@article{alicioglu_survey_2022,
	title = {A survey of visual analytics for {Explainable} {Artificial} {Intelligence} methods},
	volume = {102},
	issn = {00978493},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0097849321001886},
	doi = {10.1016/j.cag.2021.09.002},
	abstract = {Deep learning (DL) models have achieved impressive performance in various domains such as medicine, finance, and autonomous vehicle systems with advances in computing power and technologies. However, due to the black-box structure of DL models, the decisions of these learning models often need to be explained to end-users. Explainable Artificial Intelligence (XAI) provides explanations of black-box models to reveal the behavior and underlying decision-making mechanisms of the models through tools, techniques, and algorithms. Visualization techniques help to present model and prediction explanations in a more understandable, explainable, and interpretable way. This survey paper aims to review current trends and challenges of visual analytics in interpreting DL models by adopting XAI methods and present future research directions in this area. We reviewed literature based on two different aspects, model usage and visual approaches. We addressed several research questions based on our findings and then discussed missing points, research gaps, and potential future research directions. This survey provides guidelines to develop a better interpretation of neural networks through XAI methods in the field of visual analytics.},
	language = {en},
	urldate = {2023-05-12},
	journal = {Computers \& Graphics},
	author = {Alicioglu, Gulsum and Sun, Bo},
	month = feb,
	year = {2022},
	pages = {502--520},
}

Deep learning (DL) models have achieved impressive performance in various domains such as medicine, finance, and autonomous vehicle systems with advances in computing power and technologies. However, due to the black-box structure of DL models, the decisions of these learning models often need to be explained to end-users. Explainable Artificial Intelligence (XAI) provides explanations of black-box models to reveal the behavior and underlying decision-making mechanisms of the models through tools, techniques, and algorithms. Visualization techniques help to present model and prediction explanations in a more understandable, explainable, and interpretable way. This survey paper aims to review current trends and challenges of visual analytics in interpreting DL models by adopting XAI methods and present future research directions in this area. We reviewed literature based on two different aspects, model usage and visual approaches. We addressed several research questions based on our findings and then discussed missing points, research gaps, and potential future research directions. This survey provides guidelines to develop a better interpretation of neural networks through XAI methods in the field of visual analytics.

@article{he_meta-matching_2022,
	title = {Meta-matching as a simple framework to translate phenotypic predictive models from big to small data},
	copyright = {2022 The Author(s), under exclusive licence to Springer Nature America, Inc.},
	issn = {1546-1726},
	url = {https://www.nature.com/articles/s41593-022-01059-9},
	doi = {10.1038/s41593-022-01059-9},
	abstract = {We propose a simple framework—meta-matching—to translate predictive models from large-scale datasets to new unseen non-brain-imaging phenotypes in small-scale studies. The key consideration is that a unique phenotype from a boutique study likely correlates with (but is not the same as) related phenotypes in some large-scale dataset. Meta-matching exploits these correlations to boost prediction in the boutique study. We apply meta-matching to predict non-brain-imaging phenotypes from resting-state functional connectivity. Using the UK Biobank (N = 36,848) and Human Connectome Project (HCP) (N = 1,019) datasets, we demonstrate that meta-matching can greatly boost the prediction of new phenotypes in small independent datasets in many scenarios. For example, translating a UK Biobank model to 100 HCP participants yields an eight-fold improvement in variance explained with an average absolute gain of 4.0\% (minimum = −0.2\%, maximum = 16.0\%) across 35 phenotypes. With a growing number of large-scale datasets collecting increasingly diverse phenotypes, our results represent a lower bound on the potential of meta-matching.},
	language = {en},
	urldate = {2022-05-19},
	journal = {Nature Neuroscience},
	author = {He, Tong and An, Lijun and Chen, Pansheng and Chen, Jianzhong and Feng, Jiashi and Bzdok, Danilo and Holmes, Avram J. and Eickhoff, Simon B. and Yeo, B. T. Thomas},
	month = may,
	year = {2022},
	note = {Publisher: Nature Publishing Group},
	keywords = {Cognitive neuroscience, Network models},
	pages = {1--10},
}

We propose a simple framework—meta-matching—to translate predictive models from large-scale datasets to new unseen non-brain-imaging phenotypes in small-scale studies. The key consideration is that a unique phenotype from a boutique study likely correlates with (but is not the same as) related phenotypes in some large-scale dataset. Meta-matching exploits these correlations to boost prediction in the boutique study. We apply meta-matching to predict non-brain-imaging phenotypes from resting-state functional connectivity. Using the UK Biobank (N = 36,848) and Human Connectome Project (HCP) (N = 1,019) datasets, we demonstrate that meta-matching can greatly boost the prediction of new phenotypes in small independent datasets in many scenarios. For example, translating a UK Biobank model to 100 HCP participants yields an eight-fold improvement in variance explained with an average absolute gain of 4.0% (minimum = −0.2%, maximum = 16.0%) across 35 phenotypes. With a growing number of large-scale datasets collecting increasingly diverse phenotypes, our results represent a lower bound on the potential of meta-matching.

@techreport{johnston_abstract_2022,
	title = {Abstract representations emerge naturally in neural networks trained to perform multiple tasks},
	copyright = {© 2022, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), CC BY-NC 4.0, as described at http://creativecommons.org/licenses/by-nc/4.0/},
	url = {https://www.biorxiv.org/content/10.1101/2021.10.20.465187v3},
	abstract = {Humans and other animals demonstrate a remarkable ability to generalize knowledge across distinct contexts and objects during natural behavior. We posit that this ability to generalize arises from a specific representational geometry, that we call abstract and that is referred to as disentangled in machine learning. These abstract representations have been observed in recent neurophysiological studies. However, it is unknown how they emerge. Here, using feedforward neural networks, we demonstrate that the learning of multiple tasks causes abstract representations to emerge, using both supervised and reinforcement learning. We show that these abstract representations enable few-sample learning and reliable generalization on novel tasks. We conclude that abstract representations of sensory and cognitive variables may emerge from the multiple behaviors that animals exhibit in the natural world, and, as a consequence, could be pervasive in high-level brain regions. We also make several specific predictions about which variables will be represented abstractly.},
	language = {en},
	urldate = {2022-05-18},
	institution = {bioRxiv},
	author = {Johnston, W. Jeffrey and Fusi, Stefano},
	month = may,
	year = {2022},
	doi = {10.1101/2021.10.20.465187},
	note = {Section: New Results
Type: article},
	pages = {2021.10.20.465187},
}

Humans and other animals demonstrate a remarkable ability to generalize knowledge across distinct contexts and objects during natural behavior. We posit that this ability to generalize arises from a specific representational geometry, that we call abstract and that is referred to as disentangled in machine learning. These abstract representations have been observed in recent neurophysiological studies. However, it is unknown how they emerge. Here, using feedforward neural networks, we demonstrate that the learning of multiple tasks causes abstract representations to emerge, using both supervised and reinforcement learning. We show that these abstract representations enable few-sample learning and reliable generalization on novel tasks. We conclude that abstract representations of sensory and cognitive variables may emerge from the multiple behaviors that animals exhibit in the natural world, and, as a consequence, could be pervasive in high-level brain regions. We also make several specific predictions about which variables will be represented abstractly.

@article{tiedemann_one-shot_2022,
	title = {One-shot generalization in humans revealed through a drawing task},
	volume = {11},
	issn = {2050-084X},
	url = {https://doi.org/10.7554/eLife.75485},
	doi = {10.7554/eLife.75485},
	abstract = {Humans have the amazing ability to learn new visual concepts from just a single exemplar. How we achieve this remains mysterious. State-of-the-art theories suggest observers rely on internal ‘generative models’, which not only describe observed objects, but can also synthesize novel variations. However, compelling evidence for generative models in human one-shot learning remains sparse. In most studies, participants merely compare candidate objects created by the experimenters, rather than generating their own ideas. Here, we overcame this key limitation by presenting participants with 2D ‘Exemplar’ shapes and asking them to draw their own ‘Variations’ belonging to the same class. The drawings reveal that participants inferred—and synthesized—genuine novel categories that were far more varied than mere copies. Yet, there was striking agreement between participants about which shape features were most distinctive, and these tended to be preserved in the drawn Variations. Indeed, swapping distinctive parts caused objects to swap apparent category. Our findings suggest that internal generative models are key to how humans generalize from single exemplars. When observers see a novel object for the first time, they identify its most distinctive features and infer a generative model of its shape, allowing them to mentally synthesize plausible variants.},
	urldate = {2022-05-12},
	journal = {eLife},
	author = {Tiedemann, Henning and Morgenstern, Yaniv and Schmidt, Filipp and Fleming, Roland W},
	editor = {Barense, Morgan and Baker, Chris I and Bainbridge, Wilma},
	month = may,
	year = {2022},
	note = {Publisher: eLife Sciences Publications, Ltd},
	keywords = {categorization, shape perception, visual perception},
	pages = {e75485},
}

Humans have the amazing ability to learn new visual concepts from just a single exemplar. How we achieve this remains mysterious. State-of-the-art theories suggest observers rely on internal ‘generative models’, which not only describe observed objects, but can also synthesize novel variations. However, compelling evidence for generative models in human one-shot learning remains sparse. In most studies, participants merely compare candidate objects created by the experimenters, rather than generating their own ideas. Here, we overcame this key limitation by presenting participants with 2D ‘Exemplar’ shapes and asking them to draw their own ‘Variations’ belonging to the same class. The drawings reveal that participants inferred—and synthesized—genuine novel categories that were far more varied than mere copies. Yet, there was striking agreement between participants about which shape features were most distinctive, and these tended to be preserved in the drawn Variations. Indeed, swapping distinctive parts caused objects to swap apparent category. Our findings suggest that internal generative models are key to how humans generalize from single exemplars. When observers see a novel object for the first time, they identify its most distinctive features and infer a generative model of its shape, allowing them to mentally synthesize plausible variants.

