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通过能量景观分析揭示重度抑郁症及其重复经颅磁刺激干预的动态效应。

Unveiling the dynamic effects of major depressive disorder and its rTMS interventions through energy landscape analysis.

作者信息

Su Chun-Wang, Tang Yurui, Tang Nai-Long, Liu Nian, Li Jing-Wen, Qi Shun, Wang Hua-Ning, Huang Zi-Gang

机构信息

School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China.

Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China.

出版信息

Front Neurosci. 2025 Mar 5;19:1444999. doi: 10.3389/fnins.2025.1444999. eCollection 2025.

Abstract

INTRODUCTION

Brain dynamics offer a more direct insight into brain function than network structure, providing a profound understanding of dysregulation and control mechanisms within intricate brain systems. This study investigates the dynamics of functional brain networks in major depressive disorder (MDD) patients to decipher the mechanisms underlying brain dysfunction during depression and assess the impact of repetitive transcranial magnetic stimulation (rTMS) intervention.

METHODS

We employed energy landscape analysis of functional magnetic resonance imaging (fMRI) data to examine the dynamics of functional brain networks in MDD patients. The analysis focused on key dynamical indicators of the default mode network (DMN), the salience network (SN), and the central execution network (CEN). The effects of rTMS intervention on these networks were also evaluated.

RESULTS

Our findings revealed notable dynamical alterations in the pDMN, the vDMN, and the aSN, suggesting their potential as diagnostic and therapeutic markers. Particularly striking was the altered activity observed in the dDMN in the MDD group, indicative of patterns associated with depressive rumination. Notably, rTMS intervention partially reverses the identified dynamical alterations.

DISCUSSION

Our results shed light on the intrinsic dysfunction mechanisms of MDD from a dynamic standpoint and highlight the effects of rTMS intervention. The identified alterations in brain network dynamics provide promising analytical markers for the diagnosis and treatment of MDD. Future studies should further explore the clinical applications of these markers and the comprehensive dynamical effects of rTMS intervention.

摘要

引言

与网络结构相比,脑动力学能更直接地洞察脑功能,有助于深入理解复杂脑系统中的失调和控制机制。本研究调查了重度抑郁症(MDD)患者功能性脑网络的动力学,以解读抑郁症期间脑功能障碍的潜在机制,并评估重复经颅磁刺激(rTMS)干预的影响。

方法

我们采用功能磁共振成像(fMRI)数据的能量景观分析来研究MDD患者功能性脑网络的动力学。分析聚焦于默认模式网络(DMN)、突显网络(SN)和中央执行网络(CEN)的关键动力学指标。还评估了rTMS干预对这些网络的影响。

结果

我们的研究结果揭示了后扣带回默认模式网络(pDMN)、腹侧默认模式网络(vDMN)和前突显网络(aSN)中显著的动力学改变,表明它们有可能作为诊断和治疗标志物。尤其引人注目的是,在MDD组的背侧默认模式网络(dDMN)中观察到活动改变,这表明与抑郁性沉思相关的模式。值得注意的是,rTMS干预部分逆转了所确定的动力学改变。

讨论

我们的结果从动力学角度揭示了MDD的内在功能障碍机制,并突出了rTMS干预的效果。所确定的脑网络动力学改变为MDD的诊断和治疗提供了有前景的分析标志物。未来的研究应进一步探索这些标志物的临床应用以及rTMS干预的综合动力学效应。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed54/11920141/01ed7daccfb0/fnins-19-1444999-g0001.jpg

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