On the brain structure heterogeneity of autism: Parsing out acquisition site effects with significance-weighted principal component analysis

Francisco Jesús Martinez-Murcia; Meng Chuan Lai; Juan Manuel Górriz; Javier Ramírez; Adam M H Young; Sean C L Deoni; Christine Ecker; Michael V. Lombardo; Simon Baron-Cohen; Declan G M Murphy; Edward T. Bullmore; John Suckling; Anthony J. Bailey; Simon Baron-Cohen; Patrick F. Bolton; Edward T. Bullmore; Sarah Carrington; Marco Catani; Bhismadev Chakrabarti; Michael C. Craig; Eileen M. Daly; Sean C L Deoni; Christine Ecker; Francesca Happé; Julian Henty; Peter Jezzard; Patrick Johnston; Derek K. Jones; Meng Chuan Lai; Michael V. Lombardo; Anya Madden; Diane Mullins; Clodagh M. Murphy; Declan G M Murphy; Greg Pasco; Amber N V Ruigrok; Susan A. Sadek; Debbie Spain; Rose Stewart; John Suckling; Sally J. Wheelwright; Steven C. Williams

doi:10.1002/hbm.23449

On the brain structure heterogeneity of autism: Parsing out acquisition site effects with significance-weighted principal component analysis

Francisco Jesús Martinez-Murcia, Meng Chuan Lai, Juan Manuel Górriz^*, Javier Ramírez, Adam M H Young, Sean C L Deoni, Christine Ecker, Michael V. Lombardo, Simon Baron-Cohen, Declan G M Murphy, Edward T. Bullmore, John Suckling, Anthony J. Bailey, Simon Baron-Cohen, Patrick F. Bolton, Edward T. Bullmore, Sarah Carrington, Marco Catani, Bhismadev Chakrabarti, Michael C. CraigEileen M. Daly, Sean C L Deoni, Christine Ecker, Francesca Happé, Julian Henty, Peter Jezzard, Patrick Johnston, Derek K. Jones, Meng Chuan Lai, Michael V. Lombardo, Anya Madden, Diane Mullins, Clodagh M. Murphy, Declan G M Murphy, Greg Pasco, Amber N V Ruigrok, Susan A. Sadek, Debbie Spain, Rose Stewart, John Suckling, Sally J. Wheelwright, Steven C. Williams

^*Corresponding author for this work

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Abstract

Neuroimaging studies have reported structural and physiological differences that could help understand the causes and development of Autism Spectrum Disorder (ASD). Many of them rely on multisite designs, with the recruitment of larger samples increasing statistical power. However, recent large-scale studies have put some findings into question, considering the results to be strongly dependent on the database used, and demonstrating the substantial heterogeneity within this clinically defined category. One major source of variance may be the acquisition of the data in multiple centres. In this work we analysed the differences found in the multisite, multi-modal neuroimaging database from the UK Medical Research Council Autism Imaging Multicentre Study (MRC AIMS) in terms of both diagnosis and acquisition sites. Since the dissimilarities between sites were higher than between diagnostic groups, we developed a technique called Significance Weighted Principal Component Analysis (SWPCA) to reduce the undesired intensity variance due to acquisition site and to increase the statistical power in detecting group differences. After eliminating site-related variance, statistically significant group differences were found, including Broca's area and the temporo-parietal junction. However, discriminative power was not sufficient to classify diagnostic groups, yielding accuracies results close to random. Our work supports recent claims that ASD is a highly heterogeneous condition that is difficult to globally characterize by neuroimaging, and therefore different (and more homogenous) subgroups should be defined to obtain a deeper understanding of ASD. Hum Brain Mapp 38:1208–1223, 2017.

Original language	English
Pages (from-to)	1208-1223
Number of pages	16
Journal	Human Brain Mapping
Volume	38
Issue number	3
Early online date	24 Oct 2016
DOIs	https://doi.org/10.1002/hbm.23449
Publication status	Published - 1 Mar 2017

Keywords

autism spectrum disorder
structural heterogeneity
structural magnetic resonance imaging
voxel based morphometry

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On the Brain Structure Heterogeneity_MARTINEZ-MURCIA_Published online8February2017_GOLD VoR (CC-BY)Final published version, 1 MBLicence: CC BY

