Functional MRI (fMRI) researchers wishing to understand human brain processes increasingly estimate relations among regions of interest (ROIs) across time. Together, these estimates create a """"""""connectivity map"""""""" of how brain processing occurs. One ubiquitous issue for most connectivity mapping methods is that they require homogeneity across individuals for reliable and valid results to be obtained. Researchers currently have no choice but to rely on homogeneity assumptions despite consistent evidence suggesting that brain processes vary substantially across human samples within control and clinical populations. Thus to examine differences between subgroups created according to demographic, behavioral or diagnostic indices, researchers must assume that all individuals within these subgroups are the same. There is a need in the field of neuroimaging for data-driven methods for identifying subgroups of individuals from their connectivity maps to accommodate within-subgroup heterogeneity. Data-driven subgroup classification could identify brain processes which relate to suboptimal task performance or specific diagnoses by subgrouping the entire sample in addition to helping researchers understand heterogeneity within subgroups. The present project aims to fill this demand by developing a novel approach for analyzing fMRI data which: 1) arrives at valid sample-level inferences that may be generalized to the population;2) identifies subgroup classification for individuals;and 3) provides reliable parameter estimates at the individual level. After developing, validating, and implementing the new procedure, a program which builds from a successful novel algorithm developed by the present authors will be made freely available to the public.
The project, Data-driven approach for identifying subgroups using fMRI connectivity maps, aims to develop new ways of understanding how brain processes differ across people. We will produce free statistical software that will help researchers better identify subgroups to better understand heterogeneity which exists within diagnostic and control categories.
