Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

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

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.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

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.

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.

References

  1. Kessler, R. C. et al. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch. Gen. Psychiatry 62, 593–602 (2005).

    Article  PubMed  Google Scholar 

  2. Belmaker, R. & Agam, G. Major depressive disorder. N. Engl. J. Med. 358, 55–68 (2008).

    Article  CAS  PubMed  Google Scholar 

  3. Hawco, C. et al. Separable and replicable neural strategies during social brain function in people with and without severe mental illness. Am. J. Psychiatry 176, 521–530 (2019).

    Article  PubMed  Google Scholar 

  4. Etkin, A. A reckoning and research agenda for neuroimaging in psychiatry. Am. J. Psychiatry 176, 507–511 (2019).

    Article  PubMed  Google Scholar 

  5. Etkin, A. Addressing the causality gap in human psychiatric neuroscience. JAMA Psychiatry 75, 3–4 (2018).

    Article  PubMed  Google Scholar 

  6. Etkin, A. & Wager, T. D. Functional neuroimaging of anxiety: a meta-analysis of emotional processing in PTSD, social anxiety disorder, and specific phobia. Am. J. Psychiatry 164, 1476–1488 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Wu, W. et al. An electroencephalographic signature predicts antidepressant response in major depression. Nat. Biotechnol. 38, 439–447 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Fonzo, G. et al. Brain regulation of emotional conflict predicts antidepressant treatment response for depression. Nat. Hum. Behav. 3, 1319–1331 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Chin Fatt, C. R. et al. Effect of intrinsic patterns of functional brain connectivity in moderating antidepressant treatment response in major depression. Am. J. Psychiatry 177, 143–154 (2019).

    Article  PubMed  Google Scholar 

  10. Berg, A. O. Treatment of Posttraumatic Stress Disorder: An Assessment of The Evidence (National Academies Press, 2008).

  11. Etkin, A. et al. Using fMRI connectivity to define a treatment-resistant form of post-traumatic stress disorder. Sci. Transl. Med. 11, eaal3236 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Wolfers, T. et al. Mapping the heterogeneous phenotype of schizophrenia and bipolar disorder using normative models. JAMA Psychiatry 75, 1146–1155 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Marquand, A. F. et al. Beyond lumping and splitting: a review of computational approaches for stratifying psychiatric disorders. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 1, 433–447 (2016).

    PubMed  PubMed Central  Google Scholar 

  14. Poulakis, K. et al. Heterogeneous patterns of brain atrophy in Alzheimer’s disease. Neurobiol. Aging 65, 98–108 (2018).

    Article  PubMed  Google Scholar 

  15. Orban, P. et al. Subtypes of functional brain connectivity as early markers of neurodegeneration in Alzheimer’s disease. Preprint at https://doi.org/10.1101/195164 (2017).

  16. Wu, M.-J. et al. Identification and individualized prediction of clinical phenotypes in bipolar disorders using neurocognitive data, neuroimaging scans and machine learning. NeuroImage 145, 254–264 (2017).

    Article  PubMed  Google Scholar 

  17. Karalunas, S. L. et al. Subtyping attention-deficit/hyperactivity disorder using temperament dimensions: toward biologically based nosologic criteria. JAMA Psychiatry 71, 1015–1024 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Fonzo, G. A. et al. PTSD psychotherapy outcome predicted by brain activation during emotional reactivity and regulation. Am. J. Psychiatry 174, 1163–1174 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Maron-Katz, A. et al. Individual patterns of abnormality in resting-state functional connectivity reveal two data-driven PTSD subgroups. Am. J. Psychiatry 177, 244–253 (2020).

    Article  PubMed  Google Scholar 

  20. Palva, S. & Palva, J. M. Discovering oscillatory interaction networks with M/EEG: challenges and breakthroughs. Trends Cogn. Sci. 16, 219–230 (2012).

    Article  PubMed  Google Scholar 

  21. Schoffelen, J. M. & Gross, J. Source connectivity analysis with MEG and EEG. Hum. Brain Mapp. 30, 1857–1865 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  22. He, B. et al. Electrophysiological brain connectivity: theory and implementation. IEEE Trans. Biomed. Eng. 66, 2115–2137 (2019).

    Article  Google Scholar 

  23. Brunner, C. et al. Volume conduction influences scalp-based connectivity estimates. Front. Comput. Neurosci. 10, 121 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  24. O’Neill, G. C. et al. Measuring electrophysiological connectivity by power envelope correlation: a technical review on MEG methods. Phys. Med. Biol. 60, R271 (2015).

    Article  PubMed  Google Scholar 

  25. Brookes, M. J., Woolrich, M. W. & Barnes, G. R. Measuring functional connectivity in MEG: a multivariate approach insensitive to linear source leakage. NeuroImage 63, 910–920 (2012).