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Martinez-Murcia, F. J., Lai, M. C., Górriz, J. M., Ramírez, J., Young, A. M. H., Deoni, S. C. L., Ecker, C., Lombardo, M. V., Baron-Cohen, S., Murphy, D. G. M., Bullmore, E. T., Suckling, J., Bailey, A. J., Baron-Cohen, S., Bolton, P. F., Bullmore, E. T., Carrington, S., Catani, M., Chakrabarti, B., ... Williams, S. C. (2017). On the brain structure heterogeneity of autism: Parsing out acquisition site effects with significance-weighted principal component analysis. Human Brain Mapping, 38(3), 1208-1223. https://doi.org/10.1002/hbm.23449

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abstract = "Neuroimaging studies have reported structural and physiological differences that could help understand the causes and development of Autism Spectrum Disorder (ASD). Many of them rely on multisite designs, with the recruitment of larger samples increasing statistical power. However, recent large-scale studies have put some findings into question, considering the results to be strongly dependent on the database used, and demonstrating the substantial heterogeneity within this clinically defined category. One major source of variance may be the acquisition of the data in multiple centres. In this work we analysed the differences found in the multisite, multi-modal neuroimaging database from the UK Medical Research Council Autism Imaging Multicentre Study (MRC AIMS) in terms of both diagnosis and acquisition sites. Since the dissimilarities between sites were higher than between diagnostic groups, we developed a technique called Significance Weighted Principal Component Analysis (SWPCA) to reduce the undesired intensity variance due to acquisition site and to increase the statistical power in detecting group differences. After eliminating site-related variance, statistically significant group differences were found, including Broca's area and the temporo-parietal junction. However, discriminative power was not sufficient to classify diagnostic groups, yielding accuracies results close to random. Our work supports recent claims that ASD is a highly heterogeneous condition that is difficult to globally characterize by neuroimaging, and therefore different (and more homogenous) subgroups should be defined to obtain a deeper understanding of ASD. Hum Brain Mapp 38:1208–1223, 2017.",

keywords = "autism spectrum disorder, structural heterogeneity, structural magnetic resonance imaging, voxel based morphometry",

author = "Martinez-Murcia, {Francisco Jes{\'u}s} and Lai, {Meng Chuan} and G{\'o}rriz, {Juan Manuel} and Javier Ram{\'i}rez and Young, {Adam M H} and Deoni, {Sean C L} and Christine Ecker and Lombardo, {Michael V.} and Simon Baron-Cohen and Murphy, {Declan G M} and Bullmore, {Edward T.} and John Suckling and Bailey, {Anthony J.} and Simon Baron-Cohen and Bolton, {Patrick F.} and Bullmore, {Edward T.} and Sarah Carrington and Marco Catani and Bhismadev Chakrabarti and Craig, {Michael C.} and Daly, {Eileen M.} and Deoni, {Sean C L} and Christine Ecker and Francesca Happ{\'e} and Julian Henty and Peter Jezzard and Patrick Johnston and Jones, {Derek K.} and Lai, {Meng Chuan} and Lombardo, {Michael V.} and Anya Madden and Diane Mullins and Murphy, {Clodagh M.} and Murphy, {Declan G M} and Greg Pasco and Ruigrok, {Amber N V} and Sadek, {Susan A.} and Debbie Spain and Rose Stewart and John Suckling and Wheelwright, {Sally J.} and Williams, {Steven C.}",

year = "2017",

month = mar,

day = "1",

doi = "10.1002/hbm.23449",

language = "English",

volume = "38",

pages = "1208--1223",

journal = "Human Brain Mapping",

issn = "1065-9471",

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Martinez-Murcia, FJ, Lai, MC, Górriz, JM, Ramírez, J, Young, AMH, Deoni, SCL, Ecker, C, Lombardo, MV, Baron-Cohen, S, Murphy, DGM, Bullmore, ET, Suckling, J, Bailey, AJ, Baron-Cohen, S, Bolton, PF, Bullmore, ET, Carrington, S, Catani, M, Chakrabarti, B, Craig, MC , Daly, EM , Deoni, SCL , Ecker, C , Happé, F, Henty, J, Jezzard, P, Johnston, P , Jones, DK, Lai, MC, Lombardo, MV, Madden, A , Mullins, D , Murphy, CM , Murphy, DGM, Pasco, G, Ruigrok, ANV, Sadek, SA, Spain, D, Stewart, R, Suckling, J, Wheelwright, SJ & Williams, SC 2017, 'On the brain structure heterogeneity of autism: Parsing out acquisition site effects with significance-weighted principal component analysis', Human Brain Mapping, vol. 38, no. 3, pp. 1208-1223. https://doi.org/10.1002/hbm.23449

TY - JOUR

T1 - On the brain structure heterogeneity of autism

T2 - Parsing out acquisition site effects with significance-weighted principal component analysis

AU - Martinez-Murcia, Francisco Jesús

AU - Lai, Meng Chuan

AU - Górriz, Juan Manuel

AU - Ramírez, Javier

AU - Young, Adam M H

AU - Deoni, Sean C L

AU - Ecker, Christine

AU - Lombardo, Michael V.

