For many years, behavioral testing and observation have been the only way to determine if a child is autistic, often causing distress and confusion for parents. However, now the application of the standard electroencephalogram (EEG) combined with borrowed math from chaos theory, may enable doctors to read brain wave patterns and identify levels of autism one to two years earlier with 80% accuracy.
William Bosl, Ph.D., a neuroinformatics researcher in the Children’s Hospital Informatics Program states:
“Electrical activity produced by the brain has a lot more information than we realized. Computer algorithms can pick out patterns in those squiggly lines that the eye can’t see. With enough data, I’d like to follow each child’s whole trajectory from 6 to 24 months. The trend over time may be more important than a value at any particular age.”
Bosl and colleague Charles A. Nelson, Ph.D., Research Director of the Developmental Medicine Center at Children’s, recorded resting EEG signals from 79 babies 6 to 24 months of age participating in a larger study aimed at finding very early risk markers of autism. Forty-six infants had an older sibling with a confirmed diagnosis of an autism spectrum disorder (ASD); the other 33 had no family history of ASDs. As the babies watched a research assistant blowing bubbles, recordings were made via a hairnet-like cap on their scalps, studded with 64 electrodes. When possible, tests were repeated at 6, 9, 12, 18 and 24 months of age.
Bosl then took the EEG brain-wave readings for each electrode and computed their modified multiscale entropy (mMSE), a measure borrowed from chaos theory that quantifies the degree of randomness in a signal, from which characteristics of whatever is producing the signal can be inferred. In this case, patterns in the brain’s electrical activity give indirect information about how the brain is wired: the density of neurons in each part of the brain, how connections between them are organized, and the balance of short- and long-distance connections.
Chaos theory is a field of study in applied mathematics, with applications in several disciplines including physics, economics, biology, and philosophy. Chaos theory studies the behavior of dynamical systems that are highly sensitive to initial conditions; an effect which is popularly referred to as the butterfly effect. Small differences in initial conditions (such as those due to rounding errors in numerical computation) yield widely diverging outcomes for chaotic systems, rendering long-term prediction impossible in general. This happens even though these systems are deterministic, meaning that their future behavior is fully determined by their initial conditions, with no random elements involved. In other words, the deterministic nature of these systems does not make them predictable. This behavior is known as deterministic chaos, or simply chaos.
Nelson explains further:
“Many neuroscientists believe that autism reflects a ‘disconnection syndrome,’ by which distributed populations of neurons fail to communicate efficiently with one another. The current paper supports this hypothesis by suggesting that the brains of infants at high risk for developing autism exhibit different pattern of neural connectivity, though the relationship between entropy and the density of neural arbors remains to be explored.”
Although EEG testing for autism risk may seem impractical to implement on a wide scale, it is inexpensive, safe, does not require sedation (unlike MRI), takes only minutes to perform and can be done in a doctor’s office.
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Written by Sy Kraft, B.A.