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A computational investigation on using the Excitation-Inhibition (E/I balance) mechanism to optimize tDCS protocolAuthor: Snigdha Dagar Date: 2020-03-05 Report no: IIIT/TH/2020/23 Advisor:Shubhajit Roy Chowdhury,Bapi Raju Surampudi,Dipanjan roy AbstractStroke is the leading cause of severe chronic disability and the second cause of death worldwide with 15 million new cases and 50 million stroke survivors. The poststroke chronic disability may be ameliorated with early neuro rehabilitation where non-invasive brain stimulation (NIBS) techniques can be used as an adjuvant treatment to hasten the effects. However, the heterogeneity in the lesioned brain will require individualized NIBS intervention where innovative neuroimaging technologies of portable electroencephalography (EEGlabel) and functionalnear-infrared spectroscopy (fNIRS) can be leveraged for Brain State Dependent Electrotherapy (BSDE). In this thesis, we first present a theoretical model and a computational approach based on excitation–inhibition (E–I) balance hypothesis to objectively quantify the poststroke individual brain state using online fNIRS–EEG joint imaging. One of the key events that occurs following Stroke is the imbalance in local E–I (that is the ratio of Glutamate/GABA), which may be targeted with NIBS using a computational pipeline that includes individual “forward models” to predict current flow patterns through the lesioned brain or brain target region. Next, we give a proof of concept for our approach by using The Virtual Brain platform, which allows for individual, subject-specific models of the brain. We set up a pipeline that simulates tDCS on a realistic head model, and incorporated these electric field effects into the large scale brain network model to produce cortical activity. Using the pipeline set up, we investigate the effects of tDCS and E-I parameters on cortical activity, and validate it with experimental findings. Full thesis: pdf Centre for Others |
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