Neuroscientists at Georgetown University Medical Center (GUMC) have discovered a brain anomaly that explains why some people diagnosed with autism cannot easily recognize faces -- a deficit linked to the impairments in social interactions considered to be the hallmark of the disorder.
They also say that the novel neuroimaging analysis technique they developed to arrive at this finding is likely to help link behavioral deficits to differences at the neural level in a range of neurological disorders.
The final manuscript published March 15 in the online journal NeuroImage: Clinical, the scientists say that in the brains of many individuals with autism, neurons in the brain area that processes faces (the fusiform face area, or FFA) are too broadly "tuned" to finely discriminate between facial features of different people.
They made this discovery using a form of functional magnetic resonance imaging (fMRI) that scans output from the blueberry-sized FFA, located behind the right ear.
"When your brain is processing faces, you want neurons to respond selectively so that each is picking up a different aspect of individual faces. The neurons need to be finely tuned to understand what is dissimilar from one face to another," says the study's senior investigator, Maximilian Riesenhuber, PhD., an associate professor of neuroscience at GUMC.
"What we found in our 15 adult participants with autism is that in those with more severe behavioral deficits, the neurons are more broadly tuned, so that one face looks more like another, as compared with the fine tuning seen in the FFA of typical adults," he says.
"And we found evidence that reduced selectivity in FFA neurons corresponded to greater behavioral deficits in everyday face recognition in our participants. This makes sense. If your neurons cannot tell different faces apart, it makes it more difficult to tell who is talking to you or understand the facial expressions that are conveyed, which limits social interaction."
Riesenhuber adds that there is huge variation in the ability of individuals diagnosed with autism to discriminate faces, and that some autistic people have no problem with facial recognition.
"But for those that do have this challenge, it can have substantial ramifications -- some researchers believe deficits in face processing are at the root of social dysfunction in autism," he says.
The neural basis for face processing
Neuroscientists have used traditional fMRI studies in the past to probe the neural bases of behavioral differences in people with autism, but these studies have produced conflicting results, says Riesenhuber.
"The fundamental problem with traditional fMRI techniques is that they can tell which parts of the brain become active during face processing, but they are poor at directly measuring neuronal selectivity," he says, "and it is this neuronal selectivity that predicts face processing performance, as shown in our previous studies."
To test their hypothesis that differences in neuronal selectivity in the FFA are foundational to differences in face processing abilities in autism, Riesenhuber and the study's lead author, neuroscientist Xiong Jiang, PhD, developed a novel brain imaging analysis technique, termed local regional heterogeneity, to estimate neuronal selectivity.
Read the full article here
They also say that the novel neuroimaging analysis technique they developed to arrive at this finding is likely to help link behavioral deficits to differences at the neural level in a range of neurological disorders.
The final manuscript published March 15 in the online journal NeuroImage: Clinical, the scientists say that in the brains of many individuals with autism, neurons in the brain area that processes faces (the fusiform face area, or FFA) are too broadly "tuned" to finely discriminate between facial features of different people.
They made this discovery using a form of functional magnetic resonance imaging (fMRI) that scans output from the blueberry-sized FFA, located behind the right ear.
"When your brain is processing faces, you want neurons to respond selectively so that each is picking up a different aspect of individual faces. The neurons need to be finely tuned to understand what is dissimilar from one face to another," says the study's senior investigator, Maximilian Riesenhuber, PhD., an associate professor of neuroscience at GUMC.
"What we found in our 15 adult participants with autism is that in those with more severe behavioral deficits, the neurons are more broadly tuned, so that one face looks more like another, as compared with the fine tuning seen in the FFA of typical adults," he says.
"And we found evidence that reduced selectivity in FFA neurons corresponded to greater behavioral deficits in everyday face recognition in our participants. This makes sense. If your neurons cannot tell different faces apart, it makes it more difficult to tell who is talking to you or understand the facial expressions that are conveyed, which limits social interaction."
Riesenhuber adds that there is huge variation in the ability of individuals diagnosed with autism to discriminate faces, and that some autistic people have no problem with facial recognition.
"But for those that do have this challenge, it can have substantial ramifications -- some researchers believe deficits in face processing are at the root of social dysfunction in autism," he says.
The neural basis for face processing
Neuroscientists have used traditional fMRI studies in the past to probe the neural bases of behavioral differences in people with autism, but these studies have produced conflicting results, says Riesenhuber.
"The fundamental problem with traditional fMRI techniques is that they can tell which parts of the brain become active during face processing, but they are poor at directly measuring neuronal selectivity," he says, "and it is this neuronal selectivity that predicts face processing performance, as shown in our previous studies."
To test their hypothesis that differences in neuronal selectivity in the FFA are foundational to differences in face processing abilities in autism, Riesenhuber and the study's lead author, neuroscientist Xiong Jiang, PhD, developed a novel brain imaging analysis technique, termed local regional heterogeneity, to estimate neuronal selectivity.
Read the full article here
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