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<p>Poor inhibitory control and decision-making are often considered as risks for substance use and other adverse psychiatric outcomes. The Stop-Signal Task (SST) is a widely used protocol, from which inhibitory control is indexed by stop signal reaction time (SSRT). However, heretofore models of SSRT may be too simplistic to capture complex processes underlying task performance. In contrast, the Racing Diffusion Ex-Gaussian ABCD&#xa0;(RDEX-ABCD) model provides a more mechanistic framework, capturing both inhibitory control and task-general decision-making processes during the SST. Here, we applied the RDEX-ABCD model to SST data from the IMAGEN cohort (n &gt; 1000) at ages 19 and 23, and examined model parameters in relation to substance use via Elastic Net regression. Connectome-based predictive modeling was then performed to identify brain networks predicting parameters, and the association between these networks and substance use was examined. We found that parameters indexing inhibitory control had no associations with substance use and were only weakly associated with brain connectivity. In contrast, parameters reflecting general decision-making processes – such as efficiency of evidence accumulation, decision threshold (response caution), probability of go failure – and their associated brain activity were significant predictors of cannabis and cigarette use. These findings suggested that efficiency of evidence accumulation, a neurocognitive mechanism that facilitates adaptive decision making across many contexts, emerged as a robust predictor of substance use vulnerability. Overall, general decision-making mechanisms may act as more reliable indicators of vulnerability to substance use than the conventional inhibitory control measures.</p>

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Model-based analysis of stop-signal data reveals robust neural and clinical correlates of evidence accumulation but not inhibition

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月22日

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  • Yihe Weng,
  • Rory Boyle,
  • Chi Tak Lee,
  • Declan Quinn,
  • Clodagh Earley,
  • Maike Splittgerber,
  • Lili Zhang,
  • Luisa Franzen,
  • Tobias Banaschewski,
  • Arun L. W. Bokde,
  • Sylvane Desrivières,
  • Herta Flor,
  • Antoine Grigis,
  • Hugh Garavan,
  • Penny Gowland,
  • Andreas Heinz,
  • Rüdiger Brühl,
  • Jean-Luc Martinot,
  • Marie-Laure Paillère Martinot,
  • Eric Artiges,
  • Jane McGrath,
  • Frauke Nees,
  • Dimitri Papadopoulos Orfanos,
  • Luise Poustka,
  • Nathalie Holz,
  • Sarah Hohmann,
  • Michael N. Smolka,
  • Nilakshi Vaidya,
  • Gunter Schumann,
  • Henrik Walter,
  • Alexander Weigard,
  • Robert Whelan,
  • Frauke Nees,
  • Nathalie Holz,
  • Sarah Hohmann

摘要

Poor inhibitory control and decision-making are often considered as risks for substance use and other adverse psychiatric outcomes. The Stop-Signal Task (SST) is a widely used protocol, from which inhibitory control is indexed by stop signal reaction time (SSRT). However, heretofore models of SSRT may be too simplistic to capture complex processes underlying task performance. In contrast, the Racing Diffusion Ex-Gaussian ABCD (RDEX-ABCD) model provides a more mechanistic framework, capturing both inhibitory control and task-general decision-making processes during the SST. Here, we applied the RDEX-ABCD model to SST data from the IMAGEN cohort (n > 1000) at ages 19 and 23, and examined model parameters in relation to substance use via Elastic Net regression. Connectome-based predictive modeling was then performed to identify brain networks predicting parameters, and the association between these networks and substance use was examined. We found that parameters indexing inhibitory control had no associations with substance use and were only weakly associated with brain connectivity. In contrast, parameters reflecting general decision-making processes – such as efficiency of evidence accumulation, decision threshold (response caution), probability of go failure – and their associated brain activity were significant predictors of cannabis and cigarette use. These findings suggested that efficiency of evidence accumulation, a neurocognitive mechanism that facilitates adaptive decision making across many contexts, emerged as a robust predictor of substance use vulnerability. Overall, general decision-making mechanisms may act as more reliable indicators of vulnerability to substance use than the conventional inhibitory control measures.