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

<div><i>High-order functional connectivity network construction flowchart</i>. (1) Partition the entire rs-fMRI time series into multiple overlapping segments of subseries by adopting a fixed-length sliding window; (2) construct temporal low-order FC networks in each time window; (3) stack all low-order FC networks for all subjects; (4) construct a high-order FC network for each subject, by taking the low-order FC as a new vertex and the pairwise Pearson’s correlation coefficient between each pair of these new vertices as the weight. FC, functional connectivity.</div>

Computational and Mathematical Methods in Medicine

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Figure 2

Figure 2: Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network 

Figure 2 | Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network 