[article] 
inMolecular Autism > 15 (2024) . - 3p.

Titre :	Gray matter covariations in autism: out-of-sample replication using the ENIGMA autism cohort
Type de document :	Texte imprimé et/ou numérique
Auteurs :	Ting MEI, Auteur ; Alberto LLERA, Auteur ; Natalie J. FORDE, Auteur ; Daan VAN ROOIJ, Auteur ; Dorothea L. FLORIS, Auteur ; Christian F. BECKMANN, Auteur ; Jan K. BUITELAAR, Auteur
Article en page(s) :	3p.
Langues :	Anglais (eng)
Mots-clés :	Humans  Gray Matter/diagnostic imaging  Autistic Disorder/diagnostic imaging  Autism Spectrum Disorder/diagnostic imaging  Retrospective Studies  Magnetic Resonance Imaging/methods  Brain/diagnostic imaging  Autism  Gray matter volume covariation  Replication  advisory board member of, and a speaker for Janssen Cilag BV, Eli Lilly, Shire,  Lundbeck, Roche, and Servier. He is not an employee of any of these companies,  and not a stock shareholder of any of these companies. He has no other financial  or material support, including expert testimony, patents or royalties. The  present work is unrelated to the above grants and relationships. The other  authors report no biomedical financial interests or potential conflicts of  interest.
Index. décimale :	PER Périodiques
Résumé :	BACKGROUND: Autism spectrum disorder (henceforth autism) is a complex neurodevelopmental condition associated with differences in gray matter (GM) volume covariations, as reported in our previous study of the Longitudinal European Autism Project (LEAP) data. To make progress on the identification of potential neural markers and to validate the robustness of our previous findings, we aimed to replicate our results using data from the Enhancing Neuroimaging Genetics Through Meta-Analysis (ENIGMA) autism working group. METHODS: We studied 781 autistic and 927 non-autistic individuals (6-30 years, IQ???50), across 37 sites. Voxel-based morphometry was used to quantify GM volume as before. Subsequently, we used spatial maps of the two autism-related independent components (ICs) previously identified in the LEAP sample as templates for regression analyses to separately estimate the ENIGMA-participant loadings to each of these two ICs. Between-group differences in participants' loadings on each component were examined, and we additionally investigated the relation between participant loadings and autistic behaviors within the autism group. RESULTS: The two components of interest, previously identified in the LEAP dataset, showed significant between-group differences upon regressions into the ENIGMA cohort. The associated brain patterns were consistent with those found in the initial identification study. The first IC was primarily associated with increased volumes of bilateral insula, inferior frontal gyrus, orbitofrontal cortex, and caudate in the autism group relative to the control group (? = 0.129, p = 0.013). The second IC was related to increased volumes of the bilateral amygdala, hippocampus, and parahippocampal gyrus in the autism group relative to non-autistic individuals (? = 0.116, p = 0.024). However, when accounting for the site-by-group interaction effect, no significant main effect of the group can be identified (p > 0.590). We did not find significant univariate association between the brain measures and behavior in autism (p > 0.085). LIMITATIONS: The distributions of age, IQ, and sex between LEAP and ENIGMA are statistically different from each other. Owing to limited access to the behavioral data of the autism group, we were unable to further our understanding of the neural basis of behavioral dimensions of the sample. CONCLUSIONS: The current study is unable to fully replicate the autism-related brain patterns from LEAP in the ENIGMA cohort. The diverse group effects across ENIGMA sites demonstrate the challenges of generalizing the average findings of the GM covariation patterns to a large-scale cohort integrated retrospectively from multiple studies. Further analyses need to be conducted to gain additional insights into the generalizability of these two GM covariation patterns.
En ligne :	https://dx.doi.org/10.1186/s13229-024-00583-8
Permalink :	https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=537

[article] Gray matter covariations in autism: out-of-sample replication using the ENIGMA autism cohort [Texte imprimé et/ou numérique] / Ting MEI, Auteur ; Alberto LLERA, Auteur ; Natalie J. FORDE, Auteur ; Daan VAN ROOIJ, Auteur ; Dorothea L. FLORIS, Auteur ; Christian F. BECKMANN, Auteur ; Jan K. BUITELAAR, Auteur . - 3p.
Langues : Anglais (eng)
in Molecular Autism > 15 (2024) . - 3p.

Mots-clés :	Humans  Gray Matter/diagnostic imaging  Autistic Disorder/diagnostic imaging  Autism Spectrum Disorder/diagnostic imaging  Retrospective Studies  Magnetic Resonance Imaging/methods  Brain/diagnostic imaging  Autism  Gray matter volume covariation  Replication  advisory board member of, and a speaker for Janssen Cilag BV, Eli Lilly, Shire,  Lundbeck, Roche, and Servier. He is not an employee of any of these companies,  and not a stock shareholder of any of these companies. He has no other financial  or material support, including expert testimony, patents or royalties. The  present work is unrelated to the above grants and relationships. The other  authors report no biomedical financial interests or potential conflicts of  interest.
Index. décimale :	PER Périodiques
Résumé :	BACKGROUND: Autism spectrum disorder (henceforth autism) is a complex neurodevelopmental condition associated with differences in gray matter (GM) volume covariations, as reported in our previous study of the Longitudinal European Autism Project (LEAP) data. To make progress on the identification of potential neural markers and to validate the robustness of our previous findings, we aimed to replicate our results using data from the Enhancing Neuroimaging Genetics Through Meta-Analysis (ENIGMA) autism working group. METHODS: We studied 781 autistic and 927 non-autistic individuals (6-30 years, IQ???50), across 37 sites. Voxel-based morphometry was used to quantify GM volume as before. Subsequently, we used spatial maps of the two autism-related independent components (ICs) previously identified in the LEAP sample as templates for regression analyses to separately estimate the ENIGMA-participant loadings to each of these two ICs. Between-group differences in participants' loadings on each component were examined, and we additionally investigated the relation between participant loadings and autistic behaviors within the autism group. RESULTS: The two components of interest, previously identified in the LEAP dataset, showed significant between-group differences upon regressions into the ENIGMA cohort. The associated brain patterns were consistent with those found in the initial identification study. The first IC was primarily associated with increased volumes of bilateral insula, inferior frontal gyrus, orbitofrontal cortex, and caudate in the autism group relative to the control group (? = 0.129, p = 0.013). The second IC was related to increased volumes of the bilateral amygdala, hippocampus, and parahippocampal gyrus in the autism group relative to non-autistic individuals (? = 0.116, p = 0.024). However, when accounting for the site-by-group interaction effect, no significant main effect of the group can be identified (p > 0.590). We did not find significant univariate association between the brain measures and behavior in autism (p > 0.085). LIMITATIONS: The distributions of age, IQ, and sex between LEAP and ENIGMA are statistically different from each other. Owing to limited access to the behavioral data of the autism group, we were unable to further our understanding of the neural basis of behavioral dimensions of the sample. CONCLUSIONS: The current study is unable to fully replicate the autism-related brain patterns from LEAP in the ENIGMA cohort. The diverse group effects across ENIGMA sites demonstrate the challenges of generalizing the average findings of the GM covariation patterns to a large-scale cohort integrated retrospectively from multiple studies. Further analyses need to be conducted to gain additional insights into the generalizability of these two GM covariation patterns.
En ligne :	https://dx.doi.org/10.1186/s13229-024-00583-8
Permalink :	https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=537