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

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. © 2016 Wiley Periodicals, Inc.

A systematic review of structural MRI biomarkers in autism spectrum disorder: A machine learning perspective

“…This has been made possible with the availability of 3 T structural MRI data from existing databases, which are summarised in Table 2. An alternative is to aggregate MRI data from multiple sites, however this would necessitate including the site as a variable in the analysis, as the effect of this covariate may be significant (Auzias et al, 2016; Martinez‐Murcia et al, 2017). Another important consideration is accounting for confounding variables, in order to elucidate the true impact of brain structure on symptoms.…”

Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder

“…This study suggested applying a significance-weighted principal component analysis (PCA) technique to reduce the undesired intensity variance, thereby increasing the statistical power in detecting the differences between ASD and DC groups [60]. Using this technique, Broca's area and temporal-parietal junction were found to be significantly different.…”

Understanding the role of steroids in typical and atypical brain development: Advantages of using a “brain in a dish” approach

“…There is a hypothesised role of fT in atypical neurodevelopment through associative studies from our group which show significant association of elevated fT levels with autism when compared to typical individuals, and fT levels were positively associated with higher scores on the Childhood Autism Spectrum Test (CAST) and the Child Autism Spectrum Quotient (AQ‐Child) . The brain region RTPJ, which has been shown to have a greater volume with increased fT, is also known to be associated with autism . Additionally, elevated testosterone levels have been positively associated with occurrence of obsessive compulsive disorder, and Tourette syndrome, whereas elevated levels of gestational oestrogens have been suggested to increase predisposition to schizophrenia …”