Local structure-function relationships in human brain networks across the lifespan.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Network Science

Background:

• Network models are increasingly used to link brain structure to function.
• Previous research primarily utilized a limited set of models applied globally.

Purpose of the Study:

• To compare a wide array of network models for predicting brain function from structure at both global and regional levels.
• To investigate age-related changes in structure-function coupling across the human lifespan.
• To identify synergistic effects between different network predictors.

Main Methods:

• Systematic comparison of numerous stylized network models.
• Application of models at global and regional cortical levels.
• Analysis of joint predictor performance to assess synergies.
• Investigation of age-related differences in structure-function coupling.

Main Results:

• Most network models demonstrated poor predictive performance at the global level.
• Regional analysis yielded improved, yet heterogeneous, predictive performance.
• Synergistic effects were identified when using pairs of predictors.
• A global decrease in the magnitude of structure-function coupling was observed with aging.
• Age-related decreases were concentrated in sensorimotor regions, while higher-order cognitive systems maintained coupling.

Conclusions:

• Regional network analysis offers greater insight into brain function prediction than global approaches.
• Structure-function coupling exhibits significant age-dependent changes, with distinct patterns in different brain systems.
• Understanding these age-related dynamics is crucial for comprehending brain plasticity and function across the lifespan.