Classification of schizophrenia-associated brain regions in resting-state fMRI

Abstract Recently, advances in neuroscience have attracted attention to the diagnosis, treatment, and damage schizophrenia-associated brain regions using resting-state functional magnetic resonance imaging (rs-fMRI). This research is immersed endowment of machine learning approaches for discriminating schizophrenia patients provide a viable solution. Toward these goals, firstly, we implemented two sample t -tests find activation difference between healthy controls. The average control higher than patient. Secondly, correlation technique variations on presumably hidden associations structure its associated function. Moreover, current results support viewpoint that function integration helpful gain insight into pathological mechanism schizophrenia. Finally, Lasso regression used low-dimensional rs-fMRI their experimental showed SVM classifier surpasses nine algorithms provided best with good accuracy 94%.