通过多尺度形态计量相关性分析确定认知特征之间共享的神经解剖结构

Zixuan Wen, Jingxuan Bao, Shu Yang, Shannon L Risacher, Andrew J Saykin, Paul M Thompson, Christos Davatzikos, Heng Huang, Yize Zhao, Li Shen
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引用次数: 0

摘要

我们引入了一种信息量丰富的度量方法,称为形态计量相关性(morphometric correlation),作为两个认知特征之间共享神经解剖相似性的度量方法。传统的特质相关性估计可能会受到大脑形态学以外因素的干扰。为了排除这些干扰因素,我们采用高斯核来测量个体之间的形态相似性,并比较认知特质之间的纯神经解剖相关性。在实证研究中,我们采用了多尺度策略。给定一组认知特质后,我们首先对每对特质进行形态计量相关性分析,以揭示它们在全脑(或全局)水平上的共同神经解剖相关性。然后,我们将全脑概念扩展到区域形态计量相关性,并在区域(或局部)水平上估计两个认知特征之间共享的神经解剖相似性。我们的研究结果表明,形态计量学相关性可以为认知特征之间共享的神经解剖结构提供洞察力。此外,我们还估算了每种认知特质在整体和局部水平上的形态计量性,这可用于更好地理解神经解剖变化如何影响个体的认知状态。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identifying Shared Neuroanatomic Architecture between Cognitive Traits through Multiscale Morphometric Correlation Analysis.

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.

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