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Related Experiment Video

Updated: Jan 20, 2026

A Versatile Murine Model of Subcortical White Matter Stroke for the Study of Axonal Degeneration and White Matter Neurobiology
08:36

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Published on: March 17, 2016

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White matter microstructure predicts effort and reward sensitivity.

Nam Trinh1, Laurence Dricot2, Pierre Vassiliadis3

  • 1Centre 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, Bron F-69500, France..

Neuroimage
|January 18, 2026
PubMed
Summary
This summary is machine-generated.

Individual differences in effort and reward sensitivity are linked to specific white matter tracts in the brain. The supplementary motor area (SMA) pathways are key predictors of these variations in goal-directed behavior.

Keywords:
Diffusion-weighed imagingEffort processingEffort-based decision-makingFractional anisotropyMean diffusivityMotivationReward processing

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Last Updated: Jan 20, 2026

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Neuroimaging

Background:

  • Individual differences in effort and reward sensitivity significantly influence goal-directed behavior.
  • The neuroanatomical underpinnings of this variability remain largely unexplored.
  • Understanding these differences is crucial for addressing conditions like apathy and impulsivity.

Purpose of the Study:

  • To identify the white matter substrates associated with individual differences in effort and reward sensitivity.
  • To investigate the relationship between brain microstructure and decision-making.
  • To explore the predictive power of white matter features on behavioral variability.

Main Methods:

  • Combined computational modeling of effort and reward sensitivity with whole-brain diffusion MRI in 45 healthy participants.
  • Utilized data-driven, cluster-based analysis of fractional anisotropy and mean diffusivity.
  • Employed out-of-sample, multi-class machine learning for predictive analyses.

Main Results:

  • Identified 12 white matter clusters linked to effort (frontal valuation nodes) and reward sensitivity (spanning multiple networks).
  • Found strong associations within supplementary motor area (SMA)-connected clusters, some shared across both sensitivities.
  • Demonstrated that specific microstructural features reliably predicted individual differences in effort and reward sensitivity, with SMA pathways being dominant predictors.

Conclusions:

  • Distributed white matter microstructure correlates with inter-individual differences in effort and reward sensitivity.
  • SMA-connected pathways serve as central hubs in modulating these behavioral traits.
  • Localized white matter microstructure robustly predicts individual differences, offering a framework for understanding and potentially intervening in goal-directed behaviors.