Currently, the average age for a diagnosis of Autism Spectrum Disorder is 3 years old2. Autism Spectrum Disorder, ASD, is a neurodevelopmental condition that presents as difficulties with social interaction and communication in addition to restrictive or repetitive thought and behavior patterns2. An early diagnosis for ASD can not only help the child with understanding how they are atypical, but also helps the parents with understanding how to best help their child. Research shows that early detection and intervention helps most wellness cases, including the outcome of a child with ASD2. Because early detection and intervention is becoming a growing interest for researchers, people have been exploring how best to help children with ASD from very early on. A research study is designing and testing techniques to help children as young as 6 months with standard child development tasks2. The study teaches parents different strategies to help the child’s social interaction, babbling and early communication in different settings such as during meals, bath time or play2. The earlier a child receives their diagnosis, the sooner the parent can modify their techniques for helping their child’s development.
A new study from Duke University has created an algorithm to diagnosis infants at the earliest age of 1 month2. Using 45,000 children’s electronic medical records from January of 2006 until December of 2020, the researchers trained and evaluated machine-learning models2. The algorithm was precise enough to distinguish infants who later developed Attention-Deficit/Hyperactivity Disorder, or ADHD, from those who developed ASD2. The research team was particularly focused on creating an algorithm that would include groups of children that are usually overlooked by common screening techniques. Some of these groups that often miss out on the benefits of an early diagnosis include girls, children of color, and children who already have an ADHD diagnosis2. Because children with ASD are more likely to visit an ophthalmologist or neurologist, have gastrointestinal problems, or have participated in physical therapy, the program was able to recognize these patterns and differentiate who was most likely to have received a diagnosis for ASD2. If this algorithm is verified with more research, the goal would be to give doctors an alert, based on past medical issues or office visits, that the child should be closely monitored for ASD signs and symptoms. Because these issues or visits will arise even before 30 days after birth, the algorithm allows for a very early signal to the pediatrician to closely monitor for more symptoms allowing for an early diagnosis.
Maggie Guy’s article “Cortical Source Analysis of the Face Sensitive N290 ERP Component in Infants at High Risk for Autism” gives insight to her study on different groups of infants’ brain activation patterns when exposed to different stimuli1. She compared differences between typical developing infants (TD), infants with Fragile X Syndrome (FXS), and infants with an older sibling diagnosed with ASD (ASIB)1. Both infants with Fragile X Syndrome and infants with an older sibling diagnosed with ASD are at higher risk for developing ASD1. By exposing the infants to their mother’s face, a stranger’s face, a toy they are familiar with, and a new toy, she was able to recognize subtle differences in the N290 activation levels1. Guy recognized there was a developing face specialization in all participants seen through the greater activation to faces than toys in specific brain regions such as the middle fusiform gyrus1. There was an increased activation to faces in infants with FXS and muted levels in the ASIB group1. These findings allow for further research to explore these distinguishing factors even further which could help lead to early diagnoses of ASD by differentiation of facial recognition patterns in high and low risk infants.
Although both the study from Duke University and from Maggie Guy’s research aim to create a technique that allows for an early diagnosis of Autism Spectrum Disorder, they both evaluate the matter from different viewpoints. Maggie Guy explores the differences in brain activation levels between children at a high risk for a later diagnosis of ASD to those with typical developmental patterns1. Duke University confirms with their algorithm that there is a brain-body relationship with ASD and takes this approach for an early diagnosis2. An interesting next step would be to confirm if the two techniques align in their predictions and findings.
Work Cited
(1) Guy, Maggie W., et al. “Cortical Source Analysis of the Face Sensitive N290 ERP Component in Infants at High Risk for Autism.” Brain Sciences, vol. 12, no. 9, Aug. 2022, p. 1129. Crossref, https://doi.org/10.3390/brainsci12091129.
(2) Weintraub, Karen. “New Algorithm Detects Autism in Infants. How Might That Change Care?” USA Today, 8 Feb. 2023.
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