Nice to see recognitions: 1. There are many “Autisms” 2. Limitations of “behavior-based” diagnosis 3. Candor regarding “conclusions” that can be legitimately drawn using specific diagnostic / research methods
Not quite there yet: 1. Recognition of some of the “Autisms” as representing variations on a human brain continuum, and not as “automatic pathologies”. 2. Abandonment of gross “behavior-only” Diagnosis which are highly subjective, Culturally contaminated, non-discriminatory, and limited by inconsistent “Diagnostician” Training. 3. Reorganization of A “HUMAN brain spectrum” on a scientifically valid Basis that includes “Typical” Development, “Legitimate” Variations, and truly “harmful” Identifiable pathologies.
Describing the Brain in Autism in Five Dimensions—Magnetic Resonance Imaging-Assisted Diagnosis of Autism Spectrum Disorder Using a Multiparameter Classification Approach
Autism affects multiple aspects of the cerebral anatomy, which makes its neuroanatomical correlates inherently difficult to describe. Here, we used a multiparameter classification approach to characterize the complex and subtle gray matter differences in adults with ASD. SVM achieved good separation between groups, and revealed spatially distributed and largely non-overlapping patterns of regions with highest classification weights for each of five morphological features. Our results confirm that the neuroanatomy of ASD is truly multidimensional affecting multiple neural systems. The discriminating patterns detected using SVM may help further exploration of the genetic and neuropathological underpinnings of ASD.
There is good evidence to suggest that several aspects of cerebral morphology are implemented in ASD—including both volumetric and geometric features (Levitt et al., 2003; Nordahl et al., 2007). However, these are normally explored in isolation. Here, we aimed to establish a framework for multiparameter image classification to describe differences in gray matter neuroanatomy in autism in multiple dimensions, and to explore the predictive power of individual parameters for group membership. This was achieved using a multiparameter classifier incorporating volumetric and geometric features at each cerebral vertex. In the left hemisphere, SVM correctly classified 85.0% of all cases overall at a sensitivity and specificity as high as 90.0% and 80.0%, respectively, using all five morphological features. This level of sensitivity compares well with behaviorally guided diagnostic tools whose accuracies are on average ∼80%. Naturally, one would expect lower sensitivity values than the test used for defining the “autistic prototype” itself (i.e., ADI-R). Thus, if a classifier is trained on the basis of true positives identified by diagnostic tools, the maximal classification accuracy that could be reached is only as good as the measurements used to identify true positives.
The significant predictive value of pattern classification approaches may have potential clinical applications. Currently, ASD is diagnosed solely on the basis of behavioral criteria. The behavioral diagnosis is however often time consuming and can be problematic, particularly in adults. Also, different biological etiologies might result in the same behavioral phenotype [the “autisms” (Geschwind, 2007)], which is undetectable using behavioral measures alone. Thus, the existence of an ASD biomarker such as brain anatomy might be useful to facilitate and guide the behavioral diagnosis. This would, however, require further extensive exploration in the clinical setting, particularly with regards to classifier specificity to ASD rather than neurodevelopmental conditions in general.
To address the issue of clinical specificity, the established ASD classifier was used to classify individuals with ADHD—a neurodevelopmental control group. Bilaterally, the ASD classifier did not allocate the majority of ADHD subjects to the ASD category. This indicates that it does not perform equally well for other neurodevelopmental conditions, and is more specific to ASD. To further demonstrate that the classification is driven by autistic symptoms, the test margins of individuals with ASD were correlated with measures of symptom severity (Ecker et al., 2010). We found that larger margins were associated with more severe impairments in the social and communication domain of the ADI-R. The classifier therefore seems to use neuroanatomical information specifically related to ASD rather than simply reflecting nonspecific effects introduced by any kind of pathology. However, due to a recent scanner upgrade, ADHD scans were acquired with different acquisition parameters, while manufacturer, field strength, and pulse sequence remained the same. FreeSurfer has been demonstrated to show good test–retest reliability particularly within scanner-manufacturer and field strength (Han et al., 2006), but we cannot exclude the possibility that systematic differences in regional contrast may have affected the ADHD classification. Future research is thus needed to validate the ADHD findings on an independent sample.
