Abnormal multimodal neuroimaging patterns associated with social deficits in male autism spectrum disorder

Abstract

Atypical social impairments (i.e., impaired social cognition and social communication) are vital manifestations of autism spectrum disorder (ASD) patients, and the incidence rate of ASD is significantly higher in males than in females. Characterizing the atypical brain patterns underlying social deficits of ASD is significant for understanding the pathogenesis. However, there are no robust imaging biomarkers that are specific to ASD, which may be due to neurobiological complexity and limitations of single-modality research. To describe the multimodal brain patterns related to social deficits in ASD, we highlighted the potential functional role of white matter (WM) and incorporated WM functional activity and gray matter structure into multimodal fusion. Gray matter volume (GMV) and fractional amplitude of low-frequency fluctuations of WM (WM-fALFF) were combined by fusion analysis model adopting the social behavior. Our results revealed multimodal spatial patterns associated with Social Responsiveness Scale multiple scores in ASD. Specifically, GMV exhibited a consistent brain pattern, in which salience network and limbic system were commonly identified associated with all multiple social impairments. More divergent brain patterns in WM-fALFF were explored, suggesting that WM functional activity is more sensitive to ASD's complex social impairments. Moreover, brain regions related to social impairment may be potentially interconnected across modalities. Cross-site validation established the repeatability of our results. Our research findings contribute to understanding the neural mechanisms underlying social disorders in ASD and affirm the feasibility of identifying biomarkers from functional activity in WM.

Keywords: autism spectrum disorder; multimodal neuroimaging; social impairments.

FIGURE 1

Flowchart of social‐directed multimodal fusion and validation. (a) Flowchart of multimodal fusion including multimodal image processing, processing matrices and scores vectors, multimodal fusion and calculating the correlation values between loadings vectors. (b) Leave‐one‐site‐out repeatability. (c) Association with other SRS subdomains scores. IC, independent component; SRS, Social Responsiveness Scale; TMP, tissue probability map.

FIGURE 2

The identified joint component of SRS total score. (a) The spatial maps are visualized at | z | > 1.5 thresholds, where the red/blue regions indicate positive/negative contributions for the correlation between loadings of the identified components and SRS total score in GMV or WM‐fALFF. (b) The left chart is the scatterplot for the correlation between GMV loadings of IC and SRS total score; the right chart is violin plot for the two‐sample t test on GMV loadings. (c) The left chart is the 27 result of correlation between WM‐fALFF loadings of IC and SRS total score, the right chart is result of a two‐sample t test on WM‐fALFF loadings. * means p < .005.

FIGURE 3

Comparison of multimodal identified independent components. (a) The three subdomain spatial maps visualized at | z | > 1.5 thresholds and the results of correlation between loadings of IC and scores in GMV. (b) The three subdomain spatial maps visualized at | z | > 1.5 thresholds and the results of correlation between loadings of IC and scores in WM‐fALFF. * means p < .005. (c) The correlation matrix between three subdomains components and total component in GMV and WM‐fALFF.

FIGURE 4

Similarity patterns of the identified multimodal spatial maps between cohorts. Reference is a subdomain score vector. Column of “discovery” is main result of spatial maps visualized at | z | > 1.5 thresholds for all modalities. Column of “validation” is validation maps for all modalities.