MRI Scans are Transforming Autism Detection and Treatment
Early autism detection is key to personalized treatment and improved development
Posted August 9, 2017
Currently, there is no way to diagnose children with autism spectrum disorder (ASD) until behavioral traits begin to show at around 24 months of age. The Centers for Disease Control and Prevention cites a rate of 1 case of autism for every 68 children in the U.S., and while there is no single known cause, we do know that autism is presented with abnormalities in brain structure or function. We also know that autism can run in families, with children who have older siblings diagnosed with autism having a nearly 20% chance of being diagnosed with the disorder at age 2 themselves. With this knowledge at hand, functional magnetic resonance imaging, or fMRI, may be the key to helping us detect early signs of autism for high-risk children as young as 6 months old.
Early autism detection is so important because the younger a child is, the more adaptable their brain is, and this allows therapy to have a greater impact on their long-term development. Beginning a treatment program before age 2 can not only make a difference in IQ and language learning, but in the acquisition of social skills as well. This means that children with autism who receive early intervention could perform better in elementary school and face fewer challenges as they grow older. The sooner we can predict who will be diagnosed with autism, the earlier families and health providers can come up with long-term treatment plans, affording these children and families the opportunity to have the best possible outcomes.
FMRI shows great promise in early autism detection because it can measure blood flow changes in the brain and correlate this with activity in different areas of the cerebral cortex. This procedure allows researchers to look at the differences in brain activity between someone with autism performing a task, versus someone without it. The effectiveness of this analysis was demonstrated in a study conducted at Carnegie Mellon University, where researchers used fMRI neurocognitive markers to diagnose autism in participants with 97% accuracy. The team presented words describing social concepts to participants—such as “hug,” “humiliate,” “kick,” and “adore”—and by observing the underlying brain activation patterns, were able to successfully identify which participants were diagnosed with autism and which were not.
Another team at the University of North Carolina (UNC) Chapel Hill found that markers identifying autism can be found in the brains of infants as young as 6 months old. Behavioral clues to autism, such as delays in walking, talking, visual recognition and tracking patterns, are usually seen by 18 to 24 months of age. Unfortunately, there are no outwardly observable signs of autism in infants, but the UNC researchers did uncover that there are observable differences between infant brains, and were able to pinpoint specific areas of the brain that were different from neurotypical babies. They found that the brains of infants who went on to be diagnosed with autism at 24 months of age grew faster than those of infants with slower growing brains.
Earlier diagnosis also allows the most appropriate treatment to be selected and delivered at a time when a child could benefit the most. Specific treatment plans could be based on the parts of the brain that are affected, and fMRI could tell us where those are. As with other medical problems, everyone is slightly different. Studies that leverage fMRI are beginning to show where the specific problems are likely to be in a child, such as language delays or problems with social cues, and treatment could therefore be tailored accordingly.
Aside from the significant developmental benefits that early autism detection can offer, it also has the potential to translate into substantial cost savings. Therapy and medical care for children with autism costs, on average, upwards of a million dollars over the course of a lifetime, and if early diagnosis and treatment can lead to better outcomes, it can also lead to cost savings in the long run—both for families and for our larger healthcare system.