Machine Learning to Predict Clinical Response to TMS

Status:Enrolling by invitation
Therapuetic Areas:Psychiatry / Psychology
Age Range:18 - 65
Start Date:October 22, 2018
End Date:September 18, 2020

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Machine Learning to Predict Clinical Response to Transcranial Magnetic Stimulation: A Resting-State Electroencephalography Study

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.

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.
We found this trial at
Providence, Rhode Island 02908
Providence, RI
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