = {17},\n\turldate = {2023-11-28},\n\tjournal = {The Journal of Neuroscience},\n\tauthor = {Cha, Jiook and DeDora, Daniel and Nedic, Sanja and Ide, Jaime and Greenberg, Tsafrir and Hajcak, Greg and Mujica-Parodi, Lilianne R.},\n\tyear = {2016},\n\tpmid = {27122030},\n\tpmcid = {PMC6601720},\n\tpages = {4708--4718},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/KJ83WFZQ/file/view}\n}\n\n\n\n

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\n Clinical anxiety is associated with generalization of conditioned fear, in which innocuous stimuli elicit alarm. Using Pavlovian fear conditioning (electric shock), we quantify generalization as the degree to which subjects' neurobiological responses track perceptual similarity gradients to a conditioned stimulus. Previous studies show that the ventromedial prefrontal cortex (vmPFC) inversely and ventral tegmental area directly track the gradient of perceptual similarity to the conditioned stimulus in healthy individuals, whereas clinically anxious individuals fail to discriminate. Here, we extend this work by identifying specific functional roles within the prefrontal-limbic circuit. We analyzed fMRI time-series acquired from 57 human subjects during a fear generalization task using entropic measures of circuit-wide regulation and feedback (power spectrum scale invariance/autocorrelation), in combination with structural (diffusion MRI-probabilistic tractography) and functional (stochastic dynamic causal modeling) measures of prefrontal-limbic connectivity within the circuit. Group comparison and correlations with anxiety severity across 57 subjects revealed dysregulatory dynamic signatures within the inferior frontal gyrus (IFG), which our prior work has linked to impaired feedback within the circuit. Bayesian model selection then identified a fully connected prefrontal-limbic model comprising the IFG, vmPFC, and amygdala. Dysregulatory IFG dynamics were associated with weaker reciprocal excitatory connectivity between the IFG and the vmPFC. The vmPFC exhibited inhibitory influence on the amygdala. Our current results, combined with our previous work across a threat-perception spectrum of 137 subjects and a meta-analysis of 366 fMRI studies, dissociate distinct roles for three prefrontal-limbic regions, wherein the IFG provides evaluation of stimulus meaning, which then informs the vmPFC in inhibiting the amygdala., SIGNIFICANCE STATEMENT Affective neuroscience has generally treated prefrontal regions (orbitofrontal cortex, dorsolateral prefrontal cortex, inferior frontal gyrus, ventromedial prefrontal cortex) equivalently as inhibitory components of the prefrontal-limbic system. Yet research across the anxiety spectrum suggests that the inferior frontal gyrus may have a more complex role in emotion regulation, as this region shows abnormal function in disorders of both hyperarousal and hypoarousal. Using entropic measures of circuit-wide regulation and feedback, in combination with measures of structural and functional connectivity, we dissociate distinct roles for three prefrontal-limbic regions, wherein the inferior frontal gyrus provides evaluation of stimulus meaning, which then informs the ventromedial prefrontal cortex in inhibiting the amygdala. This reconfiguration coheres with studies of conceptual disambiguation also implicating the inferior frontal gyrus.\n

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\n \n\n \n \n \n \n \n \n Acute psychological stress induces short-term variable immune response.\n \n \n \n \n\n\n \n Breen, M. S.; Beliakova-Bethell, N.; Mujica-Parodi, L. R.; Carlson, J. M.; Ensign, W. Y.; Woelk, C. H.; and Rana, B. K.\n\n\n \n\n\n\n Brain, Behavior, and Immunity, 53: 172–182. 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Acute$ Paper\n \n \n \n $\"Acute$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{breen2016,\n\ttitle = {Acute psychological stress induces short-term variable immune response},\n\tvolume = {53},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {0889-1591},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0889159115300349},\n\tdoi = {10.1016/j.bbi.2015.10.008},\n\tabstract = {In spite of advances in understanding the cross-talk between the peripheral immune system and the brain, the molecular mechanisms underlying the rapid adaptation of the immune system to an acute psychological stressor remain largely unknown. Conventional approaches to classify molecular factors mediating these responses have targeted relatively few biological measurements or explored cross-sectional study designs, and therefore have restricted characterization of stress–immune interactions. This exploratory study analyzed transcriptional profiles and flow cytometric data of peripheral blood leukocytes with physiological (endocrine, autonomic) measurements collected throughout the sequence of events leading up to, during, and after short-term exposure to physical danger in humans. Immediate immunomodulation to acute psychological stress was defined as a short-term selective up-regulation of natural killer (NK) cell-associated cytotoxic and IL-12 mediated signaling genes that correlated with increased cortisol, catecholamines and NK cells into the periphery. In parallel, we observed down-regulation of innate immune toll-like receptor genes and genes of the MyD88-dependent signaling pathway. Correcting gene expression for an influx of NK cells revealed a molecular signature specific to the adrenal cortex. Subsequently, focusing analyses on discrete groups of coordinately expressed genes (modules) throughout the time-series revealed immune stress responses in modules associated to immune/defense response, response to wounding, cytokine production, TCR signaling and NK cell cytotoxicity which differed between males and females. These results offer a spring-board for future research towards improved treatment of stress-related disease including the impact of stress on cardiovascular and autoimmune disorders, and identifies an immune mechanism by which vulnerabilities to these diseases may be gender-specific.},\n\tlanguage = {en},\n\turldate = {2021-11-30},\n\tjournal = {Brain, Behavior, and Immunity},\n\tauthor = {Breen, Michael S. and Beliakova-Bethell, Nadejda and Mujica-Parodi, Lilianne R. and Carlson, Joshua M. and Ensign, Wayne Y. and Woelk, Christopher H. and Rana, Brinda K.},\n\tyear = {2016},\n\tpages = {172--182},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/DUEJIMUG/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Sulfobutyl ether β-cyclodextrin (Captisol®) and methyl β-cyclodextrin enhance and stabilize fluorescence of aqueous indocyanine green.\n \n \n \n \n\n\n \n DeDora, D. J.; Suhrland, C.; Goenka, S.; Mullick Chowdhury, S.; Lalwani, G.; Mujica-Parodi, L. R.; and Sitharaman, B.\n\n\n \n\n\n\n Journal of Biomedical Materials Research Part B: Applied Biomaterials, 104(7): 1457–1464. 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Sulfobutyl$ Paper\n \n \n \n $\"Sulfobutyl$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{dedora2016,\n\ttitle = {Sulfobutyl ether β-cyclodextrin ({Captisol}®) and methyl β-cyclodextrin enhance and stabilize fluorescence of aqueous indocyanine green},\n\tvolume = {104},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1552-4981},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jbm.b.33496},\n\tdoi = {10.1002/jbm.b.33496},\n\tabstract = {As the only FDA-approved near-infrared fluorophore, indocyanine green (ICG) is commonly used to image vasculature in vivo. ICG degrades rapidly in solution, which limits its usefulness in certain applications, including time-sensitive surgical procedures. We propose formulations that address this shortcoming via complexation with β-cyclodextrin derivatives (β-CyD), which are known to create stabilizing inclusion complexes with hydrophobic molecules. Here, we complexed ICG with highly soluble methyl β-CyD and FDA-approved sulfobutyl ether β-CyD (Captisol®) in aqueous solution. We measured the fluorescence of the complexes over 24 h. We found that both CyD+ICG complexes exhibit sustained fluorescence increases of {\\textgreater}2.0× versus ICG in water and {\\textgreater}20.0× in PBS. Using transmission electron microscopy, we found evidence of reduced aggregation in complexes versus ICG alone. We thus conclude that this reduction in aggregation helps mitigate fluorescence autoquenching of CyD+ICG complexes compared in ICG alone. We also found that while ICG complexed with methyl β-CyD severely reduced the viability of MRC-5 fibroblasts, ICG complexed with sulfobutyl ether β-CyD had no effect on viability. These results represent an important first step toward enhancing the utility of aqueous ICG by reducing aggregation-dependent fluorescence degradation. © 2015 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 104B: 1457–1464, 2016.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2021-11-30},\n\tjournal = {Journal of Biomedical Materials Research Part B: Applied Biomaterials},\n\tauthor = {DeDora, Daniel J. and Suhrland, Cassandra and Goenka, Shilpi and Mullick Chowdhury, Sayan and Lalwani, Gaurav and Mujica-Parodi, Lilianne R. and Sitharaman, Balaji},\n\tyear = {2016},\n\tpages = {1457--1464},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/8XVD9G86/file/view}\n}\n\n\n\n

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\n \n 2015\n \n \n (3)\n \n \n

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\n \n\n \n \n \n \n \n \n Using network dynamic fMRI for detection of epileptogenic foci.\n \n \n \n \n\n\n \n Nedic, S.; Stufflebeam, S. M.; Rondinoni, C.; Velasco, T. R.; dos Santos, A. C.; Leite, J. P.; Gargaro, A. C.; Mujica-Parodi, L. R.; and Ide, J. S.\n\n\n \n\n\n\n BMC Neurology, 15(1): 262. 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Using$ Paper\n \n \n \n $\"Using$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{nedic2015,\n\ttitle = {Using network dynamic {fMRI} for detection of epileptogenic foci},\n\tvolume = {15},\n\tcopyright = {All rights reserved},\n\tissn = {1471-2377},\n\turl = {https://doi.org/10.1186/s12883-015-0514-y},\n\tdoi = {10.1186/s12883-015-0514-y},\n\tabstract = {Epilepsy is one of the most prevalent neurological disorders. It remains medically intractable for about one-third of patients with focal epilepsy, for whom precise localization of the epileptogenic zone responsible for seizure initiation may be critical for successful surgery. Existing fMRI literature points to widespread network disturbances in functional connectivity. Per previous scalp and intracranial EEG studies and consistent with excessive local synchronization during interictal discharges, we hypothesized that, relative to same regions in healthy controls, epileptogenic foci would exhibit less chaotic dynamics, identifiable via entropic analyses of resting state fMRI time series.},\n\tnumber = {1},\n\turldate = {2021-11-30},\n\tjournal = {BMC Neurology},\n\tauthor = {Nedic, Sanja and Stufflebeam, Steven M. and Rondinoni, Carlo and Velasco, Tonicarlo R. and dos Santos, Antonio C. and Leite, Joao P. and Gargaro, Ana C. and Mujica-Parodi, Lilianne R. and Ide, Jaime S.},\n\tyear = {2015},\n\tpages = {262},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/JQSS6HDW/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Facilitated Attentional Orienting and Delayed Disengagement to Conscious and Nonconscious Fearful Faces.\n \n \n \n \n\n\n \n Carlson, J. M.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n Journal of Nonverbal Behavior, 39(1): 69–77. 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Facilitated$ Paper\n \n \n \n $\"Facilitated$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{carlson2015,\n\ttitle = {Facilitated {Attentional} {Orienting} and {Delayed} {Disengagement} to {Conscious} and {Nonconscious} {Fearful} {Faces}},\n\tvolume = {39},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1573-3653},\n\turl = {https://doi.org/10.1007/s10919-014-0185-1},\n\tdoi = {10.1007/s10919-014-0185-1},\n\tabstract = {Fearful facial expressions are salient nonverbal social cues that signal the existence of potential threat within the environment. These threat signals capture spatial attention both when processed consciously (unmasked) and nonconsciously (masked). Studies using masked fearful faces have most reliably found speeded orienting towards their location, but delayed disengagement from this location has also been observed. Surprisingly however, the extent to which orienting and disengagement processes underlie modulations in spatial attention to conscious/unmasked fearful faces has yet to be explored. Here, participants performed an unmasked and masked fearful face dot-probe task, which included a baseline condition to assess attentional orienting and disengagement effects. We found that both unmasked and masked fearful faces capture spatial attention through facilitated orienting and delayed disengagement. These results provide new evidence that consciously and nonconsciously processed social expressions of fear facilitate attention through similar mechanisms.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2021-11-30},\n\tjournal = {Journal of Nonverbal Behavior},\n\tauthor = {Carlson, Joshua M. and Mujica-Parodi, Lilianne R.},\n\tyear = {2015},\n\tpages = {69--77},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/APQR3JBK/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Anticipation of high arousal aversive and positive movie clips engages common and distinct neural substrates.\n \n \n \n \n\n\n \n Greenberg, T.; Carlson, J. M.; Rubin, D.; Cha, J.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n Social Cognitive and Affective Neuroscience, 10(4): 605–611. 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Anticipation$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{greenberg2015,\n\ttitle = {Anticipation of high arousal aversive and positive movie clips engages common and distinct neural substrates},\n\tvolume = {10},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1749-5024},\n\tdoi = {10.1093/scan/nsu091},\n\tabstract = {The neural correlates of anxious anticipation have been primarily studied with aversive and neutral stimuli. In this study, we examined the effect of valence on anticipation by using high arousal aversive and positive stimuli and a condition of uncertainty (i.e. either positive or aversive). The task consisted of predetermined cues warning participants of upcoming aversive, positive, 'uncertain' (either aversive or positive) and neutral movie clips. Anticipation of all affective clips engaged common regions including the anterior insula, dorsal anterior cingulate cortex, thalamus, caudate, inferior parietal and prefrontal cortex that are associated with emotional experience, sustained attention and appraisal. In contrast, the nucleus accumbens and medial prefrontal cortex, regions implicated in reward processing, were selectively engaged during anticipation of positive clips (depicting sexually explicit content) and the mid-insula, which has been linked to processing aversive stimuli, was selectively engaged during anticipation of aversive clips (depicting graphic medical procedures); these three areas were also activated during anticipation of 'uncertain' clips reflecting a broad preparatory response for both aversive and positive stimuli. These results suggest that a common circuitry is recruited in anticipation of affective clips regardless of valence, with additional areas preferentially engaged depending on whether expected stimuli are negative or positive.},\n\tlanguage = {eng},\n\tnumber = {4},\n\tjournal = {Social Cognitive and Affective Neuroscience},\n\tauthor = {Greenberg, Tsafrir and Carlson, Joshua M. and Rubin, Denis and Cha, Jiook and Mujica-Parodi, Lilianne R.},\n\tyear = {2015},\n\tpmid = {24984958},\n\tpmcid = {PMC4381244},\n\tpages = {605--611},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/NBPIBTQX/file/view}\n}\n\n\n\n

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\n \n 2014\n \n \n (6)\n \n \n

