@article{RN314,
   author = {Cirstian, R. and Pilmeyer, J. and Bernas, A. and Jansen, J. F. A. and Breeuwer, M. and Aldenkamp, A. P. and Zinger, S.},
   title = {Objective biomarkers of depression: A study of Granger causality and wavelet coherence in resting-state fMRI},
   journal = {J Neuroimaging},
   note = {Cirstian, Ramona
Pilmeyer, Jesper
Bernas, Antoine
Jansen, Jacobus F A
Breeuwer, Marcel
Aldenkamp, Albert P
Zinger, Svitlana
eng
TK11812P07/Topconsortium voor Kennis en Innovatie/
Philips/
Eindhoven Engine/
J Neuroimaging. 2023 Jan 29. doi: 10.1111/jon.13085.},
   abstract = {BACKGROUND AND PURPOSE: The lack of a robust diagnostic biomarker makes understanding depression from a neurobiological standpoint an important goal, especially in the context of brain imaging. METHODS: In this study, we aim to create novel image-based features for objective diagnosis of depression. Resting-state network time series are used to investigate neurodynamics with the help of wavelet coherence and Granger causality (G-causality). Three new features are introduced: total wavelet coherence, wavelet lead coherence, and wavelet coherence blob analysis. The fourth feature, pair-wise conditional G-causality, is used to establish the causality between resting-state networks. We use the proposed features to classify depression in adult subjects. RESULTS: We obtained an accuracy of 86% in the wavelet lead coherence, 80% in Granger causality, and 86% in wavelet coherence blob analysis. Subjects with depression showed hyperconnectivity between the dorsal attention network and the auditory network as well as between the posterior default mode network and the dorsal attention network. Hypoconnectivity was found between the anterior default mode network and the auditory network as well as the right frontoparietal network and the lateral visual network. An abnormal co-activation pattern was found between cerebellum and the lateral motor network according to the wavelet coherence blob analysis. CONCLUSION: Based on abnormal functional dynamics between brain networks, we were able to identify subjects with depression with high accuracy. The findings of this study contribute to the understanding of the impaired emotional and attention processing associated with depression, as well as decreased motor activity.},
   keywords = {causality
depression
fMRI
neurodynamics
resting-state networks
wavelet coherence},
   ISSN = {1552-6569 (Electronic)
1051-2284 (Linking)},
   DOI = {10.1111/jon.13085},
   url = {https://www.ncbi.nlm.nih.gov/pubmed/36710075},
   year = {2023},
   type = {Journal Article}
}

{"_id":"3HMJk8gb8z4aLA78v","bibbaseid":"cirstian-pilmeyer-bernas-jansen-breeuwer-aldenkamp-zinger-objectivebiomarkersofdepressionastudyofgrangercausalityandwaveletcoherenceinrestingstatefmri-2023","author_short":["Cirstian, R.","Pilmeyer, J.","Bernas, A.","Jansen, J. F. A.","Breeuwer, M.","Aldenkamp, A. P.","Zinger, S."],"bibdata":{"bibtype":"article","type":"Journal Article","author":[{"propositions":[],"lastnames":["Cirstian"],"firstnames":["R."],"suffixes":[]},{"propositions":[],"lastnames":["Pilmeyer"],"firstnames":["J."],"suffixes":[]},{"propositions":[],"lastnames":["Bernas"],"firstnames":["A."],"suffixes":[]},{"propositions":[],"lastnames":["Jansen"],"firstnames":["J.","F.","A."],"suffixes":[]},{"propositions":[],"lastnames":["Breeuwer"],"firstnames":["M."],"suffixes":[]},{"propositions":[],"lastnames":["Aldenkamp"],"firstnames":["A.","P."],"suffixes":[]},{"propositions":[],"lastnames":["Zinger"],"firstnames":["S."],"suffixes":[]}],"title":"Objective biomarkers of depression: A study of Granger causality and wavelet coherence in resting-state fMRI","journal":"J Neuroimaging","note":"Cirstian, Ramona Pilmeyer, Jesper Bernas, Antoine Jansen, Jacobus F A Breeuwer, Marcel Aldenkamp, Albert P Zinger, Svitlana eng TK11812P07/Topconsortium voor Kennis en Innovatie/ Philips/ Eindhoven Engine/ J Neuroimaging. 2023 Jan 29. doi: 10.1111/jon.13085.","abstract":"BACKGROUND AND PURPOSE: The lack of a robust diagnostic biomarker makes understanding depression from a neurobiological standpoint an important goal, especially in the context of brain imaging. METHODS: In this study, we aim to create novel image-based features for objective diagnosis of depression. Resting-state network time series are used to investigate neurodynamics with the help of wavelet coherence and Granger causality (G-causality). Three new features are introduced: total wavelet coherence, wavelet lead coherence, and wavelet coherence blob analysis. The fourth feature, pair-wise conditional G-causality, is used to establish the causality between resting-state networks. We use the proposed features to classify depression in adult subjects. RESULTS: We obtained an accuracy of 86% in the wavelet lead coherence, 80% in Granger causality, and 