Machine intelligence accelerates research on mapping brains by MR

Scientists at 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 18 in Scientific Reports, gives researchers more confidence in using this technique to unravel the stringing of the human brain and gain a better understanding of the changes. in this wiring that accompanies brain or mental disorders such as Parkinson’s disease or Alzheimer’s.

“It is essential to work out how the different brain regions are connected – what we call the brain’s connectome – to fully understand the brain and the complex processes it undergoes,” he said. Professor Kenji Doya, who heads the Neural Computing Unit at the Graduate University of the Okinawa Institute of Science and Technology (OIST).

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To identify connectomes, researchers monitor nerve cell fibers that extend throughout the brain. In animal experiments, scientists can introduce a fluorescent trace to several points in the brain and image where the cloud fibers coming from those points expand. But this process requires the analysis of hundreds of brain chips from many animals. And because it is so aggressive, it cannot be used in humans, explained Dr. Doya.

However, advances in magnetic resonance imaging (MRI) have made it possible to estimate noninvasive connections. This technique, called MRI-based fiber tracking, uses powerful magnetic fields to track signals from water molecules as they move – or spread – across nappies. A computer algorithm then uses these water signals to estimate the path of the cloud fibers throughout the entire brain.

But for now, the algorithms are not yielding definite results. Just as images may look different depending on the position of the camera chosen by a photographer, the options – or parameters – that scientists choose for these algorithms can generate different connections. .

“There are real concerns about the reliability of this approach,” said Dr. Carlos Gutierrez, the first author and postdoctoral researcher in the OIST Neural Computing Unit. “False objects can be under the control of the links, meaning they show cloud connections that don’t really exist.”

In addition, the algorithms struggle to detect nerve fibers that stretch between remote regions of the brain. But those long-distance connections are some of the most important for understanding how the brain works, Dr. Gutierrez said.