Machine Learning to Predict Clinical Response to TMS

NCT ID: NCT03847688

Last Updated: 2019-02-25

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 35 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2018-10-22
• Study Completion Date: 2020-09-18

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.

Conditions

Depression, Unipolar

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

Treatment resistant Major Depressive Disorder

Transcranial Magnetic Stimulation

Intervention Type: DEVICE

Patient receive Transcranial Magnetic Stimulation for treatment resistant depression as part of their routine care.

Interventions

Transcranial Magnetic Stimulation

Patient receive Transcranial Magnetic Stimulation for treatment resistant depression as part of their routine care.

Intervention Type: DEVICE

Eligibility 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.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 65 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Providence VA Medical Center

FED

Sponsor Role: collaborator

Brown University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Amin Zand Vakili, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

Brown University

Locations

Providence VA Medical Center

Providence, Rhode Island, United States

Countries

United States

Other Identifiers

1805002078

Identifier Type: -

Identifier Source: org_study_id