Brain Surface Anatomy in Adults With Autism

Abstract

Neuroimaging studies of brain anatomy in autism spectrum disorder (ASD) have mostly been based on measures of cortical volume (CV). However, CV is a product of 2 distinct parameters, cortical thickness (CT) and surface area (SA), that in turn have distinct genetic and developmental origins.Objective: To investigate regional differences in CV, SA, and CT as well as their relationship in a large and well-characterized sample of men with ASD and matched controls.Design: Multicenter case-control design using quantitative magnetic resonance imaging.

On the brain structure heterogeneity of autism: Parsing out acquisition site effects with significance‐weighted principal component analysis

“…Regarding intensity correction, in the MRC AIMS database used in this study [Ecker et al, 2012, 2013, 2015], a standardization procedure based on quantitative imaging [Deoni et al, 2008] was used to minimize inter‐site variance and improve the signal‐to‐noise contrast. However, as the between‐site analysis in “Experiment 1: Effect of Acquisition Site” section suggests, this strategy still results in variance that makes it easier to distinguish scanning sites than diagnostic groups.…”

“…Structural MRI were analysed from 136 adult, right‐handed males (68 with ASD and 68 matched controls) with no significant mean differences in age and full‐scale IQ, acquired from the centres contributing to the UK Medical Research Council Autism Imaging Multi‐centre Study (MRC AIMS) [Ecker et al, 2012, 2013, 2015] and recruited by advertisement. In this work, only participants recruited at the Institute of Psychiatry, King's College London (LON) and the Autism Research Centre, University of Cambridge (CAM) were included where an equivalent set of images were acquired from each participant.…”

“…For accurate registration to the standard stereotatic space of the Montreal Neurological Institute (MNI), a simulated T1‐weighted Inversion Recovery (IR) images (synT1) were created based on the qT1 maps (Ecker et al, 2012, 2013; Lai et al, 2012]. The synT1 images were then segmented using New Segment into grey (GM) and white matter (WM) maps, and normalized to the MNI space using DARTEL in SPM8 [Friston et al, 2007], with modulation (preserve volume) to retain information of regional/local GM and WM volumes, and smoothed with a 3mm FWHM Gaussian Kernel to account for inter‐subject mis‐registration.…”

“…The delimitation of both functionally and structurally affected areas in the brain in such an etiologically and neurobiologically heterogeneous condition has long been a major concern [Ecker and Murphy, 2014; Lai et al, 2013; Lenroot and Yeung, 2013]. With this context, the use of large samples is of fundamental importance, and has been addressed by establishing multi‐centre collaborations such as the UK Medical Research Council Autism Imaging Multi‐centre Study (MRC AIMS) [Ecker et al, 2012, 2013, 2015] and the Autism Brain Imaging Data Exchange (ABIDE) [Di Martino et al, 2014]…”

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

“…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.…”