Schizophrenia is a chronic mental illness. The exact cause if schizophrenia is not yet known. Extensive research has been done to identify robust biomarkers for the disease using non-invasive brain imaging techniques. A robust biomarker can be informative about pathophysiology of the disease and can guide clinicians into developing more effective interventions. The aim of this dissertation is two folds. First, we seek to identify robust biomarkers using resting state fMRI activity from a cohort of schizophrenic and healthy subjects in a purely data driven approach. We will calculate multivariate network measures and use them as features for classification of the subjects into healthy and diseased. The network measures will be calculated using nodes defined by the AAL anatomical atlas as well as a functional atlas constructed from the fMRI activity. Network measures with high classification rate may be used as potential biomarkers. We will employ double cross-validation to estimate generalizability of our results to a new population of subjects that were not used in biomarker identification. Second, we seek to identify biomarkers using electroretinogram (ERG). We will use a data driven approach to classify individuals based on the pattern of retinal activity they exhibit in response to visual stimulation. Characteristics of the ERG result in high classification rate are presented as potential biomarkers of schizophrenia.
University of Minnesota Ph.D. dissertation. November 2017. Major: Biomedical Engineering. Advisors: Kelvin Lim, Theoden Netoff. 1 computer file (PDF); vi, 109 pages.
Novel Biomarker Identification Approaches for Schizophrenia using fMRI and Retinal Electrophysiology.
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