Tracking the Brain’s Intrinsic Connectivity Networks in EEG
Tracking the Brain’s Intrinsic Connectivity Networks in EEG
Abstract Functional magnetic resonance imaging (fMRI) has identified dysfunctional network dynamics underlying a number 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. EEG-based features were then used to classify three cognitively-relevant brain networks with up to 75% accuracy. These findings could lead to affordable and non-invasive methods to objectively diagnose brain disorders involving dysfunctional network dynamics, and to track and even predict treatment responses.
McKinnon Margaret C.、Becker Suzanna、Connolly John F.、Harrison Amabilis H.、Heisz Jennifer J.、Shaw Saurabh Bhaskar
Department of Psychiatry and Behavioural Neuroscience, McMaster University||Mood Disorders Program, St. Joseph?ˉs Healthcare||Homewood Research InstituteNeuroscience Graduate Program, McMaster University||Department of Psychology Neuroscience & Behaviour, McMaster University||Vector Institute for Artificial Intelligence||Centre for Advanced Research in Experimental and Applied Linguistics (ARiEAL), Department of Linguistics and Languages, McMaster UniversityNeuroscience Graduate Program, McMaster University||Department of Psychology Neuroscience & Behaviour, McMaster University||Vector Institute for Artificial Intelligence||Centre for Advanced Research in Experimental and Applied Linguistics (ARiEAL), Department of Linguistics and Languages, McMaster University||Department of Linguistics and Languages, McMaster UniversityNeuroscience Program, Hamilton Health Sciences||Imaging Research Centre, St. Joseph?ˉs Heathcare HamiltonDepartment of Kinesiology, McMaster UniversityNeuroscience Graduate Program, McMaster University||Vector Institute for Artificial Intelligence||Centre for Advanced Research in Experimental and Applied Linguistics (ARiEAL), Department of Linguistics and Languages, McMaster University
医学研究方法神经病学、精神病学基础医学
Intrinsic Connectivity Networks (ICN)Default Mode Network (DMN)Central Executive Network (CEN)Salience Network (SN)Simultaneous EEG-fMRIMachine Learning
McKinnon Margaret C.,Becker Suzanna,Connolly John F.,Harrison Amabilis H.,Heisz Jennifer J.,Shaw Saurabh Bhaskar.Tracking the Brain’s Intrinsic Connectivity Networks in EEG[EB/OL].(2025-03-28)[2025-05-10].https://www.biorxiv.org/content/10.1101/2021.06.18.449078.点此复制
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