  Image-on-scalar regression has been a popular approach to modeling the
association between brain activities and scalar characteristics in neuroimaging
research. The associations could be heterogeneous across individuals in the
population, as indicated by recent large-scale neuroimaging studies, e.g., the
Adolescent Brain Cognitive Development (ABCD) study. The ABCD data can inform
our understanding of heterogeneous associations and how to leverage the
heterogeneity and tailor interventions to increase the number of youths who
benefit. It is of great interest to identify subgroups of individuals from the
population such that: 1) within each subgroup the brain activities have
homogeneous associations with the clinical measures; 2) across subgroups the
associations are heterogeneous; and 3) the group allocation depends on
individual characteristics. Existing image-on-scalar regression methods and
clustering methods cannot directly achieve this goal. We propose a latent
subgroup image-on-scalar regression model (LASIR) to analyze large-scale,
multi-site neuroimaging data with diverse sociodemographics. LASIR introduces
the latent subgroup for each individual and group-specific, spatially varying
effects, with an efficient stochastic expectation maximization algorithm for
inferences. We demonstrate that LASIR outperforms existing alternatives for
subgroup identification of brain activation patterns with functional magnetic
resonance imaging data via comprehensive simulations and applications to the
ABCD study. We have released our reproducible codes for public use with the
software package available on Github: https://github.com/zikaiLin/lasir.