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\n \n\n \n \n \n \n \n \n Hyper-Reactive Human Ventral Tegmental Area and Aberrant Mesocorticolimbic Connectivity in Overgeneralization of Fear in Generalized Anxiety Disorder.\n \n \n \n \n\n\n \n Carlson, J. M.; DeDora, D. J.; Greenberg, T.; Proudfit, G. H.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n The Journal of Neuroscience, 34(17): 5855–5860. 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Hyper-Reactive$ Paper\n \n \n \n $\"Hyper-Reactive$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{carlson2014a,\n\ttitle = {Hyper-{Reactive} {Human} {Ventral} {Tegmental} {Area} and {Aberrant} {Mesocorticolimbic} {Connectivity} in {Overgeneralization} of {Fear} in {Generalized} {Anxiety} {Disorder}},\n\tvolume = {34},\n\tcopyright = {All rights reserved},\n\tissn = {0270-6474},\n\turl = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6608289/},\n\tdoi = {10.1523/JNEUROSCI.4868-13.2014},\n\tabstract = {The ventral tegmental area (VTA) has been primarily implicated in reward-motivated behavior. Recently, aberrant dopaminergic VTA signaling has also been implicated in anxiety-like behaviors in animal models. These findings, however, have yet to be extended to anxiety in humans. Here we hypothesized that clinical anxiety is linked to dysfunction of the mesocorticolimbic circuit during threat processing in humans; specifically, excessive or dysregulated activity of the mesocorticolimbic aversion circuit may be etiologically related to errors in distinguishing cues of threat versus safety, also known as “overgeneralization of fear.” To test this, we recruited 32 females with generalized anxiety disorder and 25 age-matched healthy control females. We measured brain activity using fMRI while participants underwent a fear generalization task consisting of pseudo-randomly presented rectangles with systematically varying widths. A mid-sized rectangle served as a conditioned stimulus (CS; 50\\% electric shock probability) and rectangles with widths of CS ±20\\%, ±40\\%, and ±60\\% served as generalization stimuli (GS; never paired with electric shock). Healthy controls showed VTA reactivity proportional to the cue's perceptual similarity to CS (threat). In contrast, patients with generalized anxiety disorder showed heightened and less discriminating VTA reactivity to GS, a feature that was positively correlated with trait anxiety, as well as increased mesocortical and decreased mesohippocampal coupling. Our results suggest that the human VTA and the mesocorticolimbic system play a crucial role in threat processing, and that abnormalities in this system are implicated in maladaptive threat processing in clinical anxiety.},\n\tnumber = {17},\n\turldate = {2023-11-28},\n\tjournal = {The Journal of Neuroscience},\n\tauthor = {Carlson, Joshua M. and DeDora, Daniel J. and Greenberg, Tsafrir and Proudfit, Greg H. and Mujica-Parodi, Lilianne R.},\n\tyear = {2014},\n\tpmid = {24760845},\n\tpmcid = {PMC6608289},\n\tpages = {5855--5860},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/TN4UFP9R/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Influence of the BDNF genotype on amygdalo-prefrontal white matter microstructure is linked to nonconscious attention bias to threat.\n \n \n \n \n\n\n \n Carlson, J. M.; Cha, J.; Harmon-Jones, E.; Mujica-Parodi, L. R.; and Hajcak, G.\n\n\n \n\n\n\n Cerebral Cortex (New York, N.Y.: 1991), 24(9): 2249–2257. 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Influence$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{carlson2014,\n\ttitle = {Influence of the {BDNF} genotype on amygdalo-prefrontal white matter microstructure is linked to nonconscious attention bias to threat},\n\tvolume = {24},\n\tcopyright = {All rights reserved},\n\tissn = {1460-2199},\n\tdoi = {10.1093/cercor/bht089},\n\tabstract = {Cognitive processing biases, such as increased attention to threat, are gaining recognition as causal factors in anxiety. Yet, little is known about the anatomical pathway by which threat biases cognition and how genetic factors might influence the integrity of this pathway, and thus, behavior. For 40 normative adults, we reconstructed the entire amygdalo-prefrontal white matter tract (uncinate fasciculus) using diffusion tensor weighted MRI and probabilistic tractography to test the hypothesis that greater fiber integrity correlates with greater nonconscious attention bias to threat as measured by a backward masked dot-probe task. We used path analysis to investigate the relationship between brain-derived nerve growth factor genotype, uncinate fasciculus integrity, and attention bias behavior. Greater structural integrity of the amygdalo-prefrontal tract correlates with facilitated attention bias to nonconscious threat. Genetic variability associated with brain-derived nerve growth factor appears to influence the microstructure of this pathway and, in turn, attention bias to nonconscious threat. These results suggest that the integrity of amygdalo-prefrontal projections underlie nonconscious attention bias to threat and mediate genetic influence on attention bias behavior. Prefrontal cognition and attentional processing in high bias individuals appear to be heavily influenced by nonconscious threat signals relayed via the uncinate fasciculus.},\n\tlanguage = {eng},\n\tnumber = {9},\n\tjournal = {Cerebral Cortex (New York, N.Y.: 1991)},\n\tauthor = {Carlson, Joshua M. and Cha, Jiook and Harmon-Jones, Eddie and Mujica-Parodi, Lilianne R. and Hajcak, Greg},\n\tyear = {2014},\n\tpmid = {23585520},\n\tpages = {2249--2257},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/5V2BT2VR/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Network connectivity modulates power spectrum scale invariance.\n \n \n \n \n\n\n \n Rădulescu, A.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n NeuroImage, 90: 436–448. 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Network$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{radulescu2014,\n\ttitle = {Network connectivity modulates power spectrum scale invariance},\n\tvolume = {90},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1095-9572},\n\tdoi = {10.1016/j.neuroimage.2013.12.001},\n\tabstract = {Measures of complexity are sensitive in detecting disease, which has made them attractive candidates for diagnostic biomarkers; one complexity measure that has shown promise in fMRI is power spectrum scale invariance (PSSI). Even if scale-free features of neuroimaging turn out to be diagnostically useful, however, their underlying neurobiological basis is poorly understood. Using modeling and simulations of a schematic prefrontal-limbic meso-circuit, with excitatory and inhibitory networks of nodes, we present here a framework for how network density within a control system can affect the complexity of signal outputs. Our model demonstrates that scale-free behavior, similar to that observed in fMRI PSSI data, can be obtained for sufficiently large networks in a context as simple as a linear stochastic system of differential equations, although the scale-free range improves when introducing more realistic, nonlinear behavior in the system. PSSI values (reflective of complexity) vary as a function of both input type (excitatory, inhibitory) and input density (mean number of long-range connections, or strength), independent of their node-specific geometric distribution. Signals show pink noise (1/f) behavior when excitatory and inhibitory influences are balanced. As excitatory inputs are increased and decreased, signals shift towards white and brown noise, respectively. As inhibitory inputs are increased and decreased, signals shift towards brown and white noise, respectively. The results hold qualitatively at the hemodynamic scale, which we modeled by introducing a neurovascular component. Comparing hemodynamic simulation results to fMRI PSSI results from 96 individuals across a wide spectrum of anxiety-levels, we show how our model can generate concrete and testable hypotheses for understanding how connectivity affects regulation of meso-circuits in the brain.},\n\tlanguage = {eng},\n\tjournal = {NeuroImage},\n\tauthor = {Rădulescu, Anca and Mujica-Parodi, Lilianne R.},\n\tyear = {2014},\n\tpmid = {24333393},\n\tpages = {436--448},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/NBBCT8Z4/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n The fine line between ‘brave’ and ‘reckless’: Amygdala reactivity and regulation predict recognition of risk.\n \n \n \n \n\n\n \n Mujica-Parodi, L. R.; Carlson, J. M.; Cha (차지욱), J.; and Rubin, D.\n\n\n \n\n\n\n NeuroImage, 103: 1–9. 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n \n $\"The$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{mujica-parodi2014,\n\ttitle = {The fine line between ‘brave’ and ‘reckless’: {Amygdala} reactivity and regulation predict recognition of risk},\n\tvolume = {103},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1053-8119},\n\tshorttitle = {The fine line between ‘brave’ and ‘reckless’},\n\turl = {https://www.sciencedirect.com/science/article/pii/S1053811914007125},\n\tdoi = {10.1016/j.neuroimage.2014.08.038},\n\tabstract = {Background\nHigh sensation-seekers (HSS) pursue novelty even at the cost of self-harm. When challenged, HSS are less anxious, show blunted physiological (cortisol, startle) and neurobiological (prefrontal-limbic) responses, and devalue aversive outcomes. Here, we investigate how these features interact under conditions of physical danger, in distinguishing between adaptive and maladaptive approaches to risk.\nMethods\nWe recruited a cohort of individuals who voluntarily sought out recreational exposure to physical risk, and obtained serial cortisol values over two time-locked days. On the ‘baseline’ day, we scanned subjects' brains with functional and structural MRI; on the ‘skydiving day,’ subjects completed a first-time tandem skydive. During neuroimaging, subjects viewed cues that predicted aversive noise; neural data were analyzed for prefrontal-limbic reactivity (activation) and regulation (non-linear complexity), as well as cortical thickness. To probe threat perception, subjects identified aggression for ambiguous faces morphed between neutral and angry poles.\nResults\nIndividuals with prefrontal-limbic meso-circuits with less balanced regulation between excitatory and inhibitory components showed both diminished cortisol/anxiety responses to their skydives, as well as less accurate perceptual recognition of threat. This impaired control was localized to the inferior frontal gyrus, with associated cortical thinning. Structural equation modeling suggests that sensation-seeking is primarily mediated via threat-perception, which itself is primarily mediated via neural reactivity and regulation.\nConclusions\nOur results refine the sensation-seeking construct to provide important distinctions (brain-based, but with endocrine and cognitive consequences) between the brave, who feel fear but nonetheless overcome it, and the reckless, who fail to recognize danger. This distinction has important real-world implications, as those who fail to recognize risk are less likely to mitigate it.},\n\tlanguage = {en},\n\turldate = {2021-11-30},\n\tjournal = {NeuroImage},\n\tauthor = {Mujica-Parodi, Lilianne R. and Carlson, Joshua M. and Cha (차지욱), Jiook and Rubin, Denis},\n\tyear = {2014},\n\tpages = {1--9},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/FEA73QQP/file/view}\n}\n\n\n\n

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\n Background High sensation-seekers (HSS) pursue novelty even at the cost of self-harm. When challenged, HSS are less anxious, show blunted physiological (cortisol, startle) and neurobiological (prefrontal-limbic) responses, and devalue aversive outcomes. Here, we investigate how these features interact under conditions of physical danger, in distinguishing between adaptive and maladaptive approaches to risk. Methods We recruited a cohort of individuals who voluntarily sought out recreational exposure to physical risk, and obtained serial cortisol values over two time-locked days. On the ‘baseline’ day, we scanned subjects' brains with functional and structural MRI; on the ‘skydiving day,’ subjects completed a first-time tandem skydive. During neuroimaging, subjects viewed cues that predicted aversive noise; neural data were analyzed for prefrontal-limbic reactivity (activation) and regulation (non-linear complexity), as well as cortical thickness. To probe threat perception, subjects identified aggression for ambiguous faces morphed between neutral and angry poles. Results Individuals with prefrontal-limbic meso-circuits with less balanced regulation between excitatory and inhibitory components showed both diminished cortisol/anxiety responses to their skydives, as well as less accurate perceptual recognition of threat. This impaired control was localized to the inferior frontal gyrus, with associated cortical thinning. Structural equation modeling suggests that sensation-seeking is primarily mediated via threat-perception, which itself is primarily mediated via neural reactivity and regulation. Conclusions Our results refine the sensation-seeking construct to provide important distinctions (brain-based, but with endocrine and cognitive consequences) between the brave, who feel fear but nonetheless overcome it, and the reckless, who fail to recognize danger. This distinction has important real-world implications, as those who fail to recognize risk are less likely to mitigate it.\n

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\n \n\n \n \n \n \n \n \n Small-world network properties in prefrontal cortex correlate with predictors of psychopathology risk in young children: A NIRS study.\n \n \n \n \n\n\n \n Fekete, T.; Beacher, F.; Cha, J.; Rubin, D.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n NeuroImage, 85: 345–353. 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Small-world$ Paper\n \n \n \n $\"Small-world$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{fekete2014,\n\tseries = {Celebrating 20 {Years} of {Functional} {Near} {Infrared} {Spectroscopy} ({fNIRS})},\n\ttitle = {Small-world network properties in prefrontal cortex correlate with predictors of psychopathology risk in young children: {A} {NIRS} study},\n\tvolume = {85},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1053-8119},\n\tshorttitle = {Small-world network properties in prefrontal cortex correlate with predictors of psychopathology risk in young children},\n\turl = {https://www.sciencedirect.com/science/article/pii/S1053811913007775},\n\tdoi = {10.1016/j.neuroimage.2013.07.022},\n\tabstract = {Near infrared spectroscopy (NIRS) is an emerging imaging technique that is relatively inexpensive, portable, and particularly well suited for collecting data in ecological settings. Therefore, it holds promise as a potential neurodiagnostic for young children. We set out to explore whether NIRS could be utilized in assessing the risk of developmental psychopathology in young children. A growing body of work indicates that temperament at young age is associated with vulnerability to psychopathology later on in life. In particular, it has been shown that low effortful control (EC), which includes the focusing and shifting of attention, inhibitory control, perceptual sensitivity, and a low threshold for pleasure, is linked to conditions such as anxiety, depression and attention deficit hyperactivity disorder (ADHD). Physiologically, EC has been linked to a control network spanning among other sites the prefrontal cortex. Several psychopathologies, such as depression and ADHD, have been shown to result in compromised small-world network properties. Therefore we set out to explore the relationship between EC and the small-world properties of PFC using NIRS. NIRS data were collected from 44 toddlers, ages 3–5, while watching naturalistic stimuli (movie clips). Derived complex network measures were then correlated to EC as derived from the Children's Behavior Questionnaire (CBQ). We found that reduced levels of EC were associated with compromised small-world properties of the prefrontal network. Our results suggest that the longitudinal NIRS studies of complex network properties in young children hold promise in furthering our understanding of developmental psychopathology.},\n\tlanguage = {en},\n\turldate = {2021-11-30},\n\tjournal = {NeuroImage},\n\tauthor = {Fekete, Tomer and Beacher, Felix and Cha, Jiook and Rubin, Denis and Mujica-Parodi, Lilianne R.},\n\tyear = {2014},\n\tpages = {345--353},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/QZAUTIPJ/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Circuit-Wide Structural and Functional Measures Predict Ventromedial Prefrontal Cortex Fear Generalization: Implications for Generalized Anxiety Disorder.\n \n \n \n \n\n\n \n Cha, J.; Greenberg, T.; Carlson, J. M.; DeDora, D. J.; Hajcak, G.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n The Journal of Neuroscience, 34(11): 4043–4053. 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Circuit-Wide$ Paper\n \n \n \n $\"Circuit-Wide$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{cha2014,\n\ttitle = {Circuit-{Wide} {Structural} and {Functional} {Measures} {Predict} {Ventromedial} {Prefrontal} {Cortex} {Fear} {Generalization}: {Implications} for {Generalized} {Anxiety} {Disorder}},\n\tvolume = {34},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {0270-6474, 1529-2401},\n\tshorttitle = {Circuit-{Wide} {Structural} and {Functional} {Measures} {Predict} {Ventromedial} {Prefrontal} {Cortex} {Fear} {Generalization}},\n\turl = {https://www.jneurosci.org/lookup/doi/10.1523/JNEUROSCI.3372-13.2014},\n\tdoi = {10.1523/JNEUROSCI.3372-13.2014},\n\tabstract = {The ventromedial prefrontal cortex (vmPFC) plays a critical role in a number of evaluative processes, including risk assessment. Impaired discrimination between threat and safety is considered a hallmark of clinical anxiety. Here, we investigated the circuit-wide structural and functional mechanisms underlying vmPFC threat–safety assessment in humans. We tested patients with generalized anxiety disorder (GAD;\n              n\n              = 32, female) and healthy controls (\n              n\n              = 25, age-matched female) on a task that assessed the generalization of conditioned threat during fMRI scanning. The task consisted of seven rectangles of graded widths presented on a screen; only the midsize one was paired with mild electric shock [conditioned stimulus (CS)], while the others, safety cues, systematically varied in width by ±20, 40, and 60\\% [generalization stimuli (GS)] compared with the CS. We derived an index reflecting vmPFC functioning from the BOLD reactivity on a continuum of threat (CS) to safety (GS least similar to CS); patients with GAD showed less discrimination between threat and safety cues, compared with healthy controls (Greenberg et al., 2013b). Using structural, functional (i.e., resting-state), and diffusion MRI, we measured vmPFC thickness, vmPFC functional connectivity, and vmPFC structural connectivity within the corticolimbic systems. The results demonstrate that all three factors predict individual variability of vmPFC threat assessment in an independent fashion. Moreover, these neural features are also linked to GAD, most likely via an vmPFC fear generalization. Our results strongly suggest that vmPFC threat processing is closely associated with broader corticolimbic circuit anomalies, which may synergistically contribute to clinical anxiety.},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2023-11-28},\n\tjournal = {The Journal of Neuroscience},\n\tauthor = {Cha, Jiook and Greenberg, Tsafrir and Carlson, Joshua M. and DeDora, Daniel J. and Hajcak, Greg and Mujica-Parodi, Lilianne R.},\n\tyear = {2014},\n\tpages = {4043--4053},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/9I8NFJK3/file/view}\n}\n\n\n\n

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\n \n 2013\n \n \n (6)\n \n \n