@article{fu_geometry_2022,
	title = {The geometry of domain-general performance monitoring in the human medial frontal cortex},
	copyright = {Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works},
	url = {https://www.science.org/doi/epdf/10.1126/science.abm9922},
	doi = {10.1126/science.abm9922},
	abstract = {Representations of evaluative signals in human frontal cortex are both abstract and task specific.},
	language = {EN},
	urldate = {2022-05-09},
	journal = {Science},
	author = {Fu, Zhongzheng and Beam, Danielle and Chung, Jeffrey M. and Reed, Chrystal M. and Mamelak, Adam N. and Adolphs, Ralph and Rutishauser, Ueli},
	month = may,
	year = {2022},
	note = {Publisher: American Association for the Advancement of Science},
}

Representations of evaluative signals in human frontal cortex are both abstract and task specific.

@article{yeung_reporting_2022,
	title = {Reporting details of neuroimaging studies on individual traits prediction: a literature survey},
	issn = {1053-8119},
	shorttitle = {Reporting details of neuroimaging studies on individual traits prediction},
	url = {https://www.sciencedirect.com/science/article/pii/S1053811922003962},
	doi = {10.1016/j.neuroimage.2022.119275},
	abstract = {Using machine-learning tools to predict individual phenotypes from neuroimaging data is one of the most promising and hence dynamic fields in systems neuroscience. Here, we perform a literature survey of the rapidly work on phenotype prediction in healthy subjects or general population to sketch out the current state and ongoing developments in terms of data, analysis methods and reporting. Excluding papers on age-prediction and clinical applications, which form a distinct literature, we identified a total 108 papers published since 2007. In these, memory, fluid intelligence and attention were most common phenotypes to be predicted, which resonates with the observation that roughly a quarter of the papers used data from the Human Connectome Project, even though another half recruited their own cohort. Sample size (in terms of training and external test sets) and prediction accuracy (from internal and external validation respectively) did not show significant temporal trends. Prediction accuracy was negatively correlated with sample size of the training set, but not the external test set. While known to be optimistic, leave-one-out cross-validation (LOO CV) was the prevalent strategy for model validation (n = 48). Meanwhile, 27 studies used external validation with external test set. Both numbers showed no significant temporal trends. The most popular learning algorithm was connectome-based predictive modeling introduced by the Yale team. Other common learning algorithms were linear regression, relevance vector regression (RVR), support vector regression (SVR), least absolute shrinkage and selection operator (LASSO), and elastic net. Meanwhile, the amount of data from self-recruiting studies (but not studies using open, shared dataset) was positively correlated with internal validation prediction accuracy. At the same time, self-recruiting studies also reported a significantly higher internal validation prediction accuracy than those using open, shared datasets. Data type and participant age did not significantly influence prediction accuracy. Confound control also did not influence prediction accuracy after adjusted for other factors. To conclude, most of the current literature is probably quite optimistic with internal validation using LOO CV. More efforts should be made to encourage the use of external validation with external test sets to further improve generalizability of the models.},
	language = {en},
	urldate = {2022-05-09},
	journal = {NeuroImage},
	author = {Yeung, Andy Wai Kan and More, Shammi and Wu, Jianxiao and Eickhoff, Simon B.},
	month = may,
	year = {2022},
	keywords = {Individual trait, Neuroimaging, Prediction, Predictive modeling, Survey},
	pages = {119275},
}

Using machine-learning tools to predict individual phenotypes from neuroimaging data is one of the most promising and hence dynamic fields in systems neuroscience. Here, we perform a literature survey of the rapidly work on phenotype prediction in healthy subjects or general population to sketch out the current state and ongoing developments in terms of data, analysis methods and reporting. Excluding papers on age-prediction and clinical applications, which form a distinct literature, we identified a total 108 papers published since 2007. In these, memory, fluid intelligence and attention were most common phenotypes to be predicted, which resonates with the observation that roughly a quarter of the papers used data from the Human Connectome Project, even though another half recruited their own cohort. Sample size (in terms of training and external test sets) and prediction accuracy (from internal and external validation respectively) did not show significant temporal trends. Prediction accuracy was negatively correlated with sample size of the training set, but not the external test set. While known to be optimistic, leave-one-out cross-validation (LOO CV) was the prevalent strategy for model validation (n = 48). Meanwhile, 27 studies used external validation with external test set. Both numbers showed no significant temporal trends. The most popular learning algorithm was connectome-based predictive modeling introduced by the Yale team. Other common learning algorithms were linear regression, relevance vector regression (RVR), support vector regression (SVR), least absolute shrinkage and selection operator (LASSO), and elastic net. Meanwhile, the amount of data from self-recruiting studies (but not studies using open, shared dataset) was positively correlated with internal validation prediction accuracy. At the same time, self-recruiting studies also reported a significantly higher internal validation prediction accuracy than those using open, shared datasets. Data type and participant age did not significantly influence prediction accuracy. Confound control also did not influence prediction accuracy after adjusted for other factors. To conclude, most of the current literature is probably quite optimistic with internal validation using LOO CV. More efforts should be made to encourage the use of external validation with external test sets to further improve generalizability of the models.

@techreport{busch_temporal_2022,
	title = {Temporal {PHATE}: {A} multi-view manifold learning method for brain state trajectories},
	copyright = {© 2022, 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/},
	shorttitle = {Temporal {PHATE}},
	url = {https://www.biorxiv.org/content/10.1101/2022.05.03.490534v2},
	abstract = {Brain activity as measured with functional magnetic resonance imaging (fMRI) gives the illusion of intractably high dimensionality, rife with collection and biological noise. Nonlinear dimensionality reductions like UMAP, tSNE, and PHATE have proven useful for high-throughput biomedical data, but have not been extensively used in fMRI, which is known to reflect the redundancy and co-modulation of neural population activity. Here we take the manifold-geometry preserving method PHATE and extend it for use in brain activity timeseries data in a method we call temporal PHATE (T-PHATE). We observe that in addition to the intrinsically lower dimensionality of fMRI data, it also has significant autocorrelative structure that we can exploit to faithfully denoise the signal and learn brain activation manifolds. We empirically validate T-PHATE on three fMRI tasks and show that T-PHATE manifolds improve visualization fidelity, stimulus feature classification, and neural event segmentation. T-PHATE demonstrates impressive improvements over previous cutting-edge approaches to understanding the nature of cognition from fMRI and bodes potential applications broadly for high-dimensional datasets of temporally-diffuse processes.},
	language = {en},
	urldate = {2022-05-06},
	institution = {bioRxiv},
	author = {Busch, Erica L. and Huang, Jessie and Benz, Andrew and Wallenstein, Tom and Lajoie, Guillaume and Wolf, Guy and Krishnaswamy, Smita and Turk-Browne, Nicholas B.},
	month = may,
	year = {2022},
	doi = {10.1101/2022.05.03.490534},
	note = {Section: New Results
Type: article},
	pages = {2022.05.03.490534},
}

Brain activity as measured with functional magnetic resonance imaging (fMRI) gives the illusion of intractably high dimensionality, rife with collection and biological noise. Nonlinear dimensionality reductions like UMAP, tSNE, and PHATE have proven useful for high-throughput biomedical data, but have not been extensively used in fMRI, which is known to reflect the redundancy and co-modulation of neural population activity. Here we take the manifold-geometry preserving method PHATE and extend it for use in brain activity timeseries data in a method we call temporal PHATE (T-PHATE). We observe that in addition to the intrinsically lower dimensionality of fMRI data, it also has significant autocorrelative structure that we can exploit to faithfully denoise the signal and learn brain activation manifolds. We empirically validate T-PHATE on three fMRI tasks and show that T-PHATE manifolds improve visualization fidelity, stimulus feature classification, and neural event segmentation. T-PHATE demonstrates impressive improvements over previous cutting-edge approaches to understanding the nature of cognition from fMRI and bodes potential applications broadly for high-dimensional datasets of temporally-diffuse processes.

