Trajectories and predictors of response to social cognition training in people with schizophrenia: A proof-of-concept machine learning study

Kathleen Miley, Michael V. Bronstein, Sisi Ma, Hyunkyu Lee, Michael F. Green, Joseph Ventura, Christine I. Hooker, Mor Nahum, Sophia Vinogradov

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Social cognition training (SCT) can improve social cognition deficits in schizophrenia. However, little is known about patterns of response to SCT or individual characteristics that predict response. Methods: 76 adults with schizophrenia randomized to receive 8–12 weeks of remotely-delivered SCT were included in this analysis. Social cognition was measured with a composite of six assessments. Latent class growth analyses identified trajectories of social cognitive response to SCT. Random forest and logistic regression models were trained to predict membership in the trajectory group that showed improvement from baseline measures including symptoms, functioning, motivation, and cognition. Results: Five trajectory groups were identified: Group 1 (29 %) began with slightly above average social cognition, and this ability significantly improved with SCT. Group 2 (9 %) had baseline social cognition approximately one standard deviation above the sample mean and did not improve with training. Groups 3 (18 %) and 4 (36 %) began with average to slightly below-average social cognition and showed non-significant trends toward improvement. Group 5 (8 %) began with social cognition approximately one standard deviation below the sample mean, and experienced significant deterioration in social cognition. The random forest model had the best performance, predicting Group 1 membership with an area under the curve of 0.73 (SD 0.24; 95 % CI [0.51–0.87]). Conclusions: Findings suggest that there are distinct patterns of response to SCT in schizophrenia and that those with slightly above average social cognition at baseline may be most likely to experience gains. Results may inform future research seeking to individualize SCT treatment for schizophrenia.

Original languageEnglish
Pages (from-to)92-99
Number of pages8
JournalSchizophrenia Research
Volume266
DOIs
StatePublished - Apr 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2024 Elsevier B.V.

Funding

Research reported in this publication was supported by the National Institute of Mental Health (NIMH) Award R44MH091793 and by the National Center for Advancing Translational Sciences of the National Institutes of Health (Award Number UL1-TR002494 ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

FundersFunder number
National Institutes of HealthUL1-TR002494
National Institute of Mental HealthR44MH091793
National Center for Advancing Translational Sciences

    Keywords

    • Cognitive remediation
    • Machine learning
    • Precision medicine
    • Psychosis
    • Treatment response

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