Machine intelligence accelerates research into mapping brains with MR

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Machine intelligence accelerates research into mapping brains with MR

Press releases may be edited for formatting or style | December 18, 2020 Alzheimers/Neurology Artificial Intelligence MRI

In 2013, scientists launched a Japanese government-led project called Brain/MINDS (Brain Mapping by Integrated Neurotechnologies for Disease Studies) to map the brains of marmosets -- small nonhuman primates whose brains have a similar structure to human brains.

The brain/MINDS project aims to create a complete connectome of the marmoset brain by using both the non-invasive MRI imaging technique and the invasive fluorescent tracer technique.

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"The data set from this project was a really unique opportunity for us to compare the results from the same brain generated by the two techniques and determine what parameters need to be set to generate the most accurate MRI-based connectome," said Dr. Gutierrez.

In the current study, the researchers set out to fine-tune the parameters of two different widely-used algorithms so that they would reliably detect long-range fibers. They also wanted to make sure the algorithms identified as many fibers as possible while minimally pinpointing ones that were not actually present.

Instead of trying out all the different parameter combinations manually, the researchers turned to machine intelligence.

To determine the best parameters, the researchers used an evolutionary algorithm. The fiber tracking algorithm estimated the connectome from the diffusion MRI data using parameters that changed - or mutated - in each successive generation. Those parameters competed against each other and the best parameters - the ones that generated connectomes that most closely matched the neural network detected by the fluorescent tracer - advanced to the next generation.

The researchers tested the algorithms using fluorescent tracer and MRI data from ten different marmoset brains.

But choosing the best parameters wasn't simple, even for machines, the researchers found. "Some parameters might reduce the false positive rate but make it harder to detect long-range connections. There's conflict between the different issues we want to solve. So deciding what parameters to select each time always involves a trade-off," said Dr. Gutierrez.

Throughout the multiple generations of this "survival-of-the-fittest" process, the algorithms running for each brain exchanged their best parameters with each other, allowing the algorithms to settle on a more similar set of parameters. At the end of the process, the researchers took the best parameters and averaged them to create one shared set.

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