@article{benkarim_population_2022,
	title = {Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging},
	volume = {20},
	issn = {1545-7885},
	url = {https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001627},
	doi = {10.1371/journal.pbio.3001627},
	abstract = {Brain imaging research enjoys increasing adoption of supervised machine learning for single-participant disease classification. Yet, the success of these algorithms likely depends on population diversity, including demographic differences and other factors that may be outside of primary scientific interest. Here, we capitalize on propensity scores as a composite confound index to quantify diversity due to major sources of population variation. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in 2 separate clinical cohorts: the Autism Brain Imaging Data Exchange (ABIDE, n = 297) and the Healthy Brain Network (HBN, n = 551). Across various analysis scenarios, our results uncover the extent to which cross-validated prediction performances are interlocked with diversity. The instability of extracted brain patterns attributable to diversity is located preferentially in regions part of the default mode network. Collectively, our findings highlight the limitations of prevailing deconfounding practices in mitigating the full consequences of population diversity.},
	language = {en},
	number = {4},
	urldate = {2022-05-04},
	journal = {PLOS Biology},
	author = {Benkarim, Oualid and Paquola, Casey and Park, Bo-yong and Kebets, Valeria and Hong, Seok-Jun and Wael, Reinder Vos de and Zhang, Shaoshi and Yeo, B. T. Thomas and Eickenberg, Michael and Ge, Tian and Poline, Jean-Baptiste and Bernhardt, Boris C. and Bzdok, Danilo},
	month = apr,
	year = {2022},
	note = {Publisher: Public Library of Science},
	keywords = {ADHD, Autism, Autism spectrum disorder, Forecasting, Machine learning, Neural networks, Neuroimaging, Species diversity},
	pages = {e3001627},
}

Brain imaging research enjoys increasing adoption of supervised machine learning for single-participant disease classification. Yet, the success of these algorithms likely depends on population diversity, including demographic differences and other factors that may be outside of primary scientific interest. Here, we capitalize on propensity scores as a composite confound index to quantify diversity due to major sources of population variation. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in 2 separate clinical cohorts: the Autism Brain Imaging Data Exchange (ABIDE, n = 297) and the Healthy Brain Network (HBN, n = 551). Across various analysis scenarios, our results uncover the extent to which cross-validated prediction performances are interlocked with diversity. The instability of extracted brain patterns attributable to diversity is located preferentially in regions part of the default mode network. Collectively, our findings highlight the limitations of prevailing deconfounding practices in mitigating the full consequences of population diversity.

@article{wein_forecasting_2022,
	title = {Forecasting {Brain} {Activity} {Based} on {Models} of {Spatio}-{Temporal} {Brain} {Dynamics}: {A} {Comparison} of {Graph} {Neural} {Network} {Architectures}},
	shorttitle = {Forecasting {Brain} {Activity} {Based} on {Models} of {Spatio}-{Temporal} {Brain} {Dynamics}},
	url = {http://arxiv.org/abs/2112.04266},
	abstract = {Comprehending the interplay between spatial and temporal characteristics of neural dynamics can contribute to our understanding of information processing in the human brain. Graph neural networks (GNNs) provide a new possibility to interpret graph structured signals like those observed in complex brain networks. In our study we compare different spatio-temporal GNN architectures and study their ability to model neural activity distributions obtained in functional MRI (fMRI) studies. We evaluate the performance of the GNN models on a variety of scenarios in MRI studies and also compare it to a VAR model, which is currently often used for directed functional connectivity analysis. We show that by learning localized functional interactions on the anatomical substrate, GNN based approaches are able to robustly scale to large network studies, even when available data are scarce. By including anatomical connectivity as the physical substrate for information propagation, such GNNs also provide a multi-modal perspective on directed connectivity analysis, offering a novel possibility to investigate the spatio-temporal dynamics in brain networks.},
	urldate = {2022-05-02},
	journal = {arXiv:2112.04266 [q-bio, stat]},
	author = {Wein, Simon and Schüller, Alina and Tomé, Ana Maria and Malloni, Wilhelm M. and Greenlee, Mark W. and Lang, Elmar W.},
	month = apr,
	year = {2022},
	note = {arXiv: 2112.04266},
	keywords = {Quantitative Biology - Neurons and Cognition, Statistics - Machine Learning},
}

Comprehending the interplay between spatial and temporal characteristics of neural dynamics can contribute to our understanding of information processing in the human brain. Graph neural networks (GNNs) provide a new possibility to interpret graph structured signals like those observed in complex brain networks. In our study we compare different spatio-temporal GNN architectures and study their ability to model neural activity distributions obtained in functional MRI (fMRI) studies. We evaluate the performance of the GNN models on a variety of scenarios in MRI studies and also compare it to a VAR model, which is currently often used for directed functional connectivity analysis. We show that by learning localized functional interactions on the anatomical substrate, GNN based approaches are able to robustly scale to large network studies, even when available data are scarce. By including anatomical connectivity as the physical substrate for information propagation, such GNNs also provide a multi-modal perspective on directed connectivity analysis, offering a novel possibility to investigate the spatio-temporal dynamics in brain networks.

@inproceedings{avramidis_enhancing_2022,
	title = {Enhancing {Affective} {Representations} {Of} {Music}-{Induced} {Eeg} {Through} {Multimodal} {Supervision} {And} {Latent} {Domain} {Adaptation}},
	doi = {10.1109/ICASSP43922.2022.9746643},
	abstract = {The study of Music Cognition and neural responses to music has been invaluable in understanding human emotions. Brain signals, though, manifest a highly complex structure that makes processing and retrieving meaningful features challenging, particularly of abstract constructs like affect. Moreover, the performance of learning models is undermined by the limited amount of available neuronal data and their severe inter-subject variability. In this paper we extract efficient, personalized affective representations from EEG signals during music listening. To this end, we employ music signals as a supervisory modality to EEG, aiming to project their semantic correspondence onto a common representation space. We utilize a bi-modal framework by combining an LSTM-based attention model to process EEG and a pre-trained model for music tagging, along with a reverse domain discriminator to align the distributions of the two modalities, further constraining the learning process with emotion tags. The resulting framework can be utilized for emotion recognition both directly, by performing supervised predictions from either modality, and indirectly, by providing relevant music samples to EEG input queries. The experimental findings show the potential of enhancing neuronal data through stimulus information for recognition purposes and yield insights into the distribution and temporal variance of music-induced affective features.},
	booktitle = {{ICASSP} 2022 - 2022 {IEEE} {International} {Conference} on {Acoustics}, {Speech} and {Signal} {Processing} ({ICASSP})},
	author = {Avramidis, Kleanthis and Garoufis, Christos and Zlatintsi, Athanasia and Maragos, Petros},
	month = may,
	year = {2022},
	note = {ISSN: 2379-190X},
	keywords = {Brain modeling, Cross-Modal Learnin, Electroencephalograph, Electroencephalography, Emotion Recognitio, Music, Music Cognitio, Semantics, Signal processing, Speech recognition, Tagging},
	pages = {4588--4592},
}

The study of Music Cognition and neural responses to music has been invaluable in understanding human emotions. Brain signals, though, manifest a highly complex structure that makes processing and retrieving meaningful features challenging, particularly of abstract constructs like affect. Moreover, the performance of learning models is undermined by the limited amount of available neuronal data and their severe inter-subject variability. In this paper we extract efficient, personalized affective representations from EEG signals during music listening. To this end, we employ music signals as a supervisory modality to EEG, aiming to project their semantic correspondence onto a common representation space. We utilize a bi-modal framework by combining an LSTM-based attention model to process EEG and a pre-trained model for music tagging, along with a reverse domain discriminator to align the distributions of the two modalities, further constraining the learning process with emotion tags. The resulting framework can be utilized for emotion recognition both directly, by performing supervised predictions from either modality, and indirectly, by providing relevant music samples to EEG input queries. The experimental findings show the potential of enhancing neuronal data through stimulus information for recognition purposes and yield insights into the distribution and temporal variance of music-induced affective features.

@article{wein_forecasting_2022-1,
	title = {Forecasting {Brain} {Activity} {Based} on {Models} of {Spatio}-{Temporal} {Brain} {Dynamics}: {A} {Comparison} of {Graph} {Neural} {Network} {Architectures}},
	shorttitle = {Forecasting {Brain} {Activity} {Based} on {Models} of {Spatio}-{Temporal} {Brain} {Dynamics}},
	url = {http://arxiv.org/abs/2112.04266},
	abstract = {Comprehending the interplay between spatial and temporal characteristics of neural dynamics can contribute to our understanding of information processing in the human brain. Graph neural networks (GNNs) provide a new possibility to interpret graph structured signals like those observed in complex brain networks. In our study we compare different spatio-temporal GNN architectures and study their ability to model neural activity distributions obtained in functional MRI (fMRI) studies. We evaluate the performance of the GNN models on a variety of scenarios in MRI studies and also compare it to a VAR model, which is currently often used for directed functional connectivity analysis. We show that by learning localized functional interactions on the anatomical substrate, GNN based approaches are able to robustly scale to large network studies, even when available data are scarce. By including anatomical connectivity as the physical substrate for information propagation, such GNNs also provide a multi-modal perspective on directed connectivity analysis, offering a novel possibility to investigate the spatio-temporal dynamics in brain networks.},
	urldate = {2022-05-02},
	journal = {arXiv:2112.04266 [q-bio, stat]},
	author = {Wein, Simon and Schüller, Alina and Tomé, Ana Maria and Malloni, Wilhelm M. and Greenlee, Mark W. and Lang, Elmar W.},
	month = apr,
	year = {2022},
	note = {arXiv: 2112.04266},
	keywords = {Quantitative Biology - Neurons and Cognition, Statistics - Machine Learning},
}

Comprehending the interplay between spatial and temporal characteristics of neural dynamics can contribute to our understanding of information processing in the human brain. Graph neural networks (GNNs) provide a new possibility to interpret graph structured signals like those observed in complex brain networks. In our study we compare different spatio-temporal GNN architectures and study their ability to model neural activity distributions obtained in functional MRI (fMRI) studies. We evaluate the performance of the GNN models on a variety of scenarios in MRI studies and also compare it to a VAR model, which is currently often used for directed functional connectivity analysis. We show that by learning localized functional interactions on the anatomical substrate, GNN based approaches are able to robustly scale to large network studies, even when available data are scarce. By including anatomical connectivity as the physical substrate for information propagation, such GNNs also provide a multi-modal perspective on directed connectivity analysis, offering a novel possibility to investigate the spatio-temporal dynamics in brain networks.

