Villalón-Reina Julio E, Zhu Alyssa H, Nir Talia M, Thomopoulos Sophia I, Laltoo Emily, Kushan Leila, Bearden Carrie E, Jahanshad Neda, Thompson Paul M
Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA.
2023 19th Int Symp Med Inf Process Anal SIPAIM (2023). 2023 Nov;2023. doi: 10.1109/SIPAIM56729.2023.10373451.
Normative models of brain metrics based on large populations are extremely valuable for detecting brain abnormalities in patients with dementia, psychiatric, or developmental conditions. Here we present the first large-scale normative model of the brain's white matter (WM) microstructure derived from 18 international diffusion MRI (dMRI) datasets covering almost the entire lifespan (totaling N=51,830 individuals; age: 3-80 years). We extracted regional diffusion tensor imaging (DTI) metrics using a standardized analysis and quality control protocol, and used Hierarchical Bayesian Regression (HBR) to model the statistical distribution of derived WM metrics as a function of age and sex, while modeling the site effect. HBR overcomes known weaknesses of some data harmonization methods that simply scale and shift residual distributions at each site. To illustrate the method, we applied it to detect and visualize profiles of WM microstructural deviations in cohorts of patients with Alzheimer's disease, mild cognitive impairment, Parkinson's disease and in carriers of 22q11.2 copy number variants, a rare neurogenetic condition that confers increased risk for psychosis. The resulting large-scale model offers a common reference to identify disease effects in individuals or groups, as well as to compare disorders and discover factors that influence these abnormalities.
基于大量人群的大脑指标规范模型对于检测痴呆症、精神疾病或发育障碍患者的大脑异常极为重要。在此，我们展示了首个大规模的大脑白质（WM）微观结构规范模型，该模型源自18个国际扩散磁共振成像（dMRI）数据集，几乎覆盖了整个生命周期（总计N = 51,830人；年龄：3至80岁）。我们使用标准化分析和质量控制方案提取了区域扩散张量成像（DTI）指标，并使用分层贝叶斯回归（HBR）对导出的WM指标的统计分布进行建模，将其作为年龄和性别的函数，同时对位点效应进行建模。HBR克服了一些数据协调方法的已知弱点，这些方法只是简单地对每个位点的残差分布进行缩放和移位。为了说明该方法，我们将其应用于检测和可视化阿尔茨海默病、轻度认知障碍、帕金森病患者队列以及22q11.2拷贝数变异携带者（一种罕见的神经遗传疾病，会增加患精神病的风险）的WM微观结构偏差情况。由此产生的大规模模型为识别个体或群体中的疾病影响、比较疾病以及发现影响这些异常的因素提供了一个通用参考。
