White matter microstructure predicts effort and reward sensitivity.
Authors
Affiliations (5)
Affiliations (5)
- Centre for Research Training in Machine Learning (ML-Labs), School of Computing, Dublin City University, D09V209, Dublin, Ireland; Insight Research Ireland Centre for Data Analytics, School of Computing, Dublin City University, D09V209, Dublin, Ireland; Université Claude Bernard Lyon 1, CNRS, INSERM, Centre de Recherche en Neurosciences de Lyon (CRNL), U1028 UMR5292, Impact Team, F-69500, Bron, France.. Electronic address: [email protected].
- Institute of Neuroscience, Université Catholique de Louvain, 1200, Brussels, Belgium.
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, UK.; Department of Brain Sciences, Imperial College London, London, UK.
- Insight Research Ireland Centre for Data Analytics, School of Computing, Dublin City University, D09V209, Dublin, Ireland.
- Université Claude Bernard Lyon 1, CNRS, INSERM, Centre de Recherche en Neurosciences de Lyon (CRNL), U1028 UMR5292, Impact Team, F-69500, Bron, France.; Institute of Neuroscience, Université Catholique de Louvain, 1200, Brussels, Belgium.. Electronic address: [email protected].
Abstract
From rodents to humans, animals constantly face a central question: is the reward worth the effort? Effort and reward sensitivity in such situations vary substantially across individuals and ultimately shape goal-directed behavior. Yet, the neuroanatomical basis underlying this variability across individuals remain unclear. Here, we combined computational modeling of effort and reward sensitivity during decision-making with whole-brain diffusion MRI in 45 healthy participants to identify white matter substrates of individual effort and reward sensitivity. A data-driven, cluster-based analysis of fractional anisotropy and mean diffusivity revealed 12 clusters: five linked to effort sensitivity, all within tracts connected to major frontal valuation nodes (e.g., supplementary motor area [SMA], dorsal anterior cingulate cortex [dACC], orbitofrontal cortex [OFC]), and seven linked to reward sensitivity, spanning frontal valuation, fronto-parietal, and sensorimotor networks. The strongest associations involved two SMA-connected clusters, one shared across effort and reward sensitivity and another consistent across both microstructural metrics. Critically, microstructural features from the five effort-related and seven reward-related clusters reliably predicted graded individual differences in effort and reward sensitivity in out-of-sample, multi-class machine learning analyses, respectively, whereas randomly sampled clusters did not. SMA-connected tracts were the dominant predictors in these decoding analyses, with additional contributions from fronto-parietal and sensorimotor pathways for reward sensitivity. These findings reveal a distributed microstructure correlates underlying inter-individual differences in effort and reward sensitivity, with SMA pathways emerging as central hubs. They demonstrate that localized white matter microstructure can robustly predict these individual differences, offering a framework to forecast the impact of lesions or interventions on goal-directed behavior, including apathy and impulsivity.