Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network

Figure 1

Basic framework. Illustration of the basic framework of the method used. (1) Data acquisition and preprocessing; (2) network construction (the high-order functional connectivity network is constructed first, followed by construction of the minimum spanning tree network); (3) feature extraction and selection (two types of feature are extracted and selected: one is to calculate quantifiable local network features (degree, betweenness centrality, and eccentricity) with the Kolmogorov–Smirnov test used for feature selection and the other is to mine frequent subgraphs from the HC and MDD groups and select the most discriminative subnetworks as the subgraph patterns); (4) classification model construction (kernel matrix is calculated for two types of feature and then the multiple-kernel support vector machine (SVM) is adopted to combine these heterogeneous kernels for distinguishing individuals with MDD from healthy controls). AAL, automated anatomical labeling; fMRI, functional magnetic resonance imaging; FC, functional connectivity; HC, healthy controls; MDD, major depressive disorder.