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不同认知领域的皮质形态存在明显的区域性差异模式,且与认知表现相关。

Distinct Regionalization Patterns of Cortical Morphology are Associated with Cognitive Performance Across Different Domains.

机构信息

Center for Human Development, University of California, San Diego, La Jolla, CA 92161, USA.

Department of Cognitive Science, University of California, San Diego, La Jolla, CA 92093, USA.

出版信息

Cereb Cortex. 2021 Jul 5;31(8):3856-3871. doi: 10.1093/cercor/bhab054.

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.

摘要

儿童的认知表现可预测其学业和社交成果;因此,了解儿童发育过程中认知个体差异的神经生物学机制对于提高生活质量可能很重要。社会上普遍存在一种观点,即许多认知过程都基于单一的心理结构。然而,尚不清楚是否存在许多认知过程的一致神经基础。在这里,我们在一个来自青少年大脑与认知发展研究(ABCD 研究)的大型 9-11 岁儿童样本(N=10145)中发现,在控制了全局成像指标后,皮质表面积和表观厚度的分布式配置与不同类型任务的认知表现存在差异关联。这些关联的区域化模式的最小重叠对关于发展智力功能的竞争理论具有启示意义。令人惊讶的是,不控制社会人口因素会增加这些区域化模式之间的相似性。这强调了理解社会人口因素、认知和大脑结构之间的共享方差的重要性,尤其是在 ABCD 这样的基于人群的样本中。

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