Development of cortical thickness and surface area in autism spectrum disorder

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental disorder often associated with changes in cortical volume. The constituents of cortical volume – cortical thickness and surface area – have separable developmental trajectories and are related to different neurobiological processes. However, little is known about the developmental trajectories of cortical thickness and surface area in ASD. In this magnetic resonance imaging (MRI) study, we used an accelerated longitudinal design to investigate the cortical development in 90 individuals with ASD and 90 typically developing controls, aged 9 to 20 years. We quantified cortical measures using the FreeSurfer software package, and then used linear mixed model analyses to estimate the developmental trajectories for each cortical measure. Our primary finding was that the development of surface area follows a linear trajectory in ASD that differs from typically developing controls. In typical development, we found a decline in cortical surface area between the ages of 9 and 20 that was absent in ASD. We found this pattern in all regions where developmental trajectories for surface area differed between groups. When we applied a more stringent correction that takes the interdependency of measures into account, this effect on cortical surface area retained significance for left banks of superior temporal sulcus, postcentral area, and right supramarginal area. These areas have previously been implicated in ASD and are involved in the interpretation and processing of audiovisual social stimuli and distinction between self and others. Although some differences in cortical volume and thickness were found, none survived the more stringent correction for multiple testing. This study underscores the importance of distinguishing between cortical surface area and thickness in investigating cortical development, and suggests the development of cortical surface area is of importance to ASD.

Cortical surface architecture endophenotype and correlates of clinical diagnosis of autism spectrum disorder

“…Regarding the developmental trajectories of CT in ASD, quadratic age trajectories composed of three phases have been proposed: accelerated expansion during early childhood; accelerated thinning during late childhood and adolescence; and, lastly, decelerated thinning during early adulthood . Notably, recent large‐sample studies have reported a dynamic CT pattern of group differences observed between children with ASD and those with TD over broad regions of the cortex, but with differences fading over adolescence to a virtually identical CT by young adult age . Furthermore, a multivariate classification study using the Autism Brain Imaging Data Exchange dataset concluded that anatomical differences in cortical measures of CV, CT, and surface area offer very limited diagnostic value in ASD .…”

“…When examining cortical structure, surface‐based cortical matrices, such as cortical thickness (CT), fractal dimension (FD; a marker of cortical complexity), and sulcal depth (SD), are of particular interest. Unlike the conventional approach to examining regional gray matter volume, these surface‐based cortical measures focus on cortical folding . As they reflect different aspects of cortical architecture and stem from different genetic and cellular mechanisms in the brain, they have the potential to provide a more complete picture of the pathophysiology involved in the cortical architecture of ASD than gray matter volume.…”

“…Unlike the conventional approach to examining regional gray matter volume, these surface-based cortical measures focus on cortical folding. [7][8][9][10] As they reflect different aspects of cortical architecture and stem from different genetic and cellular mechanisms in the brain, 11,12 they have the potential to provide a more complete picture of the pathophysiology involved in the cortical architecture of ASD than gray matter volume.…”

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

“…Increased SA was also observed for younger children with ASD (Ohta et al, 2016), which is consistent with the findings of early overgrowth followed by accelerated decline in brain volumes (Hardan et al, 2009) and suggests that this can be a robust structural correlate of ASD. However, it is less clear on the developmental trajectory of SA with age, with one study reporting an accelerated reduction in SA with age (from 7 to 39 years) (Doyle‐Thomas et al, 2013b), while in a larger study (180 participants) the reduction in SA with age observed in healthy controls was not observed in ASD participants (Mensen et al, 2017).…”

Cortical surface architecture endophenotype and correlates of clinical diagnosis of autism spectrum disorder

“…Unlike the conventional approach to examine regional gray matter volume, these surface-based cortical measures focus on cortical folding. [7][8][9][10] Since they reflect different aspects of cortical architecture and stem from different genetic and cellular mechanisms in the brain, 11,12 they have the potential to provide a more complete picture of the pathophysiology involved in the cortical architecture of ASD than gray matter volume.…”