Most current theories of brain abnormalities underlying autism emphasize wide spread structural and functional changes (27
) and disturbances in cortical connectivity among brain regions (8
). With growing evidence that the brain disturbance underlying autism involves multiple brain regions came the need for increasingly sophisticated methods for analyzing these complex alterations. Multivariate pattern analysis is a powerful tool for investigating the pattern of these differences, and has several advantages over traditional univariate VBM approaches. In particular, such analyses are more sensitive to subtle changes in multiple brain areas that may accompany complex neuropsychiatric disorders such as autism (see (54
) for review). The interpretation of a result from an MPA analysis is that the brain regions identified are those in which there is information which can be gleaned from a pattern of voxels that can be used to assign a particular individual dataset to a group - in our case, autism or control.
Using a support vector machine (SVM) searchlight classification procedure, we found that gray matter in several cortical and subcortical regions discriminated between autism and TD groups with high classification accuracies. Some of the highest classification accuracies (near 90%) were achieved with GM in the posterior cingulate cortex, medial prefrontal cortex, and medial temporal lobes, all regions that comprise the default mode network (35
). This finding is in line with the most recent meta-analysis of structural neuroimaging studies of autism, which points to decreases in gray matter in the hippocampus and precuneus (41
). Several recent studies have supported a role for the DMN in the pathophysiology of autism. In adults with ASD, deactivation of the DMN during task performance appears abnormal (37
), and the network shows reduced functional connectivity at rest (37
). Adolescents with ASD likewise show weaker connectivity within the DMN (58
). Autism is associated with altered socioemotional responses, which have been linked to DMN function (59
). Furthermore, an activation likelihood estimation meta-analysis of twenty-four neuroimaging studies examining social processing in ASD found that medial prefrontal cortex and posterior cingulate cortex, two main nodes of the DMN, are hypoactive relative to neurotypical adults (61
). Our current results support the notion that there might be morphological differences within DMN nodes that contribute to the observed functional differences at the network level.
The present study found that the PCC not only produced the highest classification accuracy, but an individual subject’s distance from the hyperplane separating the two groups in the classification analysis were also significantly correlated with ADI-R scores. Specifically, children with the most elevated communication symptom score on the ADI-R (indicating the most severe deficits) were located furthest away from the hyperplane separating the autism and TD groups. These data indicate that our classification analyses are sensitive not only in distinguishing between autism and TD groups, but also in relating symptom severity with multi-voxel brain measures. Previous studies as well as the current study collectively suggest that atypical engagement of and connectivity within the DMN and associated networks is one possible signature of brain dysfunction in ASD (22
). Of note, both our VBM and MPA analyses showed group differences localized to the PCC, demonstrating the robustness of this result across methods.
In addition to GM differences within the DMN, we found high classification accuracies in several prefrontal, lateral temporal, and subcortical regions. The frontal and temporal lobes are also notable for showing abnormal increases in gray and white matter between 2 and 4 years of age (See (64
) for review). The posterior STS, involved with social and speech perception, has been identified in fMRI studies as a key region involved in the pathophysiology that may be compromised in adults with autism (65
). The cerebellum and caudate, which produced 85% classification accuracies in our analyses, have previously been shown to have structural abnormalities in ASD, and reportedly also discriminates between adults with ASD and neurotypical adults (33
). Caudate volume has been reported to associate with repetitive behaviors in individuals with autism (15
We found that white matter in the genu and splenium of the corpus callosum also allowed for high classification accuracies. Previous studies have shown corpus callosum abnormalities in ASD (67
), a finding that has been interpreted as resulting from alterations in interhemispheric cortical connectivity. The novel finding of the current study is that white matter along the inferior fronto-occipital fasciculus and superior longitudinal fasciculus could also distinguish children with autism from TD children. A recent meta-analysis of VBM studies of autism reports that patients with ASD showed increases of white matter volume in the left inferior fronto-occipital fasciculus (71
). Our current findings suggest that these white matter differences are also reflected in multivariate patterns after normalizing for overall volume differences.
The only published studies of classification of structural MRI data have been conducted in either adults or toddlers with autism. The current study is the first such study in children and adolescents. Some common features of these findings have emerged across age groups that may highlight key features of autism. Ecker and colleagues reported that GM data more accurately classified individuals than did white matter data, and that multivariate methods were more sensitive to group differences than were univariate VBM methods (33
), which is what we also found in the current study. The current work, however, is the first to identify the specific loci of GM and WM differences in children and adolescents with autism. The previous study used a whole-brain classification method that was not optimized for finding discriminating brain regions, rather than searchlight classification. Ecker and colleagues recently used a multiparameter classification approach (including data from both volumetric and geometric cortical features) to reveal distributed patterns of discriminating regions from structural gray matter measurements collected from adults with autism (33
). Another recent study used multivariate pattern classification to examine male toddlers with autism and found that in the age range examined (1-4 years), the classification method used could not discriminate between toddlers with autism and controls, though univariate methods did show that toddlers with autism had greater brain volume in several areas (72
). Whether this was due to heterogeneity within the autism group, choice of classification algorithm, choice of control participants, power issues, or represents a true null finding remains an open question.
The current study has several limitations. We examined the age range of 8-18 years, which spans a period of rapid and non-linear brain development. Unfortunately, there is at present no straightforward way to incorporate age covariates into the MPA analysis, which is a shortcoming of the method. Future studies can address this issue by substantially increasing the numbers of participants and dividing the samples into two smaller age ranges to more closely model maturational changes in brain morphology as they relate to autism. Also, the searchlight classification algorithm that we adopt is well suited for using local information to uncover precisely which brain regions provide the most information about group membership (autism or control). However, a limitation of this method is that it cannot identify two or more distant brain regions that together discriminate the two population groups. Methodological advances in this area will be necessary to apply this technique at the whole-brain level so as to consider these potential relationships. Lastly, while our method allows for the identification of structural brain signatures of autism, multimodal studies incorporating functional neuroimaging are needed to address the question of whether measures of functional connectivity, in conjunction with morphology, can better discriminate autism from typical development.
The elucidation of the brain basis of autism is critical for defining neurobiologic mechanisms responsible for the disorder, accounting for heterogeneity across cases, monitoring its evolution, and its response to intervention. One of the major impediments to progress in understanding ASD results from the fact that it is currently diagnosed solely on the basis of behavioral characteristics (73
). Findings from the current study and similar efforts integrating other types of neuroimaging data may eventually lead to the identification of robust brain-based biomarkers with the potential to aid in early detection and intervention in children with ASD. Discovery of such biomarkers may ultimately also be of potential use in identifying toddlers or siblings at risk for developing autism. While the initial results presented here are promising, future studies with larger samples enabling smaller age subgroups within the child population, as well as a wider range of cognitive functioning, will be important in addressing issues of heterogeneity within the population and further investigating relationships between symptomatology and brain structure.