@article{wein_forecasting_2022-2,
	title = {Forecasting {Brain} {Activity} {Based} on {Models} of {Spatio}-{Temporal} {Brain} {Dynamics}: {A} {Comparison} of {Graph} {Neural} {Network} {Architectures}},
	shorttitle = {Forecasting {Brain} {Activity} {Based} on {Models} of {Spatio}-{Temporal} {Brain} {Dynamics}},
	url = {http://arxiv.org/abs/2112.04266},
	abstract = {Comprehending the interplay between spatial and temporal characteristics of neural dynamics can contribute to our understanding of information processing in the human brain. Graph neural networks (GNNs) provide a new possibility to interpret graph structured signals like those observed in complex brain networks. In our study we compare different spatio-temporal GNN architectures and study their ability to model neural activity distributions obtained in functional MRI (fMRI) studies. We evaluate the performance of the GNN models on a variety of scenarios in MRI studies and also compare it to a VAR model, which is currently often used for directed functional connectivity analysis. We show that by learning localized functional interactions on the anatomical substrate, GNN based approaches are able to robustly scale to large network studies, even when available data are scarce. By including anatomical connectivity as the physical substrate for information propagation, such GNNs also provide a multi-modal perspective on directed connectivity analysis, offering a novel possibility to investigate the spatio-temporal dynamics in brain networks.},
	urldate = {2022-05-02},
	journal = {arXiv:2112.04266 [q-bio, stat]},
	author = {Wein, Simon and Schüller, Alina and Tomé, Ana Maria and Malloni, Wilhelm M. and Greenlee, Mark W. and Lang, Elmar W.},
	month = apr,
	year = {2022},
	note = {arXiv: 2112.04266},
	keywords = {Quantitative Biology - Neurons and Cognition, Statistics - Machine Learning},
}

Comprehending the interplay between spatial and temporal characteristics of neural dynamics can contribute to our understanding of information processing in the human brain. Graph neural networks (GNNs) provide a new possibility to interpret graph structured signals like those observed in complex brain networks. In our study we compare different spatio-temporal GNN architectures and study their ability to model neural activity distributions obtained in functional MRI (fMRI) studies. We evaluate the performance of the GNN models on a variety of scenarios in MRI studies and also compare it to a VAR model, which is currently often used for directed functional connectivity analysis. We show that by learning localized functional interactions on the anatomical substrate, GNN based approaches are able to robustly scale to large network studies, even when available data are scarce. By including anatomical connectivity as the physical substrate for information propagation, such GNNs also provide a multi-modal perspective on directed connectivity analysis, offering a novel possibility to investigate the spatio-temporal dynamics in brain networks.

@misc{noauthor_does_2022,
	title = {Does evolution estimate gradients?},
	url = {https://joramkeijser.github.io/2022/05/01/mutations.html},
	abstract = {A gradient-based (left) and a gradient-free (right) algorithm that minimise the same noisy quadratic loss function. Color indicates performance.},
	language = {en},
	urldate = {2022-05-02},
	journal = {Endless computations most beautiful},
	month = may,
	year = {2022},
}

A gradient-based (left) and a gradient-free (right) algorithm that minimise the same noisy quadratic loss function. Color indicates performance.

@article{aquino_intersection_2022,
	title = {On the intersection between data quality and dynamical modelling of large-scale {fMRI} signals},
	volume = {256},
	issn = {1053-8119},
	url = {https://www.sciencedirect.com/science/article/pii/S105381192200180X},
	doi = {10.1016/j.neuroimage.2022.119051},
	abstract = {Large-scale dynamics of the brain are routinely modelled using systems of nonlinear dynamical equations that describe the evolution of population-level activity, with distinct neural populations often coupled according to an empirically measured structural connectivity matrix. This modelling approach has been used to generate insights into the neural underpinnings of spontaneous brain dynamics, as recorded with techniques such as resting state functional MRI (fMRI). In fMRI, researchers have many degrees of freedom in the way that they can process the data and recent evidence indicates that the choice of pre-processing steps can have a major effect on empirical estimates of functional connectivity. However, the potential influence of such variations on modelling results are seldom considered. Here we show, using three popular whole-brain dynamical models, that different choices during fMRI preprocessing can dramatically affect model fits and interpretations of findings. Critically, we show that the ability of these models to accurately capture patterns in fMRI dynamics is mostly driven by the degree to which they fit global signals rather than interesting sources of coordinated neural dynamics. We show that widespread deflections can arise from simple global synchronisation. We introduce a simple two-parameter model that captures these fluctuations and performs just as well as more complex, multi-parameter biophysical models. From our combined analyses of data and simulations, we describe benchmarks to evaluate model fit and validity. Although most models are not resilient to denoising, we show that relaxing the approximation of homogeneous neural populations by more explicitly modelling inter-regional effective connectivity can improve model accuracy at the expense of increased model complexity. Our results suggest that many complex biophysical models may be fitting relatively trivial properties of the data, and underscore a need for tighter integration between data quality assurance and model development.},
	language = {en},
	urldate = {2022-05-02},
	journal = {NeuroImage},
	author = {Aquino, Kevin M. and Fulcher, Ben and Oldham, Stuart and Parkes, Linden and Gollo, Leonardo and Deco, Gustavo and Fornito, Alex},
	month = aug,
	year = {2022},
	keywords = {Connectivity, Connectome, Denoising, DiCER, GSR, Modelling, Network, Resting-state, fMRI, rsfMRI},
	pages = {119051},
}

Large-scale dynamics of the brain are routinely modelled using systems of nonlinear dynamical equations that describe the evolution of population-level activity, with distinct neural populations often coupled according to an empirically measured structural connectivity matrix. This modelling approach has been used to generate insights into the neural underpinnings of spontaneous brain dynamics, as recorded with techniques such as resting state functional MRI (fMRI). In fMRI, researchers have many degrees of freedom in the way that they can process the data and recent evidence indicates that the choice of pre-processing steps can have a major effect on empirical estimates of functional connectivity. However, the potential influence of such variations on modelling results are seldom considered. Here we show, using three popular whole-brain dynamical models, that different choices during fMRI preprocessing can dramatically affect model fits and interpretations of findings. Critically, we show that the ability of these models to accurately capture patterns in fMRI dynamics is mostly driven by the degree to which they fit global signals rather than interesting sources of coordinated neural dynamics. We show that widespread deflections can arise from simple global synchronisation. We introduce a simple two-parameter model that captures these fluctuations and performs just as well as more complex, multi-parameter biophysical models. From our combined analyses of data and simulations, we describe benchmarks to evaluate model fit and validity. Although most models are not resilient to denoising, we show that relaxing the approximation of homogeneous neural populations by more explicitly modelling inter-regional effective connectivity can improve model accuracy at the expense of increased model complexity. Our results suggest that many complex biophysical models may be fitting relatively trivial properties of the data, and underscore a need for tighter integration between data quality assurance and model development.

@article{chen_shared_2022,
	title = {Shared and unique brain network features predict cognitive, personality, and mental health scores in the {ABCD} study},
	volume = {13},
	copyright = {2022 The Author(s)},
	issn = {2041-1723},
	url = {https://www.nature.com/articles/s41467-022-29766-8},
	doi = {10.1038/s41467-022-29766-8},
	abstract = {How individual differences in brain network organization track behavioral variability is a fundamental question in systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the individual level. However, most studies focus on single behavioral traits, thus not capturing broader relationships across behaviors. In a large sample of 1858 typically developing children from the Adolescent Brain Cognitive Development (ABCD) study, we show that predictive network features are distinct across the domains of cognitive performance, personality scores and mental health assessments. On the other hand, traits within each behavioral domain are predicted by similar network features. Predictive network features and models generalize to other behavioral measures within the same behavioral domain. Although tasks are known to modulate the functional connectome, predictive network features are similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood.},
	language = {en},
	number = {1},
	urldate = {2022-05-02},
	journal = {Nature Communications},
	author = {Chen, Jianzhong and Tam, Angela and Kebets, Valeria and Orban, Csaba and Ooi, Leon Qi Rong and Asplund, Christopher L. and Marek, Scott and Dosenbach, Nico U. F. and Eickhoff, Simon B. and Bzdok, Danilo and Holmes, Avram J. and Yeo, B. T. Thomas},
	month = apr,
	year = {2022},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Cognitive neuroscience, Computational neuroscience},
	pages = {2217},
}

How individual differences in brain network organization track behavioral variability is a fundamental question in systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the individual level. However, most studies focus on single behavioral traits, thus not capturing broader relationships across behaviors. In a large sample of 1858 typically developing children from the Adolescent Brain Cognitive Development (ABCD) study, we show that predictive network features are distinct across the domains of cognitive performance, personality scores and mental health assessments. On the other hand, traits within each behavioral domain are predicted by similar network features. Predictive network features and models generalize to other behavioral measures within the same behavioral domain. Although tasks are known to modulate the functional connectome, predictive network features are similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood.

