Wen Zixuan, Bao Jingxuan, Yang Shu, Wen Junhao, Zhan Qipeng, Cui Yuhan, Erus Guray, Yang Zhijian, Thompson Paul M, Zhao Yize, Davatzikos Christos, Shen Li
Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, PA, USA.
Laboratory of AI and Biomedical Science (LABS), University of Southern California, CA, USA.
Proc IEEE Int Symp Biomed Imaging. 2024 May;2024. doi: 10.1109/isbi56570.2024.10635581. Epub 2024 Aug 22.
Morphometricity examines the global statistical association between brain morphology and an observable trait, and is defined as the proportion of the trait variation attributable to brain morphology. In this work, we propose an accurate morphometricity estimator based on the generalized random effects (GRE) model, and perform morphometricity analyses on five cognitive traits in an Alzheimer's study. Our empirical study shows that the proposed GRE model outperforms the widely used LME model on both simulation and real data. In addition, we extend morphometricity estimation from the whole brain to the focal-brain level, and examine and quantify both global and regional neuroanatomical signatures of the cognitive traits. Our global analysis reveals 1) a relatively strong anatomical basis for ADAS13, 2) intermediate ones for MMSE, CDRSB and FAQ, and 3) a relatively weak one for RAVLT.learning. The top associations identified from our regional morphometricity analysis include those between all five cognitive traits and multiple regions such as hippocampus, amygdala, and inferior lateral ventricles. As expected, the identified regional associations are weaker than the global ones. While the whole brain analysis is more powerful in identifying higher morphometricity, the regional analysis could localize the neuroanatomical signatures of the studied cognitive traits and thus provide valuable information in imaging and cognitive biomarker discovery for normal and/or disordered brain research.
形态测量学研究大脑形态与可观察特征之间的整体统计关联，并被定义为可归因于大脑形态的特征变异比例。在这项工作中，我们基于广义随机效应（GRE）模型提出了一种精确的形态测量估计器，并在一项阿尔茨海默病研究中对五种认知特征进行了形态测量分析。我们的实证研究表明，在模拟数据和真实数据上，所提出的GRE模型均优于广泛使用的线性混合效应（LME）模型。此外，我们将形态测量估计从全脑扩展到局部脑水平，并检查和量化认知特征的整体和区域神经解剖学特征。我们的整体分析揭示了：1）ADAS13有相对较强的解剖学基础；2）MMSE、CDRSB和FAQ有中等强度的解剖学基础；3）RAVLT学习有相对较弱的解剖学基础。我们从区域形态测量分析中确定的顶级关联包括所有五种认知特征与海马体、杏仁核和下侧脑室等多个区域之间的关联。正如预期的那样，所确定的区域关联比整体关联弱。虽然全脑分析在识别更高的形态测量方面更强大，但区域分析可以定位所研究认知特征的神经解剖学特征，从而为正常和/或紊乱大脑研究中的成像和认知生物标志物发现提供有价值的信息。
