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Imaging studies question connectivity theory of autism

by  /  11 June 2012

Resting activity: Functional connections within the default mode network, a brain circuit that is active (red) when an individual is idle, are weaker in people with autism than in controls.

Resting activity: Functional connections within the default mode network, a brain circuit that is active (red) when an individual is idle, are weaker in people with autism than in controls.

The results contradict the so-called connectivity theory of autism, which holds that the brains of people with the disorder have weak long-range functional connections compared with controls.

The studies all examined brain connectivity using resting-state analysis, in which researchers use functional magnetic resonance imaging (fMRI) to record brain activity as participants lie quietly in the scanner. They then use these data to infer functional connections in the brain. (Functional connections often reflect structural connections, but not always1,2.)

One study, led by Kevin Pelphrey at Yale University, confirmed previous research showing weak functional connections in the default mode network of people with autism compared with controls. The default mode network is active when the brain is not performing a cognitive task.

Comparing 28 children with autism and 28 controls, ages 8 to 17, the researchers also found that some regions in the sensory and motor networks have stronger functional connections in people with autism than in controls. But the majority of the brain showed no difference at all.

The results highlight how challenging it is to understand how the brains of people with autism differ from those of controls.

“It’s an overstatement in the broader literature to say autism is an underconnectivity disorder,” says Pelphrey, director of the Child Neuroscience Laboratory at Yale. “We could find very small specific differences, but overall the data look quite similar.”

Missed connections:

The idea that people with autism have impaired long-range connections in the brain is intriguing because it could explain their problems with language and communication, complex tasks that require the integration of information from different brain regions.

The bulk of evidence for connectivity impairments in autism comes from task-related brain imaging, in which fMRI records brain activity as a participant performs a cognitive task while lying in the scanner.

As in resting-state analysis, researchers infer connectivity among brain regions by looking for those whose activity changes in tandem. (Sometimes, however, correlations in activity are merely the result of two regions responding to the same stimulus at the same time.)

The majority of these studies have found that in people with autism, long-range synchrony in the brain is disrupted, mostly between the frontal and parietal regions.

Resting-state analysis, by contrast, measures brain activity in the absence of any stimuli. The idea is to detect correlations that reflect real interactions between brain regions, rather than regions whose activity appears related as they independently respond to a given task at the same time.  

This approach, which has focused largely on the default mode network, has also shown that the brains of people with autism show a lack of synchrony among different parts of the network.

Even minute head movements skew the results of functional connectivity studies, the studies found, making it appear as if those who move more while in the scanner, such as children and people with autism or other disorders, have weak long-range connections. 

“Every time developmental data are presented now, someone asks whether motion was controlled for,” says Pelphrey.

A second study presented at IMFAR supports concerns over movement, finding little evidence for changes in functional connectivity in people with autism when head motion is taken into account.

Dan Kennedy, a postdoctoral researcher in Ralph Adolphs’ lab at the California Institute of Technology in Pasadena, analyzed 19 adults with high-functioning autism and 20 controls using resting-state fMRI. He and his colleagues found that the connectivity maps are strikingly similar in the two groups.

Kennedy then merged the two groups and compared connectivity in those with a lot of or little head motion. Those who move more appear to have connectivity patterns that resemble those previously reported in autism, even after running mathematical analyses designed to remove the effects of motion. “That might explain previous findings,” he says.

Kennedy told that other people at IMFAR had found similar results, but the negative results had been difficult to publish.

Small differences:

Pelphrey’s study controlled for motion by removing pairs of brain images that show that the head moved a significant distance between the images.

His study is unusual in that it has fairly large numbers for brain imaging and is focused on children with autism rather than on adults. (Children with autism can be especially difficult to scan because they tend to move.) In addition, the researchers looked at correlations across the entire cortex, rather than just the default mode network.

Pelphrey says his finding that the brains of autism are mostly similar to those of controls is robust. “We know it’s not just a null finding, because we could find very specific small differences,” he says.

However, some experts say research showing weak connections in autism brains will hold up to further scrutiny.

“Animal models support it, and the bulk of literature supports it,” says Susan Bookheimer, professor of cognitive neuroscience at the University of California, Los Angeles.

Marcel Just, director of the Center for Cognitive Brain Imaging at Carnegie Mellon University in Pittsburgh, Pennsylvania, attributes the disparate findings to the fact that researchers are intentionally measuring different aspects of brain activity.

Part of the debate rests on which type of brain activity researchers consider most important in assessing functional connectivity.

