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Front Comput Neurosci. 2013 Apr 25;7:38. doi: 10.3389/fncom.2013.00038. eCollection 2013.

Characterizing Functional Connectivity Differences in Aging Adults using Machine Learning on Resting State fMRI Data.

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Medical Physics, University of Wisconsin-Madison Madison, WI, USA ; Clinical Neuroengineering Training Program, University of Wisconsin-Madison Madison, WI, USA.


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.


aging; reorganization; resting state fMRI; support vector machine

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