Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands.
Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2023 Nov;8(11):1084-1093. doi: 10.1016/j.bpsc.2022.08.011. Epub 2022 Sep 6.
Although many studies have explored atypicalities in gray matter (GM) and white matter (WM) morphology of autism, most of them relied on unimodal analyses that did not benefit from the likelihood that different imaging modalities may reflect common neurobiology. We aimed to establish brain patterns of modalities that differentiate between individuals with and without autism and explore associations between these brain patterns and clinical measures in the autism group.
We studied 183 individuals with autism and 157 nonautistic individuals (age range, 6-30 years) in a large, deeply phenotyped autism dataset (EU-AIMS LEAP [European Autism Interventions-A Multicentre Study for Developing New Medications Longitudinal European Autism Project]). Linked independent component analysis was used to link all participants' GM volume and WM diffusion tensor images, and group comparisons of modality shared variances were examined. Subsequently, we performed univariate and multivariate brain-behavior correlation analyses to separately explore the relationships between brain patterns and clinical profiles.
One multimodal pattern was significantly related to autism. This pattern was primarily associated with GM volume in bilateral insula and frontal, precentral and postcentral, cingulate, and caudate areas and co-occurred with altered WM features in the superior longitudinal fasciculus. The brain-behavior correlation analyses showed a significant multivariate association primarily between brain patterns that involved variation of WM and symptoms of restricted and repetitive behavior in the autism group.
Our findings demonstrate the assets of integrated analyses of GM and WM alterations to study the brain mechanisms that underpin autism and show that the complex clinical autism phenotype can be interpreted by brain covariation patterns that are spread across the brain involving both cortical and subcortical areas.
尽管许多研究已经探索了自闭症患者大脑灰质(GM)和白质(WM)形态的异常,但大多数研究都依赖于单模态分析,而没有利用不同成像模式可能反映共同神经生物学的可能性。我们旨在建立区分自闭症患者和非自闭症患者的模态大脑模式,并探索自闭症组中这些大脑模式与临床测量之间的关联。
我们在一个大型的、深度表型自闭症数据集(EU-AIMS LEAP [欧洲自闭症干预措施-开发新药物的多中心研究纵向欧洲自闭症项目])中研究了 183 名自闭症患者和 157 名非自闭症患者(年龄范围 6-30 岁)。使用链接独立成分分析将所有参与者的 GM 体积和 WM 扩散张量图像联系起来,并检查模态共享方差的组间比较。随后,我们进行了单变量和多变量脑-行为相关性分析,分别探索大脑模式与临床特征之间的关系。
一个多模态模式与自闭症显著相关。该模式主要与双侧岛叶和额、前中央和后中央、扣带回和尾状核区域的 GM 体积有关,同时伴有上纵束 WM 特征的改变。脑-行为相关性分析显示,自闭症组中 WM 变化的脑模式与受限和重复行为症状之间存在显著的多变量关联。
我们的发现证明了 GM 和 WM 改变的综合分析在研究自闭症的大脑机制方面的优势,并表明复杂的临床自闭症表型可以通过跨大脑分布的大脑共变模式来解释,这些模式涉及皮质和皮质下区域。