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<p>Autism is diagnosed on the basis of early difficulties in social communication and restricted repetitive behaviors. However, the single label of ‘autism’ masks a large degree of multi-scale heterogeneity and does not equitably balance differing perspectives. Here we discuss a research-oriented framework that splits autism by a type I versus type II distinction based on non-core language, intellectual, motor and adaptive functioning features in early development. These subtypes are identifiable in a highly replicable manner with data-driven machine-learning techniques and take very different developmental paths leading to highly differentiated later-life outcomes. Distinctive neurobiological mechanisms also underlie the subtypes and point toward differential contributions of rare and common genetic variation, neurogenesis and cortical patterning mechanisms. The proposed framework will help balance differing perspectives and facilitate a better individualized understanding of the autisms.</p>

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Stratifying the autisms by a type I versus type II distinction in early development

  • 期刊论文

  • 发表日期：

    2026年3月10日

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  • Michael V. Lombardo,
  • Ines Severino,
  • Veronica Mandelli

摘要

Autism is diagnosed on the basis of early difficulties in social communication and restricted repetitive behaviors. However, the single label of ‘autism’ masks a large degree of multi-scale heterogeneity and does not equitably balance differing perspectives. Here we discuss a research-oriented framework that splits autism by a type I versus type II distinction based on non-core language, intellectual, motor and adaptive functioning features in early development. These subtypes are identifiable in a highly replicable manner with data-driven machine-learning techniques and take very different developmental paths leading to highly differentiated later-life outcomes. Distinctive neurobiological mechanisms also underlie the subtypes and point toward differential contributions of rare and common genetic variation, neurogenesis and cortical patterning mechanisms. The proposed framework will help balance differing perspectives and facilitate a better individualized understanding of the autisms.