Treatment response prediction and individualized identification of first-episode drug-naïve schizophrenia using brain functional connectivity

Abstract

Identifying biomarkers in schizophrenia during the first episode without the confounding effects of treatment has been challenging. Leveraging these biomarkers to establish diagnosis and make individualized predictions of future treatment responses to antipsychotics would be of great value, but there has been limited progress. In this study, by using machine learning algorithms and the functional connections of the superior temporal cortex, we successfully identified the first-episode drug-naive (FEDN) schizophrenia patients (accuracy 78.6%) and predict their responses to antipsychotic treatment (accuracy 82.5%) at an individual level. The functional connections (FC) were derived using the mutual information and the correlations, between the blood-oxygen-level dependent signals of the superior temporal cortex and other cortical regions acquired with the resting-state functional magnetic resonance imaging. We also found that the mutual information and correlation FC was informative in identifying individual FEDN schizophrenia and prediction of treatment response, respectively. The methods and findings in this paper could provide a critical step toward individualized identification and treatment response prediction in first-episode drug-naive schizophrenia, which could complement other biomarkers in the development of precision medicine approaches for this severe mental disorder.

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Acknowledgements

Supported in part by the NARSAD Young Investigator Grant of the Brain & Behavior Research Foundation (B.C.), the Michael E. Debakey VA Medical Center and the Beth K. and Stuart C. Yudofsky Division of Neuropsychiatry, Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine in Houston, TX (R.Y.C.), and NIMH grant R01 085667, the Dunn Research Foundation and the Pat Rutherford, Jr. Endowed Chair in Psychiatry (J.C.S.).

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Correspondence to Xiang Yang Zhang.

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B.C., R.Y.C., D.C., M.X., L.W., and X.Y.Z. reported no biomedical financial interests or potential conflicts of interest. J.C.S. has received grants/research support from Forrest, BMS, J&J, Merck, Stanley Medical Research Institute, NIH and has been a speaker for Pfizer and Abbott.

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Cao, B., Cho, R.Y., Chen, D. et al. Treatment response prediction and individualized identification of first-episode drug-naïve schizophrenia using brain functional connectivity. Mol Psychiatry 25, 906–913 (2020). https://doi.org/10.1038/s41380-018-0106-5

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Keywords

  • support vector machines
  • SVM

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