Gray matter covariations in autism: out-of-sample replication using the ENIGMA autism cohort.

Autism Gray matter volume covariation Replication

Journal

Molecular autism
ISSN: 2040-2392
Titre abrégé: Mol Autism
Pays: England
ID NLM: 101534222

Informations de publication

Date de publication:
17 Jan 2024
Historique:
received: 05 01 2024
accepted: 08 01 2024
medline: 17 1 2024
pubmed: 17 1 2024
entrez: 16 1 2024
Statut: epublish

Résumé

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. 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. 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 (β = 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 (β = 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). 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. 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.

Sections du résumé

BACKGROUND 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 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 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 (β = 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 (β = 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 CONCLUSIONS
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 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.

Identifiants

pubmed: 38229192
doi: 10.1186/s13229-024-00583-8
pii: 10.1186/s13229-024-00583-8
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

3

Subventions

Organisme : China Scholarship Council grant
ID : 201806010408
Organisme : European Union's Horizon 2020 research and innovation programme
ID : Marie Skłodowska-Curie grant agreement (101025785)
Organisme : European Community's Horizon 2020 Programme (H2020/2014-2020) Grant
ID : 847818 (CANDY)
Organisme : European Community's Horizon 2020 Programme (H2020/2014-2020) Grant
ID : 642996 (BRAINVIEW)
Organisme : the Netherlands Organization for Scientific Research VICI Grant
ID : 2020/TTW/00836465
Organisme : Wellcome Trust Collaborative Award Grant
ID : 215573/Z/19/Z
Organisme : European Union Seventh Framework Programme Grant
ID : 602805 (AGGRESSOTYPE)
Organisme : European Community's Horizon 2020 Programme (H2020/2014-2020)
ID : 643051 (MiND)

Informations de copyright

© 2024. The Author(s).

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Auteurs

Ting Mei (T)

Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Kapittelweg 29, 6525EN, Nijmegen, The Netherlands. t.mei@donders.ru.nl.

Alberto Llera (A)

Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Kapittelweg 29, 6525EN, Nijmegen, The Netherlands.

Natalie J Forde (NJ)

Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Kapittelweg 29, 6525EN, Nijmegen, The Netherlands.

Daan van Rooij (D)

Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Kapittelweg 29, 6525EN, Nijmegen, The Netherlands.
Department of Psychology, Utrecht University, Utrecht, The Netherlands.

Dorothea L Floris (DL)

Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Kapittelweg 29, 6525EN, Nijmegen, The Netherlands.
Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland.

Christian F Beckmann (CF)

Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Kapittelweg 29, 6525EN, Nijmegen, The Netherlands.
Centre for Functional MRI of the Brain, University of Oxford, Oxford, UK.

Jan K Buitelaar (JK)

Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Kapittelweg 29, 6525EN, Nijmegen, The Netherlands.
Karakter Child and Adolescent Psychiatry University Centre, Nijmegen, The Netherlands.

Classifications MeSH