Quantitative Identification of Major Depression Based on Resting-State Dynamic Functional Connectivity: A Machine Learning Approach - Yan, Baoyu; Xu, Xiaopan; Liu, Mengwan; Zheng, Kaizhong; Liu, Jian; Li, Jianming; Wei, Lei; Zhang, Binjie; Lu, Hongbing; Li, Baojuan (Frontiers in Neuroscience, 2020)
Introduction: Developing a machine learning-based approach which could provide quantitative identification of major depression (MDD) is essential for the diagnosis and intervention of this disorder. However, the performances of traditional algorithms using static functional connectivity (SFC) measures were unsatisfactory. In the present work, we exploit the hidden information embedded in dynamic functional connectivity (DFC) and developed an accurate and objective image-based diagnosis system for MDD. Methods: MRI images were collected from 99 participants including 56 healthy controls and 43 MDD patients. DFC was calculated using a sliding-window algorithm. A nonlinear support vector machine (SVM) approach was then used with the DFC matrices as features to distinguish MDD patients from healthy controls. The spatiotemporal characteristics of the most discriminative features were then investigated. Results: The area under the curve (AUC) of the SVM classifier with DFC measures reached 0.9985, while this value is only 0.9216 for the algorithm using SFC measures. Spatially, the most discriminative 40 connections distributed in the dorsal attention network (DAN), default mode network (DMN), sensorimotor network (SMN), visual network (VN), central executive network (CEN), reward network (RN) and auditory network (AN), etc. Notably, a large portion of these connections were associated with the calcarine gyrus and gyrus rectus. Temporally, the most discriminative connections transited from the cortex to deeper regions. Conclusion: The results clearly suggested that DFC is superior to SFC, and provide a reliable quantitative identification method for MDD. Our findings may furnish a better understanding of the neural mechanisms of MDD, as well as improve accurate diagnosis and early intervention of this disorder.