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.

Three shades of grey: detecting brain abnormalities in children with autism using source-, voxel- and surface-based morphometry / Pappaianni, E; Siugzdaite, R; Vettori, S; Venuti, P; Job, R; Grecucci, A.. - In: EJN. EUROPEAN JOURNAL OF NEUROSCIENCE. - ISSN 1460-9568. - ELETTRONICO. - 2018, 47:6(2018), pp. 690-700. [10.1111/ejn.13704]

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

Pappaianni E;Siugzdaite R;Venuti P;Job R;Grecucci A.
2018-01-01

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.
2018
6
Pappaianni, E; Siugzdaite, R; Vettori, S; Venuti, P; Job, R; Grecucci, A.
Three shades of grey: detecting brain abnormalities in children with autism using source-, voxel- and surface-based morphometry / Pappaianni, E; Siugzdaite, R; Vettori, S; Venuti, P; Job, R; Grecucci, A.. - In: EJN. EUROPEAN JOURNAL OF NEUROSCIENCE. - ISSN 1460-9568. - ELETTRONICO. - 2018, 47:6(2018), pp. 690-700. [10.1111/ejn.13704]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/205443
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