Brainwave activity reveals potential biomarker for autism in children

October 26, 2018

Science Daily/Kanazawa University

Autism spectrum disorder (ASD) affects children's social and intellectual development. Conventional diagnostic methods for ASD rely on behavioral observation. Researchers have now identified a potential quantifiable biomarker for diagnosing ASD. Using magnetic brainwave imaging, they correlated altered gamma oscillation with the motor response of children with ASD, which is consistent with previous key hypotheses on ASD. The means of observation potentially offers a noninvasive, impartial form of early diagnosis of ASD.

Now, a team of researchers at Kanazawa University in Japan have made an important step towards identifying a biomarker based on motor-related brain activity. Their work followed on from the key hypothesis that autism results from an excitatory and inhibitory imbalance in the brain, which is associated with repetitive brainwaves called gamma oscillations. A reduction in this type of brain activity has been seen during visual, auditory, and tactile stimulation in individuals with ASD.

The researchers set out to further explore motor-induced gamma oscillations in children with ASD, and recently reported their findings in The Journal of Neuroscience.

They formed two groups of children who were 5-7 years old. Those in the first group were conventionally diagnosed with ASD, while the second group was made up of children classed as developing typically. The children each performed a video-game-like task where they had to press a button with their right finger, while in a relaxed environment. Magnetoencephalography, which records magnetic activity from neurons, was used to monitor the children's brainwaves during the task.

"We measured the button response time, motor-evoked magnetic fields, and motor-related gamma oscillations," study corresponding author Mitsuru Kikuchi says. "As found in other studies, the ASD children's response time was slightly slower and the amplitude in their magnetic fields was a bit decreased. The gamma oscillations were where we saw significant and interesting differences."

There was a considerably lower peak frequency of the gamma oscillations in the ASD group. A lower peak frequency of motor-related gamma oscillations also signaled low concentration of the inhibitory neurotransmitter GABA, which has also been found associated with ASD. The findings additionally suggest delayed development of motor control in young children with ASD. Collectively the behavioral performance and brainwave findings offer promise for ASD diagnosis.

"Early diagnosis of ASD is highly important so that we can actively manage the disorder as soon as possible," first author Kyung-min An says. "These findings may prove to be extremely useful in helping us understand the neurophysiological mechanism behind social and motor control development in children with ASD. Using magnetoencephalography in this way gives us a noninvasive and quantifiable biomarker, which is something we are in great need of."

https://www.sciencedaily.com/releases/2018/10/181026102712.htm

EEG signals accurately predict autism as early as 3 months of age

Early diagnosis by 'digital biomarkers' may allow early intervention, better outcomes

May 1, 2018

Science Daily/Boston Children's Hospital

Autism is challenging to diagnose, especially early in life. A new study shows that inexpensive EEGs, which measure brain electrical activity, accurately predict or rule out autism spectrum disorder in infants, even in some as young as three months.

"EEGs are low-cost, non-invasive and relatively easy to incorporate into well-baby checkups," says Charles Nelson, PhD, director of the Laboratories of Cognitive Neuroscience at Boston Children's Hospital and co-author of the study. "Their reliability in predicting whether a child will develop autism raises the possibility of intervening very early, well before clear behavioral symptoms emerge. This could lead to better outcomes and perhaps even prevent some of the behaviors associated with ASD."

The study analyzed data from the Infant Sibling Project (now called the Infant Screening Project), a collaboration between Boston Children's Hospital and Boston University that seeks to map early development and identify infants at risk for developing ASD and/or language and communication difficulties.

William Bosl, PhD, associate professor of Health Informatics and Clinical Psychology at the University of San Francisco, also affiliated with the Computational Health Informatics Program (CHIP) at Boston Children's Hospital, has been working for close to a decade on algorithms to interpret EEG signals, the familiar squiggly lines generated by electrical activity in the brain. Bosl's research suggests that even an EEG that appears normal contains "deep" data that reflect brain function, connectivity patterns and structure that can be found only with computer algorithms.

The Infant Screening Project provided Bosl with EEG data from 99 infants considered at high risk for ASD (having an older sibling with the diagnosis) and 89 low-risk controls (without an affected sibling). The EEGs were taken at 3, 6, 9, 12, 18, 24 and 36 months of age by fitting a net over the babies' scalps with 128 sensors as the babies sat in their mothers' laps. (An experimenter blew bubbles to distract them.) All babies also underwent extensive behavioral evaluations with the Autism Diagnostic Observation Schedule (ADOS), an established clinical diagnostic tool.

Bosl's computational algorithms analyzed six different components (frequencies) of the EEG (high gamma, gamma, beta, alpha, theta, delta), using a variety of measures of signal complexity. These measures can reflect differences in how the brain is wired and how it processes and integrates information, says Bosl.

The algorithms predicted a clinical diagnosis of ASD with high specificity, sensitivity and positive predictive value, exceeding 95 percent at some ages.

"The results were stunning," Bosl says. "Our predictive accuracy by 9 months of age was nearly 100 percent. We were also able to predict ASD severity, as indicated by the ADOS Calibrated Severity Score, with quite high reliability, also by 9 months of age."

Bosl believes that the early differences in signal complexity, drawing upon multiple aspects of brain activity, fit with the view that autism is a disorder that begins during the brain's early development but can take different trajectories. In other words, an early predisposition to autism may be influenced by other factors along the way.

"We believe that infants who have an older sibling with autism may carry a genetic liability for developing autism," says Nelson. "This increased risk, perhaps interacting with another genetic or environmental factor, leads some infants to develop autism -- although clearly not all, since we know that four of five "infant sibs" do not develop autism."

https://www.sciencedaily.com/releases/2018/05/180501085140.htm