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Measuring Delay Discounting in Humans Using an Adjusting Amount Task
Published on: January 9, 2016
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An fMRI-Based Brain Marker of Individual Differences in Delay Discounting
Leonie Koban1,2,3, Sangil Lee4, Daniela S Schelski5,6
1Marketing Area, INSEAD, F-77300 Fontainebleau, France leonie.koban@cnrs.fr.
Summary
Individual differences in delay discounting, or valuing future rewards, were predicted using a novel functional brain marker derived from fMRI data. This marker also correlated with body weight and metabolic factors, offering insights into decision-making and health.
Area of Science:
- Neuroscience
- Cognitive Psychology
- Behavioral Economics
Background:
- Individual differences in delay discounting, the preference for immediate over future rewards, are linked to life outcomes, psychopathology, and obesity.
- Understanding the neurobiological basis of these individual differences is crucial for developing targeted interventions.
Purpose of the Study:
- To develop and validate a machine learning-based functional brain marker for individual differences in delay discounting using fMRI data.
- To assess the generalizability and predictive validity of the brain marker in independent datasets.
- To explore the relationship between the brain marker, body weight, and metabolic indicators.
Main Methods:
- Machine learning algorithms were applied to fMRI data acquired during an intertemporal choice task in two independent adult cohorts.
- A functional brain marker was trained and cross-validated to predict individual differences in delay discounting.
- The marker's predictive performance was assessed, and its relationship with body mass index and metabolic blood markers (insulin, c-peptide, leptin) was examined.
Main Results:
- The functional brain marker achieved significant prediction-outcome correlations in both training (r=0.49) and independent (r=0.45) datasets.
- The marker predicted delay discounting behavior several weeks later and showed significant differences between overweight and lean individuals.
- Marker responses, but not discounting behavior, predicted fasting-state insulin, c-peptide, and leptin levels, implicating brain-metabolism links.
Conclusions:
- A generalizable functional brain marker for individual differences in delay discounting was successfully developed using machine learning and fMRI.
- This marker provides a novel, brain-based measure of intertemporal decision-making and shows potential as a transdiagnostic marker for altered decision-making.
- The findings highlight a link between neural valuation processes, cognitive control, and metabolic health, offering new avenues for research and clinical applications.