    Article  CAS  PubMed  Google Scholar 

  26. Siems, M., Pape, A.-A., Hipp, J. F. & Siegel, M. Measuring the cortical correlation structure of spontaneous oscillatory activity with EEG and MEG. NeuroImage 129, 345–355 (2016).

    Article  PubMed  Google Scholar 

  27. Toll, R. et al. An electroencephalography connectomic profile of post-traumatic stress disorder. Am. J. Psychiatry 177, 233–243 (2020).

    Article  PubMed  Google Scholar 

  28. Colclough, G. L. et al. How reliable are MEG resting-state connectivity metrics? NeuroImage 138, 284–293 (2016).

    Article  CAS  PubMed  Google Scholar 

  29. Witten, D. M. & Tibshirani, R. A framework for feature selection in clustering. J. Am. Stat. Assoc. 105, 713–726 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Kass, R. E. & Raftery, A. Bayes factors. J. Am. Stat. Assoc. 90, 773–795 (1995).

    Article  Google Scholar 

  31. Tibshirani, R., Walther, G. & Hastie, T. Estimating the number of clusters in a data set via the gap statistic. J. R. Stat. Soc. B 63, 411–423 (2001).

    Article  Google Scholar 

  32. Price, R. B. et al. Parsing heterogeneity in the brain connectivity of depressed and healthy adults during positive mood. Biol. Psychiatry 81, 347–357 (2017).

    Article  PubMed  Google Scholar 

  33. Lanius, R., Bluhm, R., Lanius, U. & Pain, C. A review of neuroimaging studies in PTSD: heterogeneity of response to symptom provocation. J. Psychiatry Res. 40, 709–729 (2006).

    Article  CAS  Google Scholar 

  34. Williams, L. M. et al. Childhood trauma predicts antidepressant response in adults with major depression: data from the randomized international study to predict optimized treatment for depression. Transl. Psychiatry 6, e799 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Liston, C. et al. Default mode network mechanisms of transcranial magnetic stimulation in depression. Biol. Psychiatry 76, 517–526 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Hipp, J. F. et al. Large-scale cortical correlation structure of spontaneous oscillatory activity. Nat. Neurosci. 15, 884–890 (2012).

    Article  CAS  PubMed  Google Scholar 

  37. Brookes, M. J. et al. Investigating the electrophysiological basis of resting state networks using magnetoencephalography. Proc. Natl Acad. Sci. USA 108, 16783–16788 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Mantini, D. et al. Electrophysiological signatures of resting state networks in the human brain. Proc. Natl Acad. Sci. USA 104, 13170–13175 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Cabral, J. et al. Exploring mechanisms of spontaneous functional connectivity in MEG: how delayed network interactions lead to structured amplitude envelopes of band-pass filtered oscillations. NeuroImage 90, 423–435 (2014).

    Article  PubMed  Google Scholar 

  40. Deco, G. et al. Single or multiple frequency generators in on-going brain activity: a mechanistic whole-brain model of empirical MEG data. NeuroImage 152, 538–550 (2017).

    Article  PubMed  Google Scholar 

  41. Drysdale, A. T. et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat. Med. 23, 28–38 (2017).

    Article  CAS  PubMed  Google Scholar 

  42. Dichter, G. S., Gibbs, D. & Smoski, M. J. A systematic review of relations between resting-state functional-MRI and treatment response in major depressive disorder. J. Affect. Disord. 172, 8–17 (2015).

    Article  PubMed  Google Scholar 

  43. Klumpp, H. et al. Resting state amygdala-prefrontal connectivity predicts symptom change after cognitive behavioral therapy in generalized social anxiety disorder. Biol. Mood Anxiety Disord. 4, 14 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Kumari, V. et al. Dorsolateral prefrontal cortex activity predicts responsiveness to cognitive-behavioral therapy in schizophrenia. Biol. Psychiatry 66, 594–602 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Reggente, N. et al. Multivariate resting-state functional connectivity predicts response to cognitive behavioral therapy in obsessive–compulsive disorder. Proc. Natl Acad. Sci. USA 115, 2222–2227 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Qin, J. et al. Predicting clinical responses in major depression using intrinsic functional connectivity. NeuroReport 26, 675–680 (2015).

    Article  CAS  PubMed  Google Scholar 

  47. Goldstein-Piekarski, A. N. et al. Intrinsic functional connectivity predicts remission on antidepressants: a randomized controlled trial to identify clinically applicable imaging biomarkers. Transl. Psychiatry 8, 57 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Spies, M. et al. Default mode network deactivation during emotion processing predicts early antidepressant response. Transl. Psychiatry 7, e1008 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Wang, Q. et al. Identification of major depressive disorder and prediction of treatment response using functional connectivity between the prefrontal cortices and subgenual anterior cingulate: a real-world study. J. Affect. Disord. 252, 365–372 (2019).

