An fMRI-based brain marker of individual differences in delay discounting.
Leonie KobanSangil LeeDaniela S SchelskiMarie-Christine SimonCaryn LermanBernd WeberJoseph W KableHilke PlassmannPublished in: The Journal of neuroscience : the official journal of the Society for Neuroscience (2023)
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 data set (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 data set (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 towards 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 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.
Keyphrases
- resting state
- functional connectivity
- white matter
- machine learning
- type diabetes
- healthcare
- insulin resistance
- big data
- decision making
- weight gain
- endothelial cells
- mental health
- public health
- metabolic syndrome
- physical activity
- blood pressure
- skeletal muscle
- adipose tissue
- body mass index
- risk assessment
- brain injury
- data analysis
- current status
- bone mineral density
- blood glucose