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

Nature Mental Health volume 1, pages 284–294 (2023)Cite this article

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Abstract

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 electroencephalogram (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 (ClinicalTrials.gov registration: NCT03343028).

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Fig. 1: Overview of the EEG treatment-predictive algorithm.
Fig. 2: Prediction performance for psychotherapy outcome and visualization of the identified EEG signature in PTSD.
Fig. 3: Prediction of psychotherapy outcome using baseline demographic and clinical variables.
Fig. 4: Within-arm and across-arm validations of the EEG connectivity signature for psychotherapy outcome prediction.
Fig. 5: Treatment response and self-report questionnaires differentiated by the EEG connectivity signature.

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Data availability

The data supporting the results in this study are available within the paper and its Supplementary Information. The dataset used in this study is governed by a data-use agreement and is therefore not publicly available.

Code availability

The custom code used in this study is available for research purpose from the corresponding author (A.E., amitetkin@altoneuroscience.com) upon reasonable request.

Change history

  • 26 May 2023

    In the version of this article initially published, there was a typo in the surname of Dawlat El-Said which is now amended in the HTML and PDF versions of the article.

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Acknowledgements

This work was funded by Cohen Veterans Bioscience grant no. CVB034. Y.Z. was supported by the National Institute of Mental Health of the National Institutes of Health grant nos. R01MH129694 and R21MH130956 and Lehigh University FIG, CORE and Accelerator grants. This research also was supported in part by the Department of Veterans Affairs Office of Academic Affiliations Advanced Fellowship Program in Mental Illness Research and Treatment, the Medical Research Service of the VA Palo Alto Health Care System, the Department of Veterans Affairs Sierra Pacific Mental Illness Research, Education, and Clinical Center (MIRECC), and STI2030-Major Projects 2022ZD0211700.

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Authors and Affiliations

  1. Department of Bioengineering, Lehigh University, Bethlehem, PA, USA

    Yu Zhang

  2. Department of Psychology, Bar-Ilan University, Ramat-Gan, Israel

    Sharon Naparstek

  3. Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA

    Sharon Naparstek, Mallissa Watts, Emmanuel Shpigel, Dawlat El-Said, Michelle L. Eisenberg & Amit Etkin

  4. Alto Neuroscience, Inc, Los Altos, CA, USA

    Joseph Gordon, Faizan S. Badami, Amit Etkin & Wei Wu

  5. Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA

    Emmanuel Shpigel, Dawlat El-Said & Amit Etkin

  6. Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA

    Michelle L. Eisenberg

  7. Department of Psychiatry, Center for Depression Research and Clinical Care, Peter O’Donnell Jr Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA

    Russell T. Toll

  8. Cohen Veterans Bioscience Inc., New York, NY, USA

    Allyson Gage

  9. New Mexico Veterans Affairs Healthcare System, Albuquerque, NM, USA

    Madeleine S. Goodkind

  10. Department of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA

    Madeleine S. Goodkind

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  1. Yu Zhang
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Contributions

Y.Z. contributed to the development of methods, analysis and interpretation of the data, and the drafting of the manuscript. S.N. contributed to the clinical data analysis and treatment outcome evaluation. J.G., M.W., E.S., D.E.-S., F.S.B., M.L.E., R.T.T. and M.S.G. contributed to EEG and clinical data collection. A.G. contributed to the interpretation of the data. A.E. provided funding and oversaw the analysis and interpretation of the data and drafting of the manuscript. W.W. oversaw analysis and interpretation of the data and contributed to the development of methods and the drafting of the manuscript.

Corresponding authors

Correspondence to Yu Zhang, Amit Etkin or Wei Wu.

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Competing interests

A.E., W.W., F.S.B. and J.G. report salary and equity from Alto Neuroscience. A.E. additionally holds equity in Akili Interactive and Mindstrong Health. None of the other authors has financial disclosures to report.

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Zhang, Y., Naparstek, S., Gordon, J. et al. Machine learning-based identification of a psychotherapy-predictive electroencephalographic signature in PTSD. Nat. Mental Health 1, 284–294 (2023). https://doi.org/10.1038/s44220-023-00049-5

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