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Neuroimaging in ASD Research

Updated: Nov 18, 2019

Overview and Implications

Understanding of the structure-function relationship of the brain in regards to ASD has become increasingly significant in light of advancements in neuroimaging technology. Breakthroughs in recent research have opened up the possibilities of earlier diagnosis of autism, which has previously been an obstacle within the autism research community.

In this study, Emerson et al. applied machine learning to functional MRI scans of 6-month old children at high risk for autism. Looking at measures of common symptoms of ASD, such as behavior, language, and repetitive behavior, the machine learning algorithm accurately predicted ASD diagnosis at 24 months of age of 59 infants. The machine correctly predicted 9 of 11 infants who received a diagnosis of ASD and all 48 infants who did not receive a diagnosis. 

Early prediction of autism, when paired with prevention tactics, can have major clinical applications for treatment of autism in children. Previous research has revealed that prevention techniques implemented before 24 months of age have the potential to lessen the severity of symptoms later on in life (Webb et al., 2014). Furthermore, this study extends previous findings that structural information from 6-12 months of age can predict ASD diagnosis, indicating that future research in this field may transform the way we view autism in children (Hazlett et al., 2017).


This paper does an excellent job of describing background information necessary to understand the more complex concepts applied in the computer algorithm. The concept of brain connectivity is heavily explored in how anatomical and functional links within the brain were applied to this study. Furthermore, the study is especially thorough in its gathering of data, incorporating a variety of cognitive and behavioral assessments in addition to the computer analysis of neuroimages. 

However, because this study’s findings are novel, its results are supported by only one other study (Hazlett et al., 2017). Thus, more replications of the experiment are necessary in order to validate its results, though the machine learning algorithm’s high prediction accuracy contributes to its credibility. Additionally, the study would benefit from a higher sample size, possibly including children at low-risk for autism as well.

Future Directions

The time period between 6 and 24 months in infants is a significant transitionary period as symptoms of ASD and atypical brain development occurs in children with autism (Wolff et al., 2012). Thus, future research into this subject could look further into the structure-function relationship of atypical brain development in comparison with typical brain development, rather than simply looking at neuroimages at a single point in time. Furthermore, the current study could expand to track the development of more clinical measures prevalent in children with autism, such as eye tracking, motor ability, and visual reception (Zwaigenbaum et al., 2005). 


Hazlett, H.C., Gu, H., Munsell, B.C., Kim, S.H., Styner, M., Wolff, J.J., Elison, J.T., Swanson, M.R., Zhu, H., Botteron, K.N., Collins, D.L., Constantino, J.N., Dager, S.R., Estes, A.M., Evans, A.C., Fonov, V.S., Gerig, G., Kostopoulos, P., McKinstry, R.C., Pandey, J., Paterson, S., Pruett, J.R., Schultz, R.T., Shaw, D.W., Zwaigenbaum, L., Piven, J. (2017). Early brain development in infants at high risk for autism spectrum disorder. Nature, vol. 542(7641), pp. 348-351.

Webb, S. L., Jones, E. J. H., Kelly, J., Dawson, G. (2014). The motivation for very early intervention for infants at high risk for autism spectrum disorders. Int. J. Speech Lang. Pathol, vol. 16(1), pp. 36–42.

Wolff, J.J., Gu, H., Gerig, G., Elison, J.T., Styner, M., Gouttard, S., Botteron, K.N., Dager, S.R., Dawson, G., Estes, A.M., Evans, A., Hazlett, H.C., Kostopoulos, P., McKinstry, R.C., Paterson, S.J., Schultz, R.T., Zwaigenbaum, L., Piven, J. (2012). Differences in white matter fiber tract development present from 6 to 24 months in infants with autism. Am J Psychiatry, vol. 169, pp. 589–600.

Zwaigenbaum, L., Bryson, S., Rogers, T., Roberts, W., Brian, J., Szatmari, P. (2005). Behavioral manifestations of autism in the first year of life. Int J Dev Neurosci, vol. 23, pp. 143–152.

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