Altered social communication network in boys with autism spectrum disorder
Yu-Chun Lo1, Yu-Jen Chen1, Yung-Chin Hsu1, Susan Shur-Fen Gau2, and Wen-Yih Isaac Tseng1

1Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan, 2Department of Psychiatry, National Taiwan University College of Medicine, Taipei, Taiwan

Synopsis

Impaired social communication skills have been consistently reported in autism spectrum disorder (ASD). We used diffusion spectrum imaging to measure white matter integrity of the social communication network, and investigated its relationships with social communication and social interaction in 62 ASD and 55 typically developing (TD) boys. ASD showed partially reduced white matter integrity of the social communication network as compared to TD. Positive correlations were found between white matter integrity and the social interaction in ASD. In conclusion, altered microstructural property of the social communication network might be a structural correlate of social communication deficits in ASD.

Introduction

Autism spectrum disorders (ASD) constitutes a group of neurodevelopment disorders with social communication deficits as one of core symptoms. Patients with high functioning autism or Asperger’s syndrome showed worse performance than typically developing (TD) people in social communication skills1. Recently, Catani and Bambini proposed a five-level anatomical model of the language network for social communication 2 including the representation of informative actions, communicative intentions, lexical / semantic processing, syntactic analysis, and pragmatic integration. The hierarchical model in relation to developmental and evolutionary trajectories includes the superior longitudinal fasciculus (SLF ) as the frontal-parietal network for informative actions (level 1); frontal aslant tracts (FAT) as the frontal aslant network for communicative intentions (level 2); uncinate fasciculus (UF), inferior longitudinal fasciculus (ILF), and inferior frontal-occipital fasciculus (IFOF) as the anterior temporal networks for lexical and semantic processing (level 3); the arcuate fasciculus (AF) as the frontal-temporal network for syntactic analysis (level 4); and tracts in temporal-parietal junction (TPJ) as the temporal-parietal network for pragmatic integration (level 5)2. Given that the social communication network underlies social language functions, we hypothesize that the microstructural property of the social communication network may be altered in ASD, and the alteration may be related to deficits in social communication and social interaction.

Methods

Sixty-two right-handed boys with ASD and 55 TD boys (aged 10 to 19) received clinical evaluations, the Chinese version of the Social Communication Questionnaire (SCQ), and MRI scans. Diffusion spectrum imaging was used to measure the microstructural property of the association fibers in the social communication network, and their relationships with social communication and social interaction were investigated. All DSI data were acquired using a twice-refocused balanced echo diffusion echo planar imaging sequence. 102 diffusion encoding gradients with the maximum diffusion sensitivity bmax = 4000 s/mm2 were sampled on the grid points in a half sphere of the 3D q-space. To obtain the transformation between individual’s DSI and the NTU-122 DSI template3, we employed a registration method under the framework of Large Deformation Diffeomorphic Metric Mapping (LDDMM) fitted to 6D features of DSI datasets, 3D in the image space and 3D in the diffusion-encoding (q) space4. The construction strategy incorporated both the gray matter anatomy provided by T1-weighted images and the white matter fiber structures provided by DSI images. Automated Anatomical Labeling (AAL) atlas was invoked to define cortical and subcortical regions as ROIs for each of the targeted tracts. A streamline-based fiber tracking algorithm was performed based on the resolved fiber vector fields provided by the NTU-122 DSI template. Targeted tracts were reconstructed on the DSI template using DSI studio (http://dsi-studio.labsolver.org) (Figure 1). A template-based approach was employed to sample generalized fractional anisotropy (GFA) in each targeted tract 5. GFA of targeted tracts for each level in the social communication network were determined using the averaged GFA of the bilateral tracts.

Results

Boys with ASD had significantly lower averaged GFA values in level 1 (SLF ), level 2 (FAT), level 3 (UF, ILF, and IFOF), and level 5 (TPJ) than did the TD boys (Figure 2). After Bonferroni correction (p < 0.01), the group difference remained significant in the GFA values of the SLF III (level 1) and FAT (level 2). There was a significant positive association between the subscales of social interaction in the Chinese SCQ and the averaged GFA of the FAT in the ASD group, even after Bonferroni correction (p < 0.01) (r = 0.437, r2= 0.191, p = 0.007) (Figure 3).

Discussion

The present study combines DSI analysis and neuropsychological tests to investigate the associations of the social communication network integrity with social communication and social interaction in ASD and TD. The integrity of the SLF (level 1) and FAT (level 2) is reduced in boys with ASD. The findings imply that the microstructural integrity of the social communication network may have been altered in late childhood, possibly attributing to the biological basis of social communication deficits in ASD. The association of the FAT with social interaction subscales suggests a supportive role for the FAT in social interaction in ASD. Our results provide evidence of microstructural alterations in the social communication network and their association with social communication deficits in ASD.

Conclusion

Our findings support our hypothesis that altered microstructural property of the social communication network might be a structural correlate of social communication deficits in ASD.

Acknowledgements

No acknowledgement found.

References

1.Knaus TA, Silver AM, Lindgren KA, Hadjikhani N, Tager-Flusberg H. fMRI activation during a language task in adolescents with ASD. J Int Neuropsychol Soc. 2008; 14(6): 967-79.

2.Catani M, Bambini V. A model for Social Communication And Language Evolution and Development (SCALED). Current opinion in neurobiology. 2014; 28C: 165-71.

3.Hsu YC, Lo YC, Chen YJ, Wedeen VJ, Isaac Tseng WY. NTU-DSI-122: A diffusion spectrum imaging template with high anatomical matching to the ICBM-152 space. Hum Brain Mapp. 2015.

4.Hsu Y-C, Hsu C-H, Tseng W-YI. A large deformation diffeomorphic metric mapping solution for diffusion spectrum imaging datasets. NeuroImage. 2012; 63(2): 818-34.

5.Chen YJ, Lo YC, Hsu YC, Fan CC, Hwang TJ, Liu CM, et al. Automatic whole brain tract-based analysis using predefined tracts in a diffusion spectrum imaging template and an accurate registration strategy. Hum Brain Mapp. 2015.

Figures

Figure 1 Seven pairs of tracts in the social communication network. SLF (green), FAT (purple), AF (red), UF (blue), IFOF (dark blue), ILF (cyan), and TPJ (yellow) were identified as the white matter tracts in the social communication network.

Figure 2 Age, white matter integrity of boys with autism spectrum disorder and typically developing boys

Figure 3 Significant association between the subscales of social interaction in the Chinese SCQ and the averaged GFA of the FAT was found in boys with autism spectrum disorder




Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
3445