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\n \n\n \n \n \n \n \n \n Optimizing complexity measures for FMRI data: algorithm, artifact, and sensitivity.\n \n \n \n \n\n\n \n Rubin, D.; Fekete, T.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n PloS One, 8(5): e63448. 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Optimizing$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{rubin2013,\n\ttitle = {Optimizing complexity measures for {FMRI} data: algorithm, artifact, and sensitivity},\n\tvolume = {8},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1932-6203},\n\tshorttitle = {Optimizing complexity measures for {FMRI} data},\n\tdoi = {10.1371/journal.pone.0063448},\n\tabstract = {INTRODUCTION: Complexity in the brain has been well-documented at both neuronal and hemodynamic scales, with increasing evidence supporting its use in sensitively differentiating between mental states and disorders. However, application of complexity measures to fMRI time-series, which are short, sparse, and have low signal/noise, requires careful modality-specific optimization.\nMETHODS: HERE WE USE BOTH SIMULATED AND REAL DATA TO ADDRESS TWO FUNDAMENTAL ISSUES: choice of algorithm and degree/type of signal processing. Methods were evaluated with regard to resilience to acquisition artifacts common to fMRI as well as detection sensitivity. Detection sensitivity was quantified in terms of grey-white matter contrast and overlap with activation. We additionally investigated the variation of complexity with activation and emotional content, optimal task length, and the degree to which results scaled with scanner using the same paradigm with two 3T magnets made by different manufacturers. Methods for evaluating complexity were: power spectrum, structure function, wavelet decomposition, second derivative, rescaled range, Higuchi's estimate of fractal dimension, aggregated variance, and detrended fluctuation analysis. To permit direct comparison across methods, all results were normalized to Hurst exponents.\nRESULTS: Power-spectrum, Higuchi's fractal dimension, and generalized Hurst exponent based estimates were most successful by all criteria; the poorest-performing measures were wavelet, detrended fluctuation analysis, aggregated variance, and rescaled range.\nCONCLUSIONS: Functional MRI data have artifacts that interact with complexity calculations in nontrivially distinct ways compared to other physiological data (such as EKG, EEG) for which these measures are typically used. Our results clearly demonstrate that decisions regarding choice of algorithm, signal processing, time-series length, and scanner have a significant impact on the reliability and sensitivity of complexity estimates.},\n\tlanguage = {eng},\n\tnumber = {5},\n\tjournal = {PloS One},\n\tauthor = {Rubin, Denis and Fekete, Tomer and Mujica-Parodi, Lilianne R.},\n\tyear = {2013},\n\tpmid = {23700424},\n\tpmcid = {PMC3660309},\n\tpages = {e63448},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/GMGP9DZ8/file/view}\n}\n\n\n\n

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\n INTRODUCTION: Complexity in the brain has been well-documented at both neuronal and hemodynamic scales, with increasing evidence supporting its use in sensitively differentiating between mental states and disorders. However, application of complexity measures to fMRI time-series, which are short, sparse, and have low signal/noise, requires careful modality-specific optimization. METHODS: HERE WE USE BOTH SIMULATED AND REAL DATA TO ADDRESS TWO FUNDAMENTAL ISSUES: choice of algorithm and degree/type of signal processing. Methods were evaluated with regard to resilience to acquisition artifacts common to fMRI as well as detection sensitivity. Detection sensitivity was quantified in terms of grey-white matter contrast and overlap with activation. We additionally investigated the variation of complexity with activation and emotional content, optimal task length, and the degree to which results scaled with scanner using the same paradigm with two 3T magnets made by different manufacturers. Methods for evaluating complexity were: power spectrum, structure function, wavelet decomposition, second derivative, rescaled range, Higuchi's estimate of fractal dimension, aggregated variance, and detrended fluctuation analysis. To permit direct comparison across methods, all results were normalized to Hurst exponents. RESULTS: Power-spectrum, Higuchi's fractal dimension, and generalized Hurst exponent based estimates were most successful by all criteria; the poorest-performing measures were wavelet, detrended fluctuation analysis, aggregated variance, and rescaled range. CONCLUSIONS: Functional MRI data have artifacts that interact with complexity calculations in nontrivially distinct ways compared to other physiological data (such as EKG, EEG) for which these measures are typically used. Our results clearly demonstrate that decisions regarding choice of algorithm, signal processing, time-series length, and scanner have a significant impact on the reliability and sensitivity of complexity estimates.\n

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\n \n\n \n \n \n \n \n \n Multiple Kernel Learning Captures a Systems-Level Functional Connectivity Biomarker Signature in Amyotrophic Lateral Sclerosis.\n \n \n \n \n\n\n \n Fekete, T.; Zach, N.; Mujica-Parodi, L. R.; and Turner, M. R.\n\n\n \n\n\n\n PLOS ONE, 8(12): e85190. 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Multiple$ Paper\n \n \n \n $\"Multiple$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{fekete2013,\n\ttitle = {Multiple {Kernel} {Learning} {Captures} a {Systems}-{Level} {Functional} {Connectivity} {Biomarker} {Signature} in {Amyotrophic} {Lateral} {Sclerosis}},\n\tvolume = {8},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1932-6203},\n\turl = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0085190},\n\tdoi = {10.1371/journal.pone.0085190},\n\tabstract = {There is significant clinical and prognostic heterogeneity in the neurodegenerative disorder amyotrophic lateral sclerosis (ALS), despite a common immunohistological signature. Consistent extra-motor as well as motor cerebral, spinal anterior horn and distal neuromuscular junction pathology supports the notion of ALS a system failure. Establishing a disease biomarker is a priority but a simplistic, coordinate-based approach to brain dysfunction using MRI is not tenable. Resting-state functional MRI reflects the organization of brain networks at the systems-level, and so changes in of motor functional connectivity were explored to determine their potential as the substrate for a biomarker signature. Intra- as well as inter-motor functional networks in the 0.03–0.06 Hz frequency band were derived from 40 patients and 30 healthy controls of similar age, and used as features for pattern detection, employing multiple kernel learning. This approach enabled an accurate classification of a group of patients that included a range of clinical sub-types. An average of 13 regions-of-interest were needed to reach peak discrimination. Subsequent analysis revealed that the alterations in motor functional connectivity were widespread, including regions not obviously clinically affected such as the cerebellum and basal ganglia. Complex network analysis showed that functional networks in ALS differ markedly in their topology, reflecting the underlying altered functional connectivity pattern seen in patients: 1) reduced connectivity of both the cortical and sub-cortical motor areas with non motor areas 2)reduced subcortical-cortical motor connectivity and 3) increased connectivity observed within sub-cortical motor networks. This type of analysis has potential to non-invasively define a biomarker signature at the systems-level. As the understanding of neurodegenerative disorders moves towards studying pre-symptomatic changes, there is potential for this type of approach to generate biomarkers for the testing of neuroprotective strategies.},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2021-11-30},\n\tjournal = {PLOS ONE},\n\tauthor = {Fekete, Tomer and Zach, Neta and Mujica-Parodi, Lilianne R. and Turner, Martin R.},\n\tyear = {2013},\n\tpages = {e85190},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/KN2F5CJ3/file/view}\n}\n\n\n\n

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\n There is significant clinical and prognostic heterogeneity in the neurodegenerative disorder amyotrophic lateral sclerosis (ALS), despite a common immunohistological signature. Consistent extra-motor as well as motor cerebral, spinal anterior horn and distal neuromuscular junction pathology supports the notion of ALS a system failure. Establishing a disease biomarker is a priority but a simplistic, coordinate-based approach to brain dysfunction using MRI is not tenable. Resting-state functional MRI reflects the organization of brain networks at the systems-level, and so changes in of motor functional connectivity were explored to determine their potential as the substrate for a biomarker signature. Intra- as well as inter-motor functional networks in the 0.03–0.06 Hz frequency band were derived from 40 patients and 30 healthy controls of similar age, and used as features for pattern detection, employing multiple kernel learning. This approach enabled an accurate classification of a group of patients that included a range of clinical sub-types. An average of 13 regions-of-interest were needed to reach peak discrimination. Subsequent analysis revealed that the alterations in motor functional connectivity were widespread, including regions not obviously clinically affected such as the cerebellum and basal ganglia. Complex network analysis showed that functional networks in ALS differ markedly in their topology, reflecting the underlying altered functional connectivity pattern seen in patients: 1) reduced connectivity of both the cortical and sub-cortical motor areas with non motor areas 2)reduced subcortical-cortical motor connectivity and 3) increased connectivity observed within sub-cortical motor networks. This type of analysis has potential to non-invasively define a biomarker signature at the systems-level. As the understanding of neurodegenerative disorders moves towards studying pre-symptomatic changes, there is potential for this type of approach to generate biomarkers for the testing of neuroprotective strategies.\n

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\n \n\n \n \n \n \n \n \n Functional and structural amygdala – Anterior cingulate connectivity correlates with attentional bias to masked fearful faces.\n \n \n \n \n\n\n \n Carlson, J. M.; Cha, J.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n Cortex, 49(9): 2595–2600. 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Functional$ Paper\n \n \n \n $\"Functional$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{carlson2013,\n\ttitle = {Functional and structural amygdala – {Anterior} cingulate connectivity correlates with attentional bias to masked fearful faces},\n\tvolume = {49},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {0010-9452},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0010945213001809},\n\tdoi = {10.1016/j.cortex.2013.07.008},\n\tabstract = {An attentional bias to threat has been causally related to anxiety. Recent research has linked nonconscious attentional bias to threat with variability in the integrity of the amygdala – anterior cingulate pathway, which sheds light on the neuroanatomical basis for a behavioral precursor to anxiety. However, the extent to which structural variability in amygdala – anterior cingulate integrity relates to the functional connectivity within this pathway and how such functional connectivity may relate to attention bias behavior, remain critical missing pieces of the puzzle. In 15 individuals we measured the structural integrity of the amygdala – prefrontal pathway with diffusion tensor-weighted MRI (magnetic resonance imaging), amygdala-seeded intrinsic functional connectivity to the anterior cingulate, and attentional bias toward backward masked fearful faces with a dot-probe task. We found that greater biases in attention to threat predicted greater levels of uncinate fasciculus integrity, greater positive amygdala – anterior cingulate functional connectivity, and greater amygdala coupling with a broader social perception network including the superior temporal sulcus, tempoparietal junction (TPJ), and somatosensory cortex. Additionally, greater levels of uncinate fasciculus integrity correlated with greater levels of amygdala – anterior cingulate intrinsic functional connectivity. Thus, high bias individuals displayed a heightened degree of amygdala – anterior cingulate connectivity during basal conditions, which we believe predisposes these individuals to focus their attention on signals of threat within their environment.},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2021-11-30},\n\tjournal = {Cortex},\n\tauthor = {Carlson, Joshua M. and Cha, Jiook and Mujica-Parodi, L. R.},\n\tyear = {2013},\n\tpages = {2595--2600},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/U5I7MBVK/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Human Gender Differences in the Perception of Conspecific Alarm Chemosensory Cues.\n \n \n \n \n\n\n \n Radulescu, A. R.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n PLOS ONE, 8(7): e68485. 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Human$ Paper\n \n \n \n $\"Human$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{radulescu2013,\n\ttitle = {Human {Gender} {Differences} in the {Perception} of {Conspecific} {Alarm} {Chemosensory} {Cues}},\n\tvolume = {8},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1932-6203},\n\turl = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0068485},\n\tdoi = {10.1371/journal.pone.0068485},\n\tabstract = {It has previously been established that, in threatening situations, animals use alarm pheromones to communicate danger. There is emerging evidence of analogous chemosensory “stress” cues in humans. For this study, we collected alarm and exercise sweat from “donors,” extracted it, pooled it and presented it to 16 unrelated “detector” subjects undergoing fMRI. The fMRI protocol consisted of four stimulus runs, with each combination of stimulus condition and donor gender represented four times. Because olfactory stimuli do not follow the canonical hemodynamic response, we used a model-free approach. We performed minimal preprocessing and worked directly with block-average time series and step-function estimates. We found that, while male stress sweat produced a comparably strong emotional response in both detector genders, female stress sweat produced a markedly stronger arousal in female than in male detectors. Our statistical tests pinpointed this gender-specificity to the right amygdala (strongest in the superficial nuclei). When comparing the olfactory bulb responses to the corresponding stimuli, we found no significant differences between male and female detectors. These imaging results complement existing behavioral evidence, by identifying whether gender differences in response to alarm chemosignals are initiated at the perceptual versus emotional level. Since we found no significant differences in the olfactory bulb (primary processing site for chemosensory signals in mammals), we infer that the specificity in responding to female fear is likely based on processing meaning, rather than strength, of chemosensory cues from each gender.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2021-11-30},\n\tjournal = {PLOS ONE},\n\tauthor = {Radulescu, Anca R. and Mujica-Parodi, Lilianne R.},\n\tyear = {2013},\n\tpages = {e68485},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/96A2MEIQ/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Neural reactivity tracks fear generalization gradients.\n \n \n \n \n\n\n \n Greenberg, T.; Carlson, J. M.; Cha, J.; Hajcak, G.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n Biological Psychology, 92(1): 2–8. 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Neural$ Paper\n \n \n \n $\"Neural$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{greenberg2013,\n\tseries = {{SI}: {Human} {Fear} {Conditioning}},\n\ttitle = {Neural reactivity tracks fear generalization gradients},\n\tvolume = {92},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {0301-0511},\n\turl = {https://www.sciencedirect.com/science/article/pii/S030105111100305X},\n\tdoi = {10.1016/j.biopsycho.2011.12.007},\n\tabstract = {Recent studies on fear generalization have demonstrated that fear-potentiated startle and skin conductance responses to a conditioned stimulus (CS) generalize to similar stimuli, with the strength of the fear response linked to perceptual similarity to the CS. The aim of the present study was to extend this work by examining neural correlates of fear generalization. An initial experiment (N=8) revealed that insula reactivity tracks the conditioned fear gradient. We then replicated this effect in a larger independent sample (N=25). Activation in the insula, anterior cingulate, right supplementary motor cortex and caudate increased reactivity as generalization stimuli (GS) were more similar to the CS, consistent with participants’ overall ratings of perceived shock likelihood and pupillary response to each stimulus.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2021-11-30},\n\tjournal = {Biological Psychology},\n\tauthor = {Greenberg, Tsafrir and Carlson, Joshua M. and Cha, Jiook and Hajcak, Greg and Mujica-Parodi, Lilianne R.},\n\tyear = {2013},\n\tpages = {2--8},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/T2JWJTTB/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Ventromedial prefrontal cortex reactivity is altered in generalized anxiety disorder during fear generalization.\n \n \n \n \n\n\n \n Greenberg, T.; Carlson, J. M.; Cha, J.; Hajcak, G.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n Depression and Anxiety, 30(3): 242–250. 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Ventromedial$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{greenberg2013a,\n\ttitle = {Ventromedial prefrontal cortex reactivity is altered in generalized anxiety disorder during fear generalization},\n\tvolume = {30},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1520-6394},\n\tdoi = {10.1002/da.22016},\n\tabstract = {BACKGROUND: Fear generalization is thought to contribute to the development and maintenance of anxiety symptoms and accordingly has been the focus of recent research. Previously, we reported that in healthy individuals (N = 25) neural reactivity in the insula, anterior cingulate cortex (ACC), supplementary motor area (SMA), and caudate follow a generalization gradient with a peak response to a conditioned stimulus (CS) that declines with greater perceptual dissimilarity of generalization stimuli (GS) to the CS. In contrast, reactivity in the ventromedial prefrontal cortex (vmPFC), a region linked to fear inhibition, showed an opposite response pattern. The aim of the current study was to examine whether neural responses to fear generalization differ in generalized anxiety disorder (GAD). A second aim was to examine connectivity of primary regions engaged by the generalization task in the GAD group versus healthy group, using psychophysiological interaction analysis.\nMETHODS: Thirty-two women diagnosed with GAD were scanned using the same generalization task as our healthy group.\nRESULTS: Individuals with GAD exhibited a less discriminant vmPFC response pattern suggestive of deficient recruitment of vmPFC during fear inhibition. Across participants, there was enhanced anterior insula (aINS) coupling with the posterior insula, ACC, SMA, and amygdala during presentation of the CS, consistent with a modulatory role for the aINS in the execution of fear responses.\nCONCLUSIONS: These findings suggest that deficits in fear regulation, rather than in the excitatory response itself, are more critical to the pathophysiology of GAD in the context of fear generalization.},\n\tlanguage = {eng},\n\tnumber = {3},\n\tjournal = {Depression and Anxiety},\n\tauthor = {Greenberg, Tsafrir and Carlson, Joshua M. and Cha, Jiook and Hajcak, Greg and Mujica-Parodi, Lilianne R.},\n\tyear = {2013},\n\tpmid = {23139148},\n\tpages = {242--250},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/DAAJV3KJ/file/view}\n}\n\n\n\n

