Neuroimaging and AI: new hope for depression treatment

Did you know that up to two-thirds of people who suffer from depression don’t find relief from the first antidepressant they try? And even after four courses of antidepressants, one-third of people with depressive symptoms still don’t get better? Neuroimaging and AI may be able to change that, according to a pair of recent studies in the American Journal of Psychiatry and Nature Human Behavior.

The new research from scientists at UT Southwestern shows that brain imaging can identify activity patterns in the brain that indicate if a person is likely to respond to a certain medication. The two studies are part of a national trial called EMBARC that is working to establish better ways to treat depression based on objective, biological evidence. They are hoping it will lead to less trial and error and more targeted, effective treatment.

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A Power Law Keeps the Brain’s Perceptions Balanced

Researchers have discovered a surprising mathematical relationship in the brain’s representations of sensory information, with possible applications to AI research.

The human brain is often described in the language of tipping points: It toes a careful line between high and low activity, between dense and sparse networks, between order and disorder. Now, by analyzing firing patterns from a record number of neurons, researchers have uncovered yet another tipping point — this time, in the neural code, the mathematical relationship between incoming sensory information and the brain’s neural representation of that information. Their findings, published in Nature in June, suggest that the brain strikes a balance between encoding as much information as possible and responding flexibly to noise, which allows it to prioritize the most significant features of a stimulus rather than endlessly cataloging smaller details. The way it accomplishes this feat could offer fresh insights into how artificial intelligence systems might work, too.

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