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. Author manuscript; available in PMC: 2016 Jan 1.
Published in final edited form as: Neurobiol Aging. 2014 Aug 30;36(0 1):S121–S131. doi: 10.1016/j.neurobiolaging.2014.04.037

Figure 5.

Figure 5

We present the results from the stratified 10-fold cross-validated (CV) support vector machine classification of controls vs. late-mild cognitive impaired subject using nine subsets of connectivity features. These features come from both a fiber connectivity method (FI) and flow connectivity method (FL) and include a variety of graph based network measures (N) along with the raw connectivity matrices (M). We evaluated the performance of each subset’s ability to classify using accuracy, sensitivity, and specificity. The CV was repeated 30 times for each feature set using corresponding CV folds and we evaluated differences using paired-sample t-tests. The bar plot shows the mean accuracy, sensitivity, and specificity over the 30 CV results along with 95% confidence intervals. FL(N) had the highest accuracy of 62.8% and was significantly different (p>0.05) in performance from all other subsets.