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.

翻译：图像标量回归已成为神经影像学研究中建模大脑活动与标量特征关联的流行方法。近期大规模神经影像学研究（如青少年脑认知发展ABCD研究）表明，这种关联在人群中可能具有个体异质性。ABCD数据有助于理解异质性关联，以及如何利用这种异质性制定个性化干预措施，使更多青少年受益。识别人群中的亚组具有重要价值，需满足：1）各亚组内大脑活动与临床指标具有同质关联；2）亚组间关联呈现异质性；3）组分配取决于个体特征。现有图像标量回归方法和聚类方法无法直接实现该目标。我们提出潜在亚组图像标量回归模型（LASIR），用于分析具有多样化社会人口学特征的大规模多站点神经影像数据。LASIR为每个个体引入潜在亚组变量，并构建组特异性空间变系数效应，辅以高效的随机期望最大化算法进行推断。通过综合模拟实验和ABCD研究应用，我们证明LASIR在基于功能磁共振成像数据的大脑激活模式亚组识别任务中优于现有替代方法。我们已将可复现代码上传至Github开源软件包供公众使用：https://github.com/zikaiLin/lasir。