Using Minimal-Redundant and Maximal-Relevant Whole-Brain Functional Connectivity to Classify Bipolar Disorder - Chen, Yen-Ling; Tu, Pei-Chi; Huang, Tzu-Hsuan; Bai, Ya-Mei; Su, Tung-Ping; Chen, Mu-Hong; Wu, Yu-Te (Frontiers in Neuroscience, 2020)
Background: A number of mental illness are often re-diagnosed to be bipolar disorder (BD). Furthermore, the prefronto-limbic-striatal regions seem to be associated with the main dysconnectivity of BD. Functional connectivity is potentially an appropriate objective neurobiological marker that can assist with BD diagnosis. Methods: Health controls (HC, n=173) and patients with BD who had been diagnosed by experienced physicians (n=192) were separated into ten folds, namely a nine-fold training set and a one-fold testing set. The classification involved feature selection of the training set using minimum-redundancy/maximum-relevance. Support vector machine was used for training. The classification was repeated ten times until each fold had been used as the testing set. Results: The mean accuracy of the ten testing sets was 76.25% and the area under the curve was 0.840. The selected functional within-network/between-network connectivity was mainly in the subcortical/cerebellar regions and the frontoparietal network. Furthermore, similarity within the BD patients, calculated by the cosine distance between two functional connectivity matrices, was smaller than between groups before feature section and greater than between groups after the feature selection. Limitations: The major limitations were that all the BD patients were receiving medication and that no independent dataset was included. Conclusions: Our approach effectively separates a relatively large group of BD patients from HCs. This was done by selecting functional connectivity, which was more similar within BD patients, and also seems to be related to the neuropathological factors associated with BD.