Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Études Cognitives, École Normale Supérieure, EHESS, CNRS, PSL University, 75005 Paris, France.
Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Études Cognitives, École Normale Supérieure, EHESS, CNRS, PSL University, 75005 Paris, France; INSERM UMR 1141, Paris Diderot University, Paris, France; Department of Child and Adolescent Psychiatry, Robert Debré Hospital, APHP, Paris, France.
Neuroimage. 2022 Jul 1;254:119118. doi: 10.1016/j.neuroimage.2022.119118. Epub 2022 Mar 19.
Studies examining cerebral asymmetries typically divide the l-R Measure (e.g., Left-Right Volume) by the L + R Measure to obtain an Asymmetry Index (AI). However, contrary to widespread belief, such a division fails to render the AI independent from the L + R Measure and/or from total brain size. As a result, variations in brain size may bias correlation estimates with the AI or group differences in AI. We investigated how to analyze brain asymmetries in to distinguish global from regional effects, and report unbiased group differences in cerebral asymmetries in the UK Biobank (N = 40, 028). We used 306 global and regional brain measures provided by the UK Biobank. Global gray and white matter volumes were taken from Freesurfer ASEG, subcortical gray matter volumes from Freesurfer ASEG and subsegmentation, cortical gray matter volumes, mean thicknesses, and surface areas from the Destrieux atlas applied on T1-and T2-weighted images, cerebellar gray matter volumes from FAST FSL, and regional white matter volumes from Freesurfer ASEG. We analyzed the extent to which the L + R Measure, Total Cerebral Measure (TCM, e.g., Total Brain Volume), and l-R TCM predict regional asymmetries. As a case study, we assessed the consequences of omitting each of these predictors on the magnitude and significance of sex differences in asymmetries. We found that the L + R Measure, the TCM, and the l-R TCM predicted the AI of more than 89% of regions and that their relationships were generally linear. Removing any of these predictors changed the significance of sex differences in 33% of regions and the magnitude of sex differences across 13-42% of regions. Although we generally report similar sex and age effects on cerebral asymmetries to those of previous large-scale studies, properly adjusting for regional and global brain size revealed additional sex and age effects on brain asymmetry.
研究检查大脑不对称性通常将 l-R 测量(例如,左-右体积)除以 L+R 测量,以获得不对称指数(AI)。然而,与普遍的看法相反,这种划分并不能使 AI 独立于 L+R 测量和/或总脑大小。因此,脑大小的变化可能会使 AI 的相关估计值和 AI 的组间差异产生偏差。我们研究了如何分析大脑不对称性,以区分全局和局部效应,并报告英国生物库(N=40,028)中大脑不对称性的无偏组间差异。我们使用了英国生物库提供的 306 个全球和区域大脑测量值。全脑灰质和白质体积来自 Freesurfer ASEG,皮质下灰质体积来自 Freesurfer ASEG 和亚分割,皮质灰质体积、平均厚度和表面积来自 Destrieux 图谱,应用于 T1 和 T2 加权图像,小脑灰质体积来自 FAST FSL,区域白质体积来自 Freesurfer ASEG。我们分析了 L+R 测量、总脑测量(TCM,例如,总脑体积)和 l-R TCM 预测区域不对称性的程度。作为案例研究,我们评估了省略这些预测因子中的任何一个对性别差异在不对称性中的幅度和显著性的影响。我们发现,L+R 测量、TCM 和 l-R TCM 预测了 89%以上区域的 AI,它们的关系通常是线性的。删除这些预测因子中的任何一个都会改变 33%的区域的性别差异的显著性,以及 13-42%的区域的性别差异的幅度。尽管我们通常报告大脑不对称性的性别和年龄效应与以前的大规模研究相似,但对区域和全球脑大小进行适当调整会揭示大脑不对称性的其他性别和年龄效应。