Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Kapittelweg 29, 6525 EN, Nijmegen, The Netherlands.
Karakter Child and Adolescent Psychiatry University Centre, Nijmegen, The Netherlands.
Mol Autism. 2023 Aug 31;14(1):32. doi: 10.1186/s13229-023-00564-3.
Neuroimaging analyses of brain structure and function in autism have typically been conducted in isolation, missing the sensitivity gains of linking data across modalities. Here we focus on the integration of structural and functional organisational properties of brain regions. We aim to identify novel brain-organisation phenotypes of autism. We utilised multimodal MRI (T1-, diffusion-weighted and resting state functional), behavioural and clinical data from the EU AIMS Longitudinal European Autism Project (LEAP) from autistic (n = 206) and non-autistic (n = 196) participants. Of these, 97 had data from 2 timepoints resulting in a total scan number of 466. Grey matter density maps, probabilistic tractography connectivity matrices and connectopic maps were extracted from respective MRI modalities and were then integrated with Linked Independent Component Analysis. Linear mixed-effects models were used to evaluate the relationship between components and group while accounting for covariates and non-independence of participants with longitudinal data. Additional models were run to investigate associations with dimensional measures of behaviour. We identified one component that differed significantly between groups (coefficient = 0.33, p = 0.02). This was driven (99%) by variance of the right fusiform gyrus connectopic map 2. While there were multiple nominal (uncorrected p < 0.05) associations with behavioural measures, none were significant following multiple comparison correction. Our analysis considered the relative contributions of both structural and functional brain phenotypes simultaneously, finding that functional phenotypes drive associations with autism. These findings expanded on previous unimodal studies by revealing the topographic organisation of functional connectivity patterns specific to autism and warrant further investigation.
神经影像学对自闭症的大脑结构和功能的分析通常是孤立进行的,因此错过了将模态之间的数据联系起来以提高敏感性的机会。在这里,我们专注于整合大脑区域的结构和功能组织特性。我们旨在确定自闭症的新的大脑组织表型。我们利用来自欧盟 AIMS 纵向欧洲自闭症项目(LEAP)的自闭症(n=206)和非自闭症(n=196)参与者的多模态 MRI(T1-、弥散加权和静息态功能)、行为和临床数据。其中,97 名参与者有 2 个时间点的数据,总共扫描了 466 次。从各自的 MRI 模式中提取了灰质密度图、概率追踪连通矩阵和连接图,并将其与链接独立成分分析相结合。线性混合效应模型用于评估组间成分的关系,同时考虑了协变量和具有纵向数据的参与者的非独立性。还运行了额外的模型来研究与行为维度测量的关联。我们确定了一个在组间差异显著的成分(系数=0.33,p=0.02)。这主要是由右侧梭状回连接图 2 的方差驱动的(99%)。虽然与行为测量有多个名义(未校正 p<0.05)关联,但在多重比较校正后,没有一个是显著的。我们的分析同时考虑了结构和功能脑表型的相对贡献,发现功能表型驱动着与自闭症的关联。这些发现通过揭示特定于自闭症的功能连通模式的拓扑组织,扩展了以前的单模态研究,并值得进一步研究。