Brainwave Test Diagnoses Autism in 2-Year-Olds, Research Shows
According to health officials, the incidence of autism is now at approximately 1 of out every 100 children and is believed to be increasing at an alarming rate. While a distinct cause and cure is not yet available, physicians have had to rely on relatively subjective behavioral exams for making a diagnosis rather than on less-biased and more accurate testing methods. However, how autism is diagnosed may soon change with the invention of a brain wave test using EEG technology and computing algorithms to measure differences in connectivity between brain regions in children as young as 2 years old.
One of the most important facets of treating autism is early diagnosis. The earlier an accurate diagnosis of autism is made, the earlier that treatment and training can begin to take advantage of a young and still-developing brain to compensate for what nature by itself needs a helping hand toward shaping a young mind.
The causes of autism appear to be multifactorial, indicating that what is happening at the neural and biochemical levels is exceedingly complex as more studies are published that both support and contradict one another. Without doubt there is a strong genetic component to autism as twin and other sibling studies have shown; however, there is convincing evidence that suspected environmental insults can add to the injury as well.
In a recent provisional article available free online published in the open access journal BMC Medicine, researchers tell us that genetic studies, Magnetic Resonance Imaging (MRI) studies and some Electroencephalogram (EEG) studies all point to plausible causes for the development of autism, but as yet have not reached fruition in a satisfactory usable diagnostic test.
On the genetic front, differentiating the cause(s) of autism can be difficult because of the presence and overlapping of genetic syndromes linked to autism such as Fragile X or Rett syndrome. Past magnetic resonance imaging (MRI) studies have hinted about potential physiological neural differences demonstrated by increased brain sizes and altered connectivity between areas of the brain in cases of autism, while current Electroencephalogram (EEG) coherence studies attempt to pinpoint MRI suspicions and have confirmed the presence of connectivity changes.
However, according to a press release issued by the Children’s Hospital in Boston, the authors point out that few studies have been done to take full advantage of the capabilities of what an EEG might reveal on a finer scale.
"We studied the typical autistic child seeing a behavioral specialist—children who typically don't cooperate well with EEGs and are very hard to study," says lead author Frank H. Duffy, MD, of the Department of Neurology. "No one has extensively studied large samples of these children with EEGs, in part because of the difficulty of getting reliable EEG recordings from them."
To surmount the hurdles of measuring EEG recordings on children with autism, the researchers modified the testing procedures and used computational algorithms to subtract away any EEG noise from excessive body and eye movements made by the children during testing that required the placing of multiple electrodes on the children's scalps.
In the study, 430 children with autism and 554 control subjects, ages 2 to 12, were measured for their brain wave patterns using EEG that sought to find altered connectivity differences such as reduced connectivity in comparisons between children with and without autism. This reduced connectivity is referred to as the level of coherence, which is somewhat likened to a radio antenna attuned to a specific radio frequency. If the radio wave is in phase (tuned) with the antenna system, then they are well-connected and are coherent. If the antenna system and radio wave do not match well, then there is a lack of clear signaling and coherence is low.
What the data revealed to the researchers was that out of 4,000 unique combinations of electrode signals, 33 coherence "factors" consistently distinguished the children with autism from the controls that were categorized in age groups of 2 to 4, 4 to 6, and 6 to 12 years of age.
"These factors allowed us to make a discriminatory rule that was highly significant and highly replicable," says Duffy. "It didn't take anything more than an EEG—the rest was computational. Our choice of variables was completely unbiased—the data told us what to do."
The researchers believe their findings could lead to a future diagnostic test of autism—particularly in children as young as two or perhaps even earlier with infants where conventional diagnostic methods are typically unreliable or inconclusive at the time of testing.
Follow this link to a related, informative article titled “Artificial Intelligence Provides Easy Autism Diagnosis in Minutes.”
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Reference: “A stable pattern of EEG spectral coherence distinguishes children with autism from neuro-typical controls - a large case control study” BMC Medicine (26 June 2012), 10:64 doi:10.1186/1741-7015-10-64; Frank H Duffy and Heidelise Als