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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
Sponsor:
Collaborator:
Providence VA Medical Center
Information provided by (Responsible Party):
Brown University

Brief Summary:

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

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Study Type : Observational
Estimated Enrollment : 35 participants
Observational Model: Cohort
Time Perspective: Prospective
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

Resource links provided by the National Library of Medicine


Group/Cohort Intervention/treatment
Treatment resistant Major Depressive Disorder Device: Transcranial Magnetic Stimulation
Patient receive Transcranial Magnetic Stimulation for treatment resistant depression as part of their routine care.




Primary Outcome Measures :
  1. 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).

  2. 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).



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Ages Eligible for Study:   18 Years to 65 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
Adult Veterans, of any sex, ages 18-65, with MDD will participate in the study at the Providence VAMC. The patients will be referred by their providers for standard neuromodulation treatment, as happens currently.
Criteria

Inclusion Criteria:

  • diagnosis of MDD, assessed by the Structured Clinical Interview of DSM-5 (SCID)
  • treatment-resistant, operationally defined as failure to achieve clinical remission (MADRS <7) remit following at least one antidepressant trial in the current major depressive episode.
  • Symptoms must be of at least moderate severity (MADRS score >19)
  • medications will be stable for at least six weeks prior to TMS, and there will be no dose changes unless medically necessary

Exclusion Criteria:

* Standard contraindications to TMS and EEG :

  • metal in the head and neck
  • history of serious head injury or loss of consciousness over 10 minutes
  • dementia
  • seizure history
  • other serious neurological disorders
  • serious or unstable medical conditions that would affect EEG signal
  • current severe substance use disorders (except for nicotine or caffeine)
  • bipolar or psychotic-spectrum disorders (e.g., schizophrenia, schizoaffective disorder, etc.)
  • Prior non-responders to TMS will also be excluded.

Information from the National Library of Medicine

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


Locations
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United States, Rhode Island
Providence VA Medical Center
Providence, Rhode Island, United States, 02908
Sponsors and Collaborators
Brown University
Providence VA Medical Center
Investigators
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Principal Investigator: Amin Zand Vakili, MD, PhD Brown University

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Responsible Party: Brown University
ClinicalTrials.gov Identifier: NCT03847688     History of Changes
Other Study ID Numbers: 1805002078
First Posted: February 20, 2019    Key Record Dates
Last Update Posted: February 25, 2019
Last Verified: February 2019
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No

Keywords provided by Brown University:
Transcranial Magnetic Stimulation
Electroencephalography
Machine Learning

Additional relevant MeSH terms:
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Depressive Disorder
Mood Disorders
Mental Disorders