Heightened effective connectivity of DLPFC-mPFC and DLPFC-ACC circuits in major depressive disorder with suicidal ideation: evidence from a TMS-EEG study

作者：Meng Chen, Xingxing Li, Wenhao Zhuang, Yongming Xu, Zhenglei Pei, Jimeng Liu, Yuanyuan Zhang, Chang Yu, Yübo Wang, Xiaoli Liu, Junfang Zhang, Guangwei Hou, Yinping Chen, Miaomiao Xu, Yafang Tang, Yan Ding, Jie Zhang, Dong-Sheng Zhou · 发表于：Translational Psychiatry · 年份：2025 · DOI：10.1038/s41398-025-03515-z · 被引用次数：7 · 研究领域：Functional Brain Connectivity Studies、Attention Deficit Hyperactivity Disorder、Transcranial Magnetic Stimulation Studies

Major depressive disorder (MDD) with suicidal ideation (SI) significantly impacts global health. Suicidal ideation is associated with alterations in brain network connectivity, yet the effective connectivity from the dorsolateral prefrontal cortex (DLPFC) to functional network nodes remains poorly understood. This study utilizes transcranial magnetic stimulation-electroencephalography (TMS-EEG) to investigate DLPFC connectivity and cortical excitability changes, providing insights into the neurobiological mechanisms and potential treatments for MDD with SI. This study recruited 166 patients with MDD and 61 healthy controls. The TMS-EEG technique was used to assess effective connectivity based on abnormal time-locked TMS evoked potentials (TEPs). Suicidal ideation was assessed using the suicidality module of the Mini International Neuropsychiatric Interview (MINI), and participants were classified into suicidal ideation (SI) and non-SI (NSI) groups based on the presence of active ideation. Subgroup analysis evaluated significant current scattering (SCS) in DLPFC-related circuits through source localization, with multiple functional networks defined as downstream regions of interest. TEP analysis at the F3 electrode revealed no significant differences between the MDD and HC groups across components. However, the SI group exhibited increased N100 amplitudes compared to the NSI group (uncorrected) and healthy controls. Source-level brain network analysis showed that the SCS of th...