Identifying Shared Neuroanatomic Architecture between Cognitive Traits through Multiscale Morphometric Correlation Analysis.
Identifying Shared Neuroanatomic Architecture between Cognitive Traits through Multiscale Morphometric Correlation Analysis.
复制标题
通过多尺度形态相关分析识别认知特征之间的共享神经解剖结构。
DOI：
10.1007/978-3-031-47425-5_21
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发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Shen,Li
中科院分区：
文献类型：
--
作者：
Wen,Zixuan;Bao,Jingxuan;Yang,Shu;Risacher,ShannonL;Saykin,AndrewJ;Thompson,PaulM;Davatzikos,Christos;Huang,Heng;Zhao,Yize;Shen,Li
We introduce an informative metric, called morphometric correlation, as a measure of shared neuroanatomic similarity between two cognitive traits. Traditional estimates of trait correlations can be confounded by factors beyond brain morphology. To exclude these confounding factors, we adopt a Gaussian kernel to measure the morphological similarity between individuals and compare pure neuroanatomic correlations among cognitive traits. In our empirical study, we employ a multiscale strategy. Given a set of cognitive traits, we first perform morphometric correlation analysis for each pair of traits to reveal their shared neuroanatomic correlation at the whole brain (or global) level. After that, we extend our whole brain concept to regional morphometric correlation and estimate shared neuroanatomic similarity between two cognitive traits at the regional (or local) level. Our results demonstrate that morphometric correlation can provide insights into shared neuroanatomic architecture between cognitive traits. Furthermore, we also estimate the morphometricity of each cognitive trait at both global and local levels, which can be used to better understand how neuroanatomic changes influence individuals’ cognitive status.