@article{kappenman_erp_2021,
	title = {{ERP} {CORE}: {An} open resource for human event-related potential research},
	volume = {225},
	issn = {10538119},
	shorttitle = {{ERP} {CORE}},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1053811920309502},
	doi = {10.1016/j.neuroimage.2020.117465},
	language = {en},
	urldate = {2022-05-02},
	journal = {NeuroImage},
	author = {Kappenman, Emily S. and Farrens, Jaclyn L. and Zhang, Wendy and Stewart, Andrew X. and Luck, Steven J.},
	month = jan,
	year = {2021},
	pages = {117465},
}

@article{barone_understanding_2021,
	title = {Understanding the {Role} of {Sensorimotor} {Beta} {Oscillations}},
	volume = {15},
	issn = {1662-5137},
	url = {https://www.frontiersin.org/articles/10.3389/fnsys.2021.655886/full},
	doi = {10.3389/fnsys.2021.655886},
	abstract = {Beta oscillations have been predominantly observed in sensorimotor cortices and basal ganglia structures and they are thought to be involved in somatosensory processing and motor control. Although beta activity is a distinct feature of healthy and pathological sensorimotor processing, the role of this rhythm is still under debate. Here we review recent findings about the role of beta oscillations during experimental manipulations (i.e., drugs and brain stimulation) and their alteration in aging and pathology. We show how beta changes when learning new motor skills and its potential to integrate sensory input with prior contextual knowledge. We conclude by discussing a novel methodological approach analyzing beta oscillations as a series of transient bursting events.},
	urldate = {2022-04-29},
	journal = {Frontiers in Systems Neuroscience},
	author = {Barone, Jacopo and Rossiter, Holly E.},
	month = may,
	year = {2021},
	pages = {655886},
}

Beta oscillations have been predominantly observed in sensorimotor cortices and basal ganglia structures and they are thought to be involved in somatosensory processing and motor control. Although beta activity is a distinct feature of healthy and pathological sensorimotor processing, the role of this rhythm is still under debate. Here we review recent findings about the role of beta oscillations during experimental manipulations (i.e., drugs and brain stimulation) and their alteration in aging and pathology. We show how beta changes when learning new motor skills and its potential to integrate sensory input with prior contextual knowledge. We conclude by discussing a novel methodological approach analyzing beta oscillations as a series of transient bursting events.

@article{botvinik-nezer_variability_2020,
	title = {Variability in the analysis of a single neuroimaging dataset by many teams},
	volume = {582},
	copyright = {2020 The Author(s), under exclusive licence to Springer Nature Limited},
	issn = {1476-4687},
	url = {https://www.nature.com/articles/s41586-020-2314-9},
	doi = {10.1038/s41586-020-2314-9},
	abstract = {Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2–5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.},
	language = {en},
	number = {7810},
	urldate = {2022-05-03},
	journal = {Nature},
	author = {Botvinik-Nezer, Rotem and Holzmeister, Felix and Camerer, Colin F. and Dreber, Anna and Huber, Juergen and Johannesson, Magnus and Kirchler, Michael and Iwanir, Roni and Mumford, Jeanette A. and Adcock, R. Alison and Avesani, Paolo and Baczkowski, Blazej M. and Bajracharya, Aahana and Bakst, Leah and Ball, Sheryl and Barilari, Marco and Bault, Nadège and Beaton, Derek and Beitner, Julia and Benoit, Roland G. and Berkers, Ruud M. W. J. and Bhanji, Jamil P. and Biswal, Bharat B. and Bobadilla-Suarez, Sebastian and Bortolini, Tiago and Bottenhorn, Katherine L. and Bowring, Alexander and Braem, Senne and Brooks, Hayley R. and Brudner, Emily G. and Calderon, Cristian B. and Camilleri, Julia A. and Castrellon, Jaime J. and Cecchetti, Luca and Cieslik, Edna C. and Cole, Zachary J. and Collignon, Olivier and Cox, Robert W. and Cunningham, William A. and Czoschke, Stefan and Dadi, Kamalaker and Davis, Charles P. and Luca, Alberto De and Delgado, Mauricio R. and Demetriou, Lysia and Dennison, Jeffrey B. and Di, Xin and Dickie, Erin W. and Dobryakova, Ekaterina and Donnat, Claire L. and Dukart, Juergen and Duncan, Niall W. and Durnez, Joke and Eed, Amr and Eickhoff, Simon B. and Erhart, Andrew and Fontanesi, Laura and Fricke, G. Matthew and Fu, Shiguang and Galván, Adriana and Gau, Remi and Genon, Sarah and Glatard, Tristan and Glerean, Enrico and Goeman, Jelle J. and Golowin, Sergej A. E. and González-García, Carlos and Gorgolewski, Krzysztof J. and Grady, Cheryl L. and Green, Mikella A. and Guassi Moreira, João F. and Guest, Olivia and Hakimi, Shabnam and Hamilton, J. Paul and Hancock, Roeland and Handjaras, Giacomo and Harry, Bronson B. and Hawco, Colin and Herholz, Peer and Herman, Gabrielle and Heunis, Stephan and Hoffstaedter, Felix and Hogeveen, Jeremy and Holmes, Susan and Hu, Chuan-Peng and Huettel, Scott A. and Hughes, Matthew E. and Iacovella, Vittorio and Iordan, Alexandru D. and Isager, Peder M. and Isik, Ayse I. and Jahn, Andrew and Johnson, Matthew R. and Johnstone, Tom and Joseph, Michael J. E. and Juliano, Anthony C. and Kable, Joseph W. and Kassinopoulos, Michalis and Koba, Cemal and Kong, Xiang-Zhen and Koscik, Timothy R. and Kucukboyaci, Nuri Erkut and Kuhl, Brice A. and Kupek, Sebastian and Laird, Angela R. and Lamm, Claus and Langner, Robert and Lauharatanahirun, Nina and Lee, Hongmi and Lee, Sangil and Leemans, Alexander and Leo, Andrea and Lesage, Elise and Li, Flora and Li, Monica Y. C. and Lim, Phui Cheng and Lintz, Evan N. and Liphardt, Schuyler W. and Losecaat Vermeer, Annabel B. and Love, Bradley C. and Mack, Michael L. and Malpica, Norberto and Marins, Theo and Maumet, Camille and McDonald, Kelsey and McGuire, Joseph T. and Melero, Helena and Méndez Leal, Adriana S. and Meyer, Benjamin and Meyer, Kristin N. and Mihai, Glad and Mitsis, Georgios D. and Moll, Jorge and Nielson, Dylan M. and Nilsonne, Gustav and Notter, Michael P. and Olivetti, Emanuele and Onicas, Adrian I. and Papale, Paolo and Patil, Kaustubh R. and Peelle, Jonathan E. and Pérez, Alexandre and Pischedda, Doris and Poline, Jean-Baptiste and Prystauka, Yanina and Ray, Shruti and Reuter-Lorenz, Patricia A. and Reynolds, Richard C. and Ricciardi, Emiliano and Rieck, Jenny R. and Rodriguez-Thompson, Anais M. and Romyn, Anthony and Salo, Taylor and Samanez-Larkin, Gregory R. and Sanz-Morales, Emilio and Schlichting, Margaret L. and Schultz, Douglas H. and Shen, Qiang and Sheridan, Margaret A. and Silvers, Jennifer A. and Skagerlund, Kenny and Smith, Alec and Smith, David V. and Sokol-Hessner, Peter and Steinkamp, Simon R. and Tashjian, Sarah M. and Thirion, Bertrand and Thorp, John N. and Tinghög, Gustav and Tisdall, Loreen and Tompson, Steven H. and Toro-Serey, Claudio and Torre Tresols, Juan Jesus and Tozzi, Leonardo and Truong, Vuong and Turella, Luca and van ‘t Veer, Anna E. and Verguts, Tom and Vettel, Jean M. and Vijayarajah, Sagana and Vo, Khoi and Wall, Matthew B. and Weeda, Wouter D. and Weis, Susanne and White, David J. and Wisniewski, David and Xifra-Porxas, Alba and Yearling, Emily A. and Yoon, Sangsuk and Yuan, Rui and Yuen, Kenneth S. L. and Zhang, Lei and Zhang, Xu and Zosky, Joshua E. and Nichols, Thomas E. and Poldrack, Russell A. and Schonberg, Tom},
	month = jun,
	year = {2020},
	note = {Number: 7810
Publisher: Nature Publishing Group},
	keywords = {Decision, Decision making, Human behaviour, Scientific community},
	pages = {84--88},
}

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2–5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.

@article{mcweeny_understanding_2020,
	title = {Understanding event‐related potentials ({ERPs}) in clinical and basic language and communication disorders research: a tutorial},
	volume = {55},
	issn = {1368-2822, 1460-6984},
	shorttitle = {Understanding event‐related potentials ({ERPs}) in clinical and basic language and communication disorders research},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/1460-6984.12535},
	doi = {10.1111/1460-6984.12535},
	language = {en},
	number = {4},
	urldate = {2022-04-29},
	journal = {International Journal of Language \& Communication Disorders},
	author = {McWeeny, Sean and Norton, Elizabeth S.},
	month = jul,
	year = {2020},
	pages = {445--457},
}

@article{jitsuishi_white_2020,
	title = {White matter dissection and structural connectivity of the human vertical occipital fasciculus to link vision-associated brain cortex},
	volume = {10},
	copyright = {2020 The Author(s)},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-020-57837-7},
	doi = {10.1038/s41598-020-57837-7},
	abstract = {The vertical occipital fasciculus (VOF) is an association fiber tract coursing vertically at the posterolateral corner of the brain. It is re-evaluated as a major fiber tract to link the dorsal and ventral visual stream. Although previous tractography studies showed the VOF’s cortical projections fall in the dorsal and ventral visual areas, the post-mortem dissection study for the validation remains limited. First, to validate the previous tractography data, we here performed the white matter dissection in post-mortem brains and demonstrated the VOF’s fiber bundles coursing between the V3A/B areas and the posterior fusiform gyrus. Secondly, we analyzed the VOF’s structural connectivity with diffusion tractography to link vision-associated cortical areas of the HCP MMP1.0 atlas, an updated map of the human cerebral cortex. Based on the criteria the VOF courses laterally to the inferior longitudinal fasciculus (ILF) and craniocaudally at the posterolateral corner of the brain, we reconstructed the VOF’s fiber tracts and found the widespread projections to the visual cortex. These findings could suggest a crucial role of VOF in integrating visual information to link the broad visual cortex as well as in connecting the dual visual stream.},
	language = {en},
	number = {1},
	urldate = {2022-04-27},
	journal = {Scientific Reports},
	author = {Jitsuishi, Tatsuya and Hirono, Seiichiro and Yamamoto, Tatsuya and Kitajo, Keiko and Iwadate, Yasuo and Yamaguchi, Atsushi},
	month = jan,
	year = {2020},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Brain, Extrastriate cortex},
	pages = {820},
}