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\n \n 2012\n \n \n (4)\n \n \n

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\n \n\n \n \n \n \n \n \n The orienting of spatial attention to backward masked fearful faces is associated with variation in the serotonin transporter gene.\n \n \n \n \n\n\n \n Carlson, J. M.; Mujica-Parodi, L. R.; Harmon-Jones, E.; and Hajcak, G.\n\n\n \n\n\n\n Emotion, 12(2): 203–207. 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{carlson2012,\n\ttitle = {The orienting of spatial attention to backward masked fearful faces is associated with variation in the serotonin transporter gene},\n\tvolume = {12},\n\tcopyright = {All rights reserved},\n\tissn = {1931-1516},\n\tdoi = {10.1037/a0025170},\n\tabstract = {Threat signals facilitate spatial attention, even when awareness of these signals has been restricted through the use of backward masking. However, unrestricted/unmasked threat cues tend to delay the disengagement of attention, whereas restricted/masked threat facilitates orienting, suggesting different underlying mechanisms. Within the general population, the serotonin transporter gene polymorphism (5HTTLPR) is associated with one's allocation of attention to unmasked threat signals. However, it is unclear to what extent the 5HTTLPR gene may be involved in nonconscious biases to masked threat, and whether or not such biases are driven by facilitated orienting or delayed disengagement. Participants were genotyped and performed a dot-probe task with backward masked fearful and neutral faces. Results indicate that short-allele carriers of the 5HTTLPR gene nonconsciously orient spatial attention to masked fearful faces. On the other hand, homozygous long-allele individuals tended to direct attention away from masked fearful faces. All participants' performance was at chance in a posttask assessment of awareness for the masked faces. The results add to current literature on the 5HTTLPR and attention biases, and suggest that threat signals facilitate the orienting of attention in short-allele carriers of the 5HTTLPR gene even under restricted processing conditions. (PsycINFO Database Record (c) 2016 APA, all rights reserved)},\n\tnumber = {2},\n\tjournal = {Emotion},\n\tauthor = {Carlson, Joshua M. and Mujica-Parodi, Lilianne R. and Harmon-Jones, Eddie and Hajcak, Greg},\n\tyear = {2012},\n\tpages = {203--207},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/R2TPJWFK/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Nonconscious attention bias to threat is correlated with anterior cingulate cortex gray matter volume: A voxel-based morphometry result and replication.\n \n \n \n \n\n\n \n Carlson, J. M.; Beacher, F.; Reinke, K. S.; Habib, R.; Harmon-Jones, E.; Mujica-Parodi, L. R.; and Hajcak, G.\n\n\n \n\n\n\n NeuroImage, 59(2): 1713–1718. 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Nonconscious$ Paper\n \n \n \n $\"Nonconscious$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{carlson2012a,\n\ttitle = {Nonconscious attention bias to threat is correlated with anterior cingulate cortex gray matter volume: {A} voxel-based morphometry result and replication},\n\tvolume = {59},\n\tcopyright = {All rights reserved},\n\tissn = {1053-8119},\n\tshorttitle = {Nonconscious attention bias to threat is correlated with anterior cingulate cortex gray matter volume},\n\turl = {https://www.sciencedirect.com/science/article/pii/S1053811911010883},\n\tdoi = {10.1016/j.neuroimage.2011.09.040},\n\tabstract = {An important aspect of the fear response is the allocation of spatial attention toward threatening stimuli. This response is so powerful that modulations in spatial attention can occur automatically without conscious awareness. Functional neuroimaging research suggests that the amygdala and anterior cingulate cortex (ACC) form a network involved in the rapid orienting of attention to threat. A hyper-responsive attention bias to threat is a common component of anxiety disorders. Yet, little is known of how individual differences in underlying brain morphometry relate to variability in attention bias to threat. Here, we performed two experiments using dot-probe tasks that measured individuals' attention bias to backward masked fearful faces. We collected whole-brain structural magnetic resonance images and used voxel-based morphometry to measure brain morphometry. We tested the hypothesis that reduced gray matter within the amygdala and ACC would be associated with reduced attention bias to threat. In Experiment 1, we found that backward masked fearful faces captured spatial attention and that elevated attention bias to masked threat was associated with greater ACC gray matter volumes. In Experiment 2, this association was replicated in a separate sample. Thus, we provide initial and replicating evidence that ACC gray matter volume is correlated with biased attention to threat. Importantly, we demonstrate that variability in affective attention bias within the healthy population is associated with ACC morphometry. This result opens the door for future research into the underlying brain morphometry associated with attention bias in clinically anxious populations.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2021-11-30},\n\tjournal = {NeuroImage},\n\tauthor = {Carlson, Joshua M. and Beacher, Felix and Reinke, Karen S. and Habib, Reza and Harmon-Jones, Eddie and Mujica-Parodi, Lilianne R. and Hajcak, Greg},\n\tyear = {2012},\n\tpages = {1713--1718},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/2452HVV8/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Second-hand stress: inhalation of stress sweat enhances neural response to neutral faces.\n \n \n \n \n\n\n \n Rubin, D.; Botanov, Y.; Hajcak, G.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n Social Cognitive and Affective Neuroscience, 7(2): 208–212. 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Second-hand$ Paper\n \n \n \n $\"Second-hand$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{rubin2012,\n\ttitle = {Second-hand stress: inhalation of stress sweat enhances neural response to neutral faces},\n\tvolume = {7},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1749-5016},\n\tshorttitle = {Second-hand stress},\n\turl = {https://doi.org/10.1093/scan/nsq097},\n\tdoi = {10.1093/scan/nsq097},\n\tabstract = {This study investigated whether human chemosensory-stress cues affect neural activity related to the evaluation of emotional stimuli. Chemosensory stimuli were obtained from the sweat of 64 male donors during both stress (first-time skydive) and control (exercise) conditions, indistinguishable by odor. We then recorded event-related potentials (ERPs) from an unrelated group of 14 participants while they viewed faces morphed with neutral-to-angry expressions and inhaled nebulized stress and exercise sweat in counter-balanced blocks, blind to condition. Results for the control condition ERPs were consistent with previous findings: the late positive potential (LPP; 400–600 ms post stimulus) in response to faces was larger for threatening than both neutral and ambiguous faces. In contrast, the stress condition was associated with a heightened LPP across all facial expressions; relative to control, the LPP was increased for both ambiguous and neutral faces in the stress condition. These results suggest that stress sweat may impact electrocortical activity associated with attention to salient environmental cues, potentially increasing attentiveness to otherwise inconspicuous stimuli.},\n\tnumber = {2},\n\turldate = {2021-11-30},\n\tjournal = {Social Cognitive and Affective Neuroscience},\n\tauthor = {Rubin, Denis and Botanov, Yevgeny and Hajcak, Greg and Mujica-Parodi, Lilianne R.},\n\tyear = {2012},\n\tpages = {208--212},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/RCFAERA8/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Trait reappraisal is associated with resilience to acute psychological stress.\n \n \n \n \n\n\n \n Carlson, J. M.; Dikecligil, G. N.; Greenberg, T.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n Journal of Research in Personality, 46(5): 609–613. 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Trait$ Paper\n \n \n \n $\"Trait$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{carlson2012b,\n\ttitle = {Trait reappraisal is associated with resilience to acute psychological stress},\n\tvolume = {46},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {0092-6566},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0092656612000803},\n\tdoi = {10.1016/j.jrp.2012.05.003},\n\tabstract = {Life is full of stressful events. However, while some individuals are negatively affected by stress, others are more resilient to its effects. The factors that contribute to variability in stress resilience are not fully understood. Here, we tested the hypothesis that trait reappraisal would be associated with greater stress resilience to a first-time tandem skydive. Specifically, we expected measures of “anxiety” to be lower and measures of “euphoria” to be higher in high trait reappraising individuals. Our findings that trait reappraisal is negatively correlated with stress reactivity as measured by cortisol, heart rate, and self-report state anxiety, but positively correlated with self-report state euphoria suggest that individuals high in trait reappraisal are more stress resilient.},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2021-11-30},\n\tjournal = {Journal of Research in Personality},\n\tauthor = {Carlson, Joshua M. and Dikecligil, Gülce N. and Greenberg, Tsafrir and Mujica-Parodi, Lilianne R.},\n\tyear = {2012},\n\tpages = {609--613},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/PEJUGE76/file/view}\n}\n\n\n\n

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\n \n 2011\n \n \n (6)\n \n \n

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\n \n\n \n \n \n \n \n \n The NIRS Analysis Package: noise reduction and statistical inference.\n \n \n \n \n\n\n \n Fekete, T.; Rubin, D.; Carlson, J. M.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n PloS One, 6(9): e24322. 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{fekete2011a,\n\ttitle = {The {NIRS} {Analysis} {Package}: noise reduction and statistical inference},\n\tvolume = {6},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1932-6203},\n\tshorttitle = {The {NIRS} {Analysis} {Package}},\n\tdoi = {10.1371/journal.pone.0024322},\n\tabstract = {Near infrared spectroscopy (NIRS) is a non-invasive optical imaging technique that can be used to measure cortical hemodynamic responses to specific stimuli or tasks. While analyses of NIRS data are normally adapted from established fMRI techniques, there are nevertheless substantial differences between the two modalities. Here, we investigate the impact of NIRS-specific noise; e.g., systemic (physiological), motion-related artifacts, and serial autocorrelations, upon the validity of statistical inference within the framework of the general linear model. We present a comprehensive framework for noise reduction and statistical inference, which is custom-tailored to the noise characteristics of NIRS. These methods have been implemented in a public domain Matlab toolbox, the NIRS Analysis Package (NAP). Finally, we validate NAP using both simulated and actual data, showing marked improvement in the detection power and reliability of NIRS.},\n\tlanguage = {eng},\n\tnumber = {9},\n\tjournal = {PloS One},\n\tauthor = {Fekete, Tomer and Rubin, Denis and Carlson, Joshua M. and Mujica-Parodi, Lilianne R.},\n\tyear = {2011},\n\tpmid = {21912687},\n\tpmcid = {PMC3166314},\n\tpages = {e24322},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/T9TJSF3H/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n A stand-alone method for anatomical localization of NIRS measurements.\n \n \n \n \n\n\n \n Fekete, T.; Rubin, D.; Carlson, J. M.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n NeuroImage, 56(4): 2080–2088. June 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{fekete2011,\n\ttitle = {A stand-alone method for anatomical localization of {NIRS} measurements},\n\tvolume = {56},\n\tcopyright = {All rights reserved},\n\tissn = {1095-9572},\n\tdoi = {10.1016/j.neuroimage.2011.03.068},\n\tabstract = {Near-infrared spectroscopy (NIRS) is a non-invasive cortical imaging technique that provides many of the advantages of cortical fMRI with additional benefits of low cost, portability, and increased temporal resolution-features that make it potentially ideal for clinical diagnostic applications. However, the usefulness of NIRS is contingent on the ability to reliably localize the measured signal cortically. Although this can be achieved by supplementing NIRS data collection with an MRI scan, a much more appealing alternative is to use a portable magnetic measuring system to record the locations of optodes. Previous work has shown that optode skull measurements can be projected to the brain consistently within reasonable error bounds. Yet, as we show, if this is done without explicitly modeling the geometry of the holder securing the NIR optodes to participants' heads, considerable bias in the projection loci results. Here, we describe an algorithm that not only overcomes this bias but also corrects for measurement error in both optode position and skull reference points (which are used to register the measurements to standard brain templates) by applying geometric constraints. This method has been implemented as part of our NIRS Analysis Package (NAP), a public domain Matlab toolbox for analysis of NIRS data.},\n\tlanguage = {eng},\n\tnumber = {4},\n\tjournal = {NeuroImage},\n\tauthor = {Fekete, Tomer and Rubin, Denis and Carlson, Joshua M. and Mujica-Parodi, Lilianne R.},\n\tmonth = jun,\n\tyear = {2011},\n\tpmid = {21459146},\n\tpages = {2080--2088},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/XQBRCC2M/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Power spectrum scale invariance identifies prefrontal dysregulation in paranoid schizophrenia.\n \n \n \n \n\n\n \n Radulescu, A. R.; Rubin, D.; Strey, H. H.; and Mujica‐Parodi, L. R.\n\n\n \n\n\n\n Human Brain Mapping, 33(7): 1582–1593. 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"Power$ Paper\n \n \n \n $\"Power$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{radulescu2011,\n\ttitle = {Power spectrum scale invariance identifies prefrontal dysregulation in paranoid schizophrenia},\n\tvolume = {33},\n\tcopyright = {All rights reserved},\n\tissn = {1065-9471},\n\turl = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6870178/},\n\tdoi = {10.1002/hbm.21309},\n\tabstract = {Theory and experimental evidence suggest that complex living systems function close to the boundary of chaos, with erroneous organization to an improper dynamical range (too stiff or chaotic) underlying system‐wide dysregulation and disease. We hypothesized that erroneous organization might therefore also characterize paranoid schizophrenia, via optimization abnormalities in the prefrontal‐limbic circuit regulating emotion. To test this, we acquired fMRI scans from 35 subjects (N = 9 patients with paranoid schizophrenia and N = 26 healthy controls), while they viewed affect‐valent stimuli. To quantify dynamic regulation, we analyzed the power spectrum scale invariance (PSSI) of fMRI time‐courses and computed the geometry of time‐delay (Poincaré) maps, a measure of variability. Patients and controls showed distinct PSSI in two clusters (k\n1: Z = 4.3215, P = 0.00002 and k\n2: Z = 3.9441, P = 0.00008), localized to the orbitofrontal/medial prefrontal cortex (Brodmann Area 10), represented by β close to white noise in patients (β ≈ 0) and in the pink noise range in controls (β ≈ −1). Interpreting the meaning of PSSI differences, the Poincaré maps indicated less variability in patients than controls (Z = −1.9437, P = 0.05 for k\n1; Z = −2.5099, P = 0.01 for k\n2). That the dynamics identified Brodmann Area 10 is consistent with previous schizophrenia research, which implicates this area in deficits of working memory, executive functioning, emotional regulation and underlying biological abnormalities in synaptic (glutamatergic) transmission. Our results additionally cohere with a large body of work finding pink noise to be the normal range of central function at the synaptic, cellular, and small network levels, and suggest that patients show less supple responsivity of this region. Hum Brain Mapp, 2011. © 2011 Wiley‐Liss, Inc.},\n\tnumber = {7},\n\turldate = {2023-11-28},\n\tjournal = {Human Brain Mapping},\n\tauthor = {Radulescu, Anca R. and Rubin, Denis and Strey, Helmut H. and Mujica‐Parodi, Lilianne R.},\n\tyear = {2011},\n\tpmid = {21567663},\n\tpmcid = {PMC6870178},\n\tpages = {1582--1593},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/FI8QGT3N/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Acute stress eliminates female advantage in detection of ambiguous negative affect.\n \n \n \n \n\n\n \n DeDora, D. J.; Carlson, J. M.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n Evolutionary Psychology: An International Journal of Evolutionary Approaches to Psychology and Behavior, 9(4): 532–542. 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"Acute$ paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{dedora2011,\n\ttitle = {Acute stress eliminates female advantage in detection of ambiguous negative affect},\n\tvolume = {9},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1474-7049},\n\tabstract = {The human stress response evolved to maximize an individual's probability of survival when threatened. The present study addressed whether physical danger modulates perception of an unrelated ambiguous threat and, if so, to what extent this response is sex- specific. The authors utilized a first-time tandem skydive as a stressor, which had been previously validated as producing a highly-controlled, genuinely stressful environment. In a counter-balanced within-subjects design, participants wore a virtual reality helmet to complete an emotion-identification task during the plane's ascent (stress condition) and in the laboratory (control condition). Participants were presented static male faces morphed between 20-80\\% aggression, which gradually emerged from degraded images. Using a binary forced-choice design, participants identified each ambiguous face as aggressive or neutral. Results showed that participants characterized emotion more rapidly under stress versus control conditions. Unexpectedly, the results also show that while women were more sensitive to affect ambiguity than men under control conditions, they exhibited a marked decrease in sensitivity equivalent to men while under stress.},\n\tlanguage = {eng},\n\tnumber = {4},\n\tjournal = {Evolutionary Psychology: An International Journal of Evolutionary Approaches to Psychology and Behavior},\n\tauthor = {DeDora, Daniel J. and Carlson, Joshua M. and Mujica-Parodi, Lilianne R.},\n\tyear = {2011},\n\tpmid = {22947993},\n\tpages = {532--542},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/XF42D47T/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Ventral striatal and medial prefrontal BOLD activation is correlated with reward-related electrocortical activity: A combined ERP and fMRI study.\n \n \n \n \n\n\n \n Carlson, J. M.; Foti, D.; Mujica-Parodi, L. R.; Harmon-Jones, E.; and Hajcak, G.\n\n\n \n\n\n\n NeuroImage, 57(4): 1608–1616. 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"Ventral$ Paper\n \n \n \n $\"Ventral$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{carlson2011,\n\ttitle = {Ventral striatal and medial prefrontal {BOLD} activation is correlated with reward-related electrocortical activity: {A} combined {ERP} and {fMRI} study},\n\tvolume = {57},\n\tcopyright = {All rights reserved},\n\tissn = {1053-8119},\n\tshorttitle = {Ventral striatal and medial prefrontal {BOLD} activation is correlated with reward-related electrocortical activity},\n\turl = {https://www.sciencedirect.com/science/article/pii/S1053811911005453},\n\tdoi = {10.1016/j.neuroimage.2011.05.037},\n\tabstract = {Functional magnetic resonance imaging (fMRI) research suggests that the ventral striatum (VS)/nucleus accumbens, medial prefrontal cortex (mPFC), and broader mesocorticolimbic dopamine system mediate aspects of reward processing from expectation of reward to pleasantness experienced upon reward attainment. In parallel, research utilizing event-related potentials (ERP) indicates that the feedback negativity (FN) is sensitive to reward vs. non-reward feedback and outcome expectation. The FN has been source localized to the mPFC and dorsal striatum, and converging evidence suggests that the FN reflects reward processing in the mesocorticolimbic system. However, the extent to which ERP and fMRI measures of reward processing are correlated has yet to be explored within the same individuals. The primary aim of the current study was to examine the convergence between fMRI (i.e., VS and mPFC) and ERP (i.e., FN) measures of reward processing in forty-two participants who completed counterbalanced fMRI and ERP sessions while performing the same monetary gambling task. For the Win{\\textgreater}Loss comparison, fMRI activation in the mesocorticolimbic reward circuit including the VS and mPFC was positively correlated with the FN. Here, we demonstrate that monetary gains activate the VS, mPFC, caudate, amygdala, and orbital frontal cortex, enhance the FN ERP component within 300ms post feedback, and that these measures are related. Thus, fMRI and ERP measures provide complementary information about mesocorticolimbic activity during reward processing, which may be useful in assessing pathological reward sensitivity.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2021-11-30},\n\tjournal = {NeuroImage},\n\tauthor = {Carlson, Joshua M. and Foti, Dan and Mujica-Parodi, Lilianne R. and Harmon-Jones, Eddie and Hajcak, Greg},\n\tyear = {2011},\n\tpages = {1608--1616},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/TXRINKZK/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Feeling anxious: anticipatory amygdalo-insular response predicts the feeling of anxious anticipation.\n \n \n \n \n\n\n \n Carlson, J. M.; Greenberg, T.; Rubin, D.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n Social Cognitive and Affective Neuroscience, 6(1): 74–81. 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"Feeling$ Paper\n \n \n \n $\"Feeling$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{carlson2011a,\n\ttitle = {Feeling anxious: anticipatory amygdalo-insular response predicts the feeling of anxious anticipation},\n\tvolume = {6},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1749-5016},\n\tshorttitle = {Feeling anxious},\n\turl = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3023082/},\n\tdoi = {10.1093/scan/nsq017},\n\tabstract = {Anticipation is a central component of anxiety and the anterior insula appears to be an important neural substrate in which this process is mediated. The anterior insula is also thought to underlie the interoceptive representation of one’s affective state. However, the degree to which individual differences in anticipation-related insula reactivity are associated with variability in the subjective experience of anxious anticipation is untested. To assess this possibility, functional magnetic resonance images were acquired while participants completed an auditory anticipation task with trial-by-trial self-report ratings of anxious anticipation. We hypothesized that the anterior insula would be positively associated with an individual’s subjective experience of anticipatory anxiety. The results provide evidence for an amygdalo-insular system involved in anxious auditory anticipation. Reactivity in the right anterior insula was predictive of individuals’ subjective experience of anxious anticipation for both aversive and neutral stimuli, whereas the amygdala was predictive of anticipatory anxiety for aversive stimuli. In addition, anxious anticipatory activation in the left insula and left amygdala covaried with participants’ level of trait anxiety, particularly when the anticipated event was proximal.},\n\tnumber = {1},\n\turldate = {2023-11-28},\n\tjournal = {Social Cognitive and Affective Neuroscience},\n\tauthor = {Carlson, Joshua M. and Greenberg, Tsafrir and Rubin, Denis and Mujica-Parodi, Lilianne R.},\n\tyear = {2011},\n\tpmid = {20207692},\n\tpmcid = {PMC3023082},\n\tpages = {74--81},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/G46VINKI/file/view}\n}\n\n\n\n

