研究人员使用对比性深度学习将自闭症谱系障碍（ASD）特有的神经解剖学变异与典型对照组参与者共有的变异分开。ASD特有的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性质的一个长期争论。至少在神经解剖学方面，个体并没有聚集成不同的亚型；相反，他们是沿着影响不同区域的连续维度组织的。
据了解，ASD是高度异质性的。识别神经解剖学的系统性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造成的变化纠缠在一起：与ASD无关的个体差异和测量伪影。
附：英文原文
Title: Contrastive machine learning reveals the structure of neuroanatomical variation within autism
Author: Aidas Aglinskas, Joshua K. Hartshorne, Stefano Anzellotti
Issue&Volume: 2022-06-03
Abstract: 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.
DOI: abm2461
Source: https://www.science.org/doi/10.1126/science.abm2461