Are Autistic Traits in the General Population Related to Global and Regional Brain Differences?

Abstract

There is accumulating evidence that autistic-related traits in the general population lie on a continuum, with autism spectrum disorders representing the extreme end of this distribution. Here, we tested the hypothesis of a possible relationship between autistic traits and brain morphometry in the general population. Participants completed the short autism-spectrum quotient-questionnaire (AQ); T1-anatomical and DWI-scans were acquired. Associations between autistic traits and gray matter, and white matter microstructural-integrity were performed on the exploration-group (N = 204; 105 males, M-age = 22.85), and validated in the validation-group (N = 304; 155 males, M-age = 22.82). No significant associations were found between AQ-scores and brain morphometry in the exploration-group, or after pooling the data. This questions the assumption that autistic traits and their morphological associations do lie on a continuum in the general population.Electronic supplementary materialThe online version of this article (doi:10.1007/s10803-015-2441-6) contains supplementary material, which is available to authorized users.

Spontaneous resting‐state gamma oscillations are not predictive of autistic traits in the general population

“…These findings indicate that the relationship between spontaneous resting‐state gamma power and autistic traits is not present in TD individuals, and suggest that more extreme (clinical) scores are needed to establish this link. This is in line with a magnetic resonance imaging (MRI) study by Koolschijn, Geurts, Van Der Leij, and Scholte () that did not find an association between autistic traits and brain morphometry in TD individuals, questioning the assumption of a broad endophenotype as well. The present lack of a relationship between spontaneous resting‐state oscillatory data and autism in a TD population is supported by the finding that not only gamma but almost none of the examined power spectra was found to be related to autistic traits in TD individuals, while they have all been related to ASD symptomatology in the clinical population (see Wang et al., , for a review).…”

Structural brain imaging correlates of ASD and ADHD across the lifespan: a hypothesis-generating review on developmental ASD–ADHD subtypes

“…A small study comparing corpus callosum volumes in young adolescents [~13 years (8–18 years)] with ASD and ADHD, reported that larger midbody areas compared to controls were found in adolescents with ASD but not ADHD (Fine et al 2014 ). Quantitative measures of ASD and ADHD in relation to gray matter volumes in healthy young adults (18–29 years) also showed non-overlapping results, with ASD symptoms correlating with the left posterior cingulate, and ADHD symptoms with the right parietal lobe, right temporal frontal cortex, bilateral thalamus, and left hippocampus/amygdala complex (Geurts et al 2013 , but see Koolschijn et al 2015 for non-replication of ASD findings). This heterogeneous mix of findings is difficult to interpret due to the lack of longitudinal brain measures used, the small sample sizes with large heterogeneity within the clinical and control groups, the often arbitrary distinction between both clinical groups where comorbid ADHD is largely ignored in ASD studies and high levels of ASD symptoms are present in subjects with ADHD (as reported by for instance Brieber et al 2007 , the ASD affected group had equally high levels of ADHD symptoms as the ADHD affected group) and the various methods used to select brain measures of interest.…”

The Amsterdam Open MRI Collection, a set of multimodal MRI datasets for individual difference analyses

“…One study 4 analyzed the functional MRI data of a subset of 20 subjects by relating features from computational models applied to the movie data to the voxelwise time series using representational similarity analysis. Another study 5 analyzed the relationship between autistic traits and voxel-based morphometry (VBM, derived from the T1-weighted scans) as well as fractional anisotropy (FA, derived from the DWI scans) in a subset of 508 subjects.…”