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It is estimated that by 2030, mental illness will cost global economy $16 trillion. To identify mental illness, we introduce electroencephalography (EEG) based connectivity biomarkers. EEG permits exploration of brain causal activities at high temporal resolution. Conventional EEG based brain connectivity studies are mostly describing connections among scalp electrode locations, which challenge the functional meaning interpretation of brain activities. In this work, we introduce a novel methodology to generate functional brain network biomarkers from source localized EEG for identifying human subjects that suffer from neurological disorders. We use sLORETA for source localization, post artifact removal, of EEG data followed by threshold binarization for marking activated and deactivated cortical estimates, and data-driven energy landscape analysis, which is rooted in statistical physics theory. This yields the brain subnetwork energy states. Furthermore, we demonstrate our novel method by a preliminary study where EEG data was recorded from 11 channels at 1000Hz from 22 schizophrenia patients and 27 healthy controls in response to transcranial magnetic stimulation administered on the left motor cortex. Sensorimotor network that is responsible for processing input and output of senses and motor activity comprises of precentral gyrus, postcentral gyrus, and paracentral gyrus was observed. In result, we found an energy state in the sensorimotor network, that significantly distinguished patients from controls (p-value<0.05) with Bonferroni correction. For future scope, we are observing other networks. Conclusively, we demonstrate a promising non-invasive low-cost data-driven method for brain network biomarker extraction at high spatiotemporal resolution for clinical applications.
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