Open AccessOn the brain structure heterogeneity of autism: Parsing out acquisition site effects with significance‐weighted principal component analysis
Author(s) -
MartinezMurcia Francisco Jesús,
Lai MengChuan,
Górriz Juan Manuel,
Ramírez Javier,
Young Adam M. H.,
Deoni Sean C. L.,
Ecker Christine,
Lombardo Michael V.,
BaronCohen Simon,
Murphy Declan G. M.,
Bullmore Edward T.,
Suckling John
Publication year - 2017
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.23449
Subject(s) - neuroimaging , autism , statistical power , psychology , principal component analysis , autism spectrum disorder , discriminative model , cognitive psychology , computer science , artificial intelligence , neuroscience , developmental psychology , statistics , mathematics
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 . © 2016 Wiley Periodicals, Inc.