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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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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 & 22 more 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

Original languageEnglish
Pages (from-to)1208-1223
Number of pages16
JournalHuman Brain Mapping
Issue number3
Early online date24 Oct 2016
Accepted/In press13 Oct 2016
E-pub ahead of print24 Oct 2016
Published1 Mar 2017


King's Authors


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.

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