Structural Connectivity of the Amygdala in Young Adults with Autism Spectrum Disorder

Poster Presentation
Friday, May 11, 2018: 11:30 AM-1:30 PM
Hall Grote Zaal (de Doelen ICC Rotterdam)
C. R. Gibbard1, D. H. Skuse2, J. D. Clayden1 and C. A. Clark1, (1)Developmental Imaging and Biophysics Section, UCL Great Ormond Street Institute of Child Health, London, United Kingdom, (2)Behavioural and Brain Sciences Unit, Population Policy and Practice Programme, UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
Background: Autism spectrum disorders (ASD) are characterised by impairments in social cognition. The amygdala is thought to be involved in abilities that directly impact on social engagement, such as emotion recognition and ‘theory of mind’. Structural magnetic resonance imaging (MRI) studies report both amygdala enlargement and reduction in ASD, whilst functional MRI studies have shown reduced amygdala activation in ASD participants in response to social cues. Little is known about the structural connectivity of the amygdala in ASD. Subdivisions of the amygdala have been identified that show specificity of structure, connectivity, and function, and recent diffusion tensor imaging studies have segmented the healthy adult amygdala into sub-regions in vivo using white matter connectivity-based parcellation schemes.

Objectives: The aim of this study was to investigate the microstructural properties of amygdala–cortical structural connections and their association with ASD behaviours, and whether connectivity of specific amygdala sub-regions is associated with particular ASD traits.

Methods: 25 high-functioning ASD (mean age: 24.7yr) and 26 neurotypical (mean age:23.2yr) participants underwent whole-brain T1-weighted (1mm3) and diffusion-weighted (2.5mm3; 60 directions at b=1000s/mm2; 3 b=0) MRI on a 1.5T Siemens Avanto scanner. Diffusion data were pre-processed using FSL. FSL FIRST and SIENAX were used to delineate amygdala and whole brain regions of interest, respectively. Cortical regions of interest were generated using FreeSurfer, and these cortical targets were grouped into frontal, parietal, occipital, and temporal lobes, and the insula using FSL utilities. All regions of interest were registered to diffusion space using FSL FLIRT and FNIRT. TractoR was used to seed probabilistic tractography from each amygdala voxel to the five cortical targets. An iterative 'winner takes all' process was used to parcellate the amygdala based on its primary cortical connection, which resulted in clusters of amygdala voxels that were maximally connected to the same cortical target. Fractional anisotropy (FA) and mean diffusivity (MD) were measured in each cluster’s white matter tracts. Group comparisons were made using ANCOVA and linear regression models. Correlations with the self-reported autism quotient (AQ), a measure of ASD symptom severity, were made within the ASD group using partial Spearman correlation. Age, gender and full-scale IQ were covariates.

Results: Amygdala volume was greater in ASD compared to controls (F(1,94)=4.19; p=0.04). In the ASD group, MD was elevated in white matter tracts connecting the right amygdala to the right cortex (t=2.35; p=0.05), which correlated with the severity of emotion recognition deficits (rho=-0.53; p=0.01). Amygdala parcellation resulted in four clusters that maximally connected to the frontal, parietal, temporal, and insula lobes, respectively. In ASD subjects, reduced FA in white matter connecting the left amygdala to the temporal cortex was associated with greater attention switching impairment, as measured by the AQ (rho=-0.61; p=0.02).

Conclusions: This study demonstrates that both amygdala volume and the microstructure of connections between the amygdala and the cortex are altered in ASD. Findings indicate that the microstructure of right amygdala white matter tracts are associated with overall ASD severity, but that investigation of amygdala sub-regions can identify more specific associations.