Department of Biomedical Engineering, University of Houston, Houston, TX, USA.
Department of Psychiatry, Medical Sciences Division, University of Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Oxford, United Kingdom.
Neuroimage. 2022 Oct 15;260:119465. doi: 10.1016/j.neuroimage.2022.119465. Epub 2022 Jul 12.
Brain neuromodulation effectively treats neurological diseases and psychiatric disorders such as Depression. However, due to patient heterogeneity, neuromodulation treatment outcomes are often highly variable, requiring patient-specific stimulation protocols throughout the recovery stages to optimize treatment outcomes. Therefore, it is critical to personalize neuromodulation protocol to optimize the patient-specific stimulation targets and parameters by accommodating inherent interpatient variability and intersession alteration during treatments. The study aims to develop a personalized repetitive transcranial magnetic stimulation (rTMS) protocol and evaluate its feasibility in optimizing the treatment efficiency using an existing dataset from an antidepressant experimental imaging study in depression. The personalization of the rTMS treatment protocol was achieved by personalizing both stimulation targets and parameters via a novel approach integrating the functional brain network controllability analysis and optimal control analysis. First, the functional brain network controllability analysis was performed to identify the optimal rTMS stimulation target from the effective connectivity network constructed from patient-specific resting-state functional magnetic resonance imaging data. The optimal control algorithm was then applied to optimize the rTMS stimulation parameters based on the optimized target. The performance of the proposed personalized rTMS technique was evaluated using datasets collected from a longitudinal antidepressant experimental imaging study in depression (n = 20). Simulation models demonstrated that the proposed personalized rTMS protocol outperformed the standard rTMS treatment by efficiently steering a depressive resting brain state to a healthy resting brain state, indicated by the significantly less control energy needed and higher model fitting accuracy achieved. The node with the maximum average controllability of each patient was designated as the optimal target region for the personalized rTMS protocol. Our results also demonstrated the theoretical feasibility of achieving comparable neuromodulation efficacy by stimulating a single node compared to stimulating multiple driver nodes. The findings support the feasibility of developing personalized neuromodulation protocols to more efficiently treat depression and other neurological diseases.
脑神经调节有效地治疗神经疾病和精神障碍，如抑郁症。然而，由于患者的异质性，神经调节治疗的结果往往差异很大，需要在整个康复阶段制定针对患者的刺激方案，以优化治疗效果。因此，通过适应治疗过程中的固有个体间变异性和个体间变化，对神经调节方案进行个性化以优化针对特定患者的刺激靶点和参数至关重要。本研究旨在开发个性化重复经颅磁刺激（rTMS）方案，并利用抑郁症抗抑郁实验成像研究中的现有数据集评估其优化治疗效率的可行性。rTMS 治疗方案的个性化是通过一种新方法实现的，该方法将功能脑网络可控性分析和最优控制分析相结合，实现了刺激靶点和参数的个性化。首先，通过从患者特定的静息态功能磁共振成像数据构建的有效连通性网络中进行功能脑网络可控性分析，确定 rTMS 刺激的最佳目标。然后，应用最优控制算法基于优化目标优化 rTMS 刺激参数。使用从抑郁症纵向抗抑郁实验成像研究中收集的数据集（n=20）评估了所提出的个性化 rTMS 技术的性能。模拟模型表明，所提出的个性化 rTMS 方案通过有效地将抑郁静息脑状态引导至健康静息脑状态，从而显著减少所需的控制能量并提高模型拟合精度，从而优于标准 rTMS 治疗。每位患者的平均可控性最大的节点被指定为个性化 rTMS 方案的最佳目标区域。我们的结果还表明，与刺激多个驱动节点相比，刺激单个节点可以实现相当的神经调节效果，这在理论上是可行的。这些发现支持开发个性化神经调节方案以更有效地治疗抑郁症和其他神经疾病的可行性。
