气候变化领域集成服务门户(CCPortal): Heritability of individualized cortical network topography

CCPortal

DOI	10.1073/pnas.2016271118
	Heritability of individualized cortical network topography
	Anderson K.M.; Ge T.; Kong R.; Patrick L.M.; Spreng R.N.; Sabuncu M.R.; Yeo B.T.T.; Holmes A.J.
发表日期	2021
ISSN	00278424
卷号	118 期号:9
英文摘要	Human cortex is patterned by a complex and interdigitated web of large-scale functional networks. Recent methodological breakthroughs reveal variation in the size, shape, and spatial topography of cortical networks across individuals. While spatial network organization emerges across development, is stable over time, and is predictive of behavior, it is not yet clear to what extent genetic factors underlie interindividual differences in network topography. Here, leveraging a nonlinear multidimensional estimation of heritability, we provide evidence that individual variability in the size and topographic organization of cortical networks are under genetic control. Using twin and family data from the Human Connectome Project (n = 1,023), we find increased variability and reduced heritability in the size of heteromodal association networks (h2: M = 0.34, SD = 0.070), relative to unimodal sensory/motor cortex (h2: M = 0.40, SD = 0.097). We then demonstrate that the spatial layout of cortical networks is influenced by genetics, using our multidimensional estimation of heritability (h2-multi; M = 0.14, SD = 0.015). However, topographic heritability did not differ between heteromodal and unimodal networks. Genetic factors had a regionally variable influence on brain organization, such that the heritability of network topography was greatest in prefrontal, precuneus, and posterior parietal cortex. Taken together, these data are consistent with relaxed genetic control of association cortices relative to primary sensory/motor regions and have implications for understanding population-level variability in brain functioning, guiding both individualized prediction and the interpretation of analyses that integrate genetics and neuroimaging. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Function brain networks; Functional connectome; Heritability; Individualized parcellation; Resting-state
语种	英语
scopus关键词	adult; article; association cortex; connectome; controlled study; genetic regulation; heritability; human; major clinical study; nerve cell network; neuroimaging; posterior parietal cortex; precuneus; prediction; sensorimotor cortex; topography
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/180479
作者单位	Department of Psychology, Yale University, New Haven, CT 06520, United States; Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, United States; Stanley Center for Psychiatric Research, Broad Institute of Massachusetts, Institute of Technology and Harvard, Cambridge, MA 02142, United States; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, United States; Department of Electrical and Computer Engineering, Centre for Sleep and Cognition, National University of Singapore119077, Singapore; Department of Electrical and Computer Engineering, Centre for Translational Magnetic Resonance Research, National University of Singapore119077, Singapore; N.1 Institute for Health, National University of Singapore119077, Singapore; Institute for Digital Medicine, National University of Singapore119077, Singapore; Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGil...
推荐引用方式 GB/T 7714	Anderson K.M.,Ge T.,Kong R.,et al. Heritability of individualized cortical network topography[J],2021,118(9).
APA	Anderson K.M..,Ge T..,Kong R..,Patrick L.M..,Spreng R.N..,...&Holmes A.J..(2021).Heritability of individualized cortical network topography.Proceedings of the National Academy of Sciences of the United States of America,118(9).
MLA	Anderson K.M.,et al."Heritability of individualized cortical network topography".Proceedings of the National Academy of Sciences of the United States of America 118.9(2021).