Using EEG to Diagnose Autism Spectrum Disorders

Introduction

A computational physicist and a cognitive neuroscientist at Children's Hospital, Boston have come up with the beginnings of a noninvasive test to evaluate an infant's autism risk. It combines the standard electroencephalogram (EEG), which records electrical activity in the brain, with machine-learning algorithms. In a pilot study, their system had 80 percent accuracy in distinguishing between 9-month-old infants known to be at high risk for autism from controls of the same age.

Main Item

Although this work, published February 22 in the online open-access journal BMC Medicine, requires validation and refinement, it suggests a safe, practical way of identifying infants at high risk for developing autism by capturing very early differences in brain organization and function. This would allow parents to begin behavioral interventions one to two years before autism can be diagnosed through traditional behavioral testing.

"Electrical activity produced by the brain has a lot more information than we realized," says William Bosl, PhD, a neuroinformatics researcher in the Children's Hospital Informatics Program. "Computer algorithms can pick out patterns in those squiggly lines that the eye can't see."

Bosl, Charles A. Nelson, PhD, Research Director of the Developmental Medicine Center at Children's, and colleagues 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.

The investigators looked at the entropy of each EEG channel, which is believed to contain information about the density of neural connections in the brain region near that electrode.

"Many neuroscientists believe that autism reflects a 'disconnection syndrome,' by which distributed populations of neurons fail to communicate efficiently with one another," explains Nelson. "The current paper supports this hypothesis by suggesting that the brains of infants at high risk for developing autism exhibit different patterns of neural connectivity, though the relationship between entropy and the density of neural arbors remains to be explored." (Neural arbors are projections of neurons that form synapses or connections with other neurons.)

On average, the most significant difference was seen at 9 months of age. The researchers note that at 9 months, babies undergo important changes in their brain function that are critical for the emergence of higher-level social and communication skills - skills often impaired in ASDs.

For reasons that still need to be explored, there was a gender difference: classification accuracy was greatest for girls at 6 months and remained high for boys at 12 and 18 months.

Overall, however, the distinction between the high-risk group and controls was smaller when infants were tested at 12 to 24 months. The authors speculate that the high-risk group may have a genetic vulnerability to autism that can be influenced and sometimes mitigated by environmental factors.

Bosl hopes to follow the high-risk group over time and compare EEG patterns in those who receive an actual ASD diagnosis and who appear to be developing normally - and then compare both groups to the controls.

"With enough data, I'd like to follow each child's whole trajectory from 6 to 24 months," Bosl adds. "The trend over time may be more important than a value at any particular age."

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. There are already data showing differences in EEG patterns for schizophrenia, major depression and PTSD, Bosl says.

Bosl also has started to collect data from older children 6 to 17 years old, and eventually hopes to have enough subjects to be able to compare EEG patterns for different types of ASDs.

About the Study

Adrienne Tierney, M.Sc., EdM in the Nelson Lab and Helen Tager-Flusberg, PhD, of Boston University were coauthors on the study. The research was supported by grants from Autism Speaks, the National Institute on Deafness and Other Communication Disorders and the Simon's Foundation.

Attribution/Source(s):

This quality-reviewed publication was selected for publishing by the editors of Disabled World due to its significant relevance to the disability community. Originally authored by Children's Hospital Boston, and published on 2011/02/22 (Edit Update: 2022/05/27), the content may have been edited for style, clarity, or brevity. For further details or clarifications, Children's Hospital Boston can be contacted at childrenshospital.org. NOTE: Disabled World does not provide any warranties or endorsements related to this article.

▶ Share This Page

Page Information, Citing and Disclaimer

Disabled World is a comprehensive online resource that provides information and news related to disabilities, assistive technologies, and accessibility issues. Founded in 2004 our website covers a wide range of topics, including disability rights, healthcare, education, employment, and independent living, with the goal of supporting the disability community and their families.

Cite This Page (APA): Children's Hospital Boston. (2011, February 22 - Last revised: 2022, May 27). Using EEG to Diagnose Autism Spectrum Disorders. Disabled World. Retrieved October 11, 2024 from www.disabled-world.com/health/neurology/autism/eegs-autism.php

Permalink: <a href="https://www.disabled-world.com/health/neurology/autism/eegs-autism.php">Using EEG to Diagnose Autism Spectrum Disorders</a>: Machine-learning system finds differences in brain connectivity, distinguishing between 9-month-old infants known to be at high risk for autism from controls of the same age.

Disabled World provides general information only. Materials presented are never meant to substitute for qualified medical care. Any 3rd party offering or advertising does not constitute an endorsement.