title: Characterizing Functional Connectivity Differences in Aging Adults using Machine Learning on Resting State fMRI Data.
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 (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.
authors: Medical Physics, University of Wisconsin-Madison Madison, WI, USA ; Clinical Neuroengineering Training Program, University of Wisconsin-Madison Madison, WI, USA.
authors: Svyatoslav
authors: S
authors: Vergun
authors: Alok S
authors: AS
authors: Deshpande
authors: Timothy B
authors: TB
authors: Meier
authors: Jie
authors: J
authors: Song
authors: Dana L
authors: DL
authors: Tudorascu
authors: Veena A
authors: VA
authors: Nair
authors: Vikas
authors: V
authors: Singh
authors: Bharat B
authors: BB
authors: Biswal
authors: M Elizabeth
authors: ME
authors: Meyerand
authors: Rasmus M
authors: RM
authors: Birn
authors: Vivek
authors: V
authors: Prabhakaran
keywords: aging; reorganization; resting state fMRI; support vector machine
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