Posts tagged EEG

ScienceDaily (June 25, 2012) — Widely available EEG testing can distinguish children with autism from neurotypical children as early as age 2, finds a study from Boston Children’s Hospital. The study is the largest, most rigorous study to date to investigate EEGs as a potential diagnostic tool for autism, and offers hope for an earlier, more definitive test.

Widely available EEG testing can distinguish children with autism from neurotypical children as early as age 2, finds a new study. The study is the largest, most rigorous study to date to investigate EEGs as a potential diagnostic tool for autism, and offers hope for an earlier, more definitive test. (Credit: © dule964 / Fotolia)

Researchers Frank H. Duffy, MD, of the Department of Neurology, and Heidelise Als, PhD, of the Department of Psychiatry at Boston Children’s Hospital, compared raw EEG data from 430 children with autism and 554 control subjects, ages 2 to 12, and found that those with autism had consistent EEG patterns indicating altered connectivity between brain regions — generally, reduced connectivity as compared with controls.

While altered connectivity occurred throughout the brain in the children with autism, the left-hemisphere language areas stood out, showing reduced connectivity as compared with neurotypical children, consistent with neuroimaging research. Findings were published June 26 in the online open-access journal BMC Medicine.

Duffy and Als focused on children with “classic” autism who had been referred for EEGs by neurologists, psychiatrists or developmental pediatricians to rule out seizure disorders. Those with diagnosed seizure disorders were excluded, as were children with Asperger’s syndrome and “high functioning” autism, who tend to dominate (and skew) the existing literature because they are relatively easy to study. The researchers also excluded children with genetic syndromes linked to autism (such as Fragile X or Rett syndrome), children being treated for other major illnesses, those with sensory disorders like blindness and deafness and those taking medications.

"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 Duffy. "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."

The researchers used techniques developed at Boston Children’s Hospital to get clean waking EEG recordings from children with autism, such as allowing them to take breaks. They used computer algorithms to adjust for the children’s body and eye movements and muscle activity, which can throw off EEG readings.

To measure connectivity in the brain, Duffy and Als compared EEG readings from multiple electrodes placed on the children’s scalps, and quantified the degree to which any two given EEG signals — in the form of waves — are synchronized, known as coherence. If two or more waves rise and fall together over time, it indicates that those brain regions are tightly connected. (Duffy likens coherence to two people singing “Mary Had a Little Lamb” together. If they can see and hear each other, they are more likely to sing in synchrony — so their coherence is high.)

In all, using computational techniques, the researchers generated coherence readings for more than 4,000 unique combinations of electrode signals, and looked for the ones that seemed to vary the most from child to child. From these, they identified 33 coherence “factors” that consistently distinguished the children with autism from the controls, across all age groups (2 to 4, 4 to 6, and 6 to 12 years).

Duffy and Als repeated their analysis 10 times, splitting their study population in half different ways and using half to identify the factors, and the other half to test and validate them. Each time, the classification scheme was validated.

"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 the findings could be the basis for a future objective diagnostic test of autism, particularly at younger ages when behavior-based measures are unreliable. Their most immediate goal is to repeat their study in children with Asperger’s syndrome and see if its EEG patterns are similar to or different from autism. They also plan to evaluate children whose autism is associated with conditions such as tuberous sclerosis, fragile X syndrome and extremely premature birth.

Source: Science Daily

(Medical Xpress) — Researchers from Western University have utilized their own game-changing technology – previously developed for use with patients in a vegetative state – to assess a more prevalent group of brain-injured patients, those in the minimally conscious state (MCS). Their findings were released today in Neurology, the medical journal of the American Academy of Neurology.

The study, led by Adrian Owen, Canada Excellence Research Chair in Cognitive Neuroscience and Imaging, and Damian Cruse of Western’s Brain and Mind Institute, is a follow-up to the team’s groundbreaking Lancet publication from November 2011 that used electroencephalography (EEG) to show that some vegetative state patients were able to reliably follow commands, even though this ability was entirely undetectable from their external behaviour. 

In the new paper, titled “The relationship between aetiology and covert cognition in the minimally-conscious state,” the MCS patients showed some inconsistent but reproducible external signs of awareness, such as being able to follow their eyes in a mirror.  Cruse says, however, that currently very little is known about their ‘internal’ state of awareness that may be hidden from their external behaviour. 

"Using our EEG approach, we found that 22 per cent of 23 MCS patients were able to complete a complex task which required them to imagine particular types of movement," says Cruse, a Post-Doctoral Fellow at the Brain and Mind Institute and the lead writer of the paper. "This tells us that these patients have a much higher level of cognitive ability than what you could detect from their behaviour."

