Latent Subgroup Identification in Image-on-scalar Regression

TL;DR

This paper introduces LASIR, a novel model for identifying latent subgroups in neuroimaging data, capturing heterogeneity in brain-phenotype associations to improve understanding and interventions.

Contribution

LASIR is the first method to simultaneously identify latent subgroups and estimate subgroup-specific brain associations in image-on-scalar regression.

Findings

LASIR outperforms existing methods in simulations.

LASIR effectively identifies subgroups in ABCD neuroimaging data.

The software is publicly available for reproducibility.

Abstract

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.