Theory-driven classification of reading difficulties from fMRI data using Bayesian latent-mixture models.

ABSTRACT: Decades of research have led to several competing theories regarding the neural contributors to impaired reading. But how can we know which theory (or theories) identifies the types of markers that indeed differentiate between individuals with reading disabilities (RD) and their typically developing (TD) peers? To answer this question, we propose a new analytical tool for theory evaluation and comparison, grounded in the Bayesian latent-mixture modeling framework. We start by constructing a series of latent-mixture classification models, each reflecting one existing theoretical claim regarding the neurofunctional markers of RD (highlighting network-level differences in either mean activation, inter-subject heterogeneity, inter-region variability, or connectivity). Then, we run each model o

SUBMITTER: Siegelman N 

PROVIDER: S-EPMC8494078 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature