This study examines the relationship of neuroanatomical volumes to stereotypies in autism. We found that the total brain volumes of children with AD were larger than those of typically developing children with neither autism nor stereotypies. We also found that in AD, lower PIQ was marginally associated with more stereotypies. However, in contrast with previous brain imaging studies of subjects from the same cohort which had reported significant morphometric differences in brain regions relevant to language (Herbert et al., 2003
), we failed to detect any volumetric differences among regions of the brain thought to be relevant to stereotypies, such as the basal ganglia. Moreover, we did not detect brain volumetric differences that correlated with loss versus persistence of stereotypies between preschool and school age assessments.
How should our negative results be interpreted in the context of other morphometric studies (Estes et al., 2011
; Hardan et al., 2003
; Sears et al., 1999
; Thakkar et al., 2008
) that report a variety of alterations that are inconsistent with each other and often based upon small sample sizes? According to the limitations of our study discussed below, we propose that the results should be characterized as “failure to detect” rather than “negative findings” or “absence of difference.” Our MRI anatomic data were acquired early in the MRI era when image resolution was lower and before functional imaging and tractography became available. We were limited to structural data as a proxy or indirect measure relevant to function. Our failure to detect any anatomical difference associated with the presence or absence of stereotypies at school age does not exclude the possibility that we might find such anatomical differences in a larger sample or with more up-to-date volumetric imaging techniques. It also does not exclude the possibility of underlying functional differences that simply do not produce a macroanatomically detectable impact. On the other hand, if our failure to detect an anatomical difference reflects an actual lack of such a difference, perhaps the influence of the underlying biology on factors affecting cell size or density was too slight in this cohort to cause aggregate volume changes measurable via MRI volumetrics. The underlying biology might include alterations in neurotransmitters/neuromodulators or in synaptic networks not associated with changes in actual neuronal or glial size or number, but rather in the neural circuitry.
There are reports of neurologic conditions, for example streptococcal infection, in which stereotypies fluctuate and may be associated with changes in volumes of the basal ganglia (Wolf & Singer, 2008
). Group analyses might wash out statistically significantly increased volumes if children at different phases of such an illness were included. But reports of such phenomena are anecdotal (Giedd, Rapoport, Garvey, Perlmutter, & Swedo, 2000
) and there is no convincing evidence that the stereotypies of autism have an encephalopathic basis. Moreover, the decrease in prevalence of stereotypies with age in our cohort suggests not so much an intermittent process as a dynamic one that tends to resolve over time. It remains to be seen whether maturation, adaptation to genetic or environmental causes of stereotypies, or development of capacities for behavioral self-control or inhibition explain the waning. Reduction in the older children of arm and finger movements but not rocking and pacing is tantalizing as a possible clue to pertinent biology, but explanation awaits availability of more refined anatomical and other neurobiological data. At least with respect to abnormal repetitive behaviors, functional rather than structural alteration may be the underpinning of these disrupting motor manifestations of autism.
Failure to detect volumetric differences in this carefully designed and implemented study brings up some critical issues inherent to autism research, some of which are responsible for the divergent and inconclusive reports in the literature. Frequent shortcomings in autism research include small sample sizes, heterogeneity in AD phenotypes, lack of means to parse people with autism according to biological etiologies, and lack of standardized tools to quantify motor manifestations such as stereotypies. Such flaws contribute to preventing investigators from interpreting positive or negative results, including those of this study, in a definitive manner.
Several limitations to this study need to be considered. First, given our sample size, a real consideration is insufficient statistical power, at least for some analyses, to detect significant volumetric differences among subgroups. Although the comparisons between the AD and TD subjects would have had adequate power to detect a difference (78.5% probability of detecting a difference of 91 cm3 in 61 children), the analyses involving only the 31 AD subjects were significantly less powered. For instance, the probability of detecting a mean volume difference of 90 cm3 between AD subjects with one to ten stereotypies versus those with more than ten was only 26.8% given our sample sizes. Similarly, the correlation of 0.34 between number of stereotypies and PIQ, assuming it is the population correlation, had only 47.8% chance of being detected. This makes the marginally significant p = .054 value obtained encouraging but not robust.
Second, another possible explanation of failure to detect volume abnormality might relate various methodological issues related to performing volumetric analyses. The anatomic parcellation units selected might not have conformed to the functionally relevant anatomical structures. Affected brain areas could have had overlapped regional boundaries or might have occupied only a portion of a morphometrically defined anatomical unit. In this case, volumetric differences could have gotten washed out by the lack of difference in that unit’s other sub-parts, or could have had different shapes but not different volumes among subjects. Brain differences could also have involved differences in tissue or metabolic composition that might not affect volumes significantly. More recent imaging methods such as shape analysis (Casanova et al., 2011
; Qiu, Adler, Crocetti, Miller, & Mostofsky, 2010
), Diffusion Tensor Imaging (DTI), or spectroscopy (Langen et al., 2012
; Sivaswamy et al., 2010
) offer alternate windows into the anatomical substrates of these movements. A third limitation to this study is the use of the “IQ status” variable. Since no IQ information was available for control subjects, they all received the presumed score of 0. However, it is possible that some TD controls had IQ scores outside the range of 85–115 and thus should have received different status scores. Although this is a legitimate concern, given the small sample size of this cohort, it is quite unlikely that having these scores would have changed our results.
A fourth limitation pertains to the fact that, while detailed quantitative videocoding of stereotypies is unique to this study, it does not guarantee a representative sample of the stereotypies of each child. Fifteen minutes is a small time sample. Finally, our choice to split the continuous variable “number of stereotypies” into a categorical variable with three levels (i.e., subjects with (1) 0, (2) 1–10 and (3) greater than 10 stereotypies during the 15 min of videocoding) is defensible; it is a reasonable way to render the great variability in the number of stereotypies more tractable. However, the arbitrary nature of this grouping may have watered down any potential meaningful relationship between stereotypy severity and brain volume changes.
It is also possible that other studies which relied on questionnaires rather than standardized scoring might have yielded less detail and precision but perhaps more reliable information on prevalence. Automated technologies for investigating movement abnormalities, such as wearable accelerometers, could overcome this concern in future studies.
4.2. Future research
Going forward and in view of these limitations, we propose that future research make every effort to include a larger cohort with equal numbers of girls and boys matched on ethnicity and IQ. Given that no significant stereotypy-related volumetric alteration at school age was found in the present cohort, we also suggest that imaging modalities aimed at assessing function such as Diffusion Tensor Imaging (DTI), magnetic resonance spectroscopy (MRS), or EEG coherence and not just or not primarily anatomical size of brain regions may be valuable to probe the potential link between stereotypies and changes in specific regions, pathways or networks. Furthermore, it is possible that the relationship between repetitive behaviors and regional brain differences might be detectable by comparing imaging analyses over time. Thus future study may benefit from longitudinal rather than cross-sectional design.
As for the behavioral assessment of stereotypies, it is expected that computerized pattern recognition of videocoded stereotypies through body sensors may yield more densely quantifiable data. Therefore future studies combining functional imaging and emerging quantitative behavioral technologies should be considered.