Aug 2026· Human Brain Mapping· Vol 47· 0 citations· 51 references
Medicine
TL;DR
These findings support a polyregional SA architecture underlying GCA, with prominent contributions from prefrontal and temporal association cortices, and highlight the value of genetically informed parcellation for identifying regional cortical contributions.
Abstract
Understanding the cortical architecture underlying individual differences in general cognitive ability (GCA) remains a central question in cognitive neuroscience. Prior work has established associations between global brain size and GCA, yet the regional effects and directionality of these relationships remain debated. Using a genetically informed cortical parcellation in 11,289 UK Biobank participants, we examined associations between cortical surface area (SA), cortical thickness (CT), and GCA measured via verbal–numerical reasoning. Total SA showed a robust positive association with GCA. At the regional level, dorsolateral prefrontal and superior temporal SA exhibited the strongest positive associations, which persisted after adjustment for global SA. In contrast, CT showed comparatively modest associations. Using Mendelian randomization (MR) with genome‐wide significant genetic instruments, we observed evidence consistent with a bidirectional relationship between total SA and GCA. At the regional level, dorsolateral prefrontal and temporal SA demonstrated evidence of MR‐inferred directional effects on GCA, while GCA showed evidence of MR‐inferred directional effects on total SA and perisylvian thickness. These findings support a polyregional SA architecture underlying GCA, with prominent contributions from prefrontal and temporal association cortices. Our results refine global brain–GCA models and highlight the value of genetically informed parcellation for identifying regional cortical contributions.
It is suggested that SCA was associated with lower PCC–prefrontal functional connectivity, but not with detectable preservation of PCC cortical thickness, which provides a potential framework for understanding heterogeneous cognitive-aging trajectories, but require confirmation in larger longitudinal cohorts.
Jie Wang, Yun-Fei Li, Tianyuyi Feng et al.· Frontiers in Aging Neuroscie...· 0 citations
White matter (WM) BOLD signals, long dismissed as non-neuronal noise, are increasingly recognized as intrinsic, anatomically organized functional activity. However, the genetic architecture of intrinsic WM functional activity remains poorly understood. Here, we performed genome-wide and phenome-wide analyses of WM frac...
OBJECTIVES
Cognitive aging shows substantial inter-individual heterogeneity, which may be shaped by both early-life developmental conditions and adult socioeconomic environments. However, how these life-course factors jointly relate to cognitive aging and the potential neuroanatomical correlates underlying these associ...
Different Magnetic Resonance Imaging-derived cortical, subcortical, and white matter measures are presumed to reflect different developmental and cellular processes, but how their genetic architecture is organised and the underlying cellular and developmental processes is unclear. We conducted genome-wide association s...
Yuan-Jun Gu, A. Ebneabbasi, Yuan-Kai He et al.· bioRxiv· 0 citations
This review examines the relationship between brain size and human intelligence by integrating findings from neuroimaging studies, genetic analyses and large-scale population research. Early theories, such as Spearman’s concept of general intelligence (g), laid the foundation for understanding cognitive ability as a un...
B. Sreeja, M. Kammar· International Journal of Far...· 0 citations
The findings suggest that genetic influences on brain morphology are expressed across multiple spatial scales, with consequences that may help to guide the design of deep learning methods to discover genomic loci associated with brain structure and brain diseases.
Emma J Gleave, Luis M. García-Marín, Z. Ceja et al.· bioRxiv· 0 citations
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