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Cited by 5Schizophrenia research and treatmentCognitive Abilities and TestingNeural and Behavioral Psychology Studies

Rethinking the effects of working memory training on executive functions in schizophrenia: A machine learning approach

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Zhang, Tongyi, Su, Meifang, Huo, Xiaoning, Zhao, Xin·2025·International Journal of Clinical and Health Psychology
DOI 10.1016/j.ijchp.2025.100628PMC PMC12513198
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Abstract

Executive dysfunction in schizophrenia profoundly impairs functional outcomes and remains insufficiently addressed by standard pharmacological treatments. While computerized cognitive training offers promise, traditional evaluation methods often fail to capture nuanced improvements along the psychosis-health continuum. This study aims to quantify executive function (EF) profile changes following working memory training and identify robust baseline predictors of treatment response. Ninety-four schizophrenia patients were randomized to adaptive N -back training ( n = 32), non-adaptive 1-back control ( n = 33), or treatment-as-usual ( n = 29). EF was assessed across working memory, cognitive flexibility, and inhibitory control domains. A support vector machine classifier, trained on an independent sample (195 patients, 169 controls) and calibrated via Platt scaling, quantified EF profile changes. An exploratory framework based on Granger causality principles identified baseline treatment predictors. Adaptive training produced significant near-transfer effects on untrained working memory tasks and reduced general psychopathology ( p fdr < 0.05), but no far-transfer effects to other EF domains. The probability of neurotypical EF classification increased substantially in the adaptive group (13.21 % to 38.79 %, p < 0.001), correlating with symptom reduction. Working memory maintenance and response inhibition emerged as the most robust baseline predictors of treatment response. Working memory training induces meaningful shifts in EF profiles in schizophrenia, promoting movement along the psychosis-health continuum toward neurotypical functioning. The classifier-based approach provides a more refined assessment compared to traditional binary measures, while the exploratory framework identifies specific EF domains predicting treatment response with potential causal relevance. These findings warrant validation through larger, multi-center trials with extended follow-up periods.

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  • Clinical conditions are heterogeneous, and emerging evidence suggests that transfer patterns may vary across etiologies.

    “This question is especially important given the heterogeneity of clinical conditions and emerging evidence that transfer patterns may vary across etiologies (Zhang et al., 2025).”

    From The asymmetry of working memory training transfer: A systematic review and meta-analysis (2026) · cites this paper as Zhang et al., 2025

    The Introduction describes heterogeneity in intervention effects within schizophrenia, and the Discussion reports near-transfer from working-memory training but no far-transfer to other executive-function domains. The paper does not compare clinical etiologies or say that transfer patterns vary across them; its findings are limited to patients with schizophrenia. (LLM, conf=0.83)

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Research works printed but not linked: 1

  1. Akiba T. Sano S. Yanase T. Ohta T. Koyama M. A next-generation hyperparameter optimization framework Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2019 2623 2631

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Authors
Zhang, T., Su, M., Huo, X., & Zhao, X.
Affiliations
Northwest Normal University (CN)
Corresponding author
Zhao, Xin
ORCID
Venue
International Journal of Clinical and Health Psychology
Publisher
Elsevier BV
Published
2025-10
Volume
25
Issue
4
Pages
100628
ISSN
1697-2600 (ISSN-L)
DOI
10.1016/j.ijchp.2025.100628
PMC
PMC12513198
OpenAlex
W4414826936
Cited by
3
References
73
Citations by year
2026: 3
FWCI
2.01
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Clinical and Health Psychology (publishedVersion, cc-by-nc-nd)
  • SSRN Electronic Journal (acceptedVersion)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
National Natural Science Foundation of China (32260207)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Abilities and Testing
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
Keywords
schizophrenia, working memory training, executive function, computerized cognitive training, treatment response prediction, response inhibition, cognitive flexibility, inhibitory control, support vector machine classification, Granger causality, baseline predictors

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