Autism is associated with interindividual variations of gray and white matter morphology

Ting Mei; Natalie J. Forde; Dorothea L. Floris; Flavio Dell’Acqua; Richard Stones; Iva Ilioska; Sarah Durston; Carolin Moessnang; Tobias Banaschewski; Rosemary Holt; Simon Baron-Cohen; Annika Rausch; Eva Loth; Bethany Oakley; Tony Charman; Christine Ecker; Declan G.M. Murphy; the EU-AIMS LEAP group; Bonnie Auyeung; Christian F. Beckmann; Alberto Llera; Jan K. Buitelaar

doi:10.1016/j.bpsc.2022.08.011

Autism is associated with interindividual variations of gray and white matter morphology

Ting Mei^*, Natalie J. Forde, Dorothea L. Floris, Flavio Dell’Acqua, Richard Stones, Iva Ilioska, Sarah Durston, Carolin Moessnang, Tobias Banaschewski, Rosemary Holt, Simon Baron-Cohen, Annika Rausch, Eva Loth, Bethany Oakley, Tony Charman, Christine Ecker, Declan G.M. Murphy, the EU-AIMS LEAP group, Bonnie Auyeung (Member of Group Organisation), Christian F. BeckmannAlberto Llera, Jan K. Buitelaar^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

Background: Although many studies have explored atypicalities in gray matter (GM) and white matter (WM) morphology of autism, most of them relied on unimodal analyses that did not benefit from the likelihood that different imaging modalities may reflect common neurobiology. We aimed to establish brain patterns of modalities that differentiate between individuals with and without autism and explore associations between these brain patterns and clinical measures in the autism group.
Methods: We studied 183 individuals with autism and 157 nonautistic individuals (age range, 6–30 years) in a large, deeply phenotyped autism dataset (EU-AIMS LEAP [European Autism Interventions—A Multicentre Study for Developing New Medications Longitudinal European Autism Project]). Linked independent component analysis was used to link all participants’ GM volume and WM diffusion tensor images, and group comparisons of modality shared variances were examined. Subsequently, we performed univariate and multivariate brain-behavior correlation analyses to separately explore the relationships between brain patterns and clinical profiles.
Results: One multimodal pattern was significantly related to autism. This pattern was primarily associated with GM volume in bilateral insula and frontal, precentral and postcentral, cingulate, and caudate areas and co-occurred with altered WM features in the superior longitudinal fasciculus. The brain-behavior correlation analyses showed a significant multivariate association primarily between brain patterns that involved variation of WM and symptoms of restricted and repetitive behavior in the autism group.
Conclusions: Our findings demonstrate the assets of integrated analyses of GM and WM alterations to study the brain mechanisms that underpin autism and show that the complex clinical autism phenotype can be interpreted by brain covariation patterns that are spread across the brain involving both cortical and subcortical areas.

Original language	English
Journal	Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
Early online date	6 Sept 2022
DOIs	https://doi.org/10.1016/j.bpsc.2022.08.011
Publication status	E-pub ahead of print - 6 Sept 2022

Keywords / Materials (for Non-textual outputs)

autism
canonical correlation analysis
gray matter
multimodal analysis
multivariate analysis
white matter

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Mei, T., Forde, N. J., Floris, D. L., Dell’Acqua, F., Stones, R., Ilioska, I., Durston, S., Moessnang, C., Banaschewski, T., Holt, R., Baron-Cohen, S., Rausch, A., Loth, E., Oakley, B., Charman, T., Ecker, C., Murphy, D. G. M., the EU-AIMS LEAP group, Auyeung, B., ... Buitelaar, J. K. (2022). Autism is associated with interindividual variations of gray and white matter morphology. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. Advance online publication. https://doi.org/10.1016/j.bpsc.2022.08.011

@article{49f5127a8bdc435dad6b98082aa4ebce,

title = "Autism is associated with interindividual variations of gray and white matter morphology",

abstract = "Background: Although many studies have explored atypicalities in gray matter (GM) and white matter (WM) morphology of autism, most of them relied on unimodal analyses that did not benefit from the likelihood that different imaging modalities may reflect common neurobiology. We aimed to establish brain patterns of modalities that differentiate between individuals with and without autism and explore associations between these brain patterns and clinical measures in the autism group. Methods: We studied 183 individuals with autism and 157 nonautistic individuals (age range, 6–30 years) in a large, deeply phenotyped autism dataset (EU-AIMS LEAP [European Autism Interventions—A Multicentre Study for Developing New Medications Longitudinal European Autism Project]). Linked independent component analysis was used to link all participants{\textquoteright} GM volume and WM diffusion tensor images, and group comparisons of modality shared variances were examined. Subsequently, we performed univariate and multivariate brain-behavior correlation analyses to separately explore the relationships between brain patterns and clinical profiles. Results: One multimodal pattern was significantly related to autism. This pattern was primarily associated with GM volume in bilateral insula and frontal, precentral and postcentral, cingulate, and caudate areas and co-occurred with altered WM features in the superior longitudinal fasciculus. The brain-behavior correlation analyses showed a significant multivariate association primarily between brain patterns that involved variation of WM and symptoms of restricted and repetitive behavior in the autism group. Conclusions: Our findings demonstrate the assets of integrated analyses of GM and WM alterations to study the brain mechanisms that underpin autism and show that the complex clinical autism phenotype can be interpreted by brain covariation patterns that are spread across the brain involving both cortical and subcortical areas.",

