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Global and Regional Structural Differences and Prediction of Autistic Traits during Adolescence
Background: Autistic traits are commonly viewed as dimensional in nature, and as continuously distributed in the general population. In this respect, the identification of predictive values of markers such as subtle autism-related alterations in brain morphology for parameter values of autistic trai...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9496772/ https://www.ncbi.nlm.nih.gov/pubmed/36138923 http://dx.doi.org/10.3390/brainsci12091187 |
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author | Nees, Frauke Banaschewski, Tobias Bokde, Arun L. W. Desrivières, Sylvane Grigis, Antoine Garavan, Hugh Gowland, Penny Grimmer, Yvonne Heinz, Andreas Brühl, Rüdiger Isensee, Corinna Becker, Andreas Martinot, Jean-Luc Paillère Martinot, Marie-Laure Artiges, Eric Papadopoulos Orfanos, Dimitri Lemaître, Hervé Stringaris, Argyris van Noort, Betteke Paus, Tomáš Penttilä, Jani Millenet, Sabina Fröhner, Juliane H. Smolka, Michael N. Walter, Henrik Whelan, Robert Schumann, Gunter Poustka, Luise |
author_facet | Nees, Frauke Banaschewski, Tobias Bokde, Arun L. W. Desrivières, Sylvane Grigis, Antoine Garavan, Hugh Gowland, Penny Grimmer, Yvonne Heinz, Andreas Brühl, Rüdiger Isensee, Corinna Becker, Andreas Martinot, Jean-Luc Paillère Martinot, Marie-Laure Artiges, Eric Papadopoulos Orfanos, Dimitri Lemaître, Hervé Stringaris, Argyris van Noort, Betteke Paus, Tomáš Penttilä, Jani Millenet, Sabina Fröhner, Juliane H. Smolka, Michael N. Walter, Henrik Whelan, Robert Schumann, Gunter Poustka, Luise |
author_sort | Nees, Frauke |
collection | PubMed |
description | Background: Autistic traits are commonly viewed as dimensional in nature, and as continuously distributed in the general population. In this respect, the identification of predictive values of markers such as subtle autism-related alterations in brain morphology for parameter values of autistic traits could increase our understanding of this dimensional occasion. However, currently, very little is known about how these traits correspond to alterations in brain morphology in typically developing individuals, particularly during a time period where changes due to brain development processes do not provide a bias. Therefore, in the present study, we analyzed brain volume, cortical thickness (CT) and surface area (SA) in a cohort of 14–15-year-old adolescents (N = 285, female: N = 162) and tested their predictive value for autistic traits, assessed with the social responsiveness scale (SRS) two years later at the age of 16–17 years, using a regression-based approach. We found that autistic traits were significantly predicted by volumetric changes in the amygdala (r = 0.181), cerebellum (r = 0.128) and hippocampus (r = −0.181, r = −0.203), both in boys and girls. Moreover, the CT of the superior frontal region was negatively correlated (r = −0.144) with SRS scores. Furthermore, we observed a significant association between the SRS total score and smaller left putamen volume, specifically in boys (r = −0.217), but not in girls. Our findings suggest that neural correlates of autistic traits also seem to lie on a continuum in the general population, are determined by limbic–striatal neuroanatomical brain areas, and are partly dependent on sex. As we imaged adolescents from a large population-based cohort within a small age range, these data may help to increase the understanding of autistic-like occasions in otherwise typically developing individuals. |
format | Online Article Text |
id | pubmed-9496772 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94967722022-09-23 Global and Regional Structural Differences and Prediction of Autistic Traits during Adolescence Nees, Frauke Banaschewski, Tobias Bokde, Arun L. W. Desrivières, Sylvane Grigis, Antoine Garavan, Hugh Gowland, Penny Grimmer, Yvonne Heinz, Andreas Brühl, Rüdiger Isensee, Corinna Becker, Andreas Martinot, Jean-Luc Paillère Martinot, Marie-Laure Artiges, Eric Papadopoulos Orfanos, Dimitri Lemaître, Hervé Stringaris, Argyris van Noort, Betteke Paus, Tomáš Penttilä, Jani Millenet, Sabina Fröhner, Juliane H. Smolka, Michael N. Walter, Henrik Whelan, Robert Schumann, Gunter Poustka, Luise Brain Sci Article Background: Autistic traits are commonly viewed as dimensional in nature, and as continuously distributed in the general population. In this respect, the identification of predictive values of markers such as subtle autism-related alterations in brain morphology for parameter values of autistic traits could increase our understanding of this dimensional occasion. However, currently, very little is known about how these traits correspond to alterations in brain morphology in typically developing individuals, particularly during a time period where changes due to brain development processes do not provide a bias. Therefore, in the present study, we analyzed brain volume, cortical thickness (CT) and surface area (SA) in a cohort of 14–15-year-old adolescents (N = 285, female: N = 162) and tested their predictive value for autistic traits, assessed with the social responsiveness scale (SRS) two years later at the age of 16–17 years, using a regression-based approach. We found that autistic traits were significantly predicted by volumetric changes in the amygdala (r = 0.181), cerebellum (r = 0.128) and hippocampus (r = −0.181, r = −0.203), both in boys and girls. Moreover, the CT of the superior frontal region was negatively correlated (r = −0.144) with SRS scores. Furthermore, we observed a significant association between the SRS total score and smaller left putamen volume, specifically in boys (r = −0.217), but not in girls. Our findings suggest that neural correlates of autistic traits also seem to lie on a continuum in the general population, are determined by limbic–striatal neuroanatomical brain areas, and are partly dependent on sex. As we imaged adolescents from a large population-based cohort within a small age range, these data may help to increase the understanding of autistic-like occasions in otherwise typically developing individuals. MDPI 2022-09-02 /pmc/articles/PMC9496772/ /pubmed/36138923 http://dx.doi.org/10.3390/brainsci12091187 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Nees, Frauke Banaschewski, Tobias Bokde, Arun L. W. Desrivières, Sylvane Grigis, Antoine Garavan, Hugh Gowland, Penny Grimmer, Yvonne Heinz, Andreas Brühl, Rüdiger Isensee, Corinna Becker, Andreas Martinot, Jean-Luc Paillère Martinot, Marie-Laure Artiges, Eric Papadopoulos Orfanos, Dimitri Lemaître, Hervé Stringaris, Argyris van Noort, Betteke Paus, Tomáš Penttilä, Jani Millenet, Sabina Fröhner, Juliane H. Smolka, Michael N. Walter, Henrik Whelan, Robert Schumann, Gunter Poustka, Luise Global and Regional Structural Differences and Prediction of Autistic Traits during Adolescence |
title | Global and Regional Structural Differences and Prediction of Autistic Traits during Adolescence |
title_full | Global and Regional Structural Differences and Prediction of Autistic Traits during Adolescence |
title_fullStr | Global and Regional Structural Differences and Prediction of Autistic Traits during Adolescence |
title_full_unstemmed | Global and Regional Structural Differences and Prediction of Autistic Traits during Adolescence |
title_short | Global and Regional Structural Differences and Prediction of Autistic Traits during Adolescence |
title_sort | global and regional structural differences and prediction of autistic traits during adolescence |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9496772/ https://www.ncbi.nlm.nih.gov/pubmed/36138923 http://dx.doi.org/10.3390/brainsci12091187 |
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