Algorithms analyze brain scans to identify people with suicidal thoughts

In a small-scale study, researchers have shown that algorithms can analyze brain scans to determine whether an individual has suicidal thoughts. During the study, the University of Pittsburgh and Carnegie Mellon University scientists mentioned words like "death," "trouble," and "carefree" to individuals undergoing fMRI scans of their brains. Apparently those kinds of words spur different brain activity in people who have suicidal thoughts compared to those who don't. The hope is that a better understanding of brain function in suicidal people could lead to better tests to assess risk of suicide and improved psychotherapy. From IEEE Spectrum:

For the study, the researchers recruited 34 volunteers between the ages of 18 and 30-half of them at risk, and the other half not at risk, of suicide. They showed the participants a series of words related to positive and negative facets of life, or words related to suicide, and asked them to think about those words.

Then the researchers recorded, with fMRI, the cerebral blood flow in the volunteers as they thought about those words, and fed the data to the algorithms, indicating which volunteers were at risk of suicide and which weren't. The algorithms then learned what the neural signatures in the brain of a suicidal person tend to look like.

Then they tested the algorithms by giving them new neural signatures to see how well they could predict, based on learning from other subjects, whether someone was suicidal or not. The classifier did it with 91% accuracy. Separately, the classifier was able to identify, with 94% accuracy, which volunteers had actually made an attempt at suicide, versus having only thought about it.

"Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth" (Nature Human Behavior)