keywords = "autism, canonical correlation analysis, gray matter, multimodal analysis, multivariate analysis, white matter",

author = "Ting Mei and Forde, {Natalie J.} and Floris, {Dorothea L.} and Flavio Dell{\textquoteright}Acqua and Richard Stones and Iva Ilioska and Sarah Durston and Carolin Moessnang and Tobias Banaschewski and Rosemary Holt and Simon Baron-Cohen and Annika Rausch and Eva Loth and Bethany Oakley and Tony Charman and Christine Ecker and Murphy, {Declan G.M.} and {the EU-AIMS LEAP group} and Buitelaar, {Jan K.} and Jumana Ahmad and Sara Ambrosino and Bonnie Auyeung and Tobias Banaschewski and Simon Baron-Cohen and Sarah Baumeister and Beckmann, {Christian F.} and Sven B{\"o}lte and Thomas Bourgeron and Carsten Bours and Michael Brammer and Daniel Brandeis and Claudia Brogna and Bruijn, {Yvette de} and Bhismadev Chakrabarti and Tony Charman and Ineke Cornelissen and Daisy Crawley and Flavio Dell{\textquoteright}Acqua and Guillaume Dumas and Sarah Durston and Christine Ecker and Jessica Faulkner and Vincent Frouin and Pilar Garc{\'e}s and David Goyard and Lindsay Ham and Hannah Hayward and Joerg Hipp and Rosemary Holt and Johnson, {Mark H.} and Jones, {Emily J.H.} and Prantik Kundu and Meng-Chuan Lai and d{\textquoteright}Ardhuy, {Xavier Liogier} and Lombardo, {Michael V.} and Eva Loth and Lythgoe, {David J.} and Ren{\'e} Mandl and Andre Marquand and Luke Mason and Maarten Mennes and Andreas Meyer-Lindenberg and Carolin Moessnang and Nico Mueller and Murphy, {Declan G.M.} and Bethany Oakley and Laurence O{\textquoteright}Dwyer and Marianne Oldehinkel and Bob Oranje and Gahan Pandina and Persico, {Antonio M.} and Annika Rausch and Barbara Ruggeri and Amber Ruigrok and Roberto Sacco and C{\'a}ceres, {Antonia San Jos{\'e}} and Emily Simonoff and Will Spooren and Julian Tillmann and Heike Tost and Jack Waldman and Williams, {Steve C.R.} and Caroline Wooldridge and Iva Ilioska and Zwiers, {Marcel P.} and Beckmann, {Christian F.} and Alberto Llera and Buitelaar, {Jan K.}",

note = "Acknowledgements: This work was primarily supported by the EU-AIMS consortium (European Autism Interventions), which receives support from Innovative Medicines Initiative Joint Undertaking (Grant No.115300), the resources of which are composed of financial contributions from the European Union{\textquoteright}s Seventh Framework Programme (Grant No. FP7/2007-2013), from the European Federation of Pharmaceutical Industries and Associations companies{\textquoteright} in-kind contributions, and by the AIMS-2-TRIALS consortium, which has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (Grant No. 777394). This Joint Undertaking receives support from the European Union{\textquoteright}s Horizon 2020 research and innovation programme; European Federation of Pharmaceutical Industries and Associations; and Autism Speaks, Autistica, and Simons Foundation Autism Research Initiative. This work reflects the authors{\textquoteright} views, and neither Innovative Medicines Initiative nor the European Union, European Federation of Pharmaceutical Industries and Associations, or any associated partners are responsible for any use that may be made of the information contained therein. This work was also supported by the China Scholarship Council (Grant No. 201806010408 [to TM]), European Union{\textquoteright}s Horizon 2020 Research and Innovation Programme under the Marie Sk{\l}odowska-Curie grant agreement (Grant No. 101025785 [to DLF]), European Union Seventh Framework Programme (Grant Nos. 602805 [AGGRESSOTYPE], 603016 [MATRICS], and 278948 [TACTICS] [to JKB], European Community{\textquoteright}s Horizon 2020 Programme H2020/2014–2020 (Grant Nos. 643051 [MiND] and 642996 [BRAINVIEW] [to JKB] and Grant No. 847818 [CANDY; to JKB and CFB], Netherlands Organization for Scientific Research VICI (Grant No. 2020/TTW/00836465 [to CFB]), Wellcome Trust Collaborative Award (Grant No. 215573/Z/19/Z [to CFB]), and Autism Research Trust (to SB-C).",