    Article  PubMed  Google Scholar 

  50. Olbrich, S. et al. Functional connectivity in major depression: increased phase synchronization between frontal cortical EEG-source estimates. Psychiatry Res. Neuroimaging 222, 91–99 (2014).

    Article  Google Scholar 

  51. George, M. S. et al. Daily repetitive transcranial magnetic stimulation (rTMS) improves mood in depression. NeuroReport 6, 1853–1856 (1995).

    Article  CAS  PubMed  Google Scholar 

  52. Voigt, J., Carpenter, L. & Leuchter, A. Cost effectiveness analysis comparing repetitive transcranial magnetic stimulation to antidepressant medications after a first treatment failure for major depressive disorder in newly diagnosed patients—a lifetime analysis. PLoS ONE 12, e0186950 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Nguyen, K.-H. & Gordon, L. G. Cost-effectiveness of repetitive transcranial magnetic stimulation versus antidepressant therapy for treatment-resistant depression. Value Health 18, 597–604 (2015).

    Article  PubMed  Google Scholar 

  54. O’Reardon, J. P. et al. Efficacy and safety of transcranial magnetic stimulation in the acute treatment of major depression: a multisite randomized controlled trial. Biol. Psychiatry 62, 1208–1216 (2007).

    Article  PubMed  Google Scholar 

  55. George, M. S. et al. Daily left prefrontal transcranial magnetic stimulation therapy for major depressive disorder: a sham-controlled randomized trial. Arch. Gen. Psychiatry 67, 507–516 (2010).

    Article  PubMed  Google Scholar 

  56. Srinivasan, R., Tucker, D. M. & Murias, M. Estimating the spatial Nyquist of the human EEG. Behav. Res. Meth. Instrum. Comput. 30, 8–19 (1998).

    Article  Google Scholar 

  57. Palva, J. M. et al. Ghost interactions in MEG/EEG source space: a note of caution on inter-areal coupling measures. NeuroImage 173, 632–643 (2018).

    Article  PubMed  Google Scholar 

  58. Olejarczyk, E., Marzetti, L., Pizzella, V. & Zappasodi, F. Comparison of connectivity analyses for resting state EEG data. J. Neural Eng. 14, 036017 (2017).

    Article  PubMed  Google Scholar 

  59. Nolte, G. et al. Identifying true brain interaction from EEG data using the imaginary part of coherency. Clin. Neurophysiol. 115, 2292–2307 (2004).

    Article  PubMed  Google Scholar 

  60. Vinck, M. et al. An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. NeuroImage 55, 1548–1565 (2011).

    Article  PubMed  Google Scholar 

  61. Geweke, J. F. Measures of conditional linear dependence and feedback between time series. J. Am. Stat. Assoc. 79, 907–915 (1984).

    Article  Google Scholar 

  62. Astolfi, L. et al. Estimation of the effective and functional human cortical connectivity with structural equation modeling and directed transfer function applied to high-resolution EEG. Magn. Reson. Imaging 22, 1457–1470 (2004).

    Article  PubMed  Google Scholar 

  63. Astolfi, L. et al. Comparison of different cortical connectivity estimators for high-resolution EEG recordings. Hum. Brain Mapp. 28, 143–157 (2007).

    Article  PubMed  Google Scholar 

  64. O’Neill, G. C. et al. Dynamics of large-scale electrophysiological networks: a technical review. NeuroImage 180, 559–576 (2018).

    Article  PubMed  Google Scholar 

  65. Weathers, F. W. et al. The Clinician-Administered PTSD Scale for DSM–5 (CAPS-5): development and initial psychometric evaluation in military veterans. Psychol. Assess. 30, 383–395 (2018).

    Article  PubMed  Google Scholar 

  66. First, M. B. in The Encyclopedia of Clinical Psychology (eds Cautin, R. L. & Lilienfeld, S. O.) 1–6 (Wiley, 2014).

  67. Mullen, T. CleanLine: Tool/Resource Info (NeuroImaging Tools and Resources Collaboratory, 2012); https://www.nitrc.org/projects/cleanline

  68. Perrin, F., Pernier, J., Bertrand, O. & Echallier, J. Spherical splines for scalp potential and current density mapping. Electroencephalogr. Clin. Neurophysiol. 72, 184–187 (1989).

