@article{b62c350d75834e65b6cc208999108c13,
title = "Personalizing repetitive transcranial magnetic stimulation for precision depression treatment based on functional brain network controllability and optimal control analysis",
abstract = "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.",
keywords = "Brain controllability, Depression, Functional network, Neuromodulation, Optimal control, Rtms",
author = "Feng Fang and Beata Godlewska and Cho, {Raymond Y.} and Savitz, {Sean I.} and Sudhakar Selvaraj and Yingchun Zhang",
note = "Funding Information: This work was supported in part by the University of Houston, NIMH R21 (1R21MH119441 – 01A1) an MRC program grant (MR/K022202/1) and the Oxford Health NIHR Biomedical Research center. The views expressed are those of the authors and not necessarily those of the National Health Service, NIHR or the Department of Health. RYC/SS has received grant/research support from NIMH R21 (1R21MH119441 – 01A1). Research supplement funds from The University of Texas Health Science Center at Houston to SS were utilized for this study. The University of Texas Health Science Center at Houston had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The content of this study is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or SAMHSA. We thank all the participants for their help with the study. SS has received research support from Flow Neuroscience AB and a sub/study investigator for Compass pathways, Relmada, Janssen and LivaNova. FF, BRG, RYC, and SIS have had no conflicting interests during the last three years. YZ owns shares at HillMed Inc and BrainHealth Tech Inc. Funding Information: This work was supported in part by the University of Houston , NIMH R21 ( 1R21MH119441 – 01A1 ) an MRC program grant (MR/K022202/1) and the Oxford Health NIHR Biomedical Research center. The views expressed are those of the authors and not necessarily those of the National Health Service, NIHR or the Department of Health. RYC/SS has received grant/research support from NIMH R21 ( 1R21MH119441 – 01A1 ). Research supplement funds from The University of Texas Health Science Center at Houston to SS were utilized for this study. The University of Texas Health Science Center at Houston had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The content of this study is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or SAMHSA. Publisher Copyright: {\textcopyright} 2022",
year = "2022",
month = oct,
day = "15",
doi = "10.1016/j.neuroimage.2022.119465",
language = "English (US)",
volume = "260",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Academic Press",
}