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\n \n 2010\n \n \n (4)\n \n \n

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\n \n\n \n \n \n \n \n \n A disposition to reappraise decreases anterior insula reactivity during anxious anticipation.\n \n \n \n \n\n\n \n Carlson, J. M.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n Biological Psychology, 85(3): 383–385. 2010.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n \n $\"A$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{carlson2010a,\n\ttitle = {A disposition to reappraise decreases anterior insula reactivity during anxious anticipation},\n\tvolume = {85},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {0301-0511},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0301051110002279},\n\tdoi = {10.1016/j.biopsycho.2010.08.010},\n\tabstract = {Across individuals there is variability in one's inherent tendency to reappraise emotional events in everyday life, which may be related to how worried one becomes in the presence of an anticipated aversive event. The extent to which this natural tendency to reappraise has neurobiological correlates during anxious anticipation is unknown. Neuroimaging research indicates that responses in the anterior insula precede anticipated aversive events and appear to represent one's affective feeling state of anxious anticipation. Successful cognitive reappraisal should weaken this anticipatory insula response. Here, functional magnetic resonance images were acquired while participants completed an anticipation task. We found increased anterior insula activation during aversive anticipation and a negative association between anxious anticipatory right anterior insula reactivity and dispositional reappraisal. Thus, even without the instruction to reappraise, individuals high in dispositional reappraisal tended to have a reduced anticipatory insula response to aversive stimuli, thereby down-regulating a neural substrate for aversive anticipation.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2021-11-30},\n\tjournal = {Biological Psychology},\n\tauthor = {Carlson, Joshua M. and Mujica-Parodi, Lilianne R.},\n\tyear = {2010},\n\tpages = {383--385},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/HSNIX9X9/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Ambulatory and Challenge-Associated Heart Rate Variability Measures Predict Cardiac Responses to Real-World Acute Emotional Stress.\n \n \n \n \n\n\n \n Dikecligil, G. N.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n Biological Psychiatry, 67(12): 1185–1190. 2010.\n \n\n\n\n
\n\n\n\n \n \n $\"Ambulatory$ Paper\n \n \n \n $\"Ambulatory$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{dikecligil2010,\n\tseries = {Amygdala {Activity} and {Anxiety}: {Stress} {Effects}},\n\ttitle = {Ambulatory and {Challenge}-{Associated} {Heart} {Rate} {Variability} {Measures} {Predict} {Cardiac} {Responses} to {Real}-{World} {Acute} {Emotional} {Stress}},\n\tvolume = {67},\n\tcopyright = {All rights reserved},\n\tissn = {0006-3223},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0006322310001149},\n\tdoi = {10.1016/j.biopsych.2010.02.001},\n\tabstract = {Background\nHeart rate variability (HRV) measures homeostatic regulation of the autonomic nervous system in response to perturbation and has been previously shown to quantify risk for cardiac events. Despite known interactions among stress vulnerability, psychiatric illness, and cardiac health, however, this is the first study to our knowledge to compare directly the value of laboratory HRV in predicting autonomic modulation of real-world emotional stress.\nMethods\nWe recorded electrocardiograms (ECG) on 56 subjects: first, within the laboratory and then during an acute emotional stressor: a first-time skydive. Laboratory sessions included two 5-min ECG recordings separated by one ambulatory 24-hour recording. To test the efficacy of introducing a mild emotional challenge, during each of the 5-min laboratory recordings, subjects viewed either aversive or benign images. Following the laboratory session, subjects participated in the acute stressor wearing a Holter ECG. Artifact-free ECGs (n = 33) were analyzed for HRV then statistically compared across laboratory and acute stress sessions.\nResults\nThere were robust correlations (r = .7–.8) between the laboratory and acute stress HRV, indicating that the two most useful paradigms (long-term wake, followed by short-term challenge) were also most sensitive to distinct components of the acute stressor: the former correlated with the fine-tuned regulatory modulation occurring immediately prior and following the acute stressor, whereas the latter correlated with gross amplitude and recovery.\nConclusions\nOur results confirmed the efficacy of laboratory-acquired HRV in predicting autonomic response to acute emotional stress and suggest that ambulatory and challenge protocols enhance predictive value.},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2021-11-30},\n\tjournal = {Biological Psychiatry},\n\tauthor = {Dikecligil, Gülce N. and Mujica-Parodi, Lilianne R.},\n\tyear = {2010},\n\tpages = {1185--1190},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/FPW7UWPG/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Blind rage? Heightened anger is associated with altered amygdala responses to masked and unmasked fearful faces.\n \n \n \n \n\n\n \n Carlson, J. M.; Greenberg, T.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n Psychiatry Research: Neuroimaging, 182(3): 281–283. 2010.\n \n\n\n\n
\n\n\n\n \n \n $\"Blind$ Paper\n \n \n \n $\"Blind$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{carlson2010,\n\ttitle = {Blind rage? {Heightened} anger is associated with altered amygdala responses to masked and unmasked fearful faces},\n\tvolume = {182},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {0925-4927},\n\tshorttitle = {Blind rage?},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0925492710000673},\n\tdoi = {10.1016/j.pscychresns.2010.02.001},\n\tabstract = {We investigated anger-related variability in the BOLD fMRI response to crude/masked and detailed/unmasked fearful faces. Anger expression positively covaried with amygdala activation to crude fear, while trait anger negatively covaried with amygdala responses to detailed fear. This differential processing may trigger aggression without the subsequent inhibition associated with distress cues.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2021-11-30},\n\tjournal = {Psychiatry Research: Neuroimaging},\n\tauthor = {Carlson, Joshua Michael and Greenberg, Tsafrir and Mujica-Parodi, Lilianne R.},\n\tyear = {2010},\n\tpages = {281--283},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/JPMNHM9W/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Power spectrum scale invariance quantifies limbic dysregulation in trait anxious adults using fMRI: Adapting methods optimized for characterizing autonomic dysregulation to neural dynamic time series.\n \n \n \n \n\n\n \n Tolkunov, D.; Rubin, D.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n NeuroImage, 50(1): 72–80. 2010.\n \n\n\n\n
\n\n\n\n \n \n $\"Power$ Paper\n \n \n \n $\"Power$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{tolkunov2010,\n\ttitle = {Power spectrum scale invariance quantifies limbic dysregulation in trait anxious adults using {fMRI}: {Adapting} methods optimized for characterizing autonomic dysregulation to neural dynamic time series},\n\tvolume = {50},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1053-8119},\n\tshorttitle = {Power spectrum scale invariance quantifies limbic dysregulation in trait anxious adults using {fMRI}},\n\turl = {https://www.sciencedirect.com/science/article/pii/S1053811909013093},\n\tdoi = {10.1016/j.neuroimage.2009.12.021},\n\tabstract = {In a well-regulated control system, excitatory and inhibitory components work closely together with minimum lag; in response to inputs of finite duration, outputs should show rapid rise and, following the input's termination, immediate return to baseline. The efficiency of this response can be quantified using the power spectrum density's scaling parameter β, a measure of self-similarity, applied to the first derivative of the raw signal. In this study, we adapted power spectrum density methods, previously used to quantify autonomic dysregulation (heart rate variability), to neural time series obtained via functional MRI. The negative feedback loop we investigated was the limbic system, using affect-valent faces as stimuli. We hypothesized that trait anxiety would be related to efficiency of regulation of limbic responses, as quantified by power-law scaling of fMRI time series. Our results supported this hypothesis, showing moderate to strong correlations of trait anxiety and β (r=0.45−0.54) for the amygdala, orbitofrontal cortex, hippocampus, superior temporal gyrus, posterior insula, and anterior cingulate. Strong anticorrelations were also found between the amygdala's β and wake heart rate variability (r=−0.61), suggesting a robust relationship between dysregulated limbic outputs and their autonomic consequences.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2021-11-30},\n\tjournal = {NeuroImage},\n\tauthor = {Tolkunov, Denis and Rubin, Denis and Mujica-Parodi, Lilianne R.},\n\tyear = {2010},\n\tpages = {72--80},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/7W9ZT4KS/file/view}\n}\n\n\n\n

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\n \n 2009\n \n \n (6)\n \n \n