year = "2022",

month = sep,

day = "6",

doi = "10.1016/j.bpsc.2022.08.011",

language = "English",

journal = "Biological Psychiatry: Cognitive Neuroscience and Neuroimaging",

issn = "2451-9022",

publisher = "Elsevier",

}

Mei, T, Forde, NJ, Floris, DL, Dell’Acqua, F, Stones, R, Ilioska, I, Durston, S, Moessnang, C, Banaschewski, T, Holt, R, Baron-Cohen, S, Rausch, A, Loth, E, Oakley, B, Charman, T, Ecker, C, Murphy, DGM, the EU-AIMS LEAP group, Auyeung, B, Beckmann, CF, Llera, A & Buitelaar, JK 2022, 'Autism is associated with interindividual variations of gray and white matter morphology', Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. https://doi.org/10.1016/j.bpsc.2022.08.011

TY - JOUR

T1 - Autism is associated with interindividual variations of gray and white matter morphology

AU - Mei, Ting

AU - Forde, Natalie J.

AU - Floris, Dorothea L.

AU - Dell’Acqua, Flavio

AU - Stones, Richard

AU - Ilioska, Iva

AU - Durston, Sarah

AU - Moessnang, Carolin

AU - Banaschewski, Tobias

AU - Holt, Rosemary

AU - Baron-Cohen, Simon

AU - Rausch, Annika

AU - Loth, Eva

AU - Oakley, Bethany

AU - Charman, Tony

AU - Ecker, Christine

AU - Murphy, Declan G.M.

AU - the EU-AIMS LEAP group

AU - Beckmann, Christian F.

AU - Llera, Alberto

AU - Buitelaar, Jan K.

A2 - Buitelaar, Jan K.

A2 - Ahmad, Jumana

A2 - Ambrosino, Sara

A2 - Auyeung, Bonnie

A2 - Banaschewski, Tobias

A2 - Baron-Cohen, Simon

A2 - Baumeister, Sarah

A2 - Beckmann, Christian F.

A2 - Bölte, Sven

A2 - Bourgeron, Thomas

A2 - Bours, Carsten

A2 - Brammer, Michael

A2 - Brandeis, Daniel

A2 - Brogna, Claudia

A2 - Bruijn, Yvette de

A2 - Chakrabarti, Bhismadev

A2 - Charman, Tony

A2 - Cornelissen, Ineke

A2 - Crawley, Daisy

A2 - Dell’Acqua, Flavio

A2 - Dumas, Guillaume

A2 - Durston, Sarah

A2 - Ecker, Christine

A2 - Faulkner, Jessica

A2 - Frouin, Vincent

A2 - Garcés, Pilar

A2 - Goyard, David

A2 - Ham, Lindsay

A2 - Hayward, Hannah

A2 - Hipp, Joerg

A2 - Holt, Rosemary

A2 - Johnson, Mark H.

A2 - Jones, Emily J.H.

A2 - Kundu, Prantik

A2 - Lai, Meng-Chuan

A2 - d’Ardhuy, Xavier Liogier

A2 - Lombardo, Michael V.

A2 - Loth, Eva

A2 - Lythgoe, David J.

A2 - Mandl, René

A2 - Marquand, Andre

A2 - Mason, Luke

A2 - Mennes, Maarten

A2 - Meyer-Lindenberg, Andreas

A2 - Moessnang, Carolin

A2 - Mueller, Nico

A2 - Murphy, Declan G.M.

A2 - Oakley, Bethany

A2 - O’Dwyer, Laurence

A2 - Oldehinkel, Marianne

A2 - Oranje, Bob

A2 - Pandina, Gahan

A2 - Persico, Antonio M.

A2 - Rausch, Annika

A2 - Ruggeri, Barbara

A2 - Ruigrok, Amber

A2 - Sacco, Roberto

A2 - Cáceres, Antonia San José

A2 - Simonoff, Emily

A2 - Spooren, Will

A2 - Tillmann, Julian

A2 - Tost, Heike

A2 - Waldman, Jack

A2 - Williams, Steve C.R.

A2 - Wooldridge, Caroline

A2 - Ilioska, Iva

A2 - Zwiers, Marcel P.

