Article Figures & Data
Figures
- Fig 1.
Classification results for patients with R-mTLE and healthy controls via leave-one-out cross-validation. The x-axis indicates the number of connections involved in the classification; the y-axis indicates classification accuracy (as represented by the generalization rate). The subplot illustrates the prediction results of all subjects with the highest accuracy, which used the first 8 and the first 23 most discriminative connections.
- Fig 2.
Classification evaluation. A, The permutation distribution of estimates produced by the linear support vector machine classifier (with 10,000 repetitions) if the first 8 most discriminating features are used. The x- and y-axes indicate the generalization rate and occurrence number; GR0, is the generalization rate obtained by the classifier trained on the actual class labels. Using the generalization rate as the test statistic, this figure demonstrates that the classifier learned the relationship between the data and the labels with a probability of being incorrect of <.0001. B, Receiver operating characteristic curves indicate the overall classification performance of the functional connectivity–based classification of patients with R-mTLE and healthy controls. The area under the receiver operating characteristic curve (AUC) was 99.6% when the first 8 connections were used for classification. C, The permutation distribution of estimates produced by the linear support vector machine classifier (with 10,000 repetitions) if the first 23 most discriminating features are used. D, Receiver operating characteristic curves indicate the overall classification performance of the functional connectivity–based classification of patients with R-mTLE and healthy controls. The area under the receiver operating characteristic curve was 100% when the first 23 connections were involved in the classification.
- Fig 3.
Region weights and connection strengths categorized by hemisphere. The connections are displayed in a surface rendering of a human brain. The thicknesses of the consensus connections in the leave-one-out cross-validation are scaled by their strengths (which were the normalized occurrences of the first 23 connections during all iterations of the leave-one-out cross-validation). A, Connections with lower strengths in patients with R-mTLE than in controls are depicted in light blue. B, Connections with greater strengths in patients with R-mTLE than in controls are displayed in orange. The ROIs related to the selected consensus connections are also scaled by their weights (calculated as the sum of the weights of all connections to and from the ROI) and are displayed. The ROIs are color-coded by functional network (cerebellum, red; cingulo-opercular network, green; DMN, blue; frontoparietal network, cyan; visual network, rose; and sensorimotor network, yellow). The numeric labels for the ROIs in this figure are provided in the On-line Table.
- Fig 4.
Region weights and connection strengths viewed from inter- and intranetwork perspectives. The connections are displayed in a surface rendering of a human brain. The thicknesses of the consensus connections in the leave-one-out cross-validation are scaled by their strengths (which were the normalized occurrences of the first 23 connections during all iterations of the leave-one-out cross-validation). A, Internetwork connections. B, Intranetwork connections. Connections with greater strengths in patients with R-mTLE than in controls are displayed in orange. Connections with lower strengths in patients with R-mTLE than in controls are depicted in light blue. The ROIs related to the selected consensus connections are also scaled by their weights (calculated as the sum of the weights of all connections to and from the ROI of interest) and are displayed. The ROIs are color-coded as in Fig 3.
