Skip to main content

Thank you for visiting 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.

Towards increasing the clinical applicability of machine learning biomarkers in psychiatry

Matters Arising to this article was published on 05 April 2021

The Original Article was published on 30 October 2017

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.


All prices are NET prices.

Fig. 1: Effects of parameter choices on the accuracy for differentiation between suicide ideators and controls.


  1. Egger, H. L. et al. Test–retest reliability of the Preschool Age Psychiatric Assessment (PAPA). J. Am. Acad. Child Adolesc. Psychiatry 45, 538–549 (2006).

    Article  Google Scholar 

  2. Hyman, S. E. The diagnosis of mental disorders: the problem of reification. Annu. Rev. Clin. Psychol. 6, 155–179 (2010).

    Article  Google Scholar 

  3. Bzdok, D. & Meyer-Lindenberg, A. Machine learning for precision psychiatry: opportunities and challenges. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 3, 223–230 (2018).

    PubMed  Google Scholar 

  4. Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44–56 (2019).

    Article  CAS  Google Scholar 

  5. Just, M. A. et al. Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth. Nat. Hum. Behav. 1, 911–919 (2017).

    Article  Google Scholar 

  6. Cui, Z. & Gong, G. The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features. NeuroImage 178, 622–637 (2018).

    Article  Google Scholar 

  7. Varoquaux, G. Cross-validation failure: small sample sizes lead to large error bars. NeuroImage 180, 68–77 (2018).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations



J.D. and S.B.E. designed the work and wrote the manuscript. S.G. and S.W. contributed to interpretation and substantively revised the manuscript.

Corresponding author

Correspondence to Simon B. Eickhoff.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Human Behaviour thanks the anonymous reviewers for their contribution to the peer review of this work.

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

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Dukart, J., Weis, S., Genon, S. et al. Towards increasing the clinical applicability of machine learning biomarkers in psychiatry. Nat Hum Behav 5, 431–432 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


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