Fang Feng, Cammon Jared, Li Rihui, Zhang Yingchun
Department of Biomedical Engineering, University of Houston, Houston, TX, United States.
Department of Psychiatry and Behavioral Sciences, Center for Interdisciplinary Brain Sciences Research, Stanford University, Stanford, CA, United States.
Front Neurosci. 2023 May 12;17:1153786. doi: 10.3389/fnins.2023.1153786. eCollection 2023.
Protocols have been proposed to optimize neuromodulation targets and parameters to increase treatment efficacies for different neuropsychiatric diseases. However, no study has investigated the temporal effects of optimal neuromodulation targets and parameters simultaneously via exploring the test-retest reliability of the optimal neuromodulation protocols. In this study, we employed a publicly available structural and resting-state functional magnetic resonance imaging (fMRI) dataset to investigate the temporal effects of the optimal neuromodulation targets and parameters inferred from our customized neuromodulation protocol and examine the test-retest reliability over scanning time. 57 healthy young subjects were included in this study. Each subject underwent a repeated structural and resting state fMRI scan in two visits with an interval of 6 weeks between two scanning visits. Brain controllability analysis was performed to determine the optimal neuromodulation targets and optimal control analysis was further applied to calculate the optimal neuromodulation parameters for specific brain states transition. Intra-class correlation (ICC) measure was utilized to examine the test-retest reliability. Our results demonstrated that the optimal neuromodulation targets and parameters had excellent test-retest reliability (both ICCs > 0.80). The test-retest reliability of model fitting accuracies between the actual final state and the simulated final state also showed a good test-retest reliability (ICC > 0.65). Our results indicated the validity of our customized neuromodulation protocol to reliably identify the optimal neuromodulation targets and parameters between visits, which may be reliably extended to optimize the neuromodulation protocols to efficiently treat different neuropsychiatric disorders.
已经有人提出了一些方案来优化神经调节靶点和参数,以提高对不同神经精神疾病的治疗效果。然而,尚无研究通过探索最佳神经调节方案的重测信度,同时研究最佳神经调节靶点和参数的时间效应。在本研究中,我们使用了一个公开可用的结构和静息态功能磁共振成像(fMRI)数据集,来研究从我们定制的神经调节方案中推断出的最佳神经调节靶点和参数的时间效应,并检查扫描时间内的重测信度。本研究纳入了57名健康的年轻受试者。每位受试者在两次扫描中重复进行了结构和静息态fMRI扫描,两次扫描之间间隔6周。进行脑可控性分析以确定最佳神经调节靶点,并进一步应用最佳控制分析来计算特定脑状态转换的最佳神经调节参数。采用组内相关系数(ICC)测量来检查重测信度。我们的结果表明,最佳神经调节靶点和参数具有出色的重测信度(两个ICC均>0.80)。实际最终状态与模拟最终状态之间模型拟合精度的重测信度也显示出良好的重测信度(ICC>0.65)。我们的结果表明,我们定制的神经调节方案能够有效可靠地识别不同扫描之间的最佳神经调节靶点和参数,这可能会被可靠地扩展,以优化神经调节方案,从而有效治疗不同的神经精神疾病。