Cruse adds that the cause of the brain injury was a determining factor in finding these cognitive abilities as 33 per cent of traumatically injured patients (e.g. traffic accident, fall) returned positive EEG results compared to zero per cent of non-traumatically injured patients (e.g. heart attack, stroke).

The research team, in collaboration with Steven Laureys at the University of Liège, Belgium, asked patients approximately 100 times each to imagine moving his or her right-hand and toes. By making recordings of the patients’ EEG, a measure of the electrical activity of the brain, the team showed that 22 per cent of the MCS patients were able to produce patterns of brain activity that were indistinguishable from a healthy individual following the same commands. 

"There are a large number of patients in the MCS worldwide, and our approach highlights the importance of using EEG and other forms of brain imaging when assessing the mental capabilities of patients following brain injury," says Cruse "It reinforces our understanding that the externally observable abilities of a patient are not necessarily a true reflection of their internal state."

Provided by University of Western Ontario

Source: medicalxpress.com

ScienceDaily (Feb. 10, 2012) — A distinctive pattern of brain activity associated with conditions including deep anesthesia, coma and congenital brain disorders appears to represent the brain’s shift into a protective, low-activity state in response to reduced metabolic energy. A mathematical model developed by a Massachusetts General Hospital (MGH)-based research team accurately predicts and explains for the first time how the condition called burst suppression is elicited when brain cells’ energy supply becomes insufficient. Their report has been released online in PNAS Early Edition.

"The seemingly unrelated brain states that lead to burst suppression — deep anesthesia, coma, hypothermia and some developmental brain disorders — all represent a depressed metabolic state," says Emery Brown, MD, PhD, of the MGH Department of Anesthesia, Critical Care and Pain Medicine, senior author of the report. "We believe we have identified something fundamental about brain neurochemistry, neuroanatomy and neurophysiology that may help us plan better therapies for brain protection and design future anesthetics."

Burst suppression is an electroencephalogram (EEG) pattern in which periods of normal, high brain activity — the bursts — are interrupted by stretches of greatly reduced activity that can last 10 seconds or longer. Burst suppression has been observed in deep general anesthesia, in induced hypothermia — used to protect the brain or other structures from damage caused by trauma or reduced blood flow — in coma, and in infants with serious neurodevelopmental disorders. It also has transiently been observed in some premature infants. Previous investigations of burst suppression focused on characterizing the structure of the EEG patterns and understanding the brain’s responsiveness to external stimuli while in this state, not on the underlying mechanism.

Lead author ShiNung Ching, PhD, a postdoctoral fellow in Brown’s lab, had been working with Nancy Kopell, PhD, a professor of Mathematics at Boston University and co-author of the PNAS article, to develop mathematical models of different brain states under general anesthesia. In developing a model for burst suppression, they focused on what the associated conditions have in common — a significant reduction in the brain’s metabolic state. In order for a signal to pass from one nerve cell to another, the balance between sodium ions outside the cell and potassium ions within the cell needs to be correct. Maintaining that balance requires that structures called ion pumps, fueled by the cellular energy molecule ATP, function correctly. The model developed by Ching and his colleagues revealed that, when brain energy supplies drop too low and cause a deficiency in ATP, potassium leaks from the nerve cells and signal transmission halts.

"It looks like burst suppression shifts the brain into an altered physiologic state to allow for the regeneration of ATP, which is the essential metabolic substrate," Ching explains. "During suppression, the brain is trying to recover enough ATP to restart. If the substrate doesn’t regenerate quickly enough, the system will have these brief bursts of activity, stop and then need to recover again. The length of suppression is governed by how quickly ATP regenerates, which matches the observation that the deeper someone is anesthetized, the longer the periods of suppression."

Brown adds, “When we use general anesthesia to place patients with serious neurologic injuries into induced comas to allow their brains to heal, we take them down to a level of burst suppression. But there are a lot of questions regarding how deeply anesthetized an individual patient should be — how often the bursts should occur — and how long we should maintain that state. By elucidating what appears to be a fundamental energy-preserving mechanism within the brain, this model may help us think about using burst suppression to guide induced coma and track recovery from brain injuries. This is also a great example of how studying anesthesia can help us learn something very basic about the brain.”

Brown is the Warren Zapol Professor of Anesthesia at Harvard Medical School. He also is a professor of Computational Neuroscience and Health Sciences and Technology at Massachusetts Institute of Technology. Additional co-authors of the PNAS report are Patrick Purdon, PhD, MGH Anesthesia, and Sujith Vijayan, PhD, Boston University Mathematics. The study was supported by grants from the National Institutes of Health and the National Science Foundation.

Source: Science Daily