Automated classification of ICA networks from resting state fMRI using Machine Learning framework
Ashish Anil Rao 1 , Hima Patel 1 , Ek Tsoon Tan 2 , Rakesh Mullick 1 , and Suresh Emmanuel Joel 1
General Electric Global Research, Bangalore,
Electric Global Research, New York, United States
Automated classification of ICA derived components in to
components of neuronal origin and components of noise
origin will be very useful. Several attempts with modest
results have been reported previously. Recently a method
for classifcation of ICA derived from high resolution,
long duration multiband scans has been reported. Here we
present accurate automated classifier at a single
subject single run level for the widely used
conventional resting state fMRI.
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