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Mood-Reading Brain Algorithm Has Potential to Treat Depression
A new study published in Nature Biotechnology reports that experimental software trained on recordings from seven patients with brain implants can decode variations in mood.
The work is part of a larger movement, funded by government agencies and industry, aimed at developing better, more personalized therapies for psychiatric conditions such as depression, anxiety, post-traumatic stress disorder and obsessive-compulsive disorder that currently use deep brain stimulation (DBS), a highly invasive surgical procedure.
Experts are working to decode how the brain normally works and what goes wrong in disease, which includes figuring out what regions are involved in a particular patient’s symptoms. That can differ from person to person, even in patients with the same diagnosis. For the Nature Biotechnology study, researchers continuously recorded the activity of hundreds of neurons for multiple days in patients being monitored for epileptic seizures. They also had these patients rate their moods every couple of hours. They used these two sets of data to train software to understand what brain activity correlated with how a person was feeling. For each patient, the signature of brain activity (or the regions that lit up) that was predictive of mood was slightly different.
The new algorithm, developed at the University of Southern California with funding from the U.S. Department of Defense, is a step toward reading and decoding mood-related brain activity more reliably. The new approach is one of several that neuroscientists are taking to enable precision medicine for the brain.
Some researchers are using already available devices, while others are aiming to develop novel algorithms for decoding brain activity, plus new hardware capable of recording and delivering stimulation. These would, theory, read and decode brain signals in real-time with the help of machine learning and then deliver small electrical pulses to correct abnormalities associated with psychiatric conditions in an adaptive way.
Source: Wall Street Journal, September 10, 2018