Nandakumar's model then identifies how the eloquent cortex is connected with the rest of the brain from a functional point of view, showing the surgeon which area to avoid during the procedure. Deep learning is a type of artificial intelligence learning that uses neural networks modeled on the human brain to analyze hefty amounts of data to perform certain tasks or functions. More specifically, his model applies deep learning techniques on a graph that summarizes cognitive activity connecting various regions of the brain. Nandakumar's model leverages the fact that regions in the brain that have synchronous blood flow patterns are more likely to be involved in similar cognitive processes. "And because this approach is non-invasive, it makes preoperative mapping techniques available to people who could not have tolerated the use of electrodes, such as young children, patients with aphasia, or patients with disabilities." "Our method improves upon the use of electrodes by working on resting-state fMRI data, which can be taken days or weeks before the surgery," said Naresh Nandakumar, a doctoral candidate in the Whiting School of Engineering's Department of Electrical and Computer Engineering and a member of the Neural Systems Analysis Laboratory.
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