A convergent structure-function substrate of cognitive imbalances in autism

Abstract

Background: Autism spectrum disorder (ASD) is a common neurodevelopmental diagnosis showing substantial phenotypic heterogeneity. A leading example can be found in verbal and nonverbal cognitive skills, which vary from elevated to impaired compared with neurotypical individuals. Moreover, deficits in verbal profiles often coexist with normal or superior performance in the nonverbal domain.

Methods: To study brain substrates underlying cognitive imbalance in ASD, we capitalized categorical and dimensional IQ profiling as well as multimodal neuroimaging.

Results: IQ analyses revealed a marked verbal to nonverbal IQ imbalance in ASD across 2 datasets (Dataset-1: 155 ASD, 151 controls; Dataset-2: 270 ASD, 490 controls). Neuroimaging analysis in Dataset-1 revealed a structure-function substrate of cognitive imbalance, characterized by atypical cortical thickening and altered functional integration of language networks alongside sensory and higher cognitive areas.

Conclusion: Although verbal and nonverbal intelligence have been considered as specifiers unrelated to autism diagnosis, our results indicate that intelligence disparities are accentuated in ASD and reflected by a consistent structure-function substrate affecting multiple brain networks. Our findings motivate the incorporation of cognitive imbalances in future autism research, which may help to parse the phenotypic heterogeneity and inform intervention-oriented subtyping in ASD.

Keywords: autism; cognitive imbalance; multimodal neuroimaging; neurosubtyping; verbal and nonverbal IQ.

Fig. 1

Verbal and nonverbal IQ profiles in ASD. A) Findings in Dataset-1. Z-scores of verbal IQ, nonverbal IQ, and the vnIQ ratio (verbal/nonverbal IQ) in ASD relative to neurotypical controls. Error bars present standard deviations. B) Replication in Dataset-2.

Fig. 2

Subtyping based on verbal and nonverbal IQ. A) Subtyping results shown at k = 2 and 4, the solutions resulting in the highest Silhouette index. B) A k = 4 solution provided subtypes reflecting cognitive imbalances, particularly when comparing ASD2 vs. ASD4. Cortical thickness and functional connectivity features were profiled across all 4 subtypes, by comparing these measures to neurotypical controls (effect sizes are presented as Cohen’s D). For the functional connectivity analysis, connections showing a significant between-group difference were counted within- and between-network separately and plotted left to the connectome for within-community comparisons and above the corresponding connectome for between-community analyses, for each subtype. The peak modulation among the functional network is marked by ^*. The language peak was observed in 3 out of the 4 identified subtypes. The bottom panels show targeted comparisons between ASD2 (high nonverbal compared with verbal IQ) and ASD4 (high verbal compared with nonverbal IQ).

Fig. 3

PCA for the distribution of verbal and nonverbal IQ in ASD. A) The direction and scores of the 2 principal components derived from verbal and nonverbal IQ profiles. PC1 reflected individual variability along a general cognitive axis, whereas PC2 reflected verbal to nonverbal IQ imbalance. B) Between-group interaction analysis of PC1 and PC2 on cortical thickness and functional connectivity. Upper panels show significant cortical thickness modulations were delineated by solid boundaries whereas uncorrected tendencies are shown in semi-transparent. Lower panels show the proportion of functional connections that undergo a significant between-group interaction for both PC1 and PC2. Findings were stratified according to functional communities as in the prior figures.

Fig. 4

Structure–function substrates of vnIQ imbalance in ASD. A) Cortical thickness analysis. Left. Interaction between diagnostic group (ASD, controls) and vnIQ scores. FWE-corrected clusters are shown in solid and with black outlines, uncorrected trends in semitransparent. Right. Correlation between cortical thickness and vnIQ in each group separately. B) Functional associations. Left. Using a functional parcellation that includes the language network (Ji et al. 2019), interaction analysis was performed at the level of parcel-to-parcel connections. Uncorrected P-values from this interaction analysis were sorted according to functional communities (primary visual, secondary visual, somatomotor, cingulo-opercular, dorsal attention, language, frontoparietal, auditory, and default mode). Parcel-wise significant interactions were summed within each network, and stratified into within- vs. between-community connections. Right. Direct correlation analysis between vnIQ and functional connectivity across different communities, carried out in ASD and controls separately. Positive/negative effects are indicated in blue/red.

Fig. 5

Functional decoding of the cortical area related to cognitive imbalance. The significant area commonly found across all previous 3 analyses was identified (i.e. left middle frontal area), and fed into the Neurosynth decoding framework. The significance of the decoding result was indicated at P < 0.001 (one tailed; z > 3.1).