Individualized structure-function coupling reveals behavioral signatures in the adolescent brain.

Area of Science:

• Neuroscience
• Developmental Neuroscience
• Computational Neuroscience

Background:

• Adolescence is a key period for brain maturation.
• The relationship between functional brain dynamics and white matter microstructure in adolescents is not well understood at the individual level.

Purpose of the Study:

• To develop a machine learning model to predict white matter microstructure (fractional anisotropy) from functional connectivity in adolescents.
• To investigate the anatomical specificity and behavioral relevance of the structure-function relationship in the developing brain.

Main Methods:

• Utilized a machine learning workflow to predict fractional anisotropy (FA) from resting-state functional connectivity (FC).
• Analyzed two large adolescent cohorts (NCANDA and HCP-Development) with longitudinal and cross-sectional data, respectively.
• Defined a "structure-function gap" as the residual between predicted and observed FA to assess deviations from the typical coupling.

Main Results:

• Modest prediction of whole-brain FA from FC was achieved (r = 0.16-0.27).
• Prediction accuracy for regional FA varied across white matter regions, with higher accuracy in tracts supporting unimodal cortical areas.
• The "structure-function gap" was linked to cognitive and behavioral measures, including memory, impulsivity, and executive function, particularly within the Corticospinal and Cingulum pathways.

Conclusions:

• Individualized structure-function coupling is a reproducible and anatomically specific marker in adolescent brain development.
• This approach provides a novel framework for linking functional connectivity patterns to white matter development and behavioral variability in youth.