Machine learning-based identification of a psychotherapy-predictive electroencephalographic signature in PTSD

Nature Mental Health, Volume 1 Issue 4, April 2023
Authored By:
Yu Zhang, Sharon Naparstek, Joseph Gordon, Mallissa Watts, Emmanuel Shpigel, Dawlat EI-Said, Faizan S. Badami, Michelle L. Eisenberg, Russell T. Toll, Allyson Gage, Madeleine S. Goodkind, Amit Etkin & Wei Wu
Although psychotherapy is at present the most effective treatment for posttraumatic stress disorder (PTSD), its efficacy is still limited for many patients, due mainly to the substantial clinical and neurobiological heterogeneity in the disease. Development of treatment-predictive algorithms by leveraging machine learning on brain connectivity data can advance our understanding of the neurobiological mechanisms underlying the disease and its treatment. Doing so with low-cost and easy-to-gather electro encephalogram (EEG) data may furthermore facilitate clinical translation of such algorithms for patients with PTSD. This study investigates whether individual patient-level resting-state EEG connectivity can predict psychotherapy outcomes in PTSD. We developed a treatment-predictive EEG signature using machine learning applied to high-density resting-state EEG collected from military veterans with PTSD. The predictive signature was dominated by theta frequency EEG connectivity differences and was able to generalize across two types of psychotherapy—prolonged exposure and cognitive processing therapy. Our results also advance a biological definition of a PTSD patient subgroup who is resistant to psychotherapy, which is currently the most evidence-based treatment for the condition. The findings support a path towards clinically translatable and scalable biomarkers that could be used to tailor interventions for each individual or drive the development of novel treatments ( registration: NCT03343028).
Published in:
Nature Mental Health
Nature Mental Health - April 2023

More Publications

May 19, 2023

Nature Reviews Neurology

Global synergistic actions to improve brain health for human development

February 13, 2023

Frontiers in Aging Neuroscience

Machine learning within the Parkinson’s progression markers initiative: Review of the current state of affairs

September 9, 2022


Deep Learning for Daily Monitoring of Parkinson’s Disease Outside the Clinic Using Wearable Sensors

June 1, 2022

JAMA Network Open

Association of Posttraumatic Stress Disorder With Accelerated Cognitive Decline in Middle-aged Women

March 23, 2022

Journal of Neurotrauma

A Framework to Advance Biomarker Development in the Diagnosis, Outcome Prediction, and Treatment of Traumatic Brain Injury

February 26, 2022

Translational Psychiatry

Plasma biomarkers associated with deployment trauma and its consequences in post-9/11 era veterans: initial findings from the TRACTS longitudinal cohort