Characterizing Functional Connectivity Differences in Aging Adults using Machine Learning on Resting State fMRI Data. Front Comput Neurosci 2013;7:38
Date
05/01/2013Pubmed ID
23630491Pubmed Central ID
PMC3635030DOI
10.3389/fncom.2013.00038Scopus ID
2-s2.0-84875970739 (requires institutional sign-in at Scopus site) 70 CitationsAbstract
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 (R (2) = 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.