N1 - Acknowledgements: This work was primarily supported by the EU-AIMS consortium (European Autism Interventions), which receives support from Innovative Medicines Initiative Joint Undertaking (Grant No.115300), the resources of which are composed of financial contributions from the European Union’s Seventh Framework Programme (Grant No. FP7/2007-2013), from the European Federation of Pharmaceutical Industries and Associations companies’ in-kind contributions, and by the AIMS-2-TRIALS consortium, which has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (Grant No. 777394). This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme; European Federation of Pharmaceutical Industries and Associations; and Autism Speaks, Autistica, and Simons Foundation Autism Research Initiative. This work reflects the authors’ views, and neither Innovative Medicines Initiative nor the European Union, European Federation of Pharmaceutical Industries and Associations, or any associated partners are responsible for any use that may be made of the information contained therein. This work was also supported by the China Scholarship Council (Grant No. 201806010408 [to TM]), European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement (Grant No. 101025785 [to DLF]), European Union Seventh Framework Programme (Grant Nos. 602805 [AGGRESSOTYPE], 603016 [MATRICS], and 278948 [TACTICS] [to JKB], European Community’s Horizon 2020 Programme H2020/2014–2020 (Grant Nos. 643051 [MiND] and 642996 [BRAINVIEW] [to JKB] and Grant No. 847818 [CANDY; to JKB and CFB], Netherlands Organization for Scientific Research VICI (Grant No. 2020/TTW/00836465 [to CFB]), Wellcome Trust Collaborative Award (Grant No. 215573/Z/19/Z [to CFB]), and Autism Research Trust (to SB-C).

PY - 2022/9/6

Y1 - 2022/9/6

N2 - Background: Although many studies have explored atypicalities in gray matter (GM) and white matter (WM) morphology of autism, most of them relied on unimodal analyses that did not benefit from the likelihood that different imaging modalities may reflect common neurobiology. We aimed to establish brain patterns of modalities that differentiate between individuals with and without autism and explore associations between these brain patterns and clinical measures in the autism group. Methods: We studied 183 individuals with autism and 157 nonautistic individuals (age range, 6–30 years) in a large, deeply phenotyped autism dataset (EU-AIMS LEAP [European Autism Interventions—A Multicentre Study for Developing New Medications Longitudinal European Autism Project]). Linked independent component analysis was used to link all participants’ GM volume and WM diffusion tensor images, and group comparisons of modality shared variances were examined. Subsequently, we performed univariate and multivariate brain-behavior correlation analyses to separately explore the relationships between brain patterns and clinical profiles. Results: One multimodal pattern was significantly related to autism. This pattern was primarily associated with GM volume in bilateral insula and frontal, precentral and postcentral, cingulate, and caudate areas and co-occurred with altered WM features in the superior longitudinal fasciculus. The brain-behavior correlation analyses showed a significant multivariate association primarily between brain patterns that involved variation of WM and symptoms of restricted and repetitive behavior in the autism group. Conclusions: Our findings demonstrate the assets of integrated analyses of GM and WM alterations to study the brain mechanisms that underpin autism and show that the complex clinical autism phenotype can be interpreted by brain covariation patterns that are spread across the brain involving both cortical and subcortical areas.

AB - Background: Although many studies have explored atypicalities in gray matter (GM) and white matter (WM) morphology of autism, most of them relied on unimodal analyses that did not benefit from the likelihood that different imaging modalities may reflect common neurobiology. We aimed to establish brain patterns of modalities that differentiate between individuals with and without autism and explore associations between these brain patterns and clinical measures in the autism group. Methods: We studied 183 individuals with autism and 157 nonautistic individuals (age range, 6–30 years) in a large, deeply phenotyped autism dataset (EU-AIMS LEAP [European Autism Interventions—A Multicentre Study for Developing New Medications Longitudinal European Autism Project]). Linked independent component analysis was used to link all participants’ GM volume and WM diffusion tensor images, and group comparisons of modality shared variances were examined. Subsequently, we performed univariate and multivariate brain-behavior correlation analyses to separately explore the relationships between brain patterns and clinical profiles. Results: One multimodal pattern was significantly related to autism. This pattern was primarily associated with GM volume in bilateral insula and frontal, precentral and postcentral, cingulate, and caudate areas and co-occurred with altered WM features in the superior longitudinal fasciculus. The brain-behavior correlation analyses showed a significant multivariate association primarily between brain patterns that involved variation of WM and symptoms of restricted and repetitive behavior in the autism group. Conclusions: Our findings demonstrate the assets of integrated analyses of GM and WM alterations to study the brain mechanisms that underpin autism and show that the complex clinical autism phenotype can be interpreted by brain covariation patterns that are spread across the brain involving both cortical and subcortical areas.

KW - autism

KW - canonical correlation analysis

KW - gray matter

KW - multimodal analysis

KW - multivariate analysis

KW - white matter

U2 - 10.1016/j.bpsc.2022.08.011

DO - 10.1016/j.bpsc.2022.08.011

M3 - Article

SN - 2451-9022

JO - Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

JF - Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

ER -

Autism is associated with interindividual variations of gray and white matter morphology

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