Scientists in Japan's brain science project have used machine intelligence to improve the accuracy and reliability of a powerful brain-mapping technique, a new study reports.
Their development, published on December 18th in Scientific Reports, gives researchers more confidence in using the technique to untangle the human brain's wiring and to better understand the changes in this wiring that accompany neurological or mental disorders such as Parkinson's or Alzheimer's disease.
"Working out how all the different brain regions are connected - what we call the connectome of the brain - is vital to fully understand the brain and all the complex processes it carries out," said Professor Kenji Doya, who leads the Neural Computation Unit at the Okinawa Institute of Science and Technology Graduate University (OIST).
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To identify connectomes, researchers track nerve cell fibers that extend throughout the brain. In animal experiments, scientists can inject a fluorescent tracer into multiple points in the brain and image where the nerve fibers originating from these points extend to. But this process requires analyzing hundreds of brain slices from many animals. And because it is so invasive, it cannot be used in humans, explained Prof. Doya.
However, advances in magnetic resonance imaging (MRI) have made it possible to estimate connectomes non-invasively. This technique, called diffusion MRI-based fiber tracking, uses powerful magnetic fields to track signals from water molecules as they move - or diffuse - along nerve fibers. A computer algorithm then uses these water signals to estimate the path of the nerve fibers throughout the whole brain.
But at present, the algorithms do not produce convincing results. Just like how photographs can look different depending on the camera settings chosen by a photographer, the settings - or parameters - chosen by scientists for these algorithms can generate very different connectomes.
"There are genuine concerns with the reliability of this method," said Dr. Carlos Gutierrez, first author and postdoctoral researcher in the OIST Neural Computation Unit. "The connectomes can be dominated by false positives, meaning they show neural connections that aren't really there."
Furthermore, the algorithms struggle to detect nerve fibers that stretch between remote regions of the brain. Yet these long-distance connections are some of the most important for understanding how the brain functions, Dr. Gutierrez said.