aNeuroscience Training Program, University of Wisconsin-Madison, Madison, WI 53705, USAbDepartment of Elec. and Comp. Engineering, University of Wisconsin-Madison, Madison, WI 53706, USAcDepartment of Medical Physics, University of Wisconsin-Madison, Madison, WI 53705, USAdDepartment of Radiology, University of Wisconsin-Madison, Madison, WI 53792, USAeDepartment of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USAfDepartment of Radiology, University of Medicine and Dentistry of New Jersey, Newark, NJ 07103, USAgDepartment of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA
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Most of what is known about the reorganization of functional brain networks that accompanies normal aging is based on neuroimaging studies in which participants perform specific tasks. In these studies, reorganization is defined by the differences in task activation between young and old adults. However, task activation differences could be the result of differences in task performance, strategy, or motivation, and not necessarily reflect reorganization. Resting-state fMRI provides a method of investigating functional brain networks without such confounds. Here, a support vector machine (SVM) classifier was used in an attempt to differentiate older adults from younger adults based on their resting-state functional connectivity. In addition, the information used by the SVM was investigated to see what functional connections best differentiated younger adult brains from older adult brains. Three separate resting-state scans from 26 younger adults (18–35 yrs) and 26 older adults (55–85) were obtained from the International Consortium for Brain Mapping (ICBM) dataset made publically available in the 1000 Functional Connectomes project www.nitrc.org/projects/fcon_1000. 100 seed-regions from four functional networks with 5 mm3 radius were defined based on a recent study using machine learning classifiers on adolescent brains. Time-series for every seed-region were averaged and three matrices of z-transformed correlation coefficients were created for each subject corresponding to each individual's three resting-state scans. SVM was then applied using leave-one-out cross-validation. The SVM classifier was 84% accurate in classifying older and younger adult brains. The majority of the connections used by the classifier to distinguish subjects by age came from seed-regions belonging to the sensorimotor and cingulo-opercular networks. These results suggest that age-related decreases in positive correlations within the cingulo-opercular and default networks, and decreases in negative correlations between the default and sensorimotor networks, are the distinguishing characteristics of age-related reorganization.