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\n \n\n \n \n \n \n \n \n Behavioral predictors of acute stress symptoms during intense military training.\n \n \n \n \n\n\n \n Taylor, M. K.; Mujica-Parodi, L. R.; Padilla, G. A.; Markham, A. E.; Potterat, E. G.; Momen, N.; Sander, T. C.; and Larson, G. E.\n\n\n \n\n\n\n Journal of Traumatic Stress, 22(3): 212–217. 2009.\n \n\n\n\n
\n\n\n\n \n \n $\"Behavioral$ Paper\n \n \n \n $\"Behavioral$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{taylor2009a,\n\ttitle = {Behavioral predictors of acute stress symptoms during intense military training},\n\tvolume = {22},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1573-6598},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jts.20413},\n\tdoi = {10.1002/jts.20413},\n\tabstract = {A better understanding of factors influencing human responses to acute stress is needed to enhance prevention and treatment of stress-related disorders. In the current study, the authors examined predictors of acute stress symptoms during intense military training in 35 men. In univariate and multivariate models, perceived stress, passive coping, and emotion-focused coping during daily living predicted acute stress symptoms in response to realistic survival training, whereas active coping and problem-focused coping did not. Baseline stress levels and coping styles, both of which may be modifiable, appear to play a fundamental role in the human response to acute uncontrollable stress. Additional research is needed to better elucidate the relative and interactive contributions of behavioral predictors of acute stress.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2021-11-30},\n\tjournal = {Journal of Traumatic Stress},\n\tauthor = {Taylor, Marcus K. and Mujica-Parodi, Lilianne R. and Padilla, Genieleah A. and Markham, Amanda E. and Potterat, Eric G. and Momen, Nausheen and Sander, Todd C. and Larson, Gerald E.},\n\tyear = {2009},\n\tpages = {212--217},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/7I2AKNV4/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n A principal component network analysis of prefrontal-limbic fMRI time series in schizophrenia patients and healthy controls.\n \n \n \n \n\n\n \n Rǎdulescu, A. R.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n Psychiatry research, 174(3): 184–194. 2009.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n \n $\"A$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{radulescu2009,\n\ttitle = {A principal component network analysis of prefrontal-limbic {fMRI} time series in schizophrenia patients and healthy controls},\n\tvolume = {174},\n\tcopyright = {All rights reserved},\n\tissn = {0165-1781},\n\turl = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2788080/},\n\tdoi = {10.1016/j.pscychresns.2009.04.017},\n\tabstract = {We investigated neural regulation of emotional arousal. We hypothesized that the interactions between the components of the prefrontal-limbic system determine the global trajectories of the individual’s brain activation, with the strengths and modulations of these interactions being potentially key components underlying the differences between healthy individuals and those with schizophrenia. Using affect-valent facial stimuli presented to N = 11 medicated schizophrenia patients and N = 65 healthy controls, we activated neural regions associated with the emotional arousal response during fMRI scanning. Performing first a random-effects analysis of the fMRI data to identify activated regions, we obtained 352 data-point time series for six brain regions: bilateral amygdala, hippocampus and two prefrontal regions (Brodmann Areas 9 and 45). Since standard statistical methods are not designed to capture system features and evolution, we used principal component analyses on two types of pre-processed data: contrasts and group averages. We captured an important characteristic of the evolution of our six-dimensional brain network: all subject trajectories are almost embedded in a two-dimensional plane. Moreover, the direction of the largest principal component was a significant differentiator between the control and patient populations: the left and right amygdala coefficients were substantially higher in the case of patients, and the coefficients of Brodmann Area 9 were, to a lesser extent, higher in controls. These results are evidence that modulations between the regions of interest are the important determinant factors for the system’s dynamical behavior. We place our results within the context of other principal component analyses used in neuroimaging, as well as of our existing theoretical model of prefrontal-limbic dysregulation.},\n\tnumber = {3},\n\turldate = {2023-11-28},\n\tjournal = {Psychiatry research},\n\tauthor = {Rǎdulescu, Anca R. and Mujica-Parodi, Lilianne R.},\n\tyear = {2009},\n\tpmid = {19880294},\n\tpmcid = {PMC2788080},\n\tpages = {184--194},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/IKWSMHET/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Higher body fat percentage is associated with increased cortisol reactivity and impaired cognitive resilience in response to acute emotional stress.\n \n \n \n \n\n\n \n Mujica-Parodi, L. R.; Renelique, R.; and Taylor, M. K.\n\n\n \n\n\n\n International Journal of Obesity (2005), 33(1): 157–165. 2009.\n \n\n\n\n
\n\n\n\n \n \n $\"Higher$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{mujica-parodi2009a,\n\ttitle = {Higher body fat percentage is associated with increased cortisol reactivity and impaired cognitive resilience in response to acute emotional stress},\n\tvolume = {33},\n\tcopyright = {All rights reserved},\n\tissn = {1476-5497},\n\tdoi = {10.1038/ijo.2008.218},\n\tabstract = {OBJECTIVE: Cortisol is elevated in individuals with both increased emotional stress and higher percentages of body fat. Cortisol is also known to affect cognitive performance, particularly spatial processing and working memory. We hypothesized that increased body fat might therefore be associated with decreased performance on a spatial processing task, in response to an acute real-world stressor.\nDESIGN: We tested two separate samples of participants undergoing their first (tandem) skydive. In the first sample (N=78), participants were tested for salivary cortisol and state anxiety (Spielberger State Anxiety Scale) during the plane's 15-min ascent to altitude in immediate anticipation of the jump. In a second sample (N=20), participants were tested for salivary cortisol, as well as cardiac variables (heart rate, autonomic regulation through heart rate variability) and performance on a cognitive task of spatial processing, selective attention and working memory.\nRESULTS: In response to the skydive, individuals with greater body fat percentages showed significantly increased reactivity for both cortisol (on both samples) and cognition, including decreased accuracy of our task of spatial processing, selective attention and working memory. These cognitive effects were restricted to the stress response and were not found under baseline conditions. There were no body fat interactions with cardiac changes in response to the stressor, suggesting that the cognitive effects were specifically hormone mediated rather than secondary to general activation of the autonomic nervous system.\nCONCLUSIONS: Our results indicate that, under real-world stress, increased body fat may be associated with endocrine stress vulnerability, with consequences for deleterious cognitive performance.},\n\tlanguage = {eng},\n\tnumber = {1},\n\tjournal = {International Journal of Obesity (2005)},\n\tauthor = {Mujica-Parodi, Lilianne R. and Renelique, R. and Taylor, M. K.},\n\tyear = {2009},\n\tpmid = {19015661},\n\tpages = {157--165},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/TMI69TMC/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Chemosensory Cues to Conspecific Emotional Stress Activate Amygdala in Humans.\n \n \n \n \n\n\n \n Mujica-Parodi, L. R.; Strey, H. H.; Frederick, B.; Savoy, R.; Cox, D.; Botanov, Y.; Tolkunov, D.; Rubin, D.; and Weber, J.\n\n\n \n\n\n\n PLOS ONE, 4(7): e6415. 2009.\n \n\n\n\n
\n\n\n\n \n \n $\"Chemosensory$ Paper\n \n \n \n $\"Chemosensory$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{mujica-parodi2009,\n\ttitle = {Chemosensory {Cues} to {Conspecific} {Emotional} {Stress} {Activate} {Amygdala} in {Humans}},\n\tvolume = {4},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1932-6203},\n\turl = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0006415},\n\tdoi = {10.1371/journal.pone.0006415},\n\tabstract = {Alarm substances are airborne chemical signals, released by an individual into the environment, which communicate emotional stress between conspecifics. Here we tested whether humans, like other mammals, are able to detect emotional stress in others by chemosensory cues. Sweat samples collected from individuals undergoing an acute emotional stressor, with exercise as a control, were pooled and presented to a separate group of participants (blind to condition) during four experiments. In an fMRI experiment and its replication, we showed that scanned participants showed amygdala activation in response to samples obtained from donors undergoing an emotional, but not physical, stressor. An odor-discrimination experiment suggested the effect was primarily due to emotional, and not odor, differences between the two stimuli. A fourth experiment investigated behavioral effects, demonstrating that stress samples sharpened emotion-perception of ambiguous facial stimuli. Together, our findings suggest human chemosensory signaling of emotional stress, with neurobiological and behavioral effects.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2021-11-30},\n\tjournal = {PLOS ONE},\n\tauthor = {Mujica-Parodi, Lilianne R. and Strey, Helmut H. and Frederick, Blaise and Savoy, Robert and Cox, David and Botanov, Yevgeny and Tolkunov, Denis and Rubin, Denis and Weber, Jochen},\n\tyear = {2009},\n\tpages = {e6415},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/3IPHQKHR/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Limbic dysregulation is associated with lowered heart rate variability and increased trait anxiety in healthy adults.\n \n \n \n \n\n\n \n Mujica-Parodi, L. R.; Korgaonkar, M.; Ravindranath, B.; Greenberg, T.; Tomasi, D.; Wagshul, M.; Ardekani, B.; Guilfoyle, D.; Khan, S.; Zhong, Y.; Chon, K.; and Malaspina, D.\n\n\n \n\n\n\n Human Brain Mapping, 30(1): 47–58. 2009.\n \n\n\n\n
\n\n\n\n \n \n $\"Limbic$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{mujica-parodi2009c,\n\ttitle = {Limbic dysregulation is associated with lowered heart rate variability and increased trait anxiety in healthy adults},\n\tvolume = {30},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1097-0193},\n\tdoi = {10.1002/hbm.20483},\n\tabstract = {OBJECTIVES: We tested whether dynamic interaction between limbic regions supports a control systems model of excitatory and inhibitory components of a negative feedback loop, and whether dysregulation of those dynamics might correlate with trait differences in anxiety and their cardiac characteristics among healthy adults.\nEXPERIMENTAL DESIGN: Sixty-five subjects received fMRI scans while passively viewing angry, fearful, happy, and neutral facial stimuli. Subjects also completed a trait anxiety inventory, and were monitored using ambulatory wake ECG. The ECG data were analyzed for heart rate variability, a measure of autonomic regulation. The fMRI data were analyzed with respect to six limbic regions (bilateral amygdala, bilateral hippocampus, Brodmann Areas 9, 45) using limbic time-series cross-correlations, maximum BOLD amplitude, and BOLD amplitude at each point in the time-series.\nPRINCIPAL OBSERVATIONS: Diminished coupling between limbic time-series in response to the neutral, fearful, and happy faces was associated with greater trait anxiety, greater sympathetic activation, and lowered heart rate variability. Individuals with greater levels of trait anxiety showed delayed activation of Brodmann Area 45 in response to the fearful and happy faces, and lowered Brodmann Area 45 activation with prolonged left amygdala activation in response to the neutral faces.\nCONCLUSIONS: The dynamics support limbic regulation as a control system, in which dysregulation, as assessed by diminished coupling between limbic time-series, is associated with increased trait anxiety and excitatory autonomic outputs. Trait-anxious individuals showed delayed inhibitory activation in response to overt-affect stimuli and diminished inhibitory activation with delayed extinction of excitatory activation in response to ambiguous-affect stimuli.},\n\tlanguage = {eng},\n\tnumber = {1},\n\tjournal = {Human Brain Mapping},\n\tauthor = {Mujica-Parodi, Lilianne R. and Korgaonkar, Mayuresh and Ravindranath, Bosky and Greenberg, Tsafrir and Tomasi, Dardo and Wagshul, Mark and Ardekani, Babak and Guilfoyle, David and Khan, Shilpi and Zhong, Yuru and Chon, Ki and Malaspina, Dolores},\n\tyear = {2009},\n\tpmid = {18041716},\n\tpmcid = {PMC2993012},\n\tpages = {47--58},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/7B5A85NM/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Anger Expression and Stress Responses in Military Men.\n \n \n \n \n\n\n \n Taylor, M. K.; Mujica-Parodi, L. R.; Potterat, E. G.; Momen, N.; Dial Ward, M. D.; Padilla, G. A.; Markham, A. E.; and Evans, K. E.\n\n\n \n\n\n\n Aviation, Space, and Environmental Medicine, 80(11): 962–967. 2009.\n \n\n\n\n
\n\n\n\n \n \n $\"Anger$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{taylor2009,\n\ttitle = {Anger {Expression} and {Stress} {Responses} in {Military} {Men}},\n\tvolume = {80},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tdoi = {10.3357/ASEM.2536.2009},\n\tabstract = {Taylor MK, Mujica-Parodi LR, Potterat EG, Momen N, Dial Ward MD, Padilla GA, Markham AE, Evans KE. Anger expression and stress responses in military men. Aviat Space Environ Med 2009; 80:962–7.Introduction: A better understanding of individual differences in the human stress response may enhance prevention and treatment of operational stress reactions. In this study, we examined the relationships of anger experience and expression to stress indices during daily living and in response to military survival training in 45 men. Methods: Prior to participation in survival training, subjects completed self-report measures of perceived stress and anger. The revised Impact of Event Scale was then administered 24 h after the conclusion of training. Results: As expected, outward anger expression was positively associated with perceived stress during free living (P {\\textless} 0.0125). Outward anger expression, inward anger expression, and angry temperament then combined to account for 25\\% of the variance in psychological impact of a stressful mock-captivity challenge. Conclusion: Anger characteristics are associated with human stress endpoints, both during daily living and in response to an ecologically valid stressor. These findings may assist in the prevention and treatment of operational stress reactions.},\n\tnumber = {11},\n\tjournal = {Aviation, Space, and Environmental Medicine},\n\tauthor = {Taylor, Marcus K. and Mujica-Parodi, Lilianne R. and Potterat, Eric G. and Momen, Nausheen and Dial Ward, Michael D. and Padilla, Genieleah A. and Markham, Amanda E. and Evans, Katherine E.},\n\tyear = {2009},\n\tpages = {962--967},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/DRI9P7NU/file/view}\n}\n\n\n\n

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\n \n 2008\n \n \n (3)\n \n \n

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\n \n\n \n \n \n \n \n \n Physical fitness influences stress reactions to extreme military training.\n \n \n \n \n\n\n \n Taylor, M. K.; Markham, A. E.; Reis, J. P.; Padilla, G. A.; Potterat, E. G.; Drummond, S. P. A.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n Military Medicine, 173(8): 738–742. 2008.\n \n\n\n\n
\n\n\n\n \n \n $\"Physical$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{taylor2008,\n\ttitle = {Physical fitness influences stress reactions to extreme military training},\n\tvolume = {173},\n\tcopyright = {All rights reserved},\n\tissn = {0026-4075},\n\tdoi = {10.7205/milmed.173.8.738},\n\tabstract = {BACKGROUND: Physical fitness and physical conditioning have long been valued by the military for their roles in enhancing mission-specific performance and reducing risk of injury in the warfighter. It is not known whether physical fitness plays a causal role in attenuating acute military stress reactions or the evolution of post-traumatic stress disorder.\nOBJECTIVE: The objective of this study was to determine whether physical fitness influences the impact of stressful events during military survival training in 31 men.\nMETHODS: Participants self-reported their most recent Physical Readiness Test scores and completed a trait anxiety measure before survival training. Participants also completed the Impact of Events Scale (IES) 24 hours after training.\nRESULTS: Aerobic fitness was inversely associated with the total IES score (p {\\textless} 0.01, adjusted R2 = 0.19). When adjusted for trait anxiety, this relationship was substantially attenuated and no longer significant (p = 0.11). Trait anxiety was inversely associated with aerobic fitness (p {\\textless} 0.05) and positively related to IES (p {\\textless} 0.001).\nCONCLUSIONS: Physical fitness may buffer stress symptoms secondary to extreme military stress and its effects may be mediated via fitness-related attenuations in trait anxiety.},\n\tlanguage = {eng},\n\tnumber = {8},\n\tjournal = {Military Medicine},\n\tauthor = {Taylor, Marcus K. and Markham, Amanda E. and Reis, Jared P. and Padilla, Genieleah A. and Potterat, Eric G. and Drummond, Sean P. A. and Mujica-Parodi, Lilianne R.},\n\tyear = {2008},\n\tpmid = {18751589},\n\tpages = {738--742},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/E4RGJWHI/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Second-Hand Stress: Neurobiological Evidence for a Human Alarm Pheromone.\n \n \n \n \n\n\n \n Mujica-Parodi, L. R.; Strey, H.; Frederick, B.; Savoy, R.; Cox, D.; Botanov, Y.; Tolkunov, D.; Rubin, D.; and Weber, J.\n\n\n \n\n\n\n Nature Precedings,1–1. 2008.\n \n\n\n\n
\n\n\n\n \n \n $\"Second-Hand$ Paper\n \n \n \n $\"Second-Hand$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{mujica-parodi2008,\n\ttitle = {Second-{Hand} {Stress}: {Neurobiological} {Evidence} for a {Human} {Alarm} {Pheromone}},\n\tcopyright = {2008 The Author(s)},\n\tissn = {1756-0357},\n\tshorttitle = {Second-{Hand} {Stress}},\n\turl = {https://www.nature.com/articles/npre.2008.2561.1},\n\tdoi = {10.1038/npre.2008.2561.1},\n\tabstract = {Alarm pheromones are airborne chemical signals, released by an individual into the environment, which transmit warning of danger to conspecifics via olfaction. Using fMRI, we provide the first neurobiological evidence for a human alarm pheromone. Individuals showed activation of the amygdala in response to sweat produced by others during emotional stress, with exercise sweat as a control; behavioral data suggest facilitated evaluation of ambiguous threat.},\n\tlanguage = {en},\n\turldate = {2023-11-28},\n\tjournal = {Nature Precedings},\n\tauthor = {Mujica-Parodi, Lilianne R. and Strey, Helmut and Frederick, Blaise and Savoy, Robert and Cox, David and Botanov, Yevgeny and Tolkunov, Denis and Rubin, Denis and Weber, Jochen},\n\tyear = {2008},\n\tpages = {1--1},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/GVKC22TK/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n A Systems Approach to Prefrontal-Limbic Dysregulation in Schizophrenia.\n \n \n \n \n\n\n \n Rădulescu, A. R.; and Mujica-Parodi, L. R.\n\n\n \n\n\n\n Neuropsychobiology, 57(4): 206–216. 2008.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n \n $\"A$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{radulescu2008,\n\ttitle = {A {Systems} {Approach} to {Prefrontal}-{Limbic} {Dysregulation} in {Schizophrenia}},\n\tvolume = {57},\n\tcopyright = {All rights reserved},\n\tissn = {0302-282X, 1423-0224},\n\turl = {https://www.karger.com/Article/FullText/151731},\n\tdoi = {10.1159/000151731},\n\tabstract = {\\textit{Introduction:} Using a prefrontal-limbic dysregulation model for schizophrenia, we tested whether a dynamic control systems approach in conjunction with neuroimaging might increase detection sensitivity in characterizing the illness. Our analyses were modeled upon diagnostic tests for other dysregulatory diseases, such as diabetes, in which trajectories for the excitatory and inhibitory components of the negative feedback loops that reestablish homeostasis are measured after system perturbation. We hypothesized that these components would show distinct coupling dynamics within the patient population, as compared to healthy controls, and that these coupling dynamics could be quantified statistically using cross-correlations between excitatory and inhibitory time series using fMRI. \\textit{Methods:} As our perturbation, we activated neural regions associated with the emotional arousal response, using affect-valent facial stimuli presented to 11 schizophrenic patients (all under psychotropic medication) and 65 healthy controls (including 11 individuals age- and sex-matched to the patients) during fMRI scanning. We first performed a random-effects analysis of the fMRI data to identify activated regions. Those regions were then analyzed for group differences, using both standard analyses with respect to the time series peaks, as well as a dynamic analysis that looked at cross-correlations between excitatory and inhibitory time series and group differences over the entire time series. \\textit{Results:} Patients and controls showed significant differences in signal dynamics between excitatory and inhibitory components of the negative feedback loop that controls emotional arousal, specifically between the right amygdala and Brodmann area 9 (BA9), when viewing angry facial expressions (p = 0.002). Further analyses were performed with respect to activation amplitudes for these areas in response to angry faces, both over the entire time series as well as for each time point along the time series. While the amygdala responses were not significantly different between groups, patients showed significantly lower BA9 activation during the beginning of the response (0.000 ≤ p ≤ 0.021) and significantly higher BA9 activation towards the end of the response (0.008 ≤ p ≤ 0.025), suggesting longer time-lags between patients’ excitatory responses and the inhibitory activation that modulates it. \\textit{Conclusions:} Our results capture a significant dysregulation between the excitatory (amygdala) and inhibitory (prefrontal) limbic regions in medicated schizophrenic patients versus healthy controls. They suggest that, analogously to diagnostic tests used in other physiological diseases, quantifying dysregulation using a control systems approach may provide an appropriate model to investigate further in developing presymptomatic neurobiological assessments of risk, or illness severity in symptomatic patients.},\n\tlanguage = {english},\n\tnumber = {4},\n\turldate = {2021-11-30},\n\tjournal = {Neuropsychobiology},\n\tauthor = {Rădulescu, Anca R. and Mujica-Parodi, Lilianne R.},\n\tyear = {2008},\n\tpages = {206--216},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/VZJEFCK6/file/view}\n}\n\n\n\n