The overall classification accuracy varied across hemispheres (79.0% left vs 65.0% right) in the absence of interhemispheric differences in parameter variability. Hemisphere laterality is an area, which remains relatively unexplored in autism. While our data suggest that the left hemisphere is better at discriminating between groups (i.e., is more “abnormal”), it is unclear whether this discrepancy is due to quantitative differences in parameters or to qualitative aspects of the discriminating patterns (i.e., additional regions). Furthermore, it is also not possible to identify whether individuals with ASD display a higher (lower) degree of cortical asymmetry relative to controls. There is some evidence to suggest that individuals with ASD show a lower degree of “leftward” (i.e., left > right) cortical symmetry than controls (Herbert et al., 2005), which may explain differences in classification accuracy. There is also evidence to suggest that the left hemisphere is under tighter genetic control than the right hemisphere (Thompson et al., 2001), which may be of relevance to a highly heritable condition such as ASD. However, a direct numerical comparison between hemispheres is needed to address this issue directly.
The classification accuracy not only varied across hemispheres but also across morphometric parameters. Bilaterally, cortical thickness provided the best classification accuracy and highest regional weights. Differences in cortical thickness have been reported previously in ASD for both increases (Chung et al., 2005; Hardan et al., 2006) as well as decreases (Chung et al., 2005; Hadjikhani et al., 2006), and in similar regions as reported here (i.e., parietal, temporal, and frontal areas). The overlap with previous studies indicates that these regions display high classification weights due to a quantitative (i.e., “true”) difference rather than high intercorrelations with thickness measures in other brain regions.
Certain geometric features such as average convexity and metric distortion provided above chance classifications as well, particularly in parietal, temporal, and frontal regions, and in areas of the cingulum. Average convexity and metric distortion measure different aspects of cortical geometry (see Materials and Methods) and have previously been linked to ASD, as has sulcal depth (Nordahl et al., 2007). Such geometric features were suggested to reflect abnormal patterns of cortical connectivity. There have also been reports of abnormal patterns of gyrification (Piven et al., 1990; Hardan et al., 2004) and large-scale displacements of the major sulci (Levitt et al., 2003). Thus, our study provides further evidence to support the hypothesis that the “autistic brain” is not just bigger or smaller but is also abnormally shaped.
While we demonstrated that the neuroanatomy of ASD is multidimensional, the etiology of such multivariate differences remains unclear. Here, little or no spatial overlap was observed between the discriminating patterns for individual parameters. Such region dependency was also observed in the regional morphometric profiles displaying the distribution of weights across multiple cortical features in a region of interest. If one assumes that different cortical features reflect different neuropathological processes, such region- and parameter-dependent variations may reflect the multifactorial etiology of ASD. For example, evidence suggests that cortical thickness and surface area reflect different neurobiological processes and are associated with different genetic mechanisms (Panizzon et al., 2009). Cortical thickness is likely to reflect dendritic arborization (Huttenlocher, 1990) or changing myelination at the gray/white matter interface (Sowell et al., 2004). In contrast, surface area is influenced by the division of progenitor cells in the embryological periventricular area, and is associated with the number of minicolumns (Rakic, 1988). Instead, geometric differences are predominantly linked with the development of neuronal connections and cortical pattern of connectivity, and are thus a marker for cerebral development (Armstrong et al., 1995; Van Essen, 1997). It is therefore likely that the reported maps reflect multiple genetic and/or neurobiological etiologies, which need further investigation. Thus, our findings should be interpreted in the context of a number of methodological limitations.
First, the classification algorithm is highly specific to the particular sample used for “training” the classifier, namely high-functioning adults with ASD. The advantage of this approach is that the classifier offers high specificity with regard to this particular subject group, but is less specific to other cohorts on the spectrum. Due to the small sample size, it was also not possible to reliably investigate differences between high-functioning autism and Asperger’s syndrome. Evidence (Howlin, 2003) suggests that by adulthood these groups are largely indistinguishable at the phenotypic level. However, the extent to which these groups differ at the level of brain anatomy is unknown, and may be investigating using SVM in the future. Second, 85% of ASD participants in our sample were diagnosed using the ADI-R, and 15% were diagnosed using the ADOS. As both diagnostic tools measure autistic symptoms at different developmental stages, the classifier may be biased toward individuals with an early diagnosis of ASD. Although it is not expected that classifier performance on the basis of ADOS and ADI differ drastically, diagnostic heterogeneity may be a potential limitation. Last, SVM is a multivariate technique and hence offers a limited degree of interpretability of specific network components. Additional analysis such as “searchlight” or “virtual lesions” approaches (Averbeck et al., 2006; Kriegeskorte et al., 2006; Pessoa and Padmala, 2007) may therefore be combined with SVM in the future to establish the relative contribution of individual regions/parameters to the overall classification performance.
Nevertheless, while classification values and specific patterns we report must be considered as preliminary, our study offers a “proof of concept” for describing the complex multidimensional gray matter differences in ASD.