The vertical occipital fasciculus (VOF) is an association fiber tract coursing vertically at the posterolateral corner of the brain. It is re-evaluated as a major fiber tract to link the dorsal and ventral visual stream. Although previous tractography studies showed the VOF’s cortical projections fall in the dorsal and ventral visual areas, the post-mortem dissection study for the validation remains limited. First, to validate the previous tractography data, we here performed the white matter dissection in post-mortem brains and demonstrated the VOF’s fiber bundles coursing between the V3A/B areas and the posterior fusiform gyrus. Secondly, we analyzed the VOF’s structural connectivity with diffusion tractography to link vision-associated cortical areas of the HCP MMP1.0 atlas, an updated map of the human cerebral cortex. Based on the criteria the VOF courses laterally to the inferior longitudinal fasciculus (ILF) and craniocaudally at the posterolateral corner of the brain, we reconstructed the VOF’s fiber tracts and found the widespread projections to the visual cortex. These findings could suggest a crucial role of VOF in integrating visual information to link the broad visual cortex as well as in connecting the dual visual stream.

@article{bakhit_superior_2020,
	title = {The superior frontal longitudinal tract: a connection between the dorsal premotor and the dorsolateral prefrontal cortices},
	volume = {10},
	copyright = {2020 The Author(s)},
	issn = {2045-2322},
	shorttitle = {The superior frontal longitudinal tract},
	url = {https://www.nature.com/articles/s41598-020-73001-7},
	doi = {10.1038/s41598-020-73001-7},
	abstract = {A few studies have identified the structural connection between the premotor area and the lateral prefrontal cortex (DLPFC) as the frontal longitudinal system (FLS). This study investigated the existence of a direct segment (none U-fibre) of the superior part of the FLS (sFLS), which connects the dorsal premotor cortex (PMd) and DLPFC and analysed its asymmetry and termination point patterns. A dataset of diffusion-weighted images from 48 subjects was used for generalised q-sampling imaging tractography. Additionally, a white-fibre dissection was conducted in two right hemispheres. An analysis of spatial location, termination points, laterality, and correlation with the subjects’ gender or handedness was performed. The sFLS was found to have a deeper longitudinal bundle directly connecting the PMd and DLPFC. The bundle is referred to hereafter as the superior frontal longitudinal tract (SFLT). The SFLT was reconstructed in 100\% of right and 88\% of left hemispheres. It exhibited variable patterns in different subjects in their posterior terminations. In addition, it was found to possess a complicated spatial relationship with the adjacent bundles. The SFLT was revealed successfully in two cadaveric right hemispheres, where the posterior terminations were found to originate in the PMd independent of the superior longitudinal fasciculus.},
	language = {en},
	number = {1},
	urldate = {2022-04-27},
	journal = {Scientific Reports},
	author = {Bakhit, Mudathir and Fujii, Masazumi and Hiruta, Ryo and Yamada, Masayuki and Iwami, Kenichiro and Sato, Taku and Saito, Kiyoshi},
	month = sep,
	year = {2020},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Anatomy, Brain, Nervous system},
	pages = {15855},
}

A few studies have identified the structural connection between the premotor area and the lateral prefrontal cortex (DLPFC) as the frontal longitudinal system (FLS). This study investigated the existence of a direct segment (none U-fibre) of the superior part of the FLS (sFLS), which connects the dorsal premotor cortex (PMd) and DLPFC and analysed its asymmetry and termination point patterns. A dataset of diffusion-weighted images from 48 subjects was used for generalised q-sampling imaging tractography. Additionally, a white-fibre dissection was conducted in two right hemispheres. An analysis of spatial location, termination points, laterality, and correlation with the subjects’ gender or handedness was performed. The sFLS was found to have a deeper longitudinal bundle directly connecting the PMd and DLPFC. The bundle is referred to hereafter as the superior frontal longitudinal tract (SFLT). The SFLT was reconstructed in 100% of right and 88% of left hemispheres. It exhibited variable patterns in different subjects in their posterior terminations. In addition, it was found to possess a complicated spatial relationship with the adjacent bundles. The SFLT was revealed successfully in two cadaveric right hemispheres, where the posterior terminations were found to originate in the PMd independent of the superior longitudinal fasciculus.

@article{guidotti_survey_2019,
	title = {A {Survey} of {Methods} for {Explaining} {Black} {Box} {Models}},
	volume = {51},
	issn = {0360-0300, 1557-7341},
	url = {https://dl.acm.org/doi/10.1145/3236009},
	doi = {10.1145/3236009},
	abstract = {In recent years, many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness, sometimes at the cost of sacrificing accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, it explicitly or implicitly delineates its own definition of interpretability and explanation. The aim of this article is to provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box system. Given a problem definition, a black box type, and a desired explanation, this survey should help the researcher to find the proposals more useful for his own work. The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective.},
	language = {en},
	number = {5},
	urldate = {2023-05-12},
	journal = {ACM Computing Surveys},
	author = {Guidotti, Riccardo and Monreale, Anna and Ruggieri, Salvatore and Turini, Franco and Giannotti, Fosca and Pedreschi, Dino},
	month = sep,
	year = {2019},
	pages = {1--42},
}

In recent years, many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness, sometimes at the cost of sacrificing accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, it explicitly or implicitly delineates its own definition of interpretability and explanation. The aim of this article is to provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box system. Given a problem definition, a black box type, and a desired explanation, this survey should help the researcher to find the proposals more useful for his own work. The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective.

@book{mecarelli_clinical_2019,
	title = {Clinical electroencephalography},
	isbn = {978-3-030-04573-9 978-3-030-04572-2},
	url = {https://link.springer.com/content/pdf/bbm%3A978-3-030-04573-9%2F1.pdf},
	abstract = {This book describes the developments and improvements in electroencephalography (EEG). In recent years, digital technology has replaced analog equipments, and it is now possible to easily record and store EEG tracings and to quickly recall previously acquired material for subsequent analysis. In addition, not only static figures, but also electronic supplementary materials can be included in books, enabling EEGs to be viewed in real-time. In clinical practice, EEG still represents the most important functional examination in the study CNS development and its anatomical and physiological integrity throughout life. In the pathological context, EEG provides indispensable diagnostic information for classification of epileptic syndromes, and it is also valuable in all the other CNS diseases (infectious, cerebrovascular, neurodegenerative, etc). Furthermore, monitoring EEG can be widely used in emergency settings, such as emergency departments or intensive care units. In comatose patients, EEG provides information regarding prognosis and evaluation of the sedative effect of anesthetic drugs. Written by a group of leading national and international experts, it offers a substantial, yet practical, EEG compendium, which serves as a reference resource for physicians and neurodiagnostic technologists as well as physicians-in-training, researchers, practicing electroencephalographers and students.},
	language = {English},
	urldate = {2022-04-29},
	publisher = {Springer},
	author = {Mecarelli, Oriano},
	year = {2019},
	note = {OCLC: 1104140146},
}

This book describes the developments and improvements in electroencephalography (EEG). In recent years, digital technology has replaced analog equipments, and it is now possible to easily record and store EEG tracings and to quickly recall previously acquired material for subsequent analysis. In addition, not only static figures, but also electronic supplementary materials can be included in books, enabling EEGs to be viewed in real-time. In clinical practice, EEG still represents the most important functional examination in the study CNS development and its anatomical and physiological integrity throughout life. In the pathological context, EEG provides indispensable diagnostic information for classification of epileptic syndromes, and it is also valuable in all the other CNS diseases (infectious, cerebrovascular, neurodegenerative, etc). Furthermore, monitoring EEG can be widely used in emergency settings, such as emergency departments or intensive care units. In comatose patients, EEG provides information regarding prognosis and evaluation of the sedative effect of anesthetic drugs. Written by a group of leading national and international experts, it offers a substantial, yet practical, EEG compendium, which serves as a reference resource for physicians and neurodiagnostic technologists as well as physicians-in-training, researchers, practicing electroencephalographers and students.

