Predicting children's literacy from task-based fMRI: Neural heterogeneity and task-dependent performance

Abstract

Reading is a complex skill with a well-characterized neural basis. Multivariate fMRI analyses have deepened our neuroscientific understanding of literacy by linking neural patterns to behavioral traits. Although task-based fMRI often outperforms resting-state fMRI in predicting cognitive traits, few studies have applied it to continuous measures of childrens reading ability. To identify neural markers of literacy, we compared predictive performance across multiple fMRI tasks and reading-related measures. In this data-driven study, we predicted literacy skills in school-aged children (6.7-10.3 years) from eleven behavioral scores grouped into Reading (fluency and comprehension), Verbal (vocabulary knowledge and verbal intelligence), and Naming (object naming speed). Predictive performance was examined across four fMRI tasks completed by subgroups of children (n = 73-97): two active tasks - phonological-lexical decisions (PhonLex) and audiovisual character learning (Learn) - and two passive tasks - word and face viewing (Localizer) and character processing (CharProc). Individual activation contrast maps, categorized as simple (single condition) or subtractive (condition contrasts), were analyzed using a machine learning model with whole-brain predictors derived from principal component analysis. Results showed the highest predictive performance for Reading and Naming with PhonLex > Learn > Localizer = CharProc, and for Verbal with PhonLex = Learn > Localizer = CharProc. Simple contrasts generally outperformed subtractive contrasts in predicting behavioral scores. Key neural predictors, identified through whole-brain and region-of-interest analyses, included the left inferior frontal gyrus, supramarginal gyrus, ventral occipitotemporal cortex, insula, and default mode network regions. Together, these findings indicate that, for predicting literacy traits in children, active tasks and tasks that engage brain systems involved in multisensory learning tend to outperform both passive paradigms and simple subtractive task contrasts. This study provides a methodological benchmark for brain-based prediction of reading ability and highlights the value of activation heterogeneity across distributed regions as a potential marker for tracking literacy development over time.