Examining the Boundary Sharpness Coefficient as an Index of Cortical Microstructure in Autism Spectrum Disorder

Abstract

Autism spectrum disorder (ASD) is associated with atypical brain development. However, the phenotype of regionally specific increased cortical thickness observed in ASD may be driven by several independent biological processes that influence the gray/white matter boundary, such as synaptic pruning, myelination, or atypical migration. Here, we propose to use the boundary sharpness coefficient (BSC), a proxy for alterations in microstructure at the cortical gray/white matter boundary, to investigate brain differences in individuals with ASD, including factors that may influence ASD-related heterogeneity (age, sex, and intelligence quotient). Using a vertex-based meta-analysis and a large multicenter structural magnetic resonance imaging (MRI) dataset, with a total of 1136 individuals, 415 with ASD (112 female; 303 male), and 721 controls (283 female; 438 male), we observed that individuals with ASD had significantly greater BSC in the bilateral superior temporal gyrus and left inferior frontal gyrus indicating an abrupt transition (high contrast) between white matter and cortical intensities. Individuals with ASD under 18 had significantly greater BSC in the bilateral superior temporal gyrus and right postcentral gyrus; individuals with ASD over 18 had significantly increased BSC in the bilateral precuneus and superior temporal gyrus. Increases were observed in different brain regions in males and females, with larger effect sizes in females. BSC correlated with ADOS-2 Calibrated Severity Score in individuals with ASD in the right medial temporal pole. Importantly, there was a significant spatial overlap between maps of the effect of diagnosis on BSC when compared with cortical thickness. These results invite studies to use BSC as a possible new measure of cortical development in ASD and to further examine the microstructural underpinnings of BSC-related differences and their impact on measures of cortical morphology.

High spatial overlap but diverging age-related trajectories of cortical MRI markers aiming to represent intracortical myelin and microstructure

“…Since cortical markers previously described have been shown to correlate with the curvature of the cortical surfaces (Olafson et al, 2021;Shafee et al, 2015), we acquired curvature estimates of the gray-white matter surface with the CIVET 2.1.0 pipeline in order to residualize the markers against mean curvature. This process is done after the smoothing procedure for analyses in the main text, and before the smoothing procedure in supplementary analyses (see supplementary figures 8-11).…”

“…In spite of these biophysical contrast mechanisms, several studies use metrics derived from the signal intensity as a representation of cortical "microstructure", a non-specific term that does not have an agreed-upon biologically meaningful definition. Furthermore, metrics derived from T1w and T2w images are often interpreted as being influenced by myeloarchitecture, and more specifically the density or concentration of gray-matter (GM) myelin (Glasser & Van Essen, 2011;Olafson et al, 2021;Salat et al, 2009). In this manuscript, we seek to characterize the similarities and differences in three measures that are often used to describe cortical myelin and microstructure.…”

“…In this manuscript, we seek to characterize the similarities and differences in three measures that are often used to describe cortical myelin and microstructure. These include: 1) The ratio of gray-to-white matter T1w signal intensities (gray-white matter ratio [GWC]; as originally proposed in Salat and colleagues (2009)), which is often used as a putative marker for myelination of deeper cortical layers (Chwa et al, 2020;Drakulich et al, 2021;Jørgensen et al, 2016;Vidal-Piñeiro et al, 2016); 2) The ratio of T1w to T2w signal (T1w/T2w ratio) has been proposed to be more sensitive to myelin than either contrast alone (Glasser & Van Essen, 2011;Grydeland et al, 2013Grydeland et al, , 2019, due to the generally inverse dependence of T1w and T2w signals on myelin; and 3) The boundary sharpness coefficient (BSC), recently proposed by our group, which examines the sharpness of the change in T1w signal intensity from superficial white matter (SWM) to gray matter (Olafson et al, 2021).…”

“…Furthermore, studies reported a generally inverted correlation of CT and GM myelin across some areas of the cortex, particularly using "virtual histology" techniques relating cell-specific gene expression to MRI contrast (Patel et al, 2020;Shafee et al, 2015). These cortical measures have also been reported as being sensitive to maturation and aging (Drakulich et al, 2021;Fjell et al, 2009;Grydeland et al, 2019;Olafson et al, 2021;Salat et al, 2009Salat et al, , 2011Vidal-Piñeiro et al, 2016), suggesting that they are sensitive to normative age-related variations. However, for these measures to be meaningful in understanding neurobiology, and if they are indeed sensitive to the same myeloarchitectonic properties, their age-related trajectories should be similar.…”

Small animal imaging presents an opportunity for improving translational research in biological psychiatry

“…Additionally, they provide a scaffold through which one can relate sources of neurobiological heterogeneity (in the form of brain anatomy and function) to clinical and behavioural heterogeneity. [4][5][6] In particular, magnetic resonance imaging (MRI) methods have been important in this regard, given the public availability of data and the multiple analysis streams that leverage different tissue properties to make inferences on brain topology and circuitry. 7,8 Arguably, this sustained strategy contributed to recent significant advances in using knowledge of brain circuitry to target novel brain stimulation approaches as treatments for neuropsychiatric disorders 9,10 and for improving our understanding of the mechanism of action of pharmacological agents (like ketamine) being repurposed for use in different clinical contexts.…”