Characterizing Functional Connectivity Differences in Aging Adults using Machine Learning on Resting State fMRI Data - Vergun, Svyatoslav; Deshpande, Alok; Meier, Timothy B.; Song, Jie; Tudorascu, Dana L.; Nair, Veena A.; Singh, Vikas; Biswal, Bharat B.; Meyerand, Mary E.; Birn, Rasmus M.; Prabhakaran, Vivek (Frontiers in Computational Neuroscience, 2013)
The brain at rest consists of spatially distributed but functionally connected regions, called intrinsic connectivity networks (ICNs). Resting state functional magnetic resonance imaging (rs-fMRI) has emerged as a way to characterize brain networks without confounds associated with task fMRI such as task difficulty and performance. Here we applied a Support Vector Machine (SVM) linear classifier as well as a Support Vector Machine Regressor (SVR) method to rs-fMRI data in order to compare age related differences in four of the major functional brain networks: the default, cingulo-opercular, fronto-parietal and sensorimotor. A linear SVM classifier discriminated between young and old subjects with 84% accuracy (p-value < 1 × 10-7). A linear SVR age predictor performed reasonably well in continuous age prediction (R2 = 0.419, p-value < 1 × 10-8). These findings reveal that differences in intrinsic connectivity as measured with rs-fMRI exist between subjects, and that SVM methods are capable of detecting and utilizing these differences for classification and prediction.