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\n Introduction: Using a prefrontal-limbic dysregulation model for schizophrenia, we tested whether a dynamic control systems approach in conjunction with neuroimaging might increase detection sensitivity in characterizing the illness. Our analyses were modeled upon diagnostic tests for other dysregulatory diseases, such as diabetes, in which trajectories for the excitatory and inhibitory components of the negative feedback loops that reestablish homeostasis are measured after system perturbation. We hypothesized that these components would show distinct coupling dynamics within the patient population, as compared to healthy controls, and that these coupling dynamics could be quantified statistically using cross-correlations between excitatory and inhibitory time series using fMRI. Methods: As our perturbation, we activated neural regions associated with the emotional arousal response, using affect-valent facial stimuli presented to 11 schizophrenic patients (all under psychotropic medication) and 65 healthy controls (including 11 individuals age- and sex-matched to the patients) during fMRI scanning. We first performed a random-effects analysis of the fMRI data to identify activated regions. Those regions were then analyzed for group differences, using both standard analyses with respect to the time series peaks, as well as a dynamic analysis that looked at cross-correlations between excitatory and inhibitory time series and group differences over the entire time series. Results: Patients and controls showed significant differences in signal dynamics between excitatory and inhibitory components of the negative feedback loop that controls emotional arousal, specifically between the right amygdala and Brodmann area 9 (BA9), when viewing angry facial expressions (p = 0.002). Further analyses were performed with respect to activation amplitudes for these areas in response to angry faces, both over the entire time series as well as for each time point along the time series. While the amygdala responses were not significantly different between groups, patients showed significantly lower BA9 activation during the beginning of the response (0.000 ≤ p ≤ 0.021) and significantly higher BA9 activation towards the end of the response (0.008 ≤ p ≤ 0.025), suggesting longer time-lags between patients’ excitatory responses and the inhibitory activation that modulates it. Conclusions: Our results capture a significant dysregulation between the excitatory (amygdala) and inhibitory (prefrontal) limbic regions in medicated schizophrenic patients versus healthy controls. They suggest that, analogously to diagnostic tests used in other physiological diseases, quantifying dysregulation using a control systems approach may provide an appropriate model to investigate further in developing presymptomatic neurobiological assessments of risk, or illness severity in symptomatic patients.\n

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\n \n 2007\n \n \n (2)\n \n \n

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\n \n\n \n \n \n \n \n \n Neurophysiologic Methods to Measure Stress During Survival, Evasion, Resistance, and Escape Training.\n \n \n \n \n\n\n \n Taylor, M. K.; Sausen, K. P.; Mujica-Parodi, L. R.; Potterat, E. G.; Yanagi, M. A.; and Kim, H.\n\n\n \n\n\n\n Aviation, Space, and Environmental Medicine, 78(5): B224–B230. 2007.\n \n\n\n\n
\n\n\n\n \n \n $\"Neurophysiologic$ paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{taylor2007,\n\ttitle = {Neurophysiologic {Methods} to {Measure} {Stress} {During} {Survival}, {Evasion}, {Resistance}, and {Escape} {Training}},\n\tvolume = {78},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tabstract = {Taylor MK, Sausen KP, Mujica-Parodi LR, Potterat EG, Yanagi MA, Kim H. Neurophysiologic methods to measure stress during survival, evasion, resistance, and escape training. Aviat Space Environ Med 2007; 78(5, Suppl.):B224-–B230.\nTraining in Survival, Evasion, Resistance,\nand Escape (SERE) is required for U.S. military members at high risk of capture. This physically and psychologically demanding course is considered an analog to the stress imposed by war, captivity, and related events, thus offering a unique and unprecedented medium in which to systematically\nexamine human stress and performance during a realistically intense operational context. Operational stress is multifaceted, manifesting cerebral, neuroendocrine, cardiac, and cognitive characteristics, and necessitating an integration of multiple methods of measurement to appropriately characterize\nits complexity. Herein we describe some of our present research methods and discuss their applicability to real-time monitoring and predicting of key aspects of human performance. A systems approach is taken, whereby some of the “key players” implicated in the stress response (e.g.,\ncerebral, neuroendocrine, cardiac) are briefly discussed, to which we link corresponding investigative techniques (fMRI, acoustic startle eye-blink reflex, heart rate variability, and neuroendocrine sampling). Background and previous research with each investigative technique and its relationship\nto the SERE context is briefly reviewed. Ultimately, we discuss the operational applicability of each measure, that is, how each may be integrated with technologies that allow computational systems to adapt to the performer during operational stress.},\n\tnumber = {5},\n\tjournal = {Aviation, Space, and Environmental Medicine},\n\tauthor = {Taylor, Marcus K. and Sausen, Kenneth P. and Mujica-Parodi, Lilianne R. and Potterat, Eric G. and Yanagi, Matthew A. and Kim, Hyung},\n\tyear = {2007},\n\tpages = {B224--B230},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/DU74AJUA/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Stressful military training: endocrine reactivity, performance, and psychological impact.\n \n \n \n \n\n\n \n Taylor, M. K.; Sausen, K. P.; Potterat, E. G.; Mujica-Parodi, L. R.; Reis, J. P.; Markham, A. E.; Padilla, G. A.; and Taylor, D. L.\n\n\n \n\n\n\n Aviation, Space, and Environmental Medicine, 78(12): 1143–1149. 2007.\n \n\n\n\n
\n\n\n\n \n \n $\"Stressful$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{taylor2007a,\n\ttitle = {Stressful military training: endocrine reactivity, performance, and psychological impact},\n\tvolume = {78},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {0095-6562},\n\tshorttitle = {Stressful military training},\n\tdoi = {10.3357/asem.2151.2007},\n\tabstract = {INTRODUCTION: We examined the responsiveness of both cortisol and dehydroepiandrosterone sulfate (DHEAS) to the stress of survival training in military men and evaluated relationships to performance, peritraumatic dissociation, and the subsequent impact of stressful events.\nMETHODS: Baseline salivary cortisol samples were self-collected by 19 men at 0900 and 1930 in a free-living (FL) environment. DHEAS samples were also collected in a subset of this sample (N = 12). Samples were subsequently taken at similar time points during a stressful captivity (SC) phase of training. Repeated-measures analyses of variance with follow-up paired t-tests examined differences across time and conditions.\nRESULTS: Significant increases were observed at both time points (0900 and 1930) from FL to SC in both cortisol (0900: 9.2 +/- 3.4 nmol x L(-1) vs. 18.4 +/- 10.5 nmol x L(-1); 1930: 3.5 +/- 3.0 nmol x L(-1) vs. 27.7 +/- 10.9 nmol x L(-1)) and DHEAS (0900: 1.7 +/- 1.3 ng x ml(-1) vs.6.7 +/- 3.5 ngx ml(-1); 1930: 1.5 0.84 ng x ml(-1) vs. 4.5 +/- 3.0 ng x ml(-1)). Also, overall performance during a high-intensity captivity-related challenge was inversely related to the DHEAS-cortisol ratio; conversely, overall performance during a low-intensity captivity-related challenge was positively related to DHEAS at the 0900 time point during SC. Dissociation was unrelated to endocrine indices measured during SC, while total impact of events was inversely related to percent change in DHEAS from FL to SC.\nCONCLUSIONS: Cortisol and DHEAS increase in response to allostatic load, and may relate to human performance during SC as well as PTSD symptoms.},\n\tlanguage = {eng},\n\tnumber = {12},\n\tjournal = {Aviation, Space, and Environmental Medicine},\n\tauthor = {Taylor, Marcus K. and Sausen, Kenneth P. and Potterat, Eric G. and Mujica-Parodi, Lilianne R. and Reis, Jared P. and Markham, Amanda E. and Padilla, Genieleah A. and Taylor, Deborah L.},\n\tyear = {2007},\n\tpmid = {18064919},\n\tpages = {1143--1149},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/GUHCJNJC/file/view}\n}\n\n\n\n

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\n \n 2005\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n \n Nonlinear Complexity and Spectral Analyses of Heart Rate Variability in Medicated and Unmedicated Patients with Schizophrenia1.\n \n \n \n \n\n\n \n Mujica-Parodi, L. R.; Yeragani, V.; and Malaspina, D.\n\n\n \n\n\n\n Neuropsychobiology, 51(1): 10–15. 2005.\n \n\n\n\n
\n\n\n\n \n \n $\"Nonlinear$ Paper\n \n \n \n $\"Nonlinear$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{mujica-parodi2005,\n\ttitle = {Nonlinear {Complexity} and {Spectral} {Analyses} of {Heart} {Rate} {Variability} in {Medicated} and {Unmedicated} {Patients} with {Schizophrenia1}},\n\tvolume = {51},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {0302-282X, 1423-0224},\n\turl = {https://www.karger.com/Article/FullText/82850},\n\tdoi = {10.1159/000082850},\n\tabstract = {\\textit{Objective:} Heart rate variability (HRV) reflects functioning of the autonomic nervous system and possibly also regulation by the neural limbic system, abnormalities of which have both figured prominently in various etiological models of schizophrenia, particularly those that address patients’ vulnerability to stress in connection to psychosis onset and exacerbation. This study provides data on cardiac functioning in a sample of schizophrenia patients that were either medication free or on atypical antipsychotics, as well as cardiac data on matched healthy controls. We included a medication-free group to investigate whether abnormalities in HRV previously reported in the literature and associated with atypical antipsychotics were solely the effect of medications or whether they might be a feature of the illness (or psychosis) itself. \\textit{Method:} We collected 24-hour ECGs on 19 patients and 24 controls. Of the patients, 9 were medication free and 10 were on atypical antipsychotics. All subject groups were matched for age and gender. Patient groups showed equivalent symptom severity and type, as well as duration of illness. We analyzed the data using nonlinear complexity (symbolic dynamic) HRV analyses as well as standard and relative spectral analyses. \\textit{Results:} For the medication-free patients as compared to the healthy controls, our data show decreased R–R intervals during sleep, and abnormal suppression of all frequency ranges, but particularly the low frequency range, which persisted even after adjusting the spectral data for the mean R–R interval. This effect was exacerbated for patients on atypical antipsychotics. Likewise, nonlinear complexity analysis showed significantly impaired HRV for medication-free patients that was exacerbated in the patients on atypical antipsychotics. \\textit{Conclusions:} Altogether, the data suggest a pattern of significantly decreased cardiac vagal function of patients with schizophrenia as compared to healthy controls, apart from and beyond any differences due to medication side effects. The data additionally confirm earlier reports of a deleterious effect of atypical antipsychotics on HRV, which may exacerbate an underlying vulnerability in patients. These results support previous evidence that autonomic abnormalities may be a core feature of the illness (or psychosis), and that an even more conservative approach to cardiac risk in schizophrenia than previously thought may therefore be clinically appropriate.},\n\tlanguage = {english},\n\tnumber = {1},\n\turldate = {2021-11-30},\n\tjournal = {Neuropsychobiology},\n\tauthor = {Mujica-Parodi, Lilianne R. and Yeragani, Vikram and Malaspina, Dolores},\n\tyear = {2005},\n\tpages = {10--15},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/MJJFTI36/file/view}\n}\n\n\n\n

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\n Objective: Heart rate variability (HRV) reflects functioning of the autonomic nervous system and possibly also regulation by the neural limbic system, abnormalities of which have both figured prominently in various etiological models of schizophrenia, particularly those that address patients’ vulnerability to stress in connection to psychosis onset and exacerbation. This study provides data on cardiac functioning in a sample of schizophrenia patients that were either medication free or on atypical antipsychotics, as well as cardiac data on matched healthy controls. We included a medication-free group to investigate whether abnormalities in HRV previously reported in the literature and associated with atypical antipsychotics were solely the effect of medications or whether they might be a feature of the illness (or psychosis) itself. Method: We collected 24-hour ECGs on 19 patients and 24 controls. Of the patients, 9 were medication free and 10 were on atypical antipsychotics. All subject groups were matched for age and gender. Patient groups showed equivalent symptom severity and type, as well as duration of illness. We analyzed the data using nonlinear complexity (symbolic dynamic) HRV analyses as well as standard and relative spectral analyses. Results: For the medication-free patients as compared to the healthy controls, our data show decreased R–R intervals during sleep, and abnormal suppression of all frequency ranges, but particularly the low frequency range, which persisted even after adjusting the spectral data for the mean R–R interval. This effect was exacerbated for patients on atypical antipsychotics. Likewise, nonlinear complexity analysis showed significantly impaired HRV for medication-free patients that was exacerbated in the patients on atypical antipsychotics. Conclusions: Altogether, the data suggest a pattern of significantly decreased cardiac vagal function of patients with schizophrenia as compared to healthy controls, apart from and beyond any differences due to medication side effects. The data additionally confirm earlier reports of a deleterious effect of atypical antipsychotics on HRV, which may exacerbate an underlying vulnerability in patients. These results support previous evidence that autonomic abnormalities may be a core feature of the illness (or psychosis), and that an even more conservative approach to cardiac risk in schizophrenia than previously thought may therefore be clinically appropriate.\n

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\n \n 2004\n \n \n (2)\n \n \n

