Anomalous gray matter patterns in specific reading comprehension deficit are independent of dyslexia

Abstract

Specific reading comprehension deficit (SRCD) affects up to 10 % of all children. SRCD is distinct from dyslexia (DYS) in that individuals with SRCD show poor comprehension despite adequate decoding skills. Despite its prevalence and considerable behavioral research, there is not yet a unified cognitive profile of SRCD. While its neuroanatomical basis is unknown, SRCD could be anomalous in regions subserving their commonly reported cognitive weaknesses in semantic processing or executive function. Here we investigated, for the first time, patterns of gray matter volume difference in SRCD as compared to DYS and typical developing (TD) adolescent readers (N = 41). A linear support vector machine algorithm was applied to whole brain gray matter volumes generated through voxel-based morphometry. As expected, DYS differed significantly from TD in a pattern that included features from left fusiform and supramarginal gyri (DYS vs. TD: 80.0 %, p < 0.01). SRCD was well differentiated not only from TD (92.5 %, p < 0.001) but also from DYS (88.0 %, p < 0.001). Of particular interest were findings of reduced gray matter volume in right frontal areas that were also supported by univariate analysis. These areas are thought to subserve executive processes relevant for reading, such as monitoring and manipulating mental representations. Thus, preliminary analyses suggest that SRCD readers possess a distinct neural profile compared to both TD and DYS readers and that these differences might be linked to domain-general abilities. This work provides a foundation for further investigation into variants of reading disability beyond DYS.

Keywords: Magnetic resonance imaging; Multivariate pattern analysis; Reading skill; Specific reading comprehension deficit; Voxel-based morphometry.

Figure 1

Schematic of the MVPA analysis pipeline. 1) Label the gray matter volume maps from the groups of interest, leaving one out. 2) The MVPA algorithm computes an optimal weighting map that distinguishes the two groups from each other. 3) Test algorithm performance by feeding in the subject that was left out. The algorithm will either correctly or incorrectly identify the novel map. 4) Iterate 2000 times, leaving out a random subject each time. This results in performance metrics such as accuracy as well as an average map of areas important for classification. 5) Conjoin multiple weighting maps to identify areas that uniquely classify each group from the other.

Figure 2

All classifiers performed at levels significantly above chance (2000 permutations).

Figure 3

Weighting maps for each MVPA classifier (p < 0.05, permutation-based correction) overlaid on the mean gray matter volume template for all subjects. Negatively weighted regions are those which contribute towards a positive classification of the comparison group (e.g. in "DYS vs. TD", high values in areas negatively weighted for SRCD would yield a prediction of TD). The blue boxes highlight negative weights in temporal language regions used in classifiers involving DYS. The red boxes highlight negative weights in right hemisphere regions contributing to classifiers involving SRCD.

Figure 4

Cortical areas of significant weighting for each conjoined set of classifier maps. Individual classifiers (e.g. "SRCD vs. DYS" and "SRCD vs. TD") were conjoined to find areas where each group was consistently characterized by increased or decreased gray matter volume relative to both comparison groups (e.g. "SRCD > TD & SRCD" and "SRCD < TD & SRCD"). Areas shown were significant in each classifier at p < 0.05 after permutation-based correction (2000 permutations).