학술논문

Distinct Regionalization Patterns of Cortical Morphology are Associated with Cognitive Performance Across Different Domains
Document Type
article
Source
Cerebral Cortex. 31(8)
Subject
Biological Psychology
Cognitive and Computational Psychology
Psychology
Mental Health
Behavioral and Social Science
Basic Behavioral and Social Science
Neurosciences
Mind and Body
1.2 Psychological and socioeconomic processes
Underpinning research
1.1 Normal biological development and functioning
Mental health
Neurological
Adolescent
Adolescent Development
Cerebral Cortex
Child
Cognition
Female
Humans
Longitudinal Studies
Magnetic Resonance Imaging
Male
Neuropsychological Tests
Psychomotor Performance
Sensitivity and Specificity
Sociodemographic Factors
adolescence
cognition
cortical morphology
development
multivariate
neuroimaging
Cognitive Sciences
Experimental Psychology
Biological psychology
Cognitive and computational psychology
Language
Abstract
Cognitive performance in children is predictive of academic and social outcomes; therefore, understanding neurobiological mechanisms underlying individual differences in cognition during development may be important for improving quality of life. The belief that a single, psychological construct underlies many cognitive processes is pervasive throughout society. However, it is unclear if there is a consistent neural substrate underlying many cognitive processes. Here, we show that a distributed configuration of cortical surface area and apparent thickness, when controlling for global imaging measures, is differentially associated with cognitive performance on different types of tasks in a large sample (N = 10 145) of 9-11-year-old children from the Adolescent Brain and Cognitive DevelopmentSM (ABCD) study. The minimal overlap in these regionalization patterns of association has implications for competing theories about developing intellectual functions. Surprisingly, not controlling for sociodemographic factors increased the similarity between these regionalization patterns. This highlights the importance of understanding the shared variance between sociodemographic factors, cognition and brain structure, particularly with a population-based sample such as ABCD.