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\n \n\n \n \n \n \n \n \n Resting neural activity distinguishes subgroups of schizophrenia patients.\n \n \n \n \n\n\n \n Malaspina, D.; Harkavy-Friedman, J.; Corcoran, C.; Mujica-Parodi, L. R.; Printz, D.; Gorman, J. M.; and Van Heertum, R.\n\n\n \n\n\n\n Biological Psychiatry, 56(12): 931–937. 2004.\n \n\n\n\n
\n\n\n\n \n \n $\"Resting$ Paper\n \n \n \n $\"Resting$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{malaspina2004,\n\ttitle = {Resting neural activity distinguishes subgroups of schizophrenia patients},\n\tvolume = {56},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {0006-3223},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0006322304010005},\n\tdoi = {10.1016/j.biopsych.2004.09.013},\n\tabstract = {Background\nSchizophrenia is etiologically heterogeneous. It is anticipated, but unproven, that subgroups will differ in neuropathology and that neuroimaging may reveal these differences. The optimal imaging condition may be at rest, where greater variability is observed than during cognitive tasks, which more consistently reveal hypofrontality. We previously demonstrated symptom and physiologic differences between familial and sporadic schizophrenia patients and hypothesized that the groups would show different resting regional cerebral blood flow (rCBF) patterns.\nMethods\nTen familial and sixteen sporadic schizophrenia patients and nine comparison subjects had single photon emission computed tomography imaging during passive visual fixation. Images were spatially normalized into Talairach coordinates and analyzed for group rCBF differences using SPM with a Z value threshold of 2.80, p {\\textless} .001.\nResults\nThe subgroups had similar age, gender, illness duration, and medication treatment. Sporadic patients had hypofrontality (anterior cingulate, paracingulate cortices, left dorsolateral and inferior-orbitofrontal), whereas familial patients had left temporoparietal hypoperfusion; all of these regions show resting activity in healthy subjects. Both groups hyperperfused the cerebellum/pons and parahippocampal gyrus; additional hyperperfusion for sporadic patients was observed in the fusiform; familial patients also hyperperfused the hippocampus, dentate, uncus, amygdala, thalamus, and putamen.\nConclusions\nFamilial and sporadic schizophrenia patients had different resting rCBF profiles, supporting the hypothesis that certain subgroups have distinct neural underpinnings. Different neuropathologic processes among subgroups of schizophrenia patients may account for the prior contradictory results of resting imaging studies.},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2021-11-30},\n\tjournal = {Biological Psychiatry},\n\tauthor = {Malaspina, Dolores and Harkavy-Friedman, Jill and Corcoran, Cheryl and Mujica-Parodi, Lilianne R. and Printz, David and Gorman, Jack M. and Van Heertum, Ronald},\n\tyear = {2004},\n\tpages = {931--937},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/KWKVJPFA/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n The Reliability and Clinical Correlates of Figure-Ground Perception in Schizophrenia.\n \n \n \n \n\n\n \n Malaspina, D.; Simon, N.; Goetz, R. R.; Corcoran, C.; Coleman, E.; Printz, D.; Mujica-Parodi, L. R.; and Wolitzky, R.\n\n\n \n\n\n\n The Journal of Neuropsychiatry and Clinical Neurosciences, 16(3): 277–283. 2004.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n \n $\"The$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{malaspina2004a,\n\ttitle = {The {Reliability} and {Clinical} {Correlates} of {Figure}-{Ground} {Perception} in {Schizophrenia}},\n\tvolume = {16},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {0895-0172},\n\turl = {https://neuro.psychiatryonline.org/doi/10.1176/jnp.16.3.277},\n\tdoi = {10.1176/jnp.16.3.277},\n\tabstract = {Schizophrenia subjects are impaired in a number of visual attention paradigms. However, their performance on tests of figure-ground visual perception (FGP), which requires subjects to visually discriminate figures embedded in a rival background, is relatively unstudied. We examined FGP in 63 schizophrenia patients and 27 control subjects and found that the patients performed the FGP test reliably and had significantly lower FGP scores than the control subjects. Figure-ground visual perception was significantly correlated with other neuropsychological test scores and was inversely related to negative symptoms. It was unrelated to antipsychotic medication treatment. Figure-ground visual perception depends on “top down” processing of visual stimuli, and thus this data suggests that dysfunction in the higher-level pathways that modulate visual perceptual processes may also be related to a core defect in schizophrenia.},\n\tnumber = {3},\n\turldate = {2023-11-28},\n\tjournal = {The Journal of Neuropsychiatry and Clinical Neurosciences},\n\tauthor = {Malaspina, Dolores and Simon, Naomi and Goetz, Raymond R. and Corcoran, Cheryl and Coleman, Eliza and Printz, David and Mujica-Parodi, Lilianne R. and Wolitzky, Rachel},\n\tyear = {2004},\n\tpages = {277--283},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/EB57VNEZ/file/view}\n}\n\n\n\n

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\n \n 2003\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n \n Using figure ground perception to examine the unitary and heterogeneity models for psychopathology in schizophrenia.\n \n \n \n \n\n\n \n Malaspina, D.; Simon, N.; Corcoran, C.; Mujica-Parodi, L. R.; Goetz, R. R.; and Gorman, J.\n\n\n \n\n\n\n Schizophrenia Research, 59(2-3): 297–299. 2003.\n \n\n\n\n
\n\n\n\n \n \n $\"Using$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{malaspina2003,\n\ttitle = {Using figure ground perception to examine the unitary and heterogeneity models for psychopathology in schizophrenia},\n\tvolume = {59},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1573-2509},\n\tdoi = {10.1016/S0920-9964(01)00380-2},\n\tabstract = {The symptoms of schizophrenia, which are typically grouped into positive, negative, and disorganized domains, are theorized to arise from different neural abnormalities or from a unitary dysfunction in the cortico-cerebellar-thalamo-cortical circuitry. If figure ground perception (FGP) were specifically associated with positive symptoms, it would lend support to the idea that the different schizophrenia symptoms have distinct neural underpinnings. This letter reports a study assessing FGP performance and positive and negative symptom measures in 47 DSM-IV schizophrenia patients. Results indicate that FGP ability was strongly associated with negative symptoms indices, across sexes and for the whole patient group using both traditional and the factor-derived solution to symptom measures. In contrast to the authors' hypothesis, FGP was not related with the positive symptom scale or factor: FGP was robustly related only to the negative symptom scales. These data provide some evidence for the hypothesis that a unitary neuroabnormality could underlie the breadth of schizophrenia symptoms. The neural network that subserves voluntary visuospatial processing and spatial representation, which is implicated in schizophrenia, may have a high convergence with this neural dysfunction. (PsycINFO Database Record (c) 2017 APA, all rights reserved)},\n\tnumber = {2-3},\n\tjournal = {Schizophrenia Research},\n\tauthor = {Malaspina, Dolores and Simon, Naomi and Corcoran, Cheryl and Mujica-Parodi, Lilianne R. and Goetz, Raymond R. and Gorman, Jack},\n\tyear = {2003},\n\tpages = {297--299},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/NGU5545M/file/view}\n}\n\n\n\n

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\n \n 2002\n \n \n (2)\n \n \n

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\n \n\n \n \n \n \n \n \n Could Stress Cause Psychosis in Individuals Vulnerable to Schizophrenia?.\n \n \n \n \n\n\n \n Corcoran, C.; Mujica-Parodi, L. R.; Yale, S.; Leitman, D.; and Malaspina, D.\n\n\n \n\n\n\n CNS spectrums, 7(1): 33–42. 2002.\n \n\n\n\n
\n\n\n\n \n \n $\"Could$ Paper\n \n \n \n $\"Could$ paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{corcoran2002,\n\ttitle = {Could {Stress} {Cause} {Psychosis} in {Individuals} {Vulnerable} to {Schizophrenia}?},\n\tvolume = {7},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1092-8529},\n\turl = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2774708/},\n\tabstract = {It has long been considered that psychosocial stress plays a role in the expression of symptoms in schizophrenia (SZ), as it interacts with latent neural vulnerability that stems from genetic liability and early environmental insult. Advances in the understanding of the neurobiology of the stress cascade in both animal and human studies lead to a plausible model by which this interaction may occur: through neurotoxic effects on the hippocampus that may involve synaptic remodeling. Of late, the neurodevelopmental model of SZ etiology has been favored. But an elaboration of this schema that credits the impact of postnatal events and considers a role for neurodegenerative changes may be more plausible, given the evidence for gene-environment interaction in SZ expression and progressive structural changes observed with magnetic resonance imaging. Furthermore, new insights into nongliotic neurotoxic effects such as apoptosis, failure of neurogenesis, and changes in circuitry lead to an expansion of the time frame in which environmental effects may mediate expression of SZ symptoms.},\n\tnumber = {1},\n\turldate = {2023-11-28},\n\tjournal = {CNS spectrums},\n\tauthor = {Corcoran, Cheryl and Mujica-Parodi, Lilianne R. and Yale, Scott and Leitman, David and Malaspina, Dolores},\n\tyear = {2002},\n\tpmid = {15254447},\n\tpmcid = {PMC2774708},\n\tpages = {33--42},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/X4DP9HKS/file/view}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n \n Are Cognitive Symptoms of Schizophrenia Mediated by Abnormalities in Emotional Arousal?.\n \n \n \n \n\n\n \n Mujica-Parodi, L. R.; Corcoran, C.; Greenberg, T.; Sackeim, H. A.; and Malaspina, D.\n\n\n \n\n\n\n CNS Spectrums, 7(1): 58–70. 2002.\n \n\n\n\n
\n\n\n\n \n \n $\"Are$ Paper\n \n \n \n $\"Are$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{mujica-parodi2002,\n\ttitle = {Are {Cognitive} {Symptoms} of {Schizophrenia} {Mediated} by {Abnormalities} in {Emotional} {Arousal}?},\n\tvolume = {7},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1092-8529, 2165-6509},\n\turl = {https://www.cambridge.org/core/journals/cns-spectrums/article/abs/are-cognitive-symptoms-of-schizophrenia-mediated-by-abnormalities-in-emotional-arousal/82209C7E2C9D105A51FF8C9BAC8A897A},\n\tdoi = {10.1017/S1092852900022276},\n\tabstract = {We tested 28 individuals with schizophrenia (SZ) and 16 healthy individuals on a test of logical reasoning and “cognitive gating,” defined as the ability to discriminate between relevant and irrelevant information in confirming or disconfirming a given belief. The Logical Reasoning and Cognitive Gating Task tests both processes under neutral and affect-laden conditions. This is done by presenting formally identical constructs using benign and emotionally arousing language. When separated by symptom profiles, we found statistically significant differences for performance and arousal response between patients with delusions, patients with formal thought disorder, and patients with neither delusions nor formal thought disorder, as well as between patients and healthy controls. When analyzed by error type, we found that nearly all errors by delusional patients were caused by overly restrictive information choice, a pattern that may be related to a delusional patient's tendency to “jump to conclusions” on Bayesian probabilistic tasks. This is in contrast to patients with formal thought disorder, whose low performance resulted also from overly extensive information choice. The tendencies towards restriction were exacerbated by arousal, which is consistent with studies on cognition and arousal in healthy individuals. After briefly examining research on emotional arousal and SZ, and the interaction between emotional arousal and restriction of perceptual cues in healthy individuals, we conclude by suggesting a model which accounts for the distinctive cognitive characteristics of delusional patients by their possessing distinct vulnerabilities to emotional arousal. Specifically, these results suggest the possibility that delusional patients process information in a manner that is essentially intact. However, delusional patients may possess an acute vulnerability to emotional arousal that might cause delusional individuals to behave cognitively as if they were healthy individuals under significantly more severe forms of stress.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2021-11-30},\n\tjournal = {CNS Spectrums},\n\tauthor = {Mujica-Parodi, Lilianne R. and Corcoran, Cheryl and Greenberg, Tsafrir and Sackeim, Harold A. and Malaspina, Dolores},\n\tyear = {2002},\n\tpages = {58--70},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/AVPF7UFX/file/view}\n}\n\n\n\n

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\n We tested 28 individuals with schizophrenia (SZ) and 16 healthy individuals on a test of logical reasoning and “cognitive gating,” defined as the ability to discriminate between relevant and irrelevant information in confirming or disconfirming a given belief. The Logical Reasoning and Cognitive Gating Task tests both processes under neutral and affect-laden conditions. This is done by presenting formally identical constructs using benign and emotionally arousing language. When separated by symptom profiles, we found statistically significant differences for performance and arousal response between patients with delusions, patients with formal thought disorder, and patients with neither delusions nor formal thought disorder, as well as between patients and healthy controls. When analyzed by error type, we found that nearly all errors by delusional patients were caused by overly restrictive information choice, a pattern that may be related to a delusional patient's tendency to “jump to conclusions” on Bayesian probabilistic tasks. This is in contrast to patients with formal thought disorder, whose low performance resulted also from overly extensive information choice. The tendencies towards restriction were exacerbated by arousal, which is consistent with studies on cognition and arousal in healthy individuals. After briefly examining research on emotional arousal and SZ, and the interaction between emotional arousal and restriction of perceptual cues in healthy individuals, we conclude by suggesting a model which accounts for the distinctive cognitive characteristics of delusional patients by their possessing distinct vulnerabilities to emotional arousal. Specifically, these results suggest the possibility that delusional patients process information in a manner that is essentially intact. However, delusional patients may possess an acute vulnerability to emotional arousal that might cause delusional individuals to behave cognitively as if they were healthy individuals under significantly more severe forms of stress.\n

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\n \n 2001\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n \n Cultural Invariance and the Diagnosis of Delusions.\n \n \n \n \n\n\n \n Mujica-Parodi, L. R.; and Sackeim, H. A.\n\n\n \n\n\n\n The Journal of Neuropsychiatry and Clinical Neurosciences, 13(3): 403–a. 2001.\n \n\n\n\n
\n\n\n\n \n \n $\"Cultural$ Paper\n \n \n \n $\"Cultural$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{mujica-parodi2001,\n\ttitle = {Cultural {Invariance} and the {Diagnosis} of {Delusions}},\n\tvolume = {13},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {0895-0172},\n\turl = {https://neuro.psychiatryonline.org/doi/full/10.1176/jnp.13.3.403-a},\n\tdoi = {10.1176/jnp.13.3.403-a},\n\tnumber = {3},\n\turldate = {2021-11-30},\n\tjournal = {The Journal of Neuropsychiatry and Clinical Neurosciences},\n\tauthor = {Mujica-Parodi, Lilianne R. and Sackeim, Harold A.},\n\tyear = {2001},\n\tpages = {403--a},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/JHS4RM5J/file/view}\n}\n\n\n\n

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\n \n 2000\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n \n Logical processing, affect, and delusional thought in schizophrenia.\n \n \n \n \n\n\n \n Mujica-Parodi, L. R.; Malaspina, D.; and Sackeim, H. A.\n\n\n \n\n\n\n Harvard Review of Psychiatry, 8(2): 73–83. 2000.\n \n\n\n\n
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@article{mujica-parodi2000,\n\ttitle = {Logical processing, affect, and delusional thought in schizophrenia},\n\tvolume = {8},\n\tcopyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},\n\tissn = {1067-3229},\n\turl = {https://content.api.getguru.com/files/view/825c59a9-11fd-417a-9def-274a47ea5090},\n\tabstract = {Deficits of logical reasoning have long been considered a hallmark of schizophrenia and delusional disorders. We provide a more precise characterization of "logic" and, by extension, of "deficits in logical reasoning." A model is offered to categorize different forms of logical deficits. This model acknowledges not only problems with making inferences, which is how logic deficits are usually conceived, but also problems in the acquisition and evaluation of premises (i.e., filtering of "input"). Early (1940-1969) and modern (1970-present) literature on logical reasoning and schizophrenia is evaluated within the context of the presented model. We argue that, despite a substantial history of interest in the topic, research to date has been inconclusive on the fundamental question of whether patients with delusional ideation show abnormalities in logical reasoning. This may be due to heterogeneous definitions of "logic," variability in the composition of patient samples, and floor effects among the healthy controls. In spite of these difficulties, the available evidence suggests that deficits in logical reasoning are more likely to occur due to faulty assessment of premises than to a defect in the structure of inferences. Such deficits seem to be provoked (in healthy individuals) or exacerbated (in patients with schizophrenia) by emotional content. The hypothesis is offered that delusional ideation is primarily affect-driven, and that a mechanism present in healthy individuals when they are emotionally challenged may be inappropriately activated in patients who are delusional.},\n\tlanguage = {eng},\n\tnumber = {2},\n\tjournal = {Harvard Review of Psychiatry},\n\tauthor = {Mujica-Parodi, Lilianne R. and Malaspina, D. and Sackeim, H. A.},\n\tyear = {2000},\n\tpmid = {10902096},\n\tpages = {73--83},\n\turl_paper={https://api.zotero.org/users/8073967/publications/items/5H4BI2A4/file/view}\n}\n\n\n\n\n\n\n\n

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