Researchers looked at 34 young adults, evenly split between suicidal participants and a control group. Each subject went through a functional magnetic resonance imaging (fMRI) and were presented with three lists of 10 words. All the words were related to suicide (words like "death," "distressed," or "fatal"), positive effects ("carefree," "kindness," "innocence"), or negative effects ("boredom," "evