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Exploring Links between Brain Image-Derived Phenotypes and Accelerometer-Measured Physical Activity in the UK

Dongliang Zhang1, Andrew Leroux2, Ciprian M Crainiceanu1

  • 1Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA.

Biorxiv : the Preprint Server for Biology
|April 10, 2026
PubMed
Summary

Physical activity (PA) is strongly linked to brain functional connectivity (FC) and predicts cardiometabolic disease risk better than brain imaging. Objective PA measures reveal key motor-related brain features.

Keywords:
UK Biobankaccelerometrycanonical correlation analysisfunctional connectivitygray matter volumemachine learning

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Area of Science:

  • Neuroimaging
  • Neuroscience
  • Public Health

Background:

  • Neurodegenerative disorders often involve altered brain functional connectivity (FC) and gray matter volume (GMV).
  • Physical activity (PA) is linked to neurodegenerative disorders, but its relationship with brain imaging phenotypes is understudied.

Purpose of the Study:

  • To investigate the multivariate association between brain image-derived phenotypes (IDPs) and PA.
  • To quantify the predictive performance of PA and brain IDPs for cardiometabolic diseases.

Main Methods:

  • Canonical correlation analysis (CCA) was used to assess associations between PA and brain IDPs (FC and GMV) from UK Biobank data.
  • Supervised and unsupervised methods quantified PA variable importance for predicting brain phenotypes.
  • Nested logistic regression models evaluated predictive performance for diabetes, stroke, coronary heart disease (CHD), and cancer.

Main Results:

  • A significant association was found between PA and FC (r = 0.50), stronger than between PA and GMV (r = 0.19).
  • Motor and attention networks were most strongly implicated.
  • PA variables, particularly intensity and circadian rhythm, predicted brain phenotypes.
  • PA variables showed greater predictive utility for CHD and diabetes than FC or GMV alone.

Conclusions:

  • Objectively measured PA is robustly associated with motor-related brain features.
  • PA provides substantial predictive information for cardiometabolic disease risk.
  • Neuroimaging measures offer limited incremental predictive value for these diseases compared to PA.