‘Noisy’ brain signals could underlie autism, study says

The glow of a lamp, the ring of a musical note, the tickle of a feather — each sensation stimulates a different part of the cortex, the outer layer of the brain. In an adult with autism, these responses vary much more than in someone without the disorder, according to a study published 20 September in Neuron¹.

Using functional magnetic resonance imaging, the researchers exposed participants to the same stimulus — a sight, sound or touch — repeatedly over dozens of trials.

When they averaged brain activity across all the trials, they found no difference between the autism and control groups. But when they looked at a single participant’s response from one trial to the next, they found that, for those with autism, the same person may show a strong response to a certain stimulus in one trial and a weak response in the next.

The results could explain why, for example, some people with autism tend to notice small visual details, and others are extremely sensitive to loud noises or bright lights.

“It makes you wonder what their own internal or phenomenological experience is,” says lead investigator Marlene Behrmann, professor of cognitive neuroscience at Carnegie Mellon University in Pittsburgh. “[It might] make the world much more confusing and less predictable.”

One theory in autism research holds that the social and communication deficits central to the disorder arise from a fundamental problem in sensory processing. The forthcoming edition of the Diagnostic and Statistical Manual of Mental Disorders for the first time proposes to add sensory issues to the diagnostic criteria for autism.

Sensory signature:

The researchers say the ‘noise’ in autism brains could be a neural signature of the disorder. “It could ultimately explain the whole slew of altered behaviors that we see in autism,” Behrmann says.

But other experts say the results don’t support that conclusion. The study is relatively small, and research over the past year has shown that experimental artifacts, such as movement in a brain scanner can mislead results.

The researchers also did not explore whether children with other developmental disorders or intellectual disability show the same type of variability.

“If you’re going to make this argument, then it’s really important to show that [the variability] is specific to autism,” says Kevin Pelphrey, associate professor of child psychiatry at the Yale Child Study Center, who was not involved in the new study. “You have to explain, developmentally, how would you get the autism phenotype by starting out with a noisy brain?”

Behrmann and her collaborators scanned the brains of 14 high-functioning adults with autism and 14 controls while they performed a difficult task. The participants watched letters flash on a screen and pressed a button when they saw one repeat.

During the task, the researchers exposed participants to various sensory stimuli: beeps in both ears, a cluster of moving dots on one side of the screen, or puffs of air on the back of their hands. Each of these stimuli activates a different part of the cortex — the auditory cortex, the visual cortex and the somatosensory cortex, respectively.

In each area, the researchers found robust and similar brain responses in both groups of participants. But individuals in the autism group showed a lower signal-to-noise ratio, the strength of their brain signals divided by the background noise from each trial.

This variability appears only in the cortex, and not in deeper regions of the brain, the study found. This is notable, Behrmann says, because imaging studies of autism tend to focus on deep regions of the brain that are involved in processing social behavior.

“We’re way outside of the social brain areas, in regions that do the most basic cortical computations that one can imagine,” Behrmann says.

Heads up:

The study, first reported at the 2011 Society for Neuroscience annual meeting in Washington, D.C., is one of only a handful in the autism literature to investigate trial-to-trial variability in a single participant.

For example, a 2008 behavioral study found that, compared with controls, children with autism have more variable reaction times from one trial to the next².

Last year, Elizabeth Milne showed that when measuring the brain response to visual patterns using electroencephalography — a non-invasive technique that measures brain waves through electrodes on the scalp — the trial-to-trial variability in the autism group is significantly larger than in controls³.

Each of these studies uses a different technique. “There’s some real scientific convergence, suggesting that it’s a reliable finding,” says Milne, lecturer in cognitive psychology at the University of Sheffield in the U.K. “It’s exciting because it provides evidence that there is a kind of general and pervasive disruption in neural processing.”

However, most of these studies have not examined whether the noisy patterns are specific to autism.

The new study may also suffer from head motion artifacts, which are common in brain scans.

The researchers performed some checks to account for this possibility. They found no difference in head motion between the autism and control groups. They also adjusted for individual movements, and note that they saw more variable responses in the cortex than in deeper regions. Head-motion artifacts typically appear all over the brain.

“If it was purely a motion effect, you’d expect it to permeate every response they looked at,” says Steve Petersen, professor of cognitive neuroscience at Washington University in St. Louis, who was not involved in the new study. “At least part of their result is probably a real result.”

Others, however, say this difference in the cortex is a red flag. “It reminds me of exactly what you’d see if you had a difference in movement,” Pelphrey says. That’s because when lying in a scanner, the outer regions of the brain — farther from the stability of the neck and spine — are most susceptible to body movements, he says.

References:

1: Dinstein I. et al. Neuron 75, 981-991 (2012) Abstract

2: Geurts H.M. et al. Neuropsychologia 46, 3030-3041 (2008) PubMed

3: Milne E. Front. Psychol. 2, 51 (2011) PubMed