bioRxivNeurosciencePreprint
Convergent and Divergent Brain-Cognition Development
Yapei XieiD, Shaoshi ZhangiD, Csaba OrbániD, Leon Qi Rong OoiiD, Ru KongiD, Dorothea L. FlorisiD, Xi‐Nian ZuoiD, Elvisha DhamalaiD, Avram J. HolmesiD, Lucina Q. UddiniD, Thomas E. NicholsiD, Adriana Di MartinoiD, B.T. Thomas YeoiD
Abstract
How brain networks and cognition co-evolve during development remains poorly understood. Here, we use resting-state functional magnetic resonance imaging (rs-fMRI) and cognitive data at baseline and Year 2 of 2,949 individuals in the Adolescent Brain Cognitive Development (ABCD) Study to examine how stable and changing features of brain network organization predict cognitive development during early adolescence. We find that baseline resting-state functional connectivity (FC) more strongly predicts future cognitive ability than baseline cognitive ability. Models trained on baseline FC to predict baseline cognition generalize better to Year 2 FC and cognition, suggesting that brain-cognition relationships strengthen over time. Intriguingly, baseline FC outperforms longitudinal FC change in predicting future cognitive ability. One potential reason is the lower reliability of FC change compared to baseline FC: ICC = 0.24 vs. 0.56. However, reducing baseline FCs reliability by shortening scan duration only partially narrows the predictive gap, suggesting reliability alone cannot be the full explanation. Furthermore, neither baseline FC nor FC change meaningfully predicts longitudinal change in cognitive ability. We also identify converging and diverging predictive network features across cross-sectional and longitudinal models of brain-cognition relationships, revealing a multivariate twist on Simpsons paradox. Together, these findings suggest that during early adolescence, stable individual differences in brain functional network organization play a more critical role than dynamic changes in shaping future cognitive outcomes.
- organism: human
- assay: imaging
- research focus: differential expression
- research focus: benchmarking
- approach: statistical model
- Citations
- 1
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