Individualized Prediction of Reading Comprehension Ability Using Gray Matter Volume

Abstract

Reading comprehension is a crucial reading skill for learning and putatively contains 2 key components: reading decoding and linguistic comprehension. Current understanding of the neural mechanism underlying these reading comprehension components is lacking, and whether and how neuroanatomical features can be used to predict these 2 skills remain largely unexplored. In the present study, we analyzed a large sample from the Human Connectome Project (HCP) dataset and successfully built multivariate predictive models for these 2 skills using whole-brain gray matter volume features. The results showed that these models effectively captured individual differences in these 2 skills and were able to significantly predict these components of reading comprehension for unseen individuals. The strict cross-validation using the HCP cohort and another independent cohort of children demonstrated the model generalizability. The identified gray matter regions contributing to the skill prediction consisted of a wide range of regions covering the putative reading, cerebellum, and subcortical systems. Interestingly, there were gender differences in the predictive models, with the female-specific model overestimating the males' abilities. Moreover, the identified contributing gray matter regions for the female-specific and male-specific models exhibited considerable differences, supporting a gender-dependent neuroanatomical substrate for reading comprehension.

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“…The Institutional Review Boards of both Penn and CHOP approved study procedures. Following exclusion of participants with major medical disorders, missing data, or low-quality imaging data (see SI Methods), we included a sample of 946 subjects aged 8-23 years (27).…”

“…Image Acquisition and Preprocessing. All participants completed multi-modal neuroimaging on the same scanner using the same sequences (27). Diffusion images were pre-processed using standard procedures, and probabilistic tractography (40, 47) was used to construct a structural connectivity matrix among 232 regions defined using a standard surface-based structural parcellation (48-50).…”

“…In the outer 2F-CV, all subjects were divided into 2 subsets. Specifically, we sorted the subjects according to their age and then assigned the individuals with an odd rank to subset 1 and the individuals with an even rank to subset 2 (26,27). We first used subset 1 as a training set, and we used subset 2 as a testing set.…”

“…We regressed out all covariates (i.e., sex, handedness, total brain volume, total network strength, and in-scanner head motion) from each brain feature (i.e., control energy of one brain region) for the training set, and the resulting coefficients were used to regress out the covariates for subjects in the testing set. Each feature was linearly scaled between zero and one across the training dataset, and the scaling parameters were also applied to scale the testing dataset (26,27). We applied an inner 2-fold cross validation (2F-CV) within training set to select the optimal r parameter.…”

“…To test this hypothesis, we capitalized on a large sample of youth who completed neuroimaging as part of the Philadelphia Neurodevelopmental Cohort (PNC) (27). We examined how white matter networks (estimated using diffusion imaging) support the transition to a frontoparietal system activation state.…”