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Reply to: Towards increasing the clinical applicability of machine learning biomarkers in psychiatry

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The Original Article was published on 05 April 2021

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Fig. 1: Distribution of classification accuracies obtained with random permutations of group membership labels.
Fig. 2: Discriminating locations produced by varying the number of voxels included from each participant.

Data availability

No new data are used in this response; all data are available in the original publication upon reasonable request from the corresponding author.

Code availability

The custom computer code that was used in the main analysis of this study is available at https://doi.org/10.5281/zenodo.4549106.

References

  1. Dukart, J., Weis, S., Genon, S. & Eickhoff, S. B. Towards increasing the clinical applicability of machine learning biomarkers in psychiatry. Nat. Hum. Behav. https://doi.org/10.1038/s41562-021-01086-9 (2021).

  2. 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 

  3. Just, M. A., Cherkassky, V. L., Buchweitz, A., Keller, T. & Mitchell, T. M. Identifying autism from neural representations of social interactions: neurocognitive markers of autism. PLoS ONE https://doi.org/10.1371/journal.pone.0113879 (2014).

  4. Just, M. A., Cherkassky, V. L., Aryal, S. & Mitchell, T. M. A neurosemantic theory of concrete noun representation based on the underlying brain codes. PLoS ONE 5, e8622 (2010).

    Article  Google Scholar 

  5. Kassam, K. S., Markey, A. R., Cherkassky, V. L., Loewenstein, G. & Just, M. A. Identifying emotions on the basis of neural activation. PLoS ONE 8, e66032 (2013).

    Article  CAS  Google Scholar 

  6. Mason, R. A. & Just, M. A. Neural representations of physics concepts. Psychol. Sci. 27, 904–913 (2016).

    Article  Google Scholar 

  7. Wang, J., Cherkassky, V. L. & Just, M. A. Predicting the brain activation pattern associated with the propositional content of a sentence: modeling neural representations of events and states. Hum. Brain Mapp. 38, 4865–4881 (2017).

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. Vargas, R. & Just, M. A. Neural representations of abstract concepts: identifying underlying neurosemantic dimensions. Cereb. Cortex 9, 2157–2166 (2019).

    Google Scholar 

  10. Bauer, A. J. & Just, M. A. A brain-based account of “basic-level” concepts. NeuroImage 161, 196–205 (2017).

    Article  Google Scholar 

  11. Yang, Y., Wang, J., Bailer, C., Cherkassky, V. L. & Just, M. A. Commonalities and differences in the neural representations of English, Portuguese, and Mandarin sentences: when knowledge of the brain-language mappings for two languages is better than one. Brain Lang. 175, 77–85 (2017).

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA

    Marcel Adam Just & Vladimir L. Cherkassky

  2. Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    David Brent

Authors
  1. Marcel Adam Just
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  2. Vladimir L. Cherkassky
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  3. David Brent
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Contributions

The data were analysed by M.A.J. and V.L.C. The paper was written by M.A.J., V.L.C. and D.B.

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Correspondence to Marcel Adam Just.

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The authors declare no competing interests.

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Peer review information Nature Human Behaviour thanks Barry Horwitz and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Information

Supplementary Discussion, Supplementary Fig. 1 and Supplementary References.

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Just, M.A., Cherkassky, V.L. & Brent, D. Reply to: Towards increasing the clinical applicability of machine learning biomarkers in psychiatry. Nat Hum Behav 5, 433–435 (2021). https://doi.org/10.1038/s41562-021-01086-9

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