@article{newson_eeg_2019,
	title = {{EEG} {Frequency} {Bands} in {Psychiatric} {Disorders}: {A} {Review} of {Resting} {State} {Studies}},
	volume = {12},
	issn = {1662-5161},
	shorttitle = {{EEG} {Frequency} {Bands} in {Psychiatric} {Disorders}},
	url = {https://www.frontiersin.org/article/10.3389/fnhum.2018.00521/full},
	doi = {10.3389/fnhum.2018.00521},
	urldate = {2022-04-29},
	journal = {Frontiers in Human Neuroscience},
	author = {Newson, Jennifer J. and Thiagarajan, Tara C.},
	month = jan,
	year = {2019},
	pages = {521},
}

@article{scheeringa_cortical_2019,
	title = {Cortical layers, rhythms and {BOLD} signals},
	volume = {197},
	issn = {10538119},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1053811917309096},
	doi = {10.1016/j.neuroimage.2017.11.002},
	language = {en},
	urldate = {2022-04-29},
	journal = {NeuroImage},
	author = {Scheeringa, René and Fries, Pascal},
	month = aug,
	year = {2019},
	pages = {689--698},
}

@article{michel_eeg_2019,
	title = {{EEG} {Source} {Imaging}: {A} {Practical} {Review} of the {Analysis} {Steps}},
	volume = {10},
	issn = {1664-2295},
	shorttitle = {{EEG} {Source} {Imaging}},
	url = {https://www.frontiersin.org/article/10.3389/fneur.2019.00325/full},
	doi = {10.3389/fneur.2019.00325},
	urldate = {2022-04-29},
	journal = {Frontiers in Neurology},
	author = {Michel, Christoph M. and Brunet, Denis},
	month = apr,
	year = {2019},
	pages = {325},
}

@article{lakens_justify_2018,
	title = {Justify your alpha},
	volume = {2},
	issn = {2397-3374},
	url = {http://www.nature.com/articles/s41562-018-0311-x},
	doi = {10.1038/s41562-018-0311-x},
	language = {en},
	number = {3},
	urldate = {2022-04-29},
	journal = {Nature Human Behaviour},
	author = {Lakens, Daniel and Adolfi, Federico G. and Albers, Casper J. and Anvari, Farid and Apps, Matthew A. J. and Argamon, Shlomo E. and Baguley, Thom and Becker, Raymond B. and Benning, Stephen D. and Bradford, Daniel E. and Buchanan, Erin M. and Caldwell, Aaron R. and Van Calster, Ben and Carlsson, Rickard and Chen, Sau-Chin and Chung, Bryan and Colling, Lincoln J. and Collins, Gary S. and Crook, Zander and Cross, Emily S. and Daniels, Sameera and Danielsson, Henrik and DeBruine, Lisa and Dunleavy, Daniel J. and Earp, Brian D. and Feist, Michele I. and Ferrell, Jason D. and Field, James G. and Fox, Nicholas W. and Friesen, Amanda and Gomes, Caio and Gonzalez-Marquez, Monica and Grange, James A. and Grieve, Andrew P. and Guggenberger, Robert and Grist, James and van Harmelen, Anne-Laura and Hasselman, Fred and Hochard, Kevin D. and Hoffarth, Mark R. and Holmes, Nicholas P. and Ingre, Michael and Isager, Peder M. and Isotalus, Hanna K. and Johansson, Christer and Juszczyk, Konrad and Kenny, David A. and Khalil, Ahmed A. and Konat, Barbara and Lao, Junpeng and Larsen, Erik Gahner and Lodder, Gerine M. A. and Lukavský, Jiří and Madan, Christopher R. and Manheim, David and Martin, Stephen R. and Martin, Andrea E. and Mayo, Deborah G. and McCarthy, Randy J. and McConway, Kevin and McFarland, Colin and Nio, Amanda Q. X. and Nilsonne, Gustav and de Oliveira, Cilene Lino and de Xivry, Jean-Jacques Orban and Parsons, Sam and Pfuhl, Gerit and Quinn, Kimberly A. and Sakon, John J. and Saribay, S. Adil and Schneider, Iris K. and Selvaraju, Manojkumar and Sjoerds, Zsuzsika and Smith, Samuel G. and Smits, Tim and Spies, Jeffrey R. and Sreekumar, Vishnu and Steltenpohl, Crystal N. and Stenhouse, Neil and Świątkowski, Wojciech and Vadillo, Miguel A. and Van Assen, Marcel A. L. M. and Williams, Matt N. and Williams, Samantha E. and Williams, Donald R. and Yarkoni, Tal and Ziano, Ignazio and Zwaan, Rolf A.},
	month = mar,
	year = {2018},
	pages = {168--171},
}

@article{clayton_many_2018,
	title = {The many characters of visual alpha oscillations},
	volume = {48},
	issn = {0953816X},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/ejn.13747},
	doi = {10.1111/ejn.13747},
	language = {en},
	number = {7},
	urldate = {2022-04-29},
	journal = {European Journal of Neuroscience},
	author = {Clayton, Michael S. and Yeung, Nick and Cohen Kadosh, Roi},
	month = oct,
	year = {2018},
	pages = {2498--2508},
}

@article{beres_time_2017,
	title = {Time is of the {Essence}: {A} {Review} of {Electroencephalography} ({EEG}) and {Event}-{Related} {Brain} {Potentials} ({ERPs}) in {Language} {Research}},
	volume = {42},
	issn = {1090-0586, 1573-3270},
	shorttitle = {Time is of the {Essence}},
	url = {http://link.springer.com/10.1007/s10484-017-9371-3},
	doi = {10.1007/s10484-017-9371-3},
	language = {en},
	number = {4},
	urldate = {2022-04-29},
	journal = {Applied Psychophysiology and Biofeedback},
	author = {Beres, Anna M.},
	month = dec,
	year = {2017},
	pages = {247--255},
}

@article{cohen_where_2017,
	title = {Where {Does} {EEG} {Come} {From} and {What} {Does} {It} {Mean}?},
	volume = {40},
	issn = {01662236},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0166223617300243},
	doi = {10.1016/j.tins.2017.02.004},
	language = {en},
	number = {4},
	urldate = {2022-04-29},
	journal = {Trends in Neurosciences},
	author = {Cohen, Michael X},
	month = apr,
	year = {2017},
	pages = {208--218},
}

@article{ahveninen_intracortical_2016,
	title = {Intracortical depth analyses of frequency-sensitive regions of human auditory cortex using {7T} {fMRI}},
	volume = {143},
	issn = {10538119},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1053811916304712},
	doi = {10.1016/j.neuroimage.2016.09.010},
	language = {en},
	urldate = {2022-05-02},
	journal = {NeuroImage},
	author = {Ahveninen, Jyrki and Chang, Wei-Tang and Huang, Samantha and Keil, Boris and Kopco, Norbert and Rossi, Stephanie and Bonmassar, Giorgio and Witzel, Thomas and Polimeni, Jonathan R.},
	month = dec,
	year = {2016},
	pages = {116--127},
}

@article{wang_firing_2016,
	title = {Firing {Frequency} {Maxima} of {Fast}-{Spiking} {Neurons} in {Human}, {Monkey}, and {Mouse} {Neocortex}},
	volume = {10},
	issn = {1662-5102},
	url = {http://journal.frontiersin.org/article/10.3389/fncel.2016.00239/full},
	doi = {10.3389/fncel.2016.00239},
	urldate = {2022-04-29},
	journal = {Frontiers in Cellular Neuroscience},
	author = {Wang, Bo and Ke, Wei and Guang, Jing and Chen, Guang and Yin, Luping and Deng, Suixin and He, Quansheng and Liu, Yaping and He, Ting and Zheng, Rui and Jiang, Yanbo and Zhang, Xiaoxue and Li, Tianfu and Luan, Guoming and Lu, Haidong D. and Zhang, Mingsha and Zhang, Xiaohui and Shu, Yousheng},
	month = oct,
	year = {2016},
}

@article{samaha_top-down_2015,
	title = {Top-down control of the phase of alpha-band oscillations as a mechanism for temporal prediction},
	volume = {112},
	issn = {0027-8424, 1091-6490},
	url = {https://pnas.org/doi/full/10.1073/pnas.1503686112},
	doi = {10.1073/pnas.1503686112},
	abstract = {Significance
            In contrast to canonical, stimulus-driven models of perception, recent proposals argue that perceptual experiences are constructed in an active manner in which top-down influences play a key role. In particular, predictions that the brain makes about the world are incorporated into each perceptual experience. Because forming the appropriate sensory predictions can have a large impact on our visual experiences and visually guided behaviors, a mechanism thought to be disrupted in certain neurological conditions like autism and schizophrenia, an understanding of the neural basis of these predictions is critical. Here, we provide evidence that perceptual expectations about when a stimulus will appear are instantiated in the brain by optimally configuring prestimulus alpha-band oscillations so as to make subsequent processing most efficacious.
          , 
            The physiological state of the brain before an incoming stimulus has substantial consequences for subsequent behavior and neural processing. For example, the phase of ongoing posterior alpha-band oscillations (8–14 Hz) immediately before visual stimulation has been shown to predict perceptual outcomes and downstream neural activity. Although this phenomenon suggests that these oscillations may phasically route information through functional networks, many accounts treat these periodic effects as a consequence of ongoing activity that is independent of behavioral strategy. Here, we investigated whether alpha-band phase can be guided by top-down control in a temporal cueing task. When participants were provided with cues predictive of the moment of visual target onset, discrimination accuracy improved and targets were more frequently reported as consciously seen, relative to unpredictive cues. This effect was accompanied by a significant shift in the phase of alpha-band oscillations, before target onset, toward each participant’s optimal phase for stimulus discrimination. These findings provide direct evidence that forming predictions about when a stimulus will appear can bias the phase of ongoing alpha-band oscillations toward an optimal phase for visual processing, and may thus serve as a mechanism for the top-down control of visual processing guided by temporal predictions.},
	language = {en},
	number = {27},
	urldate = {2022-04-29},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Samaha, Jason and Bauer, Phoebe and Cimaroli, Sawyer and Postle, Bradley R.},
	month = jul,
	year = {2015},
	pages = {8439--8444},
}

