Combined HTR1A/1B methylation and human functional connectome to recognize patients with MDD. (November 2022)

• Record Type: Journal Article
• Authors: Xu, Zhi; Gao, Chenjie; Tan, Tingting; Jiang, Wenhao; Wang, Tianyu; Chen, Zimu; Shen, Tian; Chen, Lei; Tang, Haiping; Chen, Wenji; Chen, Bingwei; Zhang, Zhijun; Yuan, Yonggui
• Is Part Of: Psychiatry research. Volume 317(2022)
• Publication Date: 2022-11
• Subjects: Major depressive disorder -- DNA methylation -- Resting-state fMRI -- Functional connectivity -- Machine-learning; Psychiatry -- Periodicals; Psychiatry -- periodicals; Psychiatrie -- Périodiques; 616.89
• DOI: 10.1016/j.psychres.2022.114842
• Languages: English
• ISSNs: 0165-1781

Abstract

Highlights: Combining the resting-state fMRI and DNA methylation data obtained more outstanding performances for the classification of patients with MDD and heathy controls than only using rs-fMRI data or DNA methylation data. The beat classification model based on machine learning methods yielded 81.78% accuracy and 0.8948 AUC in distinguishing MDD patients from healthy controls. Abstract: Objectives: This study aimed to use a machine-learning method to identify HTR1A/1B methylation and resting-state functional connectivity (rsFC) related to the diagnosis of MDD, then try to build classification models for MDD diagnosis based on the identified features. Methods: Peripheral blood samples were collected from all recruited participants, and part of the participants underwent the resting-state fMRI scan. Features including HTR1A/1B methylation and rsFC were calculated. Then, the initial feature sets of epigenetics and neuroimaging were separately input into an all-relevant feature selection to generate significant discriminative power for MDD diagnosis. Random forest classifiers were constructed and evaluated based on identified features. In addition, the SHapley Additive exPlanations (SHAP) method was adapted to interpret the diagnostic model. Results: A combination of selected HTR1A/1B methylation and rsFC feature sets achieved better performance than using either one alone - a distinction between MDD and healthy control groups was achieved at 81.78% classification accuracy and 0.8948 AUC. Conclusion: A high classification accuracy can be achieved by combining multidimensional information from epigenetics and cerebral radiomic features in MDD. Our approach can be helpful for accurate clinical diagnosis of MDD and further exploring the pathogenesis of MDD.