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Three shades of grey: detecting brain abnormalities in children with autism using source‐, voxel‐ and surface‐based morphometry.

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Title: Three shades of grey: detecting brain abnormalities in children with autism using source‐, voxel‐ and surface‐based morphometry.
Authors: Pappaianni, Edoardo, Siugzdaite, Roma, Vettori, Sofie, Venuti, Paola, Job, Remo, Grecucci, Alessandro
Source: European Journal of Neuroscience. Mar2018, Vol. 47 Issue 6, p690-700. 11p. 2 Diagrams, 5 Charts, 1 Graph.
Subjects: Children with autism spectrum disorders, Gray matter (Nerve tissue), Morphometrics, Stereotypy (Psychiatry), Social interaction
Abstract: Abstract: Autistic spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social interactions, communication and stereotyped behaviour. Recent evidence from neuroimaging supports the hypothesis that ASD deficits in adults may be related to abnormalities in a specific frontal–temporal network [Autism‐specific Structural Network (ASN)]. To see whether these results extend to younger children and to better characterize these abnormalities, we applied three morphometric methods on brain grey matter (GM) of children with and without ASD. We selected 39 sMRI images of male children with ASD and 42 typically developing (TD) from the Autism Brain Imaging Data Exchange database. We used source‐based morphometry (SoBM), a whole‐brain multivariate approach to identify GM networks, voxel‐based morphometry (VBM), a voxel‐wise comparison of the local GM concentration and surface‐based morphometry (SuBM) for the estimation of the cortical parameters. SoBM showed a bilateral frontal–parietal–temporal network different between groups, including the inferior–middle temporal gyrus, the inferior parietal lobule and the postcentral gyrus; VBM returned differences only in the right temporal lobe; SuBM returned a thinning in the right inferior temporal lobe thinner in ASD, a higher gyrification in the right superior parietal lobule in TD and in the middle frontal gyrus in ASD. For the first time, we investigated the brain abnormalities in children with ASD using three morphometric techniques. The results were relatively consistent between methods, stressing the role of an Autism‐specific Structural Network in ASD individuals. We also make methodological speculations on the relevance of using multivariate and whole‐brain neuroimaging analysis to capture ASD complexity. [ABSTRACT FROM AUTHOR]
Copyright of European Journal of Neuroscience is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Three shades of grey: detecting brain abnormalities in children with autism using source‐, voxel‐ and surface‐based morphometry.
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  Data: <searchLink fieldCode="JN" term="%22European+Journal+of+Neuroscience%22">European Journal of Neuroscience</searchLink>. Mar2018, Vol. 47 Issue 6, p690-700. 11p. 2 Diagrams, 5 Charts, 1 Graph.
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  Data: <searchLink fieldCode="DE" term="%22Children+with+autism+spectrum+disorders%22">Children with autism spectrum disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Gray+matter+%28Nerve+tissue%29%22">Gray matter (Nerve tissue)</searchLink><br /><searchLink fieldCode="DE" term="%22Morphometrics%22">Morphometrics</searchLink><br /><searchLink fieldCode="DE" term="%22Stereotypy+%28Psychiatry%29%22">Stereotypy (Psychiatry)</searchLink><br /><searchLink fieldCode="DE" term="%22Social+interaction%22">Social interaction</searchLink>
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  Data: Abstract: Autistic spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social interactions, communication and stereotyped behaviour. Recent evidence from neuroimaging supports the hypothesis that ASD deficits in adults may be related to abnormalities in a specific frontal–temporal network [Autism‐specific Structural Network (ASN)]. To see whether these results extend to younger children and to better characterize these abnormalities, we applied three morphometric methods on brain grey matter (GM) of children with and without ASD. We selected 39 sMRI images of male children with ASD and 42 typically developing (TD) from the Autism Brain Imaging Data Exchange database. We used source‐based morphometry (SoBM), a whole‐brain multivariate approach to identify GM networks, voxel‐based morphometry (VBM), a voxel‐wise comparison of the local GM concentration and surface‐based morphometry (SuBM) for the estimation of the cortical parameters. SoBM showed a bilateral frontal–parietal–temporal network different between groups, including the inferior–middle temporal gyrus, the inferior parietal lobule and the postcentral gyrus; VBM returned differences only in the right temporal lobe; SuBM returned a thinning in the right inferior temporal lobe thinner in ASD, a higher gyrification in the right superior parietal lobule in TD and in the middle frontal gyrus in ASD. For the first time, we investigated the brain abnormalities in children with ASD using three morphometric techniques. The results were relatively consistent between methods, stressing the role of an Autism‐specific Structural Network in ASD individuals. We also make methodological speculations on the relevance of using multivariate and whole‐brain neuroimaging analysis to capture ASD complexity. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of European Journal of Neuroscience is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Mar2018
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