Until relatively recently, most of our knowledge regarding the age-related reorganization of functional networks in the human brain has been based on neuroimaging studies comparing differences in task activity between younger and older adults. However, following an explosion of research into resting-state functional connectivity, it has been proposed that all functional networks that are utilized for task performance are present during rest in the form of correlations between low frequency fluctuations (Biswal et al., 1995
; Smith et al., 2009
). Resting-state fMRI allows the investigation of age-related changes in functional networks without confounds of task studies, such as performance, motivation, and the use of divergent strategies.
Relatively few studies have attempted to characterize age-related reorganization of functional networks at the brain-wide level using resting-state functional connectivity (Biswal et al., 2010
). In one such study, Meunier et al. completed a graph theoretical analysis on resting-state data investigating the effects of aging on the modular organization of functional networks (2009). They found that large modules, or networks, observed in young healthy adults were split up into smaller modules in older adults. In addition, they observed a shift in the hubs of modular connectivity, where the hubs for older adults were located in more posterior regions compared to younger adults (Meunier et al., 2009
). A separate study investigating global functional connectivity differences in homotopic regions of the brain between younger and older adults found that functional connectivity between homotopic areas decreases from adolescence to adulthood, and then increases again with advancing age in adulthood (Zuo et al., 2010
). However, several questions regarding the exact nature of the changes in brain networks that accompany age remain.
One powerful method available to investigate the age-related reorganization of functional networks is the use of machine learning classifiers (Pereira et al., 2009
). Machine learning classifiers, such as support vector machines (SVM), entail selecting independent variables, known as features, and using these features to predict the class membership of an individual example. The features are assigned parameters, called weights, by applying the SVM algorithm to a training dataset with known class labels. In essence, the feature weight corresponds to the relative contribution of a specific feature to the classifier’s ability to successfully differentiate the two groups. After feature weights are calculated, the classifier can then be applied to a separate dataset, known as the testing dataset, and the performance of the classifier can be assessed in terms of its accuracy in classifying examples to the correct class.
The use of SVM on resting-state fMRI data has several advantages over traditional univariate methods. For example, the robustness of findings on group differences can be measured in terms of the accuracy in which these findings classify individual subjects. Importantly, SVM allows the identification of features that contributed the most to subject classification, providing insight into the defining differences between two groups.
Machine learning classifiers, including SVM, have been successfully applied to resting-state fMRI data in the classification of major depressive disorder, schizophrenia, and adolescent brains from normal adult brains (Craddock et al., 2009
; Shen et al., 2010
; Supekar et al., 2009
). A recent study by Dosenbach and colleagues used SVM (and a related method support vector regression) to classify adolescent and adult brains based on resting-state functional connectivity (2010). They observed that the distinguishing characteristic for successful classification between child and adult brains was a decrease in correlations among short-range connections, along with an increase in correlations among long-range connections with increasing age.
In this study, a commonly used machine learning method namely the SVM was used to discriminate healthy younger and older adults based on their resting-state functional connectivity. We hypothesized that older and younger adult brains could be successfully classified based on their resting-state functional connectivity with the use of binary SVM. In addition, the major goal of this study was to use the information provided by SVM to identify what features (in our case functional connections) contributed the most to the success of the classifier. It is our hope that by thoroughly investigating these features we can identify patterns of network and seed-region differences that represent the distinguishing functional network changes that occur with normal aging.