Cao Zhengcao, Xiao Xiang, Zhao Yang, Jiang Yihan, Xie Cong, Paillère-Martinot Marie-Laure, Artiges Eric, Li Zheng, Daskalakis Zafiris J, Yang Yihong, Zhu Chaozhe
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States.
Front Neurosci. 2023 Jan 4;16:1079078. doi: 10.3389/fnins.2022.1079078. eCollection 2022.
It has been recognized that the efficacy of TMS-based modulation may depend on the network profile of the stimulated regions throughout the brain. However, what profile of this stimulation network optimally benefits treatment outcomes is yet to be addressed. The answer to the question is crucial for informing network-based optimization of stimulation parameters, such as coil placement, in TMS treatments. In this study, we aimed to investigate the feasibility of taking a disease-specific network as the target of stimulation network for guiding individualized coil placement in TMS treatments. We present here a novel network-based model for TMS targeting of the pathological network. First, combining E-field modeling and resting-state functional connectivity, stimulation networks were modeled from locations and orientations of the TMS coil. Second, the spatial anti-correlation between the stimulation network and the pathological network of a given disease was hypothesized to predict the treatment outcome. The proposed model was validated to predict treatment efficacy from the position and orientation of TMS coils in two depression cohorts and one schizophrenia cohort with auditory verbal hallucinations. We further demonstrate the utility of the proposed model in guiding individualized TMS treatment for psychiatric disorders. In this proof-of-concept study, we demonstrated the feasibility of the novel network-based targeting strategy that uses the whole-brain, system-level abnormity of a specific psychiatric disease as a target. Results based on empirical data suggest that the strategy may potentially be utilized to identify individualized coil parameters for maximal therapeutic effects.
人们已经认识到，基于经颅磁刺激（TMS）的调节效果可能取决于整个大脑中受刺激区域的网络特征。然而，这种刺激网络的何种特征能使治疗效果达到最优仍有待探讨。这个问题的答案对于在TMS治疗中基于网络优化刺激参数（如线圈放置）至关重要。在本研究中，我们旨在探究将疾病特异性网络作为刺激网络的靶点以指导TMS治疗中个性化线圈放置的可行性。我们在此提出一种针对病理网络的基于网络的新型TMS靶向模型。首先，结合电场建模和静息态功能连接性，根据TMS线圈的位置和方向对刺激网络进行建模。其次，假设刺激网络与特定疾病的病理网络之间存在空间反相关性以预测治疗结果。所提出的模型在两个抑郁症队列和一个伴有幻听的精神分裂症队列中，从TMS线圈的位置和方向预测治疗效果方面得到了验证。我们进一步证明了所提出模型在指导精神疾病个体化TMS治疗中的效用。在这项概念验证研究中，我们证明了这种基于网络的新型靶向策略的可行性，该策略将特定精神疾病的全脑、系统水平异常作为靶点。基于实证数据的结果表明，该策略可能潜在地用于确定个性化的线圈参数以实现最大治疗效果。
