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NCT03847688: LEARN

Machine Learning to Predict Clinical Response to TMS

Status unknown Last updated 25 February 2019
What this trial tests

trial testing Transcranial Magnetic Stimulation in Depression, Unipolar in 35 participants. Status unknown.

Timeline
22 October 2018
Primary endpoint
18 September 2020
18 September 2020

Quick facts

Lead sponsorBrown University
StatusStatus unknown
Study typeOBSERVATIONAL
Enrollment35
Start date22 October 2018
Primary completion18 September 2020
Estimated completion18 September 2020
Sites1 location across United States

Drugs / interventions tested

Conditions studied

Sponsor

Brown University

Who can join

Adults 18 to 65, any sex, with Depression, Unipolar. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

Major Depressive Disorder (MDD) is a common and debilitating illness. It affects a person's family and personal relationships, work, education, and life. It changes sleeping and eating habits and significantly impairs patients' general health. The disorder affects Veterans more than the general population, both as an isolated illness and in conjunction with posttraumatic stress disorder (PTSD) and suicidality. Symptoms in a notable proportion of patients (\~30%) do not respond to behavioral and pharmacological interventions, and new treatments are in great need. One such treatment, transcranial magnetic stimulation (TMS), has been cleared by Food and Drug Administration for treatment in MDD. TMS is effective in around 60% of patients with treatment-resistant MDD but is associated with significant financial and time burden. Further insights into the neurobiological effects of TMS and markers for functional recovery prediction and treatment progression are of great value. The goal of this proposal is to use human electrophysiology (electroencephalography, hereafter EEG, in particular) and machine learning to predict treatment response in candidates for TMS treatment and also study TMS's mechanism of action. Doing so has several benefits for patients, as prediction of treatment helps providers in screening out the patients for whom TMS is ineffective and understanding the mechanism allows us to refine and individualize the treatment. The investigators will recruit 35 patients with treatment-resistant MDD and record resting state EEG signal with a dense electrode array before and after a 6-week clinical course of TMS treatment. The investigators will use machine learning (Sparse regressions) to predict treatment outcome using functional connectivity (Coherence) maps derived from the EEG signal. The investigators also will use classifiers to track changes in functional connectivity through the course of treatment. Based on our preliminary data, the investigators hypothesize that weaker functional connectivity between prefrontal cortex (where the stimulation is delivered) and parietal/posterior midline sites predict better response to treatment and that TMS treatment will enhance these connections. The data collected here would be used as a seed and preliminary data for future federal (NIH and the VA) career development awards which will focus on the use of EEG to better understand brain function and neuromodulation treatments.

Publications & conference data

No peer-reviewed publications indexed yet for this trial.

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