86% in wavelet coherence blob analysis. Subjects with depression showed hyperconnectivity between the dorsal attention network and the auditory network as well as between the posterior default mode network and the dorsal attention network. Hypoconnectivity was found between the anterior default mode network and the auditory network as well as the right frontoparietal network and the lateral visual network. An abnormal co-activation pattern was found between cerebellum and the lateral motor network according to the wavelet coherence blob analysis. CONCLUSION: Based on abnormal functional dynamics between brain networks, we were able to identify subjects with depression with high accuracy. The findings of this study contribute to the understanding of the impaired emotional and attention processing associated with depression, as well as decreased motor activity.","keywords":"causality depression fMRI neurodynamics resting-state networks wavelet coherence","issn":"1552-6569 (Electronic) 1051-2284 (Linking)","doi":"10.1111/jon.13085","url":"https://www.ncbi.nlm.nih.gov/pubmed/36710075","year":"2023","bibtex":"@article{RN314,\n author = {Cirstian, R. and Pilmeyer, J. and Bernas, A. and Jansen, J. F. A. and Breeuwer, M. and Aldenkamp, A. P. and Zinger, S.},\n title = {Objective biomarkers of depression: A study of Granger causality and wavelet coherence in resting-state fMRI},\n journal = {J Neuroimaging},\n note = {Cirstian, Ramona\nPilmeyer, Jesper\nBernas, Antoine\nJansen, Jacobus F A\nBreeuwer, Marcel\nAldenkamp, Albert P\nZinger, Svitlana\neng\nTK11812P07/Topconsortium voor Kennis en Innovatie/\nPhilips/\nEindhoven Engine/\nJ Neuroimaging. 2023 Jan 29. doi: 10.1111/jon.13085.},\n abstract = {BACKGROUND AND PURPOSE: The lack of a robust diagnostic biomarker makes understanding depression from a neurobiological standpoint an important goal, especially in the context of brain imaging. METHODS: In this study, we aim to create novel image-based features for objective diagnosis of depression. Resting-state network time series are used to investigate neurodynamics with the help of wavelet coherence and Granger causality (G-causality). Three new features are introduced: total wavelet coherence, wavelet lead coherence, and wavelet coherence blob analysis. The fourth feature, pair-wise conditional G-causality, is used to establish the causality between resting-state networks. We use the proposed features to classify depression in adult subjects. RESULTS: We obtained an accuracy of 86% in the wavelet lead coherence, 80% in Granger causality, and 86% in wavelet coherence blob analysis. Subjects with depression showed hyperconnectivity between the dorsal attention network and the auditory network as well as between the posterior default mode network and the dorsal attention network. Hypoconnectivity was found between the anterior default mode network and the auditory network as well as the right frontoparietal network and the lateral visual network. An abnormal co-activation pattern was found between cerebellum and the lateral motor network according to the wavelet coherence blob analysis. CONCLUSION: Based on abnormal functional dynamics between brain networks, we were able to identify subjects with depression with high accuracy. The findings of this study contribute to the understanding of the impaired emotional and attention processing associated with depression, as well as decreased motor activity.},\n keywords = {causality\ndepression\nfMRI\nneurodynamics\nresting-state networks\nwavelet coherence},\n ISSN = {1552-6569 (Electronic)\n1051-2284 (Linking)},\n DOI = {10.1111/jon.13085},\n url = {https://www.ncbi.nlm.nih.gov/pubmed/36710075},\n year = {2023},\n type = {Journal Article}\n}\n\n","author_short":["Cirstian, R.","Pilmeyer, J.","Bernas, A.","Jansen, J. F. A.","Breeuwer, M.","Aldenkamp, A. P.","Zinger, S."],"key":"RN314","id":"RN314","bibbaseid":"cirstian-pilmeyer-bernas-jansen-breeuwer-aldenkamp-zinger-objectivebiomarkersofdepressionastudyofgrangercausalityandwaveletcoherenceinrestingstatefmri-2023","role":"author","urls":{"Paper":"https://www.ncbi.nlm.nih.gov/pubmed/36710075"},"keyword":["causality depression fMRI neurodynamics resting-state networks wavelet coherence"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"https://raw.githubusercontent.com/jansenjfa1/bibbase.github.io/master/jansenjfa.bib","dataSources":["TCkfRWJAZvbLAZi29"],"keywords":["causality depression fmri neurodynamics resting-state networks wavelet coherence"],"search_terms":["objective","biomarkers","depression","study","granger","causality","wavelet","coherence","resting","state","fmri","cirstian","pilmeyer","bernas","jansen","breeuwer","aldenkamp","zinger"],"title":"Objective biomarkers of depression: A study of Granger causality and wavelet coherence in resting-state fMRI","year":2023}