AU - Baron-Cohen, Simon

AU - Murphy, Declan G M

AU - Bullmore, Edward T.

AU - Suckling, John

AU - Bailey, Anthony J.

AU - Baron-Cohen, Simon

AU - Bolton, Patrick F.

AU - Bullmore, Edward T.

AU - Carrington, Sarah

AU - Catani, Marco

AU - Chakrabarti, Bhismadev

AU - Craig, Michael C.

AU - Daly, Eileen M.

AU - Deoni, Sean C L

AU - Ecker, Christine

AU - Happé, Francesca

AU - Henty, Julian

AU - Jezzard, Peter

AU - Johnston, Patrick

AU - Jones, Derek K.

AU - Lai, Meng Chuan

AU - Lombardo, Michael V.

AU - Madden, Anya

AU - Mullins, Diane

AU - Murphy, Clodagh M.

AU - Murphy, Declan G M

AU - Pasco, Greg

AU - Ruigrok, Amber N V

AU - Sadek, Susan A.

AU - Spain, Debbie

AU - Stewart, Rose

AU - Suckling, John

AU - Wheelwright, Sally J.

AU - Williams, Steven C.

PY - 2017/3/1

Y1 - 2017/3/1

N2 - Neuroimaging studies have reported structural and physiological differences that could help understand the causes and development of Autism Spectrum Disorder (ASD). Many of them rely on multisite designs, with the recruitment of larger samples increasing statistical power. However, recent large-scale studies have put some findings into question, considering the results to be strongly dependent on the database used, and demonstrating the substantial heterogeneity within this clinically defined category. One major source of variance may be the acquisition of the data in multiple centres. In this work we analysed the differences found in the multisite, multi-modal neuroimaging database from the UK Medical Research Council Autism Imaging Multicentre Study (MRC AIMS) in terms of both diagnosis and acquisition sites. Since the dissimilarities between sites were higher than between diagnostic groups, we developed a technique called Significance Weighted Principal Component Analysis (SWPCA) to reduce the undesired intensity variance due to acquisition site and to increase the statistical power in detecting group differences. After eliminating site-related variance, statistically significant group differences were found, including Broca's area and the temporo-parietal junction. However, discriminative power was not sufficient to classify diagnostic groups, yielding accuracies results close to random. Our work supports recent claims that ASD is a highly heterogeneous condition that is difficult to globally characterize by neuroimaging, and therefore different (and more homogenous) subgroups should be defined to obtain a deeper understanding of ASD. Hum Brain Mapp 38:1208–1223, 2017.

AB - Neuroimaging studies have reported structural and physiological differences that could help understand the causes and development of Autism Spectrum Disorder (ASD). Many of them rely on multisite designs, with the recruitment of larger samples increasing statistical power. However, recent large-scale studies have put some findings into question, considering the results to be strongly dependent on the database used, and demonstrating the substantial heterogeneity within this clinically defined category. One major source of variance may be the acquisition of the data in multiple centres. In this work we analysed the differences found in the multisite, multi-modal neuroimaging database from the UK Medical Research Council Autism Imaging Multicentre Study (MRC AIMS) in terms of both diagnosis and acquisition sites. Since the dissimilarities between sites were higher than between diagnostic groups, we developed a technique called Significance Weighted Principal Component Analysis (SWPCA) to reduce the undesired intensity variance due to acquisition site and to increase the statistical power in detecting group differences. After eliminating site-related variance, statistically significant group differences were found, including Broca's area and the temporo-parietal junction. However, discriminative power was not sufficient to classify diagnostic groups, yielding accuracies results close to random. Our work supports recent claims that ASD is a highly heterogeneous condition that is difficult to globally characterize by neuroimaging, and therefore different (and more homogenous) subgroups should be defined to obtain a deeper understanding of ASD. Hum Brain Mapp 38:1208–1223, 2017.

KW - autism spectrum disorder

KW - structural heterogeneity

KW - structural magnetic resonance imaging

KW - voxel based morphometry

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JO - Human Brain Mapping

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On the brain structure heterogeneity of autism: Parsing out acquisition site effects with significance-weighted principal component analysis

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Keywords

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