The Autistic Brain: Thinking Across the Spectrum by Temple Grandin, Richard Panek Read Free Book Online Page A

sensitivity rate of 92 percent. (Other researchers have questioned the reliability of comparing subjects who were awake and subjects who were sedated—factors that Hirsch’s team felt they accounted for. As always in science, further tests will or will not reinforce the validity of the findings.)
    Another way that research groups are searching for a biomarker is by taking a sample of autistic and control subjects, focusing on one aspect of the brain that the researchers have reason to believe is associated with autistic behavior, and seeing if they can create an algorithm that can tell one kind of brain from another. Jeffrey S. Anderson, from the University of Utah, offers this simplified description: “We use a whole bunch of normal brains and brains of individuals with autism, and we make a template of each one”—of autistic brain and neurotypical brain—”and we take a new subject in and just ask, ‘Well, which one does it match more?’”
    The point isn’t to identify this brain or that brain as belonging to an autistic person or a neurotypical. It’s to find an aggregate that could help identify areas of potential interest that might be biomarkers.
    In a major studythat Anderson’s group published in 2011, the aspect of the brain under consideration was connectivity. The earlier studies indicating that autistic brains tend to have local overconnectivity and long-distance underconnectivity had focused on a small number of discrete brain regions. Anderson and his colleagues instead studied the connectivity of the entirety of the gray matter. Using a variation of fMRI called functional connectivity MRI, they obtained connectivity measurements among 7,266 “regions of interest.” In a group of forty male adolescents and young adults with autism and a like sample of forty typically developing subjects, Anderson found that the connectivity test could identify whether a brain was autistic or typical with 79 percent accuracy overall and 89 percent accuracy for subjects who were under the age of twenty.
    That level of accuracy is consistent with results from other research groups. A 2011 MRI studyfrom the University of Louisville found that in a sample of seventeen autistic and seventeen neurotypical subjects, the length of the centerline of the corpus callosum could be used to distinguish between the two types of brains with a level of accuracy ranging from 82 percent to 94 percent, depending on statistical confidence levels.
    In another MRI studyfrom 2011, researchers at the Stanford University School of Medicine and Lucille Packard Children’s Hospital looked not at the size of an individual part of the brain, as structural MRI studies usually do, but at the topology of the gray matter’s folds—the brain’s cliffs and valleys. In a sample of twenty-four autistic children and twenty-four typically developing children (all aged eight to eighteen), they identified differences between the two groups in the default mode network, a system associated with daydreaming and other brain-at-rest, nontask activities. The study subjects whose brains showed the greatest deviations from the norm also exhibited the most severe communication deficits. Volume measurements of the posterior cingulate cortex in particular achieved an accuracy rate of 92 percent in telling one kind of brain from the other.
    Accuracy rates in the 80 to 90 percent range are not high enough for researchers to claim they’ve discovered a marker for autism, but it’s progress of a sort that would have been difficult to imagine only a decade ago. And it’s certainly high enough to inspire confidence in the algorithmic approach.
    One of the goals for further research is to adapt these techniques to younger subjects. As Utah’s Anderson says, “It’s not really helpful to diagnose a teenager with autism, because we already know it.” The younger the subject, the earlier the possibility of intervention. The earlier the intervention, the greater the