Use of machine learning in predicting clinical response to transcranial magnetic stimulation in comorbid posttraumatic stress disorder and major depression: A resting state electroencephalography study.
Journal:
Journal of affective disorders
PMID:
30978624
Abstract
BACKGROUND: Repetitive transcranial magnetic stimulation (TMS) is clinically effective for major depressive disorder (MDD) and investigational for other conditions including posttraumatic stress disorder (PTSD). Understanding the mechanisms of TMS action and developing biomarkers predicting response remain important goals. We applied a combination of machine learning and electroencephalography (EEG), testing whether machine learning analysis of EEG coherence would (1) predict clinical outcomes in individuals with comorbid MDD and PTSD, and (2) determine whether an individual had received a TMS course.
Authors
Keywords
Comorbidity
Depressive Disorder, Major
Electroencephalography
Female
Humans
Machine Learning
Male
Middle Aged
Predictive Value of Tests
Prefrontal Cortex
Rhode Island
Self Report
Sensitivity and Specificity
Stress Disorders, Post-Traumatic
Support Vector Machine
Transcranial Magnetic Stimulation
Treatment Outcome