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Using a new way of analyzing brain-imaging data, scientists have mapped out how the complex networks of connections in the brain evolve as children age. The researchers are now using the technology to examine how brain development in children with specific disorders, such as autism, veers off the norm. Ultimately, researchers aim to use the technology to predict, for example, whether a child at risk for autism will actually develop the disorder, or what treatments might work best for that individual.

Previous research by the same team at the Washington University School of Medicine had shown that between age five and 30, the short-range connections in the brain tend to weaken, while longer-range connections get stronger. In the new study, scientists plotted the trajectory for normal brain development and showed for the first time that they could determine a child’s development based on a brain scan.

Brain-imaging technologies that measure neural activity, such as functional magnetic resonance imaging (fMRI), have proven to be a valuable scientific tool over the last decade, but they’re still rarely used in medicine. That’s because most fMRI studies aggregate data from multiple people, making the technology inappropriate for diagnosing patients. “We realized that if the promise of fMRI is going to apply to understanding individual patients, we need to come up with a new approach, one that can take advantage of an individual’s data,” says Bradley Schlaggar, a pediatric neurologist at Washington University. Schlaggar led the new study.

The researchers developed a twist on functional connectivity–a relatively new approach to analyzing brain-imaging data. With this approach, fMRI is used to record spontaneous fluctuations in brain activity as an individual lies quietly in the brain scanner and does nothing in particular. Brain areas that are well-connected will fluctuate in synchrony, providing an indirect way of mapping the brain’s functional networks.

Schlaggar’s team studied more than 200 people age five to 30 and recorded brain activity in 160 brain areas. These areas had been identified in previous studies as important nodes of neural connectivity. The team then used a machine-learning algorithm to examine how activity in each of those areas fluctuated with every other area. The analysis revealed patterns in the data “that the naked eye wouldn’t be able to surmise,” Schlaggar says. He adds that the work requires such heavy computational processing that it probably would have been impossible on the computers available to the researchers five to 10 years ago.

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Credit: Science/AAAS

Tagged: Biomedicine, machine learning, brain imaging, fMRI, neural networks, functional connectivity

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