Brain Structural Integrity and Intrinsic Functional Connectivity Forecast 6 Year Longitudinal Growth in Children's Numerical Abilities.
Early numerical proficiency lays the foundation for acquiring quantitative skills essential in today's technological society. Identification of cognitive and brain markers associated with long-term growth of children's basic numerical computation abilities is therefore of utmost importance. Previous attempts to relate brain structure and function to numerical competency have focused on behavioral measures from a single time point. Thus, little is known about the brain predictors of individual differences in growth trajectories of numerical abilities. Using a longitudinal design, with multimodal imaging and machine-learning algorithms, we investigated whether brain structure and intrinsic connectivity in early childhood are predictive of 6 year outcomes in numerical abilities spanning childhood and adolescence. Gray matter volume at age 8 in distributed brain regions, including the ventrotemporal occipital cortex (VTOC), the posterior parietal cortex, and the prefrontal cortex, predicted longitudinal gains in numerical, but not reading, abilities. Remarkably, intrinsic connectivity analysis revealed that the strength of functional coupling among these regions also predicted gains in numerical abilities, providing novel evidence for a network of brain regions that works in concert to promote numerical skill acquisition. VTOC connectivity with posterior parietal, anterior temporal, and dorsolateral prefrontal cortices emerged as the most extensive network predicting individual gains in numerical abilities. Crucially, behavioral measures of mathematics, IQ, working memory, and reading did not predict children's gains in numerical abilities. Our study identifies, for the first time, functional circuits in the human brain that scaffold the development of numerical skills, and highlights potential biomarkers for identifying children at risk for learning difficulties.


CONCLUSIONS
Children show substantial individual differences in math abilities and ease of math learning. Early numerical abilities provide the foundation for future academic and professional success in an increasingly technological society. Understanding the early identification of poor math skills has therefore taken on great significance. This work provides important new insights into brain structure and connectivity measures that can predict longitudinal growth of children's math skills over a 6 year period, and may eventually aid in the early identification of children who might benefit from targeted interventions.
Department of Psychiatry and Behavioral Sciences, evanst@stanford.edu menon@stanford.edu.
Tanya M
TM
Evans
Department of Psychiatry and Behavioral Sciences.
John
J
Kochalka
Department of Psychiatry and Behavioral Sciences.
Tricia J
TJ
Ngoon
Department of Psychiatry and Behavioral Sciences.
Sarah S
SS
Wu
Department of Psychiatry and Behavioral Sciences.
Shaozheng
http://orcid.org/0000-0002-1859-2150
S
Qin
Department of Psychiatry and Behavioral Sciences.
Christian
http://orcid.org/0000-0001-6898-0891
C
Battista
Department of Psychiatry and Behavioral Sciences, Department of Neurology and Neurological Sciences, Stanford Neuroscience Institute, Symbolic Systems Program, Stanford University School of Medicine, Stanford, California 94305 evanst@stanford.edu menon@stanford.edu.
Vinod
V
Menon
D000293:Adolescent; D000375:Aging / Q000473:pathology / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods; D002540:Cerebral Cortex / Q000033:anatomy & histology* / Q000502:physiology*; D002648:Child; D003071:Cognition / Q000502:physiology; D005260:Female; D005544:Forecasting; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008433:Mathematics; D009415:Nerve Net / Q000033:anatomy & histology* / Q000502:physiology*; D011340:Problem Solving / Q000502:physiology
cognition; connectivity; development; longitudinal; pediatric; prediction