In Vivo Evidence of Reduced Integrity of the Gray–White Matter Boundary in Autism Spectrum Disorder

Abstract

Atypical cortical organization and reduced integrity of the gray–white matter boundary have been reported by postmortem studies in individuals with autism spectrum disorder (ASD). However, there are no in vivo studies that examine these particular features of cortical organization in ASD. Hence, we used structural magnetic resonance imaging to examine differences in tissue contrast between gray and white matter in 98 adults with ASD and 98 typically developing controls, to test the hypothesis that individuals with ASD have significantly reduced tissue contrast. More specifically, we examined contrast as a percentage between gray and white matter tissue signal intensities (GWPC) sampled at the gray–white matter boundary, and across different cortical layers. We found that individuals with ASD had significantly reduced GWPC in several clusters throughout the cortex (cluster, P < 0.05). As expected, these reductions were greatest when tissue intensities were sampled close to gray–white matter interface, which indicates a less distinct gray–white matter boundary in ASD. Our in vivo findings of reduced GWPC in ASD are therefore consistent with prior postmortem findings of a less well-defined gray–white matter boundary in ASD. Taken together, these results indicate that GWPC might be utilized as an in vivo proxy measure of atypical cortical microstructural organization in future studies.

A systematic review of structural MRI biomarkers in autism spectrum disorder: A machine learning perspective

“…Furthermore, alterations in the frontal lobe are consistent with the known role of frontal striatal circuitry with repetitive behaviours (Kohls et al, 2014), making it a potentially useful endophenotype of these symptoms. A further nine studies reported CT changes in the temporal lobes, however the direction of these findings was inconsistent with four reporting regional increases in temporal lobe CT (Andrews et al, 2017; Haar et al, 2016; Hyde et al, 2009; Sharda et al, 2016b), and five reporting regional decreases in temporal lobe CT (Andrews et al, 2017; Doyle‐Thomas et al, 2013a; Ecker et al, 2013a; Pappaianni et al, 2018; Shi et al, 2013). As with the temporal lobe, the remaining findings showed a comparable number of studies reporting increases and decreases in the CT of other brain regions.…”

No evidence for differences in contrast of the grey-white matter boundary in Autism Spectrum Disorders: An open replication

“…We analysed the two hemispheres separately, which was taken into account using a Bonferroni correction. We accounted for multiple testing across brain vertices using Random Field Theory (RFT) as in the original article by Andrews et al (2017), and also using Monte Carlo simulations. RFT analyses were performed using the Matlab/Octave SurfStat Toolbox (Worsley et al 1999, http://www.math.mcgill.ca/keith/surfstat ).…”

“…To date, only the study of Andrews et al (2017) has analysed the contrast of this grey-white matter boundary, and reported a significant decrease in contrast at different cortical levels in ASD subjects. This study, however, was based on a relatively small sample of 98 individuals with ASD and 98 controls.…”

Change in emotional self‐concept following socio‐cognitive training relates to structural plasticity of the prefrontal cortex

“…In line with the above, the data described in our manuscript using a CFT/CDT of p  < .025 with p  < .05 for family‐wise‐error (FWE) correction (two‐tailed) should have resulted in false‐positive rates no larger than 5%–10%. The applied thresholds furthermore conform to previous research on other (Andrews et al., 2017; Bernhardt et al., 2013; Ecker et al., 2013; Hong et al., 2016) as well as the same data set (Valk et al., 2017), particularly for 20 mm FWHM smoothed surface‐based 2D thickness data where higher smoothing kernels relate to more readily fulfilled assumptions of Gaussian Random Field Theory (Eklund et al., 2016; Flandin & Friston, 2016; Greve & Fischl, 2017). In addition, the effects observed at the above thresholds within the three brain areas of interest are significant for each training cohort separately as evident from post hoc analyses—except for one association at a marginal level of p  = .054.…”

“…Statistical results were corrected for multiple comparisons by means of random field theory using both a typically used (Andrews et al., 2017; Bernhardt et al., 2013; Ecker et al., 2013; Hong, Bernhardt, Schrader, Bernasconi, & Bernasconi, 2016) cluster‐determining threshold (CDT) of p  < .025 and FWE of p  < .05 (two‐tailed) for 20 mm FWHM smoothed surface‐based 2D thickness data where higher smoothing kernels relate to more readily fulfilled assumptions of Gaussian Random Field Theory (Eklund, Nichols, & Knutsson, 2016; Flandin & Friston, 2016; Greve & Fischl, 2017), as well as a more conservative cluster‐determining threshold recently recommended for the analysis of surface‐based anatomical cortical thickness data (Greve & Fischl, 2017) of CDT p  < .01 and FWE p  < .02 (two‐tailed). Moreover, we verified consistency of results across the two separate training cohorts.…”