Back

Personalized Functional Brain Network Topography Predicts Individual Differences in Youth Cognition

Keller, A. S.; Pines, A. R.; Sydnor, V. J.; Cui, Z.; Bertolero, M. A.; Barzilay, R.; Alexander-Bloch, A.; Byington, N.; Chen, A. A.; Conan, G. M.; Davatazikos, C.; Feczko, E. J.; Hendrickson, T. J.; Houghton, A.; Larsen, B.; Li, H.; Miranda Dominguez, O.; Roalf, D. R.; Perrone, A.; Shanmugan, S.; Shinohara, R. T.; Fan, Y.; Fair, D. A.; Satterthwaite, T. D.

2022-10-14 neuroscience
10.1101/2022.10.11.511823 bioRxiv
Show abstract

Individual differences in cognition during childhood are associated with important social, physical, and mental health outcomes in adolescence and adulthood. Given that cortical surface arealization during development reflects the brains functional prioritization, quantifying variation in the topography of functional brain networks across the developing cortex may provide insight regarding individual differences in cognition. We test this idea by defining personalized functional networks (PFNs) that account for interindividual heterogeneity in functional brain network topography in 9-10 year olds from the Adolescent Brain Cognitive DevelopmentSM Study. Across matched discovery (n=3,525) and replication (n=3,447) samples, the total cortical representation of fronto-parietal PFNs positively correlated with general cognition. Cross-validated ridge regressions trained on PFN topography predicted cognition across domains, with prediction accuracy increasing along the cortexs sensorimotor-association organizational axis. These results establish that functional network topography heterogeneity is associated with individual differences in cognition before the critical transition into adolescence.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.