Massachusetts Institute of Technology (MIT) researchers are developing a computer system that uses multiple types of data to help predict the effects of disease on brain anatomy.
The researchers trained a machine-learning system on magnetic resonance imaging (MRI) data from patients with neurodegenerative diseases and found supplementing that training with other patient information improved the system's predictions.
"We take our model, and we turn off the genetic information and the demographic and clinical information, and we see that with combined information, we can predict anatomical changes better," says MIT professor Polina Golland.
The researchers used data from the Alzheimer's Disease Neuroimaging Initiative, which includes MRI scans of the same subjects taken months or years apart. Each scan is represented as a three-dimensional model consisting of millions of voxels, and the researchers produced a generic brain template by averaging the voxel values of hundreds of randomly selected MRI scans. They then characterized each scan in the training set for the machine-learning algorithm.
The researchers conducted several experiments, including one in which they trained the system on scans of both healthy subjects and those displaying evidence of mild cognitive impairment. In that experiment, the researchers trained the system twice, once using just the MRI scans and the second time adding additional information to the scans; in cases where there were significant changes, the supplementary data made a significant difference.
From MIT News
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