In Vivo Evidence of Reduced Integrity of the Grey-White Matter Boundary in Autism Spectrum Disorder

Background: Histology studies have revealed abnormal cell patterning along the boundary between cortical layer VI and underlying white matter in Autism Spectrum Disorder (ASD) (Avino & Hutsler, 2010). These findings may be indicative of potential neural migration deficits in the condition. However, there are no in vivo studies that examine these particular features of cortical organization in ASD.

Objectives: The current study sought to replicate previous histology findings using in vivo neuroimaging based measures of grey-white matter signal intensity ratios (GWR). As individuals with ASD are thought to have less distinct grey and white matter boundaries we expected to find significant decreases in mean GWR amongst our ASD patients, including brain regions previously indicated by Avino & Hutsler (2010) as having abnormal cell patterning at the grey-white matter interface, namely the superior temporal gyrus, dorsolateral frontal lobe, and dorsal parietal lobe.

Methods: 98 right-handed adults with ASD (49 males & 49 females) and 98 matched typically developing controls (51 males and 47 females) aged 18-42 years were recruited and assessed (ICD-10, ADI-R, & ADOS) at the IoPPN, London, and the ARC, Cambridge. A quantitative T1-mapping MRI protocol (Deoni et al., 2008) was used to derive T1-weighted images. Tessellated cortical surface reconstructions were produced using FreeSurfer software (http://surfer.nmr.mgh.harvard.edu/). Tissue intensities were measured from at intervals from 10 to 60% into the thickness of the cortical ribbon from the pial surface for grey matter (G) and 1 mm subjacent to the grey-white boundary along the surface normal for white matter (W). GWR at each sampling depth was calculated as a percentage of grey to white matter contrast at each vertex (i): 100*(W_i-G_i)/0.5*(W_i+G_i) (Salat et al. 2009). GWR measures were subsequently smoothed using a 10mm FWHM Gaussian kernel. Vertex-wise statistical analysis of GWR was estimated by regression of a general linear model at each vertex with diagnostic group, sex, and site as categorical fixed-effects factors, and age and full scale IQ as continuous covariates. Corrections for multiple comparisons across the whole brain were performed using ‘random field theory’ based cluster analysis for non-isotropic images (p<0.05) (Worsley et al. 1999).

Results: When co-varing for the effects of biological sex, scanning site, age, and IQ, we found significant decreases in GWR measures in individuals with ASD compared to TD controls (RFT-based cluster-corrected, p<.05) in clusters centered on the parahippocampal gyrus (BA 36), fusiform gyrus (BA 20), inferior parietal lobule (BA 40), superior and middle temporal gyri (BA 21), and in the anterior cingulate (BA 32). As expected, these reductions were greatest when tissue intensities were sampled close to grey-white matter interface, which indicates a less distinct grey-white matter boundary in ASD.

Conclusions: Individuals with ASD have reductions in GWR measures in several brain regions across the cortex, which have previously been implicated in ASD. Our findings indicate that the boundary between grey and white matter in these regions is less distinct in ASD, which supports previous histology studies suggesting that ASD is associated with potential neural migration deficits in the brain.