Autism Spectrum Disorder (ASD) is a category of pervasive developmental disorders (PDD) that affect 1 in 150 children (Rapin and Tuchman, 2008). Autistic symptomatology is characterized by severe impairments that mainly affect social interaction, communication, while sparing basic cognitive skills (Misra, 2014), and not implying emotional disturbance (Rapin and Tuchman, 2008). The term ASD was introduced by Allen (1988) and included: autistic disorder (AD), Asperger syndrome (AS), and PDD not otherwise specified (Levy et al., 2009; Pina-Camacho et al., 2012). Recently, the DSM V revised the conceptualization of those disorders and currently diagnostic classifications only include Autism Spectrum Disorders (ASD) (Lord and Bishop, 2015). Nevertheless, the diagnostic criteria adopted in the current study still refer to DSM IV, and we focused primarily on autism disorder (AD). Consequently, we use the words “autism” and “autistic” to specifically refer to AD.

The mind-blindness theory proposed that characteristic problem in social interaction arises because AD have difficulties in mentalizing and understanding psychological dynamics in other people and in oneself (Baron-Cohen et al., 1985; Frith et al., 1991). Support for this model comes from behavioral studies showing that autistics poorly perform on tasks that require theory of mind (ToM) abilities (Baron-Cohen et al., 1985; Misra, 2014). The poor performance of people with autism on ToM task is thought to be due to a conceptual deficit or to processing peculiarities. In normally developing children ToM tasks elicit intuitive social insights into people. In contrast, autistic children treat these tasks as logical-reasoning problems relying on language and non-social cognitive functions (Tager-Flusberg et al., 2009). In line with this hypothesis, functional MRI investigations of AD have highlighted reduced responses in cortical areas related to social interaction (Frith, 2003; Di Martino et al., 2009; Lombardo et al., 2011; Gliga et al., 2014), and the engagement of areas associated with general problem solving abilities (Frith, 2003). Moreover, functional connectivity in a group with autism shows abnormal pattern in areas mediating ToM and in the mirror neuron system (MNS) as well (Fishman et al., 2014; Cheng et al., 2015; Kana et al., 2015).

On the other hand, studies of volumetric changes in the autistic brain resulted in inconsistent brain structures across studies: prefrontal cortex (Courchesne et al., 2011b), cerebellum (Sparks et al., 2002), temporal lobe (Palmen et al., 2006), both the frontal (Herbert et al., 2004; Jiao et al., 2010) and parietal lobes (Courchesne et al., 1993) and amygdala (Schumann et al., 2004). Some inconsistencies in the structural MRI literature may be attributed to differences in methodology, age, heterogeneity of the disorder (Geschwind, 2009), or diffuse structural abnormalities. Diffuse structural abnormalities in autism could reflect impairment of many, if not most, brain networks (Müller, 2007).

Recently, predictive models of autism based on pattern recognition in structural MR images have been successfully developed. Ecker et al. (2010) adopted a support vector machine (SVM) method to discriminate ASD individuals from controls. Brain areas in the temporal lobe, precuneus, hippocampal, and fusiform gyri were crucial for discrimination. Neural abnormalities discovered with such methodologies, however, rarely correlate with clinical criteria, or correlate only with generic total scores, and not with specific subscales (Eliez and Reiss, 2000; Lord et al., 2000; Hardan et al., 2006a,b; Ecker et al., 2010; Griebling et al., 2010). In other words, these correlations may refer more to a general severity of pathology than to specific deficits.

We suggest that neuroanatomical markers of autism disorder may be better understood by studying large-scale anatomical networks (Minshew and Williams, 2007; Schaer et al., 2013). SBM is a data-driven multivariate alternative to the standard Voxel-Based Morphometry (VBM), and it may be particularly suitable to the investigation of anatomical changes in autism. SBM takes into account information across different voxels and identifies unpredicted, naturally occurring patterns of covariance across brain regions (Xu et al., 2009). Notably, such anatomical covariance has been shown to reflect functional connectivity (Evans, 2013). For these reasons, we expect SBM to individuate large anatomical networks of gray-matter which show aberrant patterns of covariance in AD, as compared to control. We also expect those networks to include brain areas that previous studies showed to be anatomically or functionally abnormal.

