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Algorithm Can Identify Suicidal People Using Brain Scans (wired.com)
An anonymous reader quotes a report from WIRED: In a study published today in Nature Human Behavior, researchers at Carnegie Mellon and the University of Pittsburgh analyzed how suicidal individuals think and feel differently about life and death, by looking at patterns of how their brains light up in an fMRI machine. Then they trained a machine learning algorithm to isolate those signals -- a frontal lobe flare at the mention of the word "death," for example. The computational classifier was able to pick out the suicidal ideators with more than 90 percent accuracy (Warning: source may be paywalled; alternative source). Furthermore, it was able to distinguish people who had actually attempted self-harm from those who had only thought about it. In today's study, the researchers started with 17 young adults between the ages of 18 and 30 who had recently reported suicidal ideation to their therapists. Then they recruited 17 neurotypical control participants and put them each inside an fMRI scanner. While inside the tube, subjects saw a random series of 30 words. Ten were generally positive, 10 were generally negative, and 10 were specifically associated with death and suicide. Then researchers asked the subjects to think about each word for three seconds as it showed up on a screen in front of them. "What does 'trouble' mean for you?" "What about 'carefree,' what's the key concept there?" For each word, the researchers recorded the subjects' cerebral blood flow to find out which parts of their brains seemed to be at work.
The full paper (or at least some version of it) is available online here:
https://nocklab.fas.harvard.edu/files/nocklab/files/just_2017_machlearn_suicide_emotion_youth.pdf
It's probably a preprint without the last-minute changes, but it should be good enough to understand the research.