Network-specific corpus callosum aging and age-moderated cognitive associations using tract-to-region analysis

The corpus callosum (CC), the brain's largest white matter commissure, undergoes significant age-related atrophy that varies across subregions. However, how network-specific callosal connections age and relate to cognitive performance remains poorly understood. We analyzed diffusion-weighted imaging data from 718 healthy adults (ages 36-100 years) from the Human Connectome Project-Aging dataset. Using a tract-to-region approach, we quantified CC tract density within seven canonical functional networks. Cubic polynomial models examined network-specific aging trajectories, while correlation and moderation analyses investigated relationships with cognitive and motor performance across age groups. Network-specific CC tract densities showed distinct aging patterns. Somatomotor and Default Mode networks exhibited highest baseline tract density but steepest age-related declines (β = -0.068 and -0.025, respectively, p < 0.001), while Visual and Limbic networks showed relative preservation. CC tract density showed small-to-medium associations with executive function, memory, and motor performance (r = -0.32 to 0.33). Critically, age moderated these brain-behavior relationships: associations were minimal in younger adults but became progressively stronger in older adults across cognitive domains. The CC follows network-specific aging trajectories, with high-order association networks showing accelerated decline while primary sensory networks remain preserved. Strengthening brain-behavior associations with advancing age suggest callosal integrity becomes increasingly critical for maintaining cognitive performance in later life.

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Publication Details

Journal
Communications Biology
Published
2025-11-21
DOI
https://doi.org/10.1038/s42003-025-09219-w
Citations
1
Primary Topic
Advanced Neuroimaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
1.36
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article

Network-specific corpus callosum aging and age-moderated cognitive associations using tract-to-region analysis

Mohammad Hadi Aarabi
1 citations
Communications Biology
Advanced Neuroimaging Techniques and Applications
1.36
article

Network-specific corpus callosum aging and age-moderated cognitive associations using tract-to-region analysis

Mohammad Hadi Aarabi
article en
1 citations

Abstract

The corpus callosum (CC), the brain's largest white matter commissure, undergoes significant age-related atrophy that varies across subregions. However, how network-specific callosal connections age and relate to cognitive performance remains poorly understood. We analyzed diffusion-weighted imaging data from 718 healthy adults (ages 36-100 years) from the Human Connectome Project-Aging dataset. Using a tract-to-region approach, we quantified CC tract density within seven canonical functional networks. Cubic polynomial models examined network-specific aging trajectories, while correlation and moderation analyses investigated relationships with cognitive and motor performance across age groups. Network-specific CC tract densities showed distinct aging patterns. Somatomotor and Default Mode networks exhibited highest baseline tract density but steepest age-related declines (β = -0.068 and -0.025, respectively, p < 0.001), while Visual and Limbic networks showed relative preservation. CC tract density showed small-to-medium associations with executive function, memory, and motor performance (r = -0.32 to 0.33). Critically, age moderated these brain-behavior relationships: associations were minimal in younger adults but became progressively stronger in older adults across cognitive domains. The CC follows network-specific aging trajectories, with high-order association networks showing accelerated decline while primary sensory networks remain preserved. Strengthening brain-behavior associations with advancing age suggest callosal integrity becomes increasingly critical for maintaining cognitive performance in later life.

Communications BiologyVol. 8(1)
University of Padua (IT)
Openalex Percentile: Top 14%
Advanced Neuroimaging Techniques and Applications
1.36
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