Given the heterogeneity of AD and the need for large-scale samples of MR and clinical measures, the present study utilizes the ABIDE (Autism Brain Imaging Data Exchange) database.

We extracted eight independent components (Figure 1). However, only the 7th component was significantly different [t₍₃₁₎ = 2.9482, p_(FWE) = 0.0042] between AD and controls. We call this component “autism-specific structural network” (ASN). Anatomical labels of the regions composing ASN were obtained using the WFU PickAtlas (Tzourio-Mazoyer et al., 2002). Among regions that differ between AD and controls we found: inferior, middle, superior temporal gyri, fusiform gyrus, parahippocampal gyrus, paracentral lobule, precuneus, cerebellar tonsil, and portions of the inferior, middle, and superior frontal gyri (Figure 2). All the gray-matter regions of ASN are presented in Table 2 in Supplementary Material, and in Figure 2.

To ensure that differences in IQ (see Table 1 in Supplementary Material) did not account for brain differences we correlated ASN loading coefficients (i.e., 7th column of the mixing matrix) against IQ values. The correlation was not significant (p = 0.6336), thus excluding the relevance of IQ on this component.

Notably, ASN significantly correlated with the total scores of ADOS, and also with two ADOS subscales measuring highly relevant impairments in AD, i.e., difficulties in social interactions and stereotyped behavior. Both variables show significant correlation with the loading coefficient of ASN, after Bonferroni correction for multiple comparisons. Classic Total ADOS Score: r = 0.4708, p_(FWE) = 0.0065. Social Total subscore: r = 0.4269, p_(FWE) = 0.0148. Stereotyped behaviors and restricted interest: r = 0.4152, p_(FWE) = 0.0181. See Figure 2.

In this study we presented for the first time a whole brain morphometric method (SBM), based on independent component analysis, which shows alterations in gray-matter between AD individuals and controls. This innovative multivariate procedure was applied to detect brain networks that exhibit abnormal pattern of gray-matter covariance in AD. We showed that morphometric changes in autism, as detected by SBM, are significantly associated with observable social and behavioral deficits (ADOS scores).

SBM addresses a different question in comparison with massive univariate approaches to morphological changes, as Voxel-Base-Morphometry (VBM) or ROI differences between groups. Univariate approaches detect voxel- or ROI-based changes in gray-matter concentrations, while SBM individuates different levels of expression in maximally independent gray-matter networks. Differences in network expression in turn implicate differences in gray-matter concentrations, distributed along patterns of voxel-covariation. SBM is particularly suitable to study autism as spectrum disorder, since anatomical changes are likely to be distributed along networks of brain regions. In this scenario, such anatomical changes may not be locally detectable by univariate approaches.

In the present study we found an autism-specific structural network (ASN) which covers brain regions found to exhibit functional and structural abnormalities in previous studies compared with controls (Castelli et al., 2002; Saxe and Wexler, 2005; Amaral et al., 2008; Courchesne et al., 2011b; Ecker et al., 2012; Nickl-Jockschat et al., 2012; Pappaianni et al., 2016).

Autism is a behaviorally diagnosed pathology, although evidence of brain abnormalities in AD, such as atypical neural “connectivity” (Ecker et al., 2012), are increasing. MRI investigation is a precious tool for shedding light on both the neurological causes and the neurodevelopment of AD. This knowledge is needed to get fast and objective diagnosis, but also for developing appropriate treatments. The present study, along with increasing evidence (Ecker et al., 2010; Jiao et al., 2010), suggests that structural MRI may become a diagnostic instrument useful to improve the traditional behavior-based diagnosis.

AG: Experimental design, data collection and writing. DR: Experimental design, data collection, analyses and writing. RS: Experimental design, data collection, analyses, and writing. LS: writing of the paper. RJ: writing of the paper.