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The goal of this clinical trial is to create a machine learning algorithm to improve active repetitive transcranial magnetic stimulation (rTMS) treatments for veterans and/or active military personnel by alleviating Gulf War Illness related headaches and body pain (GWI-HAP). This study aims to develop and validate a Support Vector Machine (SVM) model that could replace the trial-and-error process by assessing functional connectivity provided by resting state functional magnetic resonance imaging (rs-fMRI) data to predict the most effective rTMS protocol for each person. All participants will be receiving active rTMS treatment.
The main questions it intends to answer are:
Participants will undergo the following:
Full description
This study aims to enroll a total of 140 veterans and/or active military personnel over the 4-year study period at the VA San Diego Healthcare System (VASDHS). Participants will be randomized into receiving treatments at the left DLPFC or left DLPFC and LMC, then placed into predicted respondent or non-respondent groups. They will be assigned to 1 of 4 groups:
Group A: Predicted Respondent at Left DLPFC Group B: Predicted Non-respondent at Left DLPFC Group C: Predicted Respondent at Left DLPFC and LMC Group D: Predicted Non-respondent at Left DLPFC and LMC
Participation in this study will require 15 total visits to the VASDHS over the course of 3-4 months. The visits will be separated in the following phases:
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140 participants in 4 patient groups
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Central trial contact
Albert Y Leung, MD; Caleb Lopez, BS
Data sourced from clinicaltrials.gov
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