Significance In contrast to canonical, stimulus-driven models of perception, recent proposals argue that perceptual experiences are constructed in an active manner in which top-down influences play a key role. In particular, predictions that the brain makes about the world are incorporated into each perceptual experience. Because forming the appropriate sensory predictions can have a large impact on our visual experiences and visually guided behaviors, a mechanism thought to be disrupted in certain neurological conditions like autism and schizophrenia, an understanding of the neural basis of these predictions is critical. Here, we provide evidence that perceptual expectations about when a stimulus will appear are instantiated in the brain by optimally configuring prestimulus alpha-band oscillations so as to make subsequent processing most efficacious. , The physiological state of the brain before an incoming stimulus has substantial consequences for subsequent behavior and neural processing. For example, the phase of ongoing posterior alpha-band oscillations (8–14 Hz) immediately before visual stimulation has been shown to predict perceptual outcomes and downstream neural activity. Although this phenomenon suggests that these oscillations may phasically route information through functional networks, many accounts treat these periodic effects as a consequence of ongoing activity that is independent of behavioral strategy. Here, we investigated whether alpha-band phase can be guided by top-down control in a temporal cueing task. When participants were provided with cues predictive of the moment of visual target onset, discrimination accuracy improved and targets were more frequently reported as consciously seen, relative to unpredictive cues. This effect was accompanied by a significant shift in the phase of alpha-band oscillations, before target onset, toward each participant’s optimal phase for stimulus discrimination. These findings provide direct evidence that forming predictions about when a stimulus will appear can bias the phase of ongoing alpha-band oscillations toward an optimal phase for visual processing, and may thus serve as a mechanism for the top-down control of visual processing guided by temporal predictions.

@article{cavanagh_frontal_2014,
	title = {Frontal theta as a mechanism for cognitive control},
	volume = {18},
	issn = {13646613},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1364661314001077},
	doi = {10.1016/j.tics.2014.04.012},
	language = {en},
	number = {8},
	urldate = {2022-04-29},
	journal = {Trends in Cognitive Sciences},
	author = {Cavanagh, James F. and Frank, Michael J.},
	month = aug,
	year = {2014},
	pages = {414--421},
}

@article{jackson_neurophysiological_2014,
	title = {The neurophysiological bases of {EEG} and {EEG} measurement: {A} review for the rest of us: {Neurophysiological} bases of {EEG}},
	volume = {51},
	issn = {00485772},
	shorttitle = {The neurophysiological bases of {EEG} and {EEG} measurement},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/psyp.12283},
	doi = {10.1111/psyp.12283},
	language = {en},
	number = {11},
	urldate = {2022-04-29},
	journal = {Psychophysiology},
	author = {Jackson, Alice F. and Bolger, Donald J.},
	month = nov,
	year = {2014},
	pages = {1061--1071},
}

@article{cavanagh_frontal_2014-1,
	title = {Frontal theta as a mechanism for cognitive control},
	volume = {18},
	issn = {13646613},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1364661314001077},
	doi = {10.1016/j.tics.2014.04.012},
	language = {en},
	number = {8},
	urldate = {2022-04-29},
	journal = {Trends in Cognitive Sciences},
	author = {Cavanagh, James F. and Frank, Michael J.},
	month = aug,
	year = {2014},
	pages = {414--421},
}

@article{gramfort_meg_2013,
	title = {{MEG} and {EEG} data analysis with {MNE}-{Python}},
	volume = {7},
	issn = {1662453X},
	url = {http://journal.frontiersin.org/article/10.3389/fnins.2013.00267/abstract},
	doi = {10.3389/fnins.2013.00267},
	urldate = {2022-04-29},
	journal = {Frontiers in Neuroscience},
	author = {Gramfort, Alexandre},
	year = {2013},
}

@article{friston_history_2012,
	title = {The history of the future of the {Bayesian} brain},
	volume = {62},
	issn = {10538119},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1053811911011657},
	doi = {10.1016/j.neuroimage.2011.10.004},
	language = {en},
	number = {2},
	urldate = {2022-05-02},
	journal = {NeuroImage},
	author = {Friston, Karl},
	month = aug,
	year = {2012},
	pages = {1230--1233},
}

@article{knyazev_eeg_2012,
	title = {{EEG} delta oscillations as a correlate of basic homeostatic and motivational processes},
	volume = {36},
	issn = {01497634},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0149763411001849},
	doi = {10.1016/j.neubiorev.2011.10.002},
	language = {en},
	number = {1},
	urldate = {2022-04-29},
	journal = {Neuroscience \& Biobehavioral Reviews},
	author = {Knyazev, Gennady G.},
	month = jan,
	year = {2012},
	pages = {677--695},
}

@article{leuchter_resting-state_2012,
	title = {Resting-{State} {Quantitative} {Electroencephalography} {Reveals} {Increased} {Neurophysiologic} {Connectivity} in {Depression}},
	volume = {7},
	issn = {1932-6203},
	url = {https://dx.plos.org/10.1371/journal.pone.0032508},
	doi = {10.1371/journal.pone.0032508},
	language = {en},
	number = {2},
	urldate = {2022-04-29},
	journal = {PLoS ONE},
	author = {Leuchter, Andrew F. and Cook, Ian A. and Hunter, Aimee M. and Cai, Chaochao and Horvath, Steve},
	editor = {El-Deredy, Wael},
	month = feb,
	year = {2012},
	pages = {e32508},
}

@article{buzsaki_origin_2012,
	title = {The origin of extracellular fields and currents — {EEG}, {ECoG}, {LFP} and spikes},
	volume = {13},
	issn = {1471-003X, 1471-0048},
	url = {http://www.nature.com/articles/nrn3241},
	doi = {10.1038/nrn3241},
	language = {en},
	number = {6},
	urldate = {2022-04-29},
	journal = {Nature Reviews Neuroscience},
	author = {Buzsáki, György and Anastassiou, Costas A. and Koch, Christof},
	month = jun,
	year = {2012},
	pages = {407--420},
}

@book{marr_vision_2010,
	address = {Cambridge, Mass},
	title = {Vision: a computational investigation into the human representation and processing of visual information},
	isbn = {978-0-262-51462-0},
	shorttitle = {Vision},
	publisher = {MIT Press},
	author = {Marr, David},
	year = {2010},
	note = {OCLC: ocn472791457},
	keywords = {Data processing, Human information processing, Mathematical models, Vision},
}

@article{garrido_mismatch_2009,
	title = {The mismatch negativity: {A} review of underlying mechanisms},
	volume = {120},
	issn = {13882457},
	shorttitle = {The mismatch negativity},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1388245708012686},
	doi = {10.1016/j.clinph.2008.11.029},
	language = {en},
	number = {3},
	urldate = {2022-05-02},
	journal = {Clinical Neurophysiology},
	author = {Garrido, Marta I. and Kilner, James M. and Stephan, Klaas E. and Friston, Karl J.},
	month = mar,
	year = {2009},
	pages = {453--463},
}

@article{moser_sleep_2009,
	title = {Sleep {Classification} {According} to {AASM} and {Rechtschaffen} \& {Kales}: {Effects} on {Sleep} {Scoring} {Parameters}},
	volume = {32},
	issn = {0161-8105, 1550-9109},
	shorttitle = {Sleep {Classification} {According} to {AASM} and {Rechtschaffen} \& {Kales}},
	url = {https://academic.oup.com/sleep/article-lookup/doi/10.1093/sleep/32.2.139},
	doi = {10.1093/sleep/32.2.139},
	language = {en},
	number = {2},
	urldate = {2022-04-29},
	journal = {Sleep},
	author = {Moser, Doris and Anderer, Peter and Gruber, Georg and Parapatics, Silvia and Loretz, Erna and Boeck, Marion and Kloesch, Gerhard and Heller, Esther and Schmidt, Andrea and Danker-Hopfe, Heidi and Saletu, Bernd and Zeitlhofer, Josef and Dorffner, Georg},
	month = feb,
	year = {2009},
	pages = {139--149},
}

@book{buzsaki_rhythms_2006,
	title = {Rhythms of the {Brain}},
	isbn = {978-0-19-530106-9},
	url = {https://oxford.universitypressscholarship.com/view/10.1093/acprof:oso/9780195301069.001.0001/acprof-9780195301069},
	urldate = {2022-04-29},
	publisher = {Oxford University Press},
	author = {Buzsáki, György},
	month = oct,
	year = {2006},
	doi = {10.1093/acprof:oso/9780195301069.001.0001},
}

@article{pascual-marqui_low_1994,
	title = {Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain},
	volume = {18},
	issn = {01678760},
	shorttitle = {Low resolution electromagnetic tomography},
	url = {https://linkinghub.elsevier.com/retrieve/pii/016787608490014X},
	doi = {10.1016/0167-8760(84)90014-X},
	language = {en},
	number = {1},
	urldate = {2022-04-29},
	journal = {International Journal of Psychophysiology},
	author = {Pascual-Marqui, R.D. and Michel, C.M. and Lehmann, D.},
	month = oct,
	year = {1994},
	pages = {49--65},
}

@article{dobs_brain-like_nodate,
	title = {Brain-like functional specialization emerges spontaneously in deep neural networks},
	volume = {8},
	url = {https://www.science.org/doi/10.1126/sciadv.abl8913},
	doi = {10.1126/sciadv.abl8913},
	number = {11},
	urldate = {2022-05-06},
	journal = {Science Advances},
	author = {Dobs, Katharina and Martinez, Julio and Kell, Alexander J. E. and Kanwisher, Nancy},
	note = {Publisher: American Association for the Advancement of Science},
	pages = {eabl8913},
}