Machine Learning to Predict Clinical Response to TMS (LEARN)
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|ClinicalTrials.gov Identifier: NCT03847688|
Recruitment Status : Enrolling by invitation
First Posted : February 20, 2019
Last Update Posted : February 25, 2019
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.
|Condition or disease||Intervention/treatment|
|Depression, Unipolar||Device: Transcranial Magnetic Stimulation|
|Study Type :||Observational|
|Estimated Enrollment :||35 participants|
|Official Title:||Machine Learning to Predict Clinical Response to Transcranial Magnetic Stimulation: A Resting-State Electroencephalography Study|
|Actual Study Start Date :||October 22, 2018|
|Estimated Primary Completion Date :||September 18, 2020|
|Estimated Study Completion Date :||September 18, 2020|
|Treatment resistant Major Depressive Disorder||
Device: Transcranial Magnetic Stimulation
Patient receive Transcranial Magnetic Stimulation for treatment resistant depression as part of their routine care.
- Changes in functional connectivity maps (i.e., EEG coherence) in patients before and after clinical TMS [ Time Frame: Clinical symptoms are assessed and the EEG signal is recorded twice within 2 weeks before the first treatment session, twice in the 2 weeks following the last treatment session (typically 36th session), and at 3 and 6-month following the last treatment. ]The investigators test the hypothesis that TMS modulates cortical networks in a predictable/reproducible way, by using machine learning algorithms (classifiers) to identify changes in post-treatment EEG functional connectivity (quantified by calculating EEG signal Coherence) at different frequency bands (Alpha, Beta, Delta, and Theta).
- Prediction of clinical outcomes based on pre-treatment EEG functional connectivity [ Time Frame: Clinical symptoms are assessed and the EEG signal is recorded twice within 2 weeks before the first treatment session. The two recordings would be used to asses test-retest validity. ]The investigators will use baseline/pre-treatment cortical functional connectivity (quantified by calculating EEG signal Coherence), to predict clinical response to Transcranial Magnetic Stimulation treatment in patients with Major Depressive Disorder. The ability to predict the outcome would be assessed by calculating the coefficient of determination (R2).
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03847688
|United States, Rhode Island|
|Providence VA Medical Center|
|Providence, Rhode Island, United States, 02908|
|Principal Investigator:||Amin Zand Vakili, MD, PhD||Brown University|