    Article  CAS  PubMed  Google Scholar 

  69. Bell, A. J. & Sejnowski, T. J. An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 7, 1129–1159 (1995).

    Article  CAS  PubMed  Google Scholar 

  70. Glover, G. H. et al. Function biomedical informatics research network recommendations for prospective multicenter functional MRI studies. J. Magn. Reson. Imaging 36, 39–54 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Esteban, O. et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat. Methods 16, 111–116 (2019).

    Article  CAS  PubMed  Google Scholar 

  72. Jenkinson, M. et al. FSL. NeuroImage 62, 782–790 (2012).

    Article  PubMed  Google Scholar 

  73. Foa, E. B. et al. A comparison of exposure therapy, stress inoculation training, and their combination for reducing posttraumatic stress disorder in female assault victims. J. Consult. Clin. Psychol. 67, 194–200 (1999).

    Article  CAS  PubMed  Google Scholar 

  74. Resick, P. A. Cognitive therapy for posttraumatic stress disorder. J. Cogn. Psychother. 15, 321–329 (2001).

    Article  Google Scholar 

  75. Trivedi, M. H. et al. Establishing moderators and biosignatures of antidepressant response in clinical care (EMBARC): rationale and design. J. Psychiatry Res. 78, 11–23 (2016).

    Article  Google Scholar 

  76. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013).

  77. van Buuren, S. & Groothuis-Oudshoorn, K. Mice: multivariate imputation by chained equations in R. J. Stat. Softw. 45, 1–67 (2011).

    Google Scholar 

  78. Donse, L. et al. Simultaneous rTMS and psychotherapy in major depressive disorder: clinical outcomes and predictors from a large naturalistic study. Brain Stimul. 11, 337–345 (2018).

    Article  PubMed  Google Scholar 

  79. Krepel, N. et al. Non-replication of neurophysiological predictors of non-response to rTMS in depression and neurophysiological data-sharing proposal. Brain Stimul. 11, 639–641 (2018).

    Article  PubMed  Google Scholar 

  80. Arns, M., Drinkenburg, W. H., Fitzgerald, P. B. & Kenemans, J. L. Neurophysiological predictors of non-response to rTMS in depression. Brain Stimul. 5, 569–576 (2012).

    Article  PubMed  Google Scholar 

  81. Mir-Moghtadaei, A. et al. Concordance between BeamF3 and MRI-neuronavigated target sites for repetitive transcranial magnetic stimulation of the left dorsolateral prefrontal cortex. Brain Stimul. 8, 965–973 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Beck, A. T. The current state of cognitive therapy: a 40-year retrospective. Arch. Gen. Psychiatry 62, 953–959 (2005).

    Article  PubMed  Google Scholar 

  83. Lovibond, P. F. & Lovibond, S. H. The structure of negative emotional states: comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behav. Res. Ther. 33, 335–343 (1995).

    Article  CAS  PubMed  Google Scholar 

  84. Hauk, O. Keep it simple: a case for using classical minimum norm estimation in the analysis of EEG and MEG data. NeuroImage 21, 1612–1621 (2004).

    Article  PubMed  Google Scholar 

  85. Gramfort, A., Papadopoulo, T., Olivi, E. & Clerc, M. OpenMEEG: opensource software for quasistatic bioelectromagnetics. Biomed. Eng. Online 9, 45 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Fischl, B. FreeSurfer. NeuroImage 62, 774–781 (2012).

    Article  PubMed  Google Scholar 

  87. Hämäläinen, M. MNE Software User’s Guide 59–75 (NMR Centre, Mass General Hospital, Harvard Univ., 2005).

  88. Lin, F.-H. et al. Assessing and improving the spatial accuracy in MEG source localization by depth-weighted minimum-norm estimates. NeuroImage 31, 160–171 (2006).

    Article  PubMed  Google Scholar 

  89. Chen, A. C. et al. Causal interactions between fronto-parietal central executive and default-mode networks in humans. Proc. Natl Acad. Sci. USA 110, 19944–19949 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Tibshirani, R., Wainwright, M. & Hastie, T. Statistical Learning with Sparsity: The Lasso and Generalizations (Chapman and Hall/CRC, 2015).

  91. Caliński, T. & Harabasz, J. A dendrite method for cluster analysis. Commun. Stat. Theory Methods 3, 1–27 (1974).

    Article  Google Scholar 

  92. Tipping, M. E. Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 1, 211–244 (2001).

    Google Scholar 

  93. Zhu, H. et al. Multivariate classification of earthquake survivors with post‐traumatic stress disorder based on large‐scale brain networks. Acta Psychiatr. Scand. 141, 285–298 (2020).

    Article  CAS  PubMed  Google Scholar 

Download references

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

Authors and Affiliations

Authors

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.

Ethics declarations

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.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary figures, tables, discussion and references.

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41551-020-00614-8

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing