Tracking the Brain's Intrinsic Connectivity Networks In
bioRxiv preprint doi: https://doi.org/10.1101/2021.06.18.449078; this version posted June 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Tracking the Brain’s Intrinsic Connectivity Networks in EEG Saurabh Bhaskar Shawa,c,d, Margaret C. McKinnone,f,g, Jennifer J. Heiszh, Amabilis H. Harrisoni,j, John F. Connollya,b,c,d,k, Suzanna Beckera,b,c,d aNeuroscience Graduate Program, McMaster University, Hamilton, ON, Canada bDepartment of Psychology Neuroscience & Behaviour, McMaster University, Hamilton, ON, Canada cVector Institute for Artificial Intelligence, Toronto dCentre for Advanced Research in Experimental and Applied Linguistics (ARiEAL), Department of Linguistics and Languages, McMaster University, Hamilton, ON, Canada eDepartment of Psychiatry and Behavioural Neuroscience, McMaster University, Hamilton, ON, Canada fMood Disorders Program, St. Joseph’s Healthcare, Hamilton, ON, Canada gHomewood Research Institute, Guelph, ON, Canada hDepartment of Kinesiology, McMaster University, Hamilton, ON, Canada iNeuroscience Program, Hamilton Health Sciences, Hamilton ON jImaging Research Centre, St. Joseph’s Heathcare Hamilton, Hamilton ON kDepartment of Linguistics and Languages, McMaster University, Hamilton, ON, Canada Abstract Functional magnetic resonance imaging (fMRI) has identified dysfunctional network dynamics underlying a num- ber of psychopathologies, including post-traumatic stress disorder, depression and schizophrenia. There is tremendous potential for the development of network-based clinical biomarkers to better characterize these disorders. However, to realize this potential requires the ability to track brain networks using a more affordable imaging modality, such as Electroencephalography (EEG). Here we present a novel analysis pipeline capable of tracking brain networks from EEG alone, after training on supervisory signals derived from data simultaneously recorded in EEG and fMRI, while people engaged in various cognitive tasks.
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