Department of Psychology, Yale University, New Haven, United States; Kavli Institute for Neuroscience, Yale University, New Haven, United States.
Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore.
Neuroimage. 2022 Oct 15;260:119485. doi: 10.1016/j.neuroimage.2022.119485. Epub 2022 Jul 14.
Individual differences in brain anatomy can be used to predict variations in cognitive ability. Most studies to date have focused on broad population-level trends, but the extent to which the observed predictive features are shared across sexes and age groups remains to be established. While it is standard practice to account for intracranial volume (ICV) using proportion correction in both regional and whole-brain morphometric analyses, in the context of brain-behavior predictions the possible differential impact of ICV correction on anatomical features and subgroups within the population has yet to be systematically investigated. In this work, we evaluate the effect of proportional ICV correction on sex-independent and sex-specific predictive models of individual cognitive abilities across multiple anatomical properties (surface area, gray matter volume, and cortical thickness) in healthy young adults (Human Connectome Project; n = 1013, 548 females) and typically developing children (Adolescent Brain Cognitive Development study; n = 1823, 979 females). We demonstrate that ICV correction generally reduces predictive accuracies derived from surface area and gray matter volume, while increasing predictive accuracies based on cortical thickness in both adults and children. Furthermore, the extent to which predictive models generalize across sexes and age groups depends on ICV correction: models based on surface area and gray matter volume are more generalizable without ICV correction, while models based on cortical thickness are more generalizable with ICV correction. Finally, the observed neuroanatomical features predictive of cognitive abilities are unique across age groups regardless of ICV correction, but whether they are shared or unique across sexes (within age groups) depends on ICV correction. These findings highlight the importance of considering individual differences in ICV, and show that proportional ICV correction does not remove the effects of cranial volume from anatomical measurements and can introduce ICV bias where previously there was none. ICV correction choices affect not just the strength of the relationships captured, but also the conclusions drawn regarding the neuroanatomical features that underlie those relationships.
大脑解剖结构的个体差异可用于预测认知能力的变化。迄今为止,大多数研究都集中在广泛的人口水平趋势上,但观察到的预测特征在性别和年龄组之间的共享程度仍有待确定。虽然在区域性和全脑形态计量学分析中使用比例校正来考虑颅内体积(ICV)是标准做法,但在大脑-行为预测的背景下,ICV 校正对人口中解剖特征和亚组的可能差异影响尚未得到系统研究。在这项工作中,我们评估了比例 ICV 校正对健康年轻成年人(人类连接组计划;n=1013,548 名女性)和正常发育的儿童(青少年大脑认知发展研究;n=1823,979 名女性)多个解剖学特征(表面积、灰质体积和皮质厚度)的个体认知能力的性别独立和性别特异性预测模型的影响。我们证明,ICV 校正通常会降低来自表面积和灰质体积的预测准确性,同时增加成人和儿童基于皮质厚度的预测准确性。此外,预测模型在性别和年龄组之间的通用性程度取决于 ICV 校正:基于表面积和灰质体积的模型在没有 ICV 校正的情况下更具通用性,而基于皮质厚度的模型在有 ICV 校正的情况下更具通用性。最后,无论是否进行 ICV 校正,可预测认知能力的神经解剖特征在不同年龄组中都是独特的,但它们在性别之间是否共享或独特(在年龄组内)取决于 ICV 校正。这些发现强调了考虑 ICV 个体差异的重要性,并表明比例 ICV 校正不会从解剖测量中消除颅腔体积的影响,并且可以在以前没有的地方引入 ICV 偏差。ICV 校正选择不仅影响所捕获的关系的强度,还影响关于构成这些关系的神经解剖特征的结论。