The Barriers in Obtaining an Early Diagnosis of Autism Spectrum Disorder

 

            Autism spectrum disorder (ASD) has high heterogeneity in how symptoms are presented and when the onset of symptoms is noticeable. ASD is a spectrum disorder, meaning there is a wide range of the severity of symptoms, for example, those who once were diagnosed as having Asperger’s are now considered to be on the lower end of the ASD spectrum, since many can assimilate into neurotypical society, but on the higher end of the spectrum, there are individuals who will require care for the rest of their lives. When a disorder presents so differently from person to person, it can be hard for medical professionals to accurately diagnosis. According to the Centers for Disease Control and Prevention (CDC), while some cases of ASD can be diagnosed as early as 18 months of age, but many children go undiagnosed until later childhood or even as late as adulthood. Better understanding of how a child is diagnosed with ASD and the issues with the current diagnostic criteria can lead to exploration on how changes can be initiated to ensure all children have the opportunity for an accurate ASD diagnosis early in their development.

            The current process of assessing risk and diagnosing ASD in childhood has some innate problems. In a journal review, “Predicting Autism in Infancy” by Jason Wolff and Joseph Piven, they discuss how the current diagnostic criteria found in the DSM-5, requires presentation of symptoms in early development, however there is an acknowledgement that this is not true for many ASD cases. As children grow older, they are presented to new social situations which can bring out these symptoms not seen when they were an infant. Wolff and Piven discuss the findings of another study that found six-month-old and twelve-month-old children that ASD was later diagnosed, they had no differences from typically developing children, such as smiling and holding eye contact on time. The rigid criteria of the DSM-5 makes it impossible to evaluate the risk of ASD in every child and this calls for the need of new, definitive diagnostic tools.

            Biomarkers are incredibly important in the clinical setting because they provide a way for medical professionals to give an empirical-based, unbiased diagnosis. Biomarkers are measures that can be taken from a patient and accurately predicts a disease or disorder that the patient has or could develop. Wolff and Piven explained the importance of a biomarker for ASD by noting that the current screening processes are more accurate for children who are white and have a higher socioeconomic standing, a biomarker could help eliminate these disparities. However, the task of finding a biomarker that can accurately predict ASD is not easy. The current research has turned to measures such as electroencephalography (EEG) and electrooculography (EOG) to find predictors of ASD in infants. Wolff and Piven identify neuroimaging in conjunction with EEG and EOG techniques to be a viable way forward in ASD diagnostics, especially for infants who are high risk and asymptomatic.

            A recent study, “Face-Sensitive Brain Responses in the First Year of Life” by Conte et al. investigates how event related potentials (ERPs) detected by EEG in response to facial stimuli change during early development in infants. The study discovered specific ERPs, such as N290, that were more responsive to facial stimuli in children across 4.5-month-olds to 12-month-olds. There is a belief that individuals with ASD may have impaired facial processing due to clinical symptoms involving difficulties in maintaining eye contact and picking up on implicit facial cues. Examining the differences between these facial recognition ERPs in neurotypical children to children at high risk for ASD could result in a discovery of a tool to better detect ASD during infancy.

            EEG would be an ideal tool for detecting ASD in infants due to the equipment becoming lower in costs and decreased sensitivity to movement when compared to neuroimaging techniques such as MRI. Current ASD diagnostic criteria misses many childhood cases and is highly affected by bias, but a discrete measure found to be an accurate predictor of ASD would change the lives of many children to come. Children with ASD benefit greatly from early diagnoses, allowing them to receive support for challenges unique to the disorder. The work from Conte et al. brings new hope to the field of ASD research during the search of an adequate biomarker for ASD.

References

Centers for Disease Control and Prevention. (2020, March 13). Screening and diagnosis of autism spectrum disorder. https://www.cdc.gov/ncbddd/autism/screening.html

Conte, S., Richards, J.E., Guy, M.W., Xie, W., & Roberts, J.E. (2020). Face-sensitive brain responses in the first year of life. NeuroImage, 211. https://doi.org/10.1016/j.neuroimage.2020.1166025

Wolff, J.J., & Piven, J. (2021). Predicting autism in infancy. Journal of the American Academy of Child & Adolescent Psychiatry, 60(8), 958-967. https://doi.org/10.1016/j.jaac.2020.07.910