Personalizing repetitive transcranial magnetic stimulation for precision depression treatment based on functional brain network controllability and optimal control analysis

被引:14
|
作者
Fang, Feng [1 ]
Godlewska, Beata [2 ,3 ]
Cho, Raymond Y. [4 ,5 ]
Savitz, Sean I. [6 ]
Selvaraj, Sudhakar [7 ]
Zhang, Yingchun [1 ]
机构
[1] Univ Houston, Dept Biomed Engn, Houston, TX 77004 USA
[2] Univ Oxford, Dept Psychiat, Med Sci Div, Oxford, England
[3] Oxford Hlth NHS Fdn Trust, Oxford, England
[4] Baylor Coll Med, Dept Psychiat & Behav Sci, Houston, TX USA
[5] Menninger Clin, Houston, TX USA
[6] UT Hlth Houston, McGovern Med Sch, Dept Neurol, Houston, TX USA
[7] UT Hlth Houston, McGovern Med Sch, Dept Psychiat & Behav Sci, Louis A Faillace, Houston, TX 77030 USA
关键词
Functional network; Brain controllability; Optimal control; Neuromodulation; Rtms; Depression; FMRI; FREQUENCY; RTMS; CONNECTIVITY; AMPLITUDE; METAANALYSIS; RELIABILITY; PHASE; POWER;
D O I
10.1016/j.neuroimage.2022.119465
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Brain neuromodulation effectively treats neurological diseases and psychiatric disorders such as Depression. How-ever, 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 pa-rameters 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 control-lability 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 antide-pressant 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 rest-ing 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 neuromod-ulation protocols to more efficiently treat depression and other neurological diseases.
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收藏
页数:12
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