Schizophrenia is a chronic mental disorder that affects millions in the US and tens of millions globally. It is largely believed to be caused by structural and functional differences in the brain, but its exact cause is unknown. Due to the complicated structure of the human brain, its functional connections are often represented by networks. In this thesis, we utilize brain networks generated by functional magnetic resonance imaging (fMRI) data to develop machine learning classification models that can accurately make inferences on single subjects to predict the diagnosis of schizophrenia. We look at a number of local and global connectivity measures derived from correlation-based functional connectivity matrices to do so, using a dataset provided by the National Institute of Health Center of Biomedical Research Excellence (COBRE) and 1000 Functional Connectomes project. Preprocessing and analysis of data is done through the CONN functional connectivity toolbox and MATLAB. Using a subset of subjects and global metrics, we first conduct a preliminary group comparison to determine the existence of a significant difference between patient and control groups with respect to the selected metrics. Then, we investigate machine learning classifiers using k-nearest neighbors and support vector machine models on the full dataset using an expanded set of metrics. Using these models, we observe classification accuracy rates of up to approximately 84% on testing sets using 10-fold cross validation, with sensitivity of approximately 91% and specificity of 77% using a polynomial kernel. This rate is fairly consistent with that of other studies, which generally report classification accuracies of 60-90%. As such, the models we have developed demonstrate the potential of networks in determining the nature of schizophrenia and the uses of statistical learning in the diagnosis of neuropsychiatric disorders.Bachelor of Scienc
