"Combining parameters was an important step. Individual brains vary, so there will always be a unique combination of parameters that works best for one specific brain. But our aim was to come up with the best generic set of parameters that would work well for all marmoset brains," explained Dr. Gutierrez.
The team found that the algorithm with the generic set of optimized parameters also generated a more accurate connectome in new marmoset brains that weren't part of the original training set, compared to the default parameters used previously.
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The striking difference between the images constructed by algorithms using the default and optimized parameters sends out a stark warning about MRI-based connectome research, the researchers said.
"It calls into question any research using algorithms that have not been optimized or validated," cautioned Dr. Gutierrez.
In the future, the scientists hope to make the process of using machine intelligence to identify the best parameters faster, and to use the improved algorithm to more accurately determine the connectome of brains with neurological or mental disorders.
"Ultimately, diffusion MRI-based fiber tracking could be used to map the whole human brain and pinpoint the differences between healthy and diseased brains," said Dr. Gutierrez. "This could bring us one step closer to learning how to treat these disorders."
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