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, padj = 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.

中文翻译：

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将功能和结构性大脑组织与自闭症行为联系起来：多模式 EU-AIMS 纵向欧洲自闭症项目 (LEAP) 研究

对自闭症患者大脑结构和功能的神经影像分析通常是孤立进行的，错过了跨模式链接数据的敏感性增益。在这里，我们重点关注大脑区域结构和功能组织特性的整合。我们的目标是识别自闭症的新的大脑组织表型。我们利用了来自 EU AIMS 纵向欧洲自闭症项目 (LEAP) 的多模态 MRI（T1 弥散加权和静息态功能）、自闭症 (n = 206) 和非自闭症 (n = 196) 参与者的行为和临床数据。其中，97 个具有来自 2 个时间点的数据，总扫描数量为 466。从各自的 MRI 模式中提取灰质密度图、概率纤维束成像连接矩阵和连接图，然后与关联独立成分分析相集成。线性混合效应模型用于评估成分和组之间的关系，同时考虑纵向数据参与者的协变量和非独立性。运行其他模型来研究与行为维度测量的关联。我们确定了一个在各组之间存在显着差异的成分（系数 = 0.33，padj = 0.02）。这是由右侧梭状回连接图 2 的方差驱动的 (99%)。虽然与行为测量存在多个名义上的（未校正的 p < 0.05）关联，但经过多重比较校正后，没有一个是显着的。我们的分析同时考虑了结构和功能性大脑表型的相对贡献，发现功能性表型与自闭症相关。这些发现扩展了之前的单峰研究，揭示了自闭症特有的功能连接模式的拓扑组织，值得进一步研究。