Classification of resting state fMRI datasets using dynamic network clusters

Resting state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating intrinsic and spontaneous brain activity. The application of univariate and multivariate methods such as multi voxel pattern analysis has been instrumental in localizing neural correlates to various cognitive states and psychiatric disease. However, many existing methods of rsfMRI analysis are insufficient for investigating the true mechanism of brain activity since they make implicit assumptions that are agnostic of the temporal and spatial dynamics of brain activity. The proposed method aims to create a superior feature space for representing brain activity using k-means and to create interpretable generalizations on these features for studying group differences using support vector machine classifiers.