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

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.

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Journal
Neuropsychopharmacology
Published
2026-04-22
DOI
https://doi.org/10.1038/s41386-026-02401-6
Primary Topic
Functional Brain Connectivity Studies
Type
article
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article

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

Sylvane Desrivières, Hugh P Garavan, Rüdiger Brühl, Maike Splittgerber et al.
Neuropsychopharmacology
Functional Brain Connectivity Studies
article

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

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

Abstract

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.

Neuropsychopharmacology
University of Vermont (US), Physikalisch-Technische Bundesanstalt (DE), Centre National de la Recherche Scientifique (FR), University of Nottingham (GB), Inserm (FR), Universität Hamburg (DE), University of Mannheim (DE), King's College London (GB), Trinity College Dublin (IE), Heidelberg University (DE), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Université Paris-Saclay (FR), University of Michigan (US), University Hospital Heidelberg (DE), Maastricht University (NL), Humboldt-Universität zu Berlin (DE), Sorbonne Université (FR), Assistance Publique – Hôpitaux de Paris (FR), Central Institute of Mental Health (DE), University Medical Center Hamburg-Eppendorf (DE), German Center for Infection Research (DE), Pitié-Salpêtrière Hospital (FR), CEA Paris-Saclay (FR), GFZ Helmholtz Centre for Geosciences (DE), Huashan Hospital (CN), Dublin City University (IE), European Telecommunications Standards Institute (FR), Freie Universität Berlin (DE), Technische Universität Dresden (DE), Charité - Universitätsmedizin Berlin (DE), University of Tübingen (DE), Ludwig-Maximilians-Universität München (DE)
Good health and well-being
Openalex Percentile: Top 10%
Functional Brain Connectivity Studies
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