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      Brain structural correlates of autistic traits across the diagnostic divide: A grey matter and white matter microstructure study

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          Highlights

          • Autistic traits exist along a continuum across the population.

          • Autistic traits linked to brain structure differences, measured using MRI and DTI.

          • Gray matter differences noted in regions including OFC and Lingual Gyrus.

          • White matter differences noted in tracts including ILF, IFOF, SLF.

          Abstract

          Autism Spectrum Disorders (ASD) are a set of neurodevelopmental conditions characterised by difficulties in social interaction and communication as well as stereotyped and restricted patterns of interest. Autistic traits exist in a continuum across the general population, whilst the extreme end of this distribution is diagnosed as clinical ASD. While many studies have investigated brain structure in autism using a case-control design, few have used a dimensional approach. To add to this growing body of literature, we investigated the structural brain correlates of autistic traits in a mixed sample of adult participants (25 ASD and 66 neurotypicals; age: 18–60 years). We examined the relationship between regional brain volumes (using voxel-based morphometry and surface-based morphometry) and white matter microstructure properties (using Diffusion Tensor Imaging) and autistic traits (using Autism Spectrum Quotient). Our findings show grey matter differences in regions including the orbitofrontal cortex and lingual gyrus, and suggestive evidence for white matter microstructure differences in tracts including the superior longitudinal fasciculus being related to higher autistic traits. These grey matter and white matter microstructure findings from our study are consistent with previous reports and support the brain structural differences in ASD. These findings provide further support for shared aetiology for autistic traits across the diagnostic divide.

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          Most cited references74

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          FreeSurfer.

          FreeSurfer is a suite of tools for the analysis of neuroimaging data that provides an array of algorithms to quantify the functional, connectional and structural properties of the human brain. It has evolved from a package primarily aimed at generating surface representations of the cerebral cortex into one that automatically creates models of most macroscopically visible structures in the human brain given any reasonable T1-weighted input image. It is freely available, runs on a wide variety of hardware and software platforms, and is open source. Copyright © 2012 Elsevier Inc. All rights reserved.
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            Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data.

            There has been much recent interest in using magnetic resonance diffusion imaging to provide information about anatomical connectivity in the brain, by measuring the anisotropic diffusion of water in white matter tracts. One of the measures most commonly derived from diffusion data is fractional anisotropy (FA), which quantifies how strongly directional the local tract structure is. Many imaging studies are starting to use FA images in voxelwise statistical analyses, in order to localise brain changes related to development, degeneration and disease. However, optimal analysis is compromised by the use of standard registration algorithms; there has not to date been a satisfactory solution to the question of how to align FA images from multiple subjects in a way that allows for valid conclusions to be drawn from the subsequent voxelwise analysis. Furthermore, the arbitrariness of the choice of spatial smoothing extent has not yet been resolved. In this paper, we present a new method that aims to solve these issues via (a) carefully tuned non-linear registration, followed by (b) projection onto an alignment-invariant tract representation (the "mean FA skeleton"). We refer to this new approach as Tract-Based Spatial Statistics (TBSS). TBSS aims to improve the sensitivity, objectivity and interpretability of analysis of multi-subject diffusion imaging studies. We describe TBSS in detail and present example TBSS results from several diffusion imaging studies.
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              The autism-spectrum quotient (AQ): evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians.

              Currently there are no brief, self-administered instruments for measuring the degree to which an adult with normal intelligence has the traits associated with the autistic spectrum. In this paper, we report on a new instrument to assess this: the Autism-Spectrum Quotient (AQ). Individuals score in the range 0-50. Four groups of subjects were assessed: Group 1: 58 adults with Asperger syndrome (AS) or high-functioning autism (HFA); Group 2: 174 randomly selected controls. Group 3: 840 students in Cambridge University; and Group 4: 16 winners of the UK Mathematics Olympiad. The adults with AS/HFA had a mean AQ score of 35.8 (SD = 6.5), significantly higher than Group 2 controls (M = 16.4, SD = 6.3). 80% of the adults with AS/HFA scored 32+, versus 2% of controls. Among the controls, men scored slightly but significantly higher than women. No women scored extremely highly (AQ score 34+) whereas 4% of men did so. Twice as many men (40%) as women (21%) scored at intermediate levels (AQ score 20+). Among the AS/HFA group, male and female scores did not differ significantly. The students in Cambridge University did not differ from the randomly selected control group, but scientists (including mathematicians) scored significantly higher than both humanities and social sciences students, confirming an earlier study that autistic conditions are associated with scientific skills. Within the sciences, mathematicians scored highest. This was replicated in Group 4, the Mathematics Olympiad winners scoring significantly higher than the male Cambridge humanities students. 6% of the student sample scored 32+ on the AQ. On interview, 11 out of 11 of these met three or more DSM-IV criteria for AS/HFA, and all were studying sciences/mathematics, and 7 of the 11 met threshold on these criteria. Test-retest and interrater reliability of the AQ was good. The AQ is thus a valuable instrument for rapidly quantifying where any given individual is situated on the continuum from autism to normality. Its potential for screening for autism spectrum conditions in adults of normal intelligence remains to be fully explored.
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                Author and article information

                Contributors
                Journal
                Neuroimage Clin
                Neuroimage Clin
                NeuroImage : Clinical
                Elsevier
                2213-1582
                23 November 2021
                2021
                23 November 2021
                : 32
                : 102897
                Affiliations
                [a ]Centre for Autism, School of Psychology and Clinical Language Sciences (SPCLS), University of Reading, UK
                [b ]Center for Mind and Brain, University of California Davis, Davis, CA, USA
                [c ]School of Psychology and Clinical Language Sciences, University of Reading, Harry Pitt Building, Earley Gate, Whiteknights Road, Reading RG6 6AL, UK
                [d ]Centro de Ciencia Cognitiva, Facultad de Lenguas y Educación, Universidad Antonio de Nebrija, Calle de Sta. Cruz de Marcenado, 27, 28015 Madrid, Spain
                [e ]Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women’s and Children’s Health, Karolinska Institutet & Stockholm Health Care Services, Stockholm, Sweden
                [f ]School of Mind and Brain, Humboldt University, Berlin, Germany
                [g ]Department of Language and Culture, UiT- The Arctic University of Norway, Hansine Hansens veg 18, 9019 Tromsø, Norway
                [h ]Department of Psychology, Ashoka University, Sonipat, India
                [i ]India Autism Center, Kolkata, India
                Author notes
                [* ]Corresponding author at: School of Psychology and Clinical Language Sciences (SPCLS), University of Reading, UK. v.arunachalamchandran@ 123456pgr.reading.ac.uk
                Article
                S2213-1582(21)00341-7 102897
                10.1016/j.nicl.2021.102897
                8641248
                34911200
                020522f7-a58f-4fba-8f41-6567ce55b85e
                © 2021 Published by Elsevier Inc.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 28 May 2021
                : 25 October 2021
                : 22 November 2021
                Categories
                Regular Article

                autism spectrum disorders,autistic traits,grey matter,white matter microstructure,structural mri,diffusion tensor imaging

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