Repetitive Transcranial Magnetic Stimulation for Chronic Multisymptom Illness
(SVM - GWI Trial)
What You Need to Know Before You Apply
What is the purpose of this trial?
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:
1. Does the SVM model predict a more effective treatment response rate for predicted respondents undergoing active rTMS at the left dorsolateral prefrontal cortex (DLPFC) compared to predicted non-respondents?
2. Does the SVM model predict a more effective treatment response rate while undergoing active rTMS at the left dorsolateral prefrontal cortex (DLPFC) and left motor cortex (LMC) in predicted respondents compared to predicted non-respondents?
Participants will undergo the following:
1. Receive a total of 13 active rTMS treatment sessions over 3-4 months.
2. Visit the clinic for a total of 15 visits for assessments, check ups, and treatments.
3. Keep a daily log of their headaches, muscle and joint pain throughout the study.
Are You a Good Fit for This Trial?
Inclusion Criteria
What Are the Treatments Tested in This Trial?
Interventions
- Active Repetitive Transcranial Magnetic Stimulation
- Machine Learning Algorithm
How Is the Trial Designed?
4
Treatment groups
Active Control
Find a Clinic Near You
Who Is Running the Clinical Trial?
Veterans Medical Research Foundation
Lead Sponsor
Congressionally Directed Medical Research Programs
Collaborator
San Diego Veterans Healthcare System
Collaborator
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