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Related Experiment Video

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A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
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Uncovering functional connectivity patterns predictive of cognition in youth using interpretable predictive modeling.

Hongming Li1,2,3, Matthew Cieslak4,5,6, Taylor Salo4,5,6

  • 1Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104.

Proceedings of the National Academy of Sciences of the United States of America
|October 16, 2025
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This study introduces a new model to link brain function patterns to cognitive traits in youth. The model effectively identifies brain networks associated with cognition, improving our understanding of brain-behavior relationships.

Keywords:
cognitionfunctional connectivitygeneralizabilityinterpretabilitypredictive modeling

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Data Science

Background:

  • Brain-wide association studies (BWAS) using functional MRI (fMRI) link brain function to behavior.
  • Existing models (whole-brain, region-wise) have limitations in generalizability and interpretability for functional connectivity (FC) data.
  • High dimensionality of FC data challenges whole-brain models, while region-wise models limit capturing integrated brain patterns.

Purpose of the Study:

  • To develop an interpretable predictive model for fine-grained functional connectivity (FC) patterns associated with behavioral traits.
  • To jointly learn regional relevance and prediction functions for a comprehensive characterization of FC-trait associations.
  • To capture the collective contribution of brain-wide FC patterns to predicting cognitive traits.

Main Methods:

  • Introduced a novel interpretable predictive model integrating regional and participant-level FC data.
  • The model learns relevance scores and prediction functions for each brain region.
  • Regional predictions are weighted by relevance scores for a participant-level prediction of cognitive traits.

Main Results:

  • Validated the model using fMRI data from 6,798 participants in the Adolescent Brain and Cognitive Development (ABCD) study.
  • Identified specific brain networks (cingulo-parietal, retrosplenial-temporal, dorsal attention, cingulo-opercular) as predictive of cognitive traits.
  • Achieved competitive prediction accuracy and detailed characterization of fine-grained FC differences across cognitive domains.

Conclusions:

  • The developed method effectively characterizes generalizable and fine-grained FC patterns linked to cognition in youth.
  • Learned relevance scores improved predictions of longitudinal cognitive measures and traits in independent cohorts.
  • The model offers enhanced interpretability and generalizability for understanding brain-behavior relationships.