Healthy adults have robust individual differences in neuroanatomy and cognitive ability not captured by demographics or gross morphology (). We used a hierarchical independent component analysis (hICA) to create novel characterizations of individual differences in our participants (N = 190). These components fused data across multiple cognitive tests and neuroanatomical variables. The first level contained four independent, underlying sources of phenotypic variance that predominately modeled broad relationships within types of data (e.g., “white matter,” or “subcortical gray matter”), but were not reflective of traditional individual difference measures such as sex, age, or intracranial volume. After accounting for the novel individual difference measures, a second level analysis identified two underlying sources of phenotypic variation. One of these made strong, joint contributions to both the anatomical structures associated with the core fronto-parietal “rich club” network (), and to cognitive factors. These findings suggest that a hierarchical, data-driven approach is able to identify underlying sources of individual difference that contribute to cognitive-anatomical variation in healthy young adults.
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