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Identification of psychiatric disorder subtypes from functional connectivity patterns in resting-state electroencephalography

Nature Biomedical Engineering volume 5, pages 309–323 (2021)Cite this article

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Abstract

The understanding and treatment of psychiatric disorders, which are known to be neurobiologically and clinically heterogeneous, could benefit from the data-driven identification of disease subtypes. Here, we report the identification of two clinically relevant subtypes of post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) on the basis of robust and distinct functional connectivity patterns, prominently within the frontoparietal control network and the default mode network. We identified the disease subtypes by analysing, via unsupervised and supervised machine learning, the power-envelope-based connectivity of signals reconstructed from high-density resting-state electroencephalography in four datasets of patients with PTSD and MDD, and show that the subtypes are transferable across independent datasets recorded under different conditions. The subtype whose functional connectivity differed most from those of healthy controls was less responsive to psychotherapy treatment for PTSD and failed to respond to an antidepressant medication for MDD. By contrast, both subtypes responded equally well to two different forms of repetitive transcranial magnetic stimulation therapy for MDD. Our data-driven approach may constitute a generalizable solution for connectome-based diagnosis.

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Fig. 1: Resting-state EEG PEC biomarkers define two subtypes in the discovery PTSD dataset.
Fig. 2: PEC difference between the two subtypes.
Fig. 3: Replication of the identified PEC subtypes in the two cohorts within dataset 2 (PTSD replication).
Fig. 4: Replication of the identified PEC subtypes in the two MDD datasets.
Fig. 5: Validation of subtype transferability across independent datasets.
Fig. 6: Responsiveness of subtypes to treatment across diagnoses and treatment modalities.

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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 3 (EMBARC data) is publicly available through the National Institute of Mental Health (NIMH) Data Archive (https://nda.nih.gov/edit_collection.html?id=2199). Access to the other datasets is governed by data-use agreements or sponsor restrictions, and they are therefore not publicly available.

Code availability

The custom code used in this study is available for research purposes from the corresponding author on reasonable request.

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Acknowledgements

This study was supported by grants from the Steven and Alexandra Cohen Foundation, Cohen Veterans Bioscience grant no. CVB034 and NIH grant nos. U01MH092221 and U01MH092250. A.E. was additionally funded by NIH grant no. DP1MH116506, and was previously supported by the Sierra-Pacific Mental Illness Research, Education and Clinical Center at the Veterans Affairs Palo Alto Healthcare System. Y.L. was supported by the Key R&D Program of Guangdong Province, China under grant no. 2018B030339001, National Key Research and Development Plan of China (no. 2017YFB1002505), and National Natural Science Foundation of China (no. 61633010). The support from N. Krepel in coordinating the TMS is acknowledged.

Author information

Author notes

  1. These authors contributed equally: Yu Zhang, Wei Wu.

Authors and Affiliations

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

    Yu Zhang, Wei Wu, Sharon Naparstek, Adi Maron-Katz, Mallissa Watts, Joseph Gordon, Jisoo Jeong, Emmanuel Shpigel, Parker Longwell, Kamron Sarhadi, Dawlat El-Said & Amit Etkin

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

    Yu Zhang, Wei Wu, Sharon Naparstek, Adi Maron-Katz, Mallissa Watts, Joseph Gordon, Jisoo Jeong, Emmanuel Shpigel, Parker Longwell, Kamron Sarhadi, Dawlat El-Said & Amit Etkin

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

    Yu Zhang

  4. School of Automation Science and Engineering, South China University of Technology, Guangzhou, China

    Wei Wu & Yuanqing Li

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

    Wei Wu, Joseph Gordon & Amit Etkin

  6. 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, Crystal Cooper, Cherise Chin-Fatt & Madhukar H. Trivedi

  7. Department of Computer, Control and Management Engineering “Antonio Ruberti”, University of Rome Sapienza, Rome, Italy

    Laura Astolfi

  8. IRCCF Fondazione Santa Lucia, Rome, Italy

    Laura Astolfi

  9. Pazhou Lab, Guangzhou, China

    Yuanqing Li

  10. Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, The Netherlands

    Martijn Arns

  11. neuroCare Group, Munich, Germany

    Martijn Arns

  12. Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Location AMC, Amsterdam Neuroscience, Amsterdam, The Netherlands

    Martijn Arns

  13. Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands

    Martijn Arns

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

    Madeleine S. Goodkind

  15. O’Donnell Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Madhukar H. Trivedi

  16. Steven and Alexandra Cohen Veterans Center for Post-traumatic Stress and Traumatic Brain Injury, New York University Langone School of Medicine, New York, NY, USA

    Charles R. Marmar

  17. Center for Alcohol Use Disorder and PTSD, New York University Langone School of Medicine, New York, NY, USA

    Charles R. Marmar

  18. Department of Psychiatry, New York University Langone School of Medicine, New York, NY, USA

    Charles R. Marmar

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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. W.W. contributed to the development of the methods, analysis and interpretation of the data and the drafting of the manuscript. R.T.T. contributed to the implementation of the connectivity calculation. S.N. contributed to the clinical data analysis. A.M.-K. and J.J. contributed to the fMRI data pre-processing. M.W., J.G., E.S., P.L., K.S., D.E.-S., C.C. and C.C.-F. contributed to the clinical and EEG collection. M.A. and M.S.G. oversaw the collection of clinical data. L.A. and Y.L. provided analytic input. M.H.T., C.R.M. and A.E. provided funding, oversaw the analysis and interpretation of the data, and drafting of the manuscript.

Corresponding author

Correspondence to Amit Etkin.

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Competing interests

A.E. receives equity and salary from Alto Neuroscience, along with equity from Mindstrong Health, Akili Interactive and Sizung. W.W. and J.G. receive equity and salary from Alto Neuroscience. C.R.M. receives equity from Receptor Life Sciences and consulting income from Otsuka Pharmaceuticals. The remaining authors declare no competing interests.

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Zhang, Y., Wu, W., Toll, R.T. et al. Identification of psychiatric disorder subtypes from functional connectivity patterns in resting-state electroencephalography. Nat Biomed Eng 5, 309–323 (2021). https://doi.org/10.1038/s41551-020-00614-8

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