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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 brain’s 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 Development℠ Study. Across matched discovery (n = 3525) and replication (n = 3447) samples, the total cortical representation of fronto-parietal PFNs positively correlates with general cognition. Cross-validated ridge regressions trained on PFN topography predict cognition in unseen data across domains, with prediction accuracy increasing along the cortex’s 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.
Individual differences in cognitive abilities during childhood are associated with important outcomes in adolescence. Here, the authors show associations between youth cognition and individual-specific patterns of cortical brain network organization.
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1 University of Pennsylvania, Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972); University of Pennsylvania, Department of Psychiatry, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972)
2 Chinese Institute for Brain Research, Beijing, China (GRID:grid.510934.a)
3 University of Pennsylvania, Department of Psychiatry, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972); Children’s Hospital of Philadelphia, Department of Child and Adolescent Psychiatry and Behavioral Science, Philadelphia, USA (GRID:grid.239552.a) (ISNI:0000 0001 0680 8770); Children’s Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Lifespan Brain Institute, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972)
4 Medical School, University of Minnesota, Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Minneapolis, USA (GRID:grid.17635.36) (ISNI:0000 0004 1936 8657)
5 University of Pennsylvania, Penn Statistics in Imaging and Visualization Center, Department of Biostatistics and Epidemiology, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972)
6 University of Pennsylvania, Department of Radiology, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972)
7 Medical School, University of Minnesota, Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Minneapolis, USA (GRID:grid.17635.36) (ISNI:0000 0004 1936 8657); University of Minnesota Informatics Institute, University of Minnesota, Minneapolis, USA (GRID:grid.17635.36) (ISNI:0000 0004 1936 8657)
8 University of Pennsylvania, Department of Psychiatry, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972); Children’s Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Lifespan Brain Institute, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972)
9 University of Pennsylvania, Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972); University of Pennsylvania, Department of Psychiatry, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972); Children’s Hospital of Philadelphia, Department of Child and Adolescent Psychiatry and Behavioral Science, Philadelphia, USA (GRID:grid.239552.a) (ISNI:0000 0001 0680 8770)