An fMRI-based brain marker of individual differences in delay discounting

Leonie Koban (), Sangil Lee, Daniela Schelski, Marie-Christine Simon, Caryn Lerman, Bernd Weber, Joseph Kable and Hilke Plassmann
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Leonie Koban: CRNL - Centre de recherche en neurosciences de Lyon - Lyon Neuroscience Research Center - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - UJM - Université Jean Monnet - Saint-Étienne - Université de Lyon - INSERM - Institut National de la Santé et de la Recherche Médicale - CNRS - Centre National de la Recherche Scientifique, ICM - Institut du Cerveau = Paris Brain Institute - AP-HP - Assistance publique - Hôpitaux de Paris (AP-HP) - INSERM - Institut National de la Santé et de la Recherche Médicale - CHU Pitié-Salpêtrière [AP-HP] - AP-HP - Assistance publique - Hôpitaux de Paris (AP-HP) - SU - Sorbonne Université - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique, INSEAD - Institut Européen d'administration des Affaires
Sangil Lee: University of Pennsylvania
Daniela Schelski: Universität Bonn = University of Bonn, Universität Bonn = University of Bonn
Marie-Christine Simon: Universität Bonn = University of Bonn
Caryn Lerman: USC - University of Southern California
Bernd Weber: Universität Bonn = University of Bonn, Universität Bonn = University of Bonn
Joseph Kable: University of Pennsylvania
Hilke Plassmann: INSEAD - Institut Européen d'administration des Affaires, ICM - Institut du Cerveau = Paris Brain Institute - AP-HP - Assistance publique - Hôpitaux de Paris (AP-HP) - INSERM - Institut National de la Santé et de la Recherche Médicale - CHU Pitié-Salpêtrière [AP-HP] - AP-HP - Assistance publique - Hôpitaux de Paris (AP-HP) - SU - Sorbonne Université - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique

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Abstract: Individual differences in delay discounting-how much we discount future compared to immediate rewards-are associated with general life outcomes, psychopathology, and obesity. Here, we use machine learning on fMRI activity during an intertemporal choice task to develop a functional brain marker of these individual differences in human adults. Training and cross-validating the marker in one dataset (Study 1, N = 110 male adults) resulted in a significant prediction-outcome correlation (r = 0.49), generalized to predict individual differences in a completely independent dataset (Study 2: N = 145 male and female adults, r = 0.45), and predicted discounting several weeks later. Out-of-sample responses of the functional brain marker, but not discounting behavior itself, differed significantly between overweight and lean individuals in both studies, and predicted fasting-state blood levels of insulin, c-peptide, and leptin in Study 1. Significant predictive weights of the marker were found in cingulate, insula, and frontoparietal areas, among others, suggesting an interplay among regions associated with valuation, conflict processing, and cognitive control. This new functional brain marker is a step toward a generalizable brain model of individual differences in delay discounting. Future studies can evaluate it as a potential transdiagnostic marker of altered decision-making in different clinical and developmental populations.SIGNIFICANCE STATEMENT People differ substantially in how much they prefer smaller sooner rewards or larger later rewards such as spending money now versus saving it for retirement. These individual differences are generally stable over time and have been related to differences in mental and bodily health. What is their neurobiological basis? We applied machine learning to brain-imaging data to identify a novel brain activity pattern that accurately predicts how much people prefer sooner versus later rewards, and which can be used as a new brain-based measure of intertemporal decision-making in future studies. The resulting functional brain marker also predicts overweight and metabolism-related blood markers, providing new insight into the possible links between metabolism and the cognitive and brain processes involved in intertemporal decision-making.

Keywords: fMRI; decision-making; prediction; individual differences; delay discounting; intertemporal choices; machine-learning; brain model; brain signature (search for similar items in EconPapers)
Date: 2023
Note: View the original document on HAL open archive server: https://hal.science/hal-03957745v1
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Published in Journal of Neuroscience, 2023, 43 (9), pp.1600-1613. ⟨10.1523/JNEUROSCI.1343-22.2022⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03957745

DOI: 10.1523/JNEUROSCI.1343-22.2022

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