AUTHOR=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 TITLE=Characterizing Functional Connectivity Differences in Aging Adults using Machine Learning on Resting State fMRI Data JOURNAL=Frontiers in Computational Neuroscience VOLUME=7 YEAR=2013 URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2013.00038 DOI=10.3389/fncom.2013.00038 ISSN=1662-5188 ABSTRACT=

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 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.