Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Jiangsu Key Laboratory of Brain Science and Medicine, Southeast University, Nanjing, Jiangsu, China.
Department of Radiology, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China.
CNS Neurosci Ther. 2024 Nov;30(11):e70116. doi: 10.1111/cns.70116.
Self-regulation (SR) dysfunction is a crucial risk factor for major depressive disorder (MDD). However, neural substrates of SR linking MDD remain unclear.
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.
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.
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.
自我调节（SR）功能障碍是重度抑郁症（MDD）的一个关键风险因素。然而，将 MDD 与 SR 相关的神经基础尚不清楚。
招募了 68 名健康对照者和 75 名 MDD 患者，以完成监管重点问卷（RFQ）和监管模式问卷（RMQ）的监管方向评估。节点内和节点间网络功能连接（FC）被定义为 46 个丘脑亚核（TS）或 88 个 AAL 脑区网络内的 FC 总和，以及两个网络之间的 FC 总和。采用两样本 t 检验比较组间体积和功能差异。采用 Pearson 相关分析和中介分析探讨影像学参数与两种行为之间的关系。采用典型相关分析（CCA）探讨与行为子量表相关的 TS 相关的网络间 FC 模式。结合强大的脑成像特征的基于网络的统计学与机器学习相结合，用于预测个体行为子量表。
MDD 患者在 46 个 TS 中没有表现出组水平的体积差异，但 TS 体积和节点 FC 与行为子量表之间存在显著相关性。特别地，右额上回眶部和左辅助运动区的网络间 FC 与 RFQ/RMQ 子量表与抑郁严重程度之间的相关性存在中介作用。此外，CCA 确定了两种行为是如何通过 TS 的网络间 FC 模式联系起来的。更重要的是，丘脑功能子网络可以预测 MDD 个体的 RFQ/RMQ 子量表和精神运动迟缓。
这些发现为 SR 影响 MDD 患者抑郁严重程度提供了神经学证据，并提出了潜在的生物标志物来识别 MDD 个体基于 SR 的风险表型。
