Department of Psychology and Neuroscience, Boston College, Boston, MA 02467, USA.
Science. 2022 Jun 3;376(6597):1070-1074. doi: 10.1126/science.abm2461. Epub 2022 Jun 2.
Autism spectrum disorder (ASD) is highly heterogeneous. Identifying systematic individual differences in neuroanatomy could inform diagnosis and personalized interventions. The challenge is that these differences are entangled with variation because of other causes: individual differences unrelated to ASD and measurement artifacts. We used contrastive deep learning to disentangle ASD-specific neuroanatomical variation from variation shared with typical control participants. ASD-specific variation correlated with individual differences in symptoms. The structure of this ASD-specific variation also addresses a long-standing debate about the nature of ASD: At least in terms of neuroanatomy, individuals do not cluster into distinct subtypes; instead, they are organized along continuous dimensions that affect distinct sets of regions.
自闭症谱系障碍（ASD）具有高度异质性。识别神经解剖学中的系统个体差异可以为诊断和个性化干预提供信息。挑战在于，这些差异与其他原因引起的变异交织在一起：与 ASD 无关的个体差异和测量伪影。我们使用对比深度学习来分离 ASD 特异性神经解剖学变异与与典型对照组共享的变异。ASD 特异性变异与症状的个体差异相关。这种 ASD 特异性变异的结构也解决了关于 ASD 本质的长期争论：至少在神经解剖学方面，个体不会聚类成不同的亚型；相反，它们沿着影响不同区域集的连续维度组织。