Just, for example, has focused on activity linked to cognitive processing, such as when the brain is focused on completing a language task.

“In my view, what is altered in autism is the coordination of information processing, such as occurs in language comprehension or social processing, among frontal and posterior centers of processing,” he says. “From this perspective, it makes no sense to filter out the frequencies that correspond to the cognitive processing, as some investigators do.”


1: van den Heuvel M.P. et al. Human Brain Mapp. 30, 3127–3141 (2009) PubMed

2: Greicius M.D. et al. Cereb. Cortex19, 72-78 (2009) PubMed

  • Jon Brock

    Thanks for this Emily. I was struck by the same thing. There was also a poster session where half the posters were saying underconnectivity and the other half were saying overconnectivity!

    The big problem is that nobody (as far as I can tell) actually knows what functional “connectivity” in the context of fMRI actually measures. A less misleading terminology would be “functional coactivation”.

    The time resolution of fMRI is generally of the order of several seconds, which is far slower than most of the cognitive processes that are being investigated. Certainly far too slow to correspond to different brain regions talking to one another!

    So I’m not sure what Marcel Just means when he says:

    “it makes no sense to filter out the frequencies that correspond to the cognitive processing, as some investigators do.”

    By using fMRI, that’s precisely what he is doing himself!!

  • Mary

    Functional connectivity as measured by high density EEG would go a long way to validate these results, as both default networks and functional connectivity measure via fMRI are not fully understood. fMRI functional connectivity, whether in default mode or task oriented, plays very loose and fast with the sampling theorem when it comes to analyzing the fMRI time courses for correlations and/or causal relationships. The connectivity picture in autism, as well as in other disorders, probably won’t be clear until multimodal diffusion structural, functional MRI, and EEG data are integrated into one model.

  • Steve White

    Thanks for the article. Our son was in the Autism Phenome Project at the MIND Institute at UC Davis Med School, and they found he has a very small corpus callosum. I have been wondering if this is meaningful but no one seems to know yet, or at least to have published if they do. I am wondering if there is somewhere to see a list of mental and behavioral traits related to known brain abnormalities, especially for this connectivity question. Anyone know?

    • emilysinger

      Thanks for your comment. Here is a short piece we ran on a study of the corpus callosum in autism:
      I’ll ask one of the scientists on that study if they have any answer to your question about a list of behavioral traits linked to brain abnormalities.

      • emilysinger

        I am not aware of a list such as that and really it would be difficult to generate because there is no 1:1 correspondence between structure (or even functional brain abnormalities) and specific cognitive deficits. There are general relationships though. For example, any whole brain change is likely to impact overall ability, changes in the frontal lobes or in connectivity (such as a small or absent corpus callosum) may affect executive functioning (planning, organizing, sequencing, developing a gestalt from complex social information, perspective taking, etc.), changes in the lateral temporal lobes typically affect language (expressive and/or receptive if left hemisphere language), changes in the mesial temporal lobes often influence memory and affect processing, changes in the parietal lobes can affect visual perception.

        But ultimately these relationships are very dependent on the specific location, developmental history, etc.

        Thomas W. Frazier, PhD
        Staff and Research Director, Center for Autism
        Pediatric Institute, Cleveland Clinic

  • Franco

    Re the first comment by Mary, there is at least one study that used electroencephalography (EEG) to assess dynamic brain connectivity in ASD:

    and its findings strongly support the long-distance underconnectivity theory. Quoting from its abstract:

    “Over the last years, increasing evidence has fuelled the hypothesis that Autism Spectrum Disorder (ASD) is a condition of altered brain functional connectivity. The great majority of these empirical studies rely on functional magnetic resonance imaging (fMRI) which has a relatively poor temporal resolution. Only a handful of studies have examined networks emerging from dynamic coherence at the millisecond resolution and there are no investigations of coherence at the lowest frequencies in the power spectrum – which has recently been shown to reflect long-range cortico-cortical connections. Here we used electroencephalography (EEG) to assess dynamic brain connectivity in ASD focusing in the low-frequency (delta) range. We found that connectivity patterns were distinct in ASD and control populations and reflected a double dissociation: ASD subjects lacked long-range connections, with a most prominent deficit in fronto-occipital connections. Conversely, individuals with ASD showed increased short-range connections in lateral-frontal electrodes. This effect between categories showed a consistent parametric dependency: as ASD severity increased, short-range coherence was more pronounced and long-range coherence decreased.”


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