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<p>Brain aging is characterized by complex alterations in both anatomical structure and neural function. While the interdependence between structural connectivity (SC) and functional connectivity (FC) is well-established, the patterns of structural-functional coupling (SFC) during aging remain largely unexplored, despite being crucial for elucidating the neural mechanisms of age-related changes. Moreover, traditional resting-state fMRI studies have predominantly focused on linear correlations, often overlooking nonlinear causal interactions that may play a pivotal role in the aging brain. To address this, we employed a Nonlinear Granger Causality (NGC) model to investigate SFC at the whole-brain level. The study included 227 healthy participants, stratified into a young group (20–35 years, <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\:N\:=\:153\)</EquationSource> </InlineEquation>) and an older group (59–77 years, <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\:N\:=\:74\)</EquationSource> </InlineEquation>), with further subgrouping by sex. We analyzed SFC from both static and dynamic perspectives at regional and subnetwork levels. Our results demonstrated that the young group exhibited significantly stronger NGC-based SFC compared to the sex-matched older group. Additionally, males displayed a higher proportion of strong SFC connections than age-matched females. Notably, a widespread age-related decline in nonlinear causal coupling was observed across both regional and subnetwork scales, particularly within networks governing cognitive control and attention. Furthermore, dynamic analyses across sliding windows confirmed the persistence of these aging patterns throughout the scanning duration, despite increased temporal variability observed in the elderly. This study underscores the importance of incorporating nonlinear causal relationships into brain network research, as this approach offers deeper insights into the potential mechanisms underlying age-related cognitive decline and neurodegenerative processes.</p>

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Mapping Whole-Brain Nonlinear Structure-Function Dynamics in Aging via Neural Granger Causality

  • 期刊论文

  • 发表日期：

    2026年6月23日

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  • Meng Niu,
  • Shanli Ren,
  • Chen Lin,
  • QingChen Wang,
  • Yongzhi Yin,
  • Hanning Guo,
  • Yu Fu

摘要

Brain aging is characterized by complex alterations in both anatomical structure and neural function. While the interdependence between structural connectivity (SC) and functional connectivity (FC) is well-established, the patterns of structural-functional coupling (SFC) during aging remain largely unexplored, despite being crucial for elucidating the neural mechanisms of age-related changes. Moreover, traditional resting-state fMRI studies have predominantly focused on linear correlations, often overlooking nonlinear causal interactions that may play a pivotal role in the aging brain. To address this, we employed a Nonlinear Granger Causality (NGC) model to investigate SFC at the whole-brain level. The study included 227 healthy participants, stratified into a young group (20–35 years, \(\:N\:=\:153\) ) and an older group (59–77 years, \(\:N\:=\:74\) ), with further subgrouping by sex. We analyzed SFC from both static and dynamic perspectives at regional and subnetwork levels. Our results demonstrated that the young group exhibited significantly stronger NGC-based SFC compared to the sex-matched older group. Additionally, males displayed a higher proportion of strong SFC connections than age-matched females. Notably, a widespread age-related decline in nonlinear causal coupling was observed across both regional and subnetwork scales, particularly within networks governing cognitive control and attention. Furthermore, dynamic analyses across sliding windows confirmed the persistence of these aging patterns throughout the scanning duration, despite increased temporal variability observed in the elderly. This study underscores the importance of incorporating nonlinear causal relationships into brain network research, as this approach offers deeper insights into the potential mechanisms underlying age-related cognitive decline and neurodegenerative processes.