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Thalamic Volumes and Functional Networks Linked With Self‐Regulation Dysfunction in Major Depressive Disorder

Year: 2024
Type: article
Study design: Observational Study
Abstract: AIMS: Self-regulation (SR) dysfunction is a crucial risk factor for major depressive disorder (MDD). However, neural substrates of SR linking MDD remain unclear. METHODS: Sixty-eight healthy controls and 75 MDD patients were recruited to complete regulatory orientation assessments with the Regulatory Focus Questionnaire (RFQ) and Regulatory Mode Questionnaire (RMQ). Nodal intra and inter-network functional connectivity (FC) was defined as FC sum within networks of 46 thalamic subnuclei (TS) or 88 AAL brain regions, and between the two networks separately. Group-level volumetric and functional difference were compared by two sample t-tests. Pearson's correlation analysis and mediation analysis were utilized to investigate the relationship among imaging parameters and the two behaviors. Canonical correlation analysis (CCA) was conducted to explore the inter-network FC mode of TS related to behavioral subscales. Network-based Statistics with machine learning combining powerful brain imaging features was applied to predict individual behavioral subscales. RESULTS: MDD patients showed no group-level volumetric difference in 46 TS but represented significant correlation of TS volume and nodal FC with behavioral subscales. Specially, inter-network FC of the orbital part of the right superior frontal gyrus and the left supplementary motor area mediated the correlation between RFQ/RMQ subscales and depressive severity. Furthermore, CCA identified how the two behaviors are linked via the inter-network FC mode of TS. More crucially, thalamic functional subnetworks could predict RFQ/RMQ subscales and psychomotor retardation for MDD individuals. CONCLUSION: These findings provided neurological evidence for SR affecting depressive severity in the MDD patients and proposed potential biomarkers to identify the SR-based risk phenotype of MDD individuals.
Authors: Zhang Ling, He Cancan, Liu Xinyi, Fan Dandan, Zhang Haisan, Zhang Hongxing, Xie Chunming
Language: English
Open Access status: gold
Estimated APC paid: $3,580
OpenAlex ID: W4404251484
FWCI: 0.687
Cites: 68
Cited by: 3
Related to: 10