Ressa Haylee J, Newman Benjamin T, Jacokes Zachary, McPartland James C, Kleinhans Natalia M, Druzgal T Jason, Pelphrey Kevin A, Van Horn John Darrell
Department of Psychology, University of Virginia, Gilmer Hall, Charlottesville, VA 22903.
Department of Neurology, University of Virginia, School of Medicine, Gilmer Hall, Charlottesville, VA 2290.
bioRxiv. 2024 Nov 28:2024.09.04.611183. doi: 10.1101/2024.09.04.611183.
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and repetitive behaviors. Our lab has previously found that g-ratio, the proportion of axon width to myelin diameter, and axonal conduction velocity, which is associated with the capacity of an axon to carry information, are both decreased in ASD individuals. By associating these differences with performance on cognitive and behavioral tests, this study aims to first associate a broad array of behavioral metrics with neuroimaging markers of ASD, and to explore the prevalence of ASD subtypes using a neuroimaging driven perspective. Analyzing 273 participants (148 with ASD) ages 8 to 17 through an NIH-sponsored Autism Centers of Excellence network (MH100028), we observe widespread associations between behavioral and cognitive evaluations of autism and between behavioral and microstructural metrics, alongside different directional correlations between different behavioral metrics. Stronger associations with individual subcategories from each test rather than summary scores suggest that different neuronal profiles may be masked by composite test scores. Machine learning cluster analyses applied to neuroimaging data reinforce the association between neuroimaging and behavioral metrics and suggest that age-related maturation of brain metrics may drive changes in ASD behavior. This suggests that if ASD can be definitively subtyped, these subtypes may show different behavioral trajectories across the developmental period. Clustering identified a pattern of restrictive and repetitive behavior in some participants and a second group that was defined by high sensory sensitivity and language performance.
自闭症谱系障碍（ASD）是一种神经发育障碍，其特征是社交沟通缺陷和重复行为。我们实验室之前发现，轴突宽度与髓鞘直径之比的g值以及与轴突携带信息能力相关的轴突传导速度，在ASD个体中均有所下降。通过将这些差异与认知和行为测试的表现相关联，本研究旨在首先将一系列广泛的行为指标与ASD的神经影像学标志物相关联，并从神经影像学驱动的角度探索ASD亚型的患病率。通过美国国立卫生研究院资助的卓越自闭症中心网络（MH100028）对273名年龄在8至17岁之间的参与者（148名患有ASD）进行分析，我们观察到自闭症的行为与认知评估之间以及行为与微观结构指标之间存在广泛关联，同时不同行为指标之间存在不同方向的相关性。与每个测试的各个子类别而非汇总分数有更强的关联表明，不同的神经元特征可能会被综合测试分数掩盖。应用于神经影像学数据的机器学习聚类分析强化了神经影像学与行为指标之间的关联，并表明大脑指标的年龄相关成熟可能会推动ASD行为的变化。这表明，如果ASD能够明确地进行亚型分类，那么这些亚型在整个发育时期可能会表现出不同的行为轨迹。聚类分析在一些参与者中确定了一种限制性和重复性的行为模式，以及另一组以高感官敏感性和语言表现为特征的人群。
