Massachusetts Institute of Technology (MIT) researchers have developed an algorithm that can analyze information from medical images to identify diseased areas of the brain and their connections with other regions.
"It’s quite hard for a person looking at all of that data to integrate it into a model of what is going on, because we’re not good at processing lots of numbers," says MIT professor Polina Golland.
The algorithm first compares the data from the brain scans of healthy people with those of patients with a particular disease, in order to identify differences in the connections between the two groups that indicate disruptions caused by the disorder. The algorithm then analyzes the network of connections to create a map of the areas of the brain most affected by the disease.
"Our methods extract from the data this set of regions that can explain the disruption of connectivity that we see," Golland says. The algorithm hypothesizes what disruptions in signaling it would expect to see if a certain region were affected. "It basically finds the subset of regions that best explains the observed changes in connectivity between the normal control scan and the patient scan," Golland says.
From MIT News
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