Shared and disorder-specific computational mechanisms of interference and response inhibition in schizophrenia and major depressive disorder
- 1School of Psychology, Northwest Normal University, Lanzhou, China
- 2The Third People’s Hospital of Lanzhou, Lanzhou, China
- 3The Third People’s Hospital of Tianshui, Tianshui, China
Abstract
Inhibitory control deficits are consistently observed in both schizophrenia (SCZ) and major depressive disorder (MDD), yet whether these impairments reflect shared tansdiagnostic mechanisms or disorder-specific dysfunction remains poorly understood. We recruited N = 259 participants (n = 87 SCZ, n = 86 MDD, n = 86 healthy controls) who completed Stroop and Go/No-Go tasks assessing interference and response inhibition, respectively. Hierarchical drift-diffusion modeling (HDDM) was used to decompose behavioral performance into latent computational parameters. Both patient groups exhibited significant behavioral impairments across most measures, with SCZ showing greater severity. At the computational level, reduced drift rate—reflecting less efficient evidence accumulation—emerged as a core transdiagnostic deficit across both inhibitory subcomponents and both disorders, with more pronounced reductions in SCZ. By contrast, prolonged non-decision time was disorder-differentiating, being consistently elevated across both tasks in SCZ but, in MDD, confined to response inhibition and not robust to processing-speed adjustment. Within-group clinical correlates further diverged, relating to negative-symptom severity and illness duration in SCZ and to current depressive symptom severity in MDD, although these diagnosis-specific associations were not directly compared across disorders. Machine learning classification corroborated these findings, with drift rate serving as the primary shared contributor to patient–control discrimination (area under the receiver operating characteristic curve [AUC] = 0.762–0.846), and non-decision time on the interference task the top-ranked feature in the SCZ–MDD classifier (AUC = 0.718). Together, these findings delineate shared versus disorder-specific computational signatures of inhibitory control impairment in SCZ and MDD, and suggest that computational phenotyping of inhibitory control may serve as a quantitative adjunct in precision psychiatry. Preregistration: This study was preregistered with the Chinese Clinical Trial Registry prior to data collection (registration number: ChiCTR2500111117; https://www.chictr.org.cn/showproj.html?proj=290089).
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BibTeX
@article{zhang2026shared,
title = {{Shared and disorder-specific computational mechanisms of interference and response inhibition in schizophrenia and major depressive disorder}},
author = {Tongyi Zhang and Sirui Wang and Haoyang Shi and Jia Li and Xiaolong Yang and Jin Jiao and Xiaoning Huo and Xin Zhao},
journal = {Translational Psychiatry},
year = {2026},
month = jul,
doi = {10.1038/s41398-026-04345-3},
url = {https://doi.org/10.1038/s41398-026-04345-3},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Shared and disorder-specific computational mechanisms of interference and response inhibition in schizophrenia and major depressive disorder
AU - Tongyi Zhang
AU - Sirui Wang
AU - Haoyang Shi
AU - Jia Li
AU - Xiaolong Yang
AU - Jin Jiao
AU - Xiaoning Huo
AU - Xin Zhao
PY - 2026
DA - 2026/07/01
JO - Translational Psychiatry
DO - 10.1038/s41398-026-04345-3
UR - https://doi.org/10.1038/s41398-026-04345-3
AB - Inhibitory control deficits are consistently observed in both schizophrenia (SCZ) and major depressive disorder (MDD), yet whether these impairments reflect shared tansdiagnostic mechanisms or disorder-specific dysfunction remains poorly understood. We recruited N = 259 participants (n = 87 SCZ, n = 86 MDD, n = 86 healthy controls) who completed Stroop and Go/No-Go tasks assessing interference and response inhibition, respectively. Hierarchical drift-diffusion modeling (HDDM) was used to decompose behavioral performance into latent computational parameters. Both patient groups exhibited significant behavioral impairments across most measures, with SCZ showing greater severity. At the computational level, reduced drift rate—reflecting less efficient evidence accumulation—emerged as a core transdiagnostic deficit across both inhibitory subcomponents and both disorders, with more pronounced reductions in SCZ. By contrast, prolonged non-decision time was disorder-differentiating, being consistently elevated across both tasks in SCZ but, in MDD, confined to response inhibition and not robust to processing-speed adjustment. Within-group clinical correlates further diverged, relating to negative-symptom severity and illness duration in SCZ and to current depressive symptom severity in MDD, although these diagnosis-specific associations were not directly compared across disorders. Machine learning classification corroborated these findings, with drift rate serving as the primary shared contributor to patient–control discrimination (area under the receiver operating characteristic curve [AUC] = 0.762–0.846), and non-decision time on the interference task the top-ranked feature in the SCZ–MDD classifier (AUC = 0.718). Together, these findings delineate shared versus disorder-specific computational signatures of inhibitory control impairment in SCZ and MDD, and suggest that computational phenotyping of inhibitory control may serve as a quantitative adjunct in precision psychiatry. Preregistration: This study was preregistered with the Chinese Clinical Trial Registry prior to data collection (registration number: ChiCTR2500111117; https://www.chictr.org.cn/showproj.html?proj=290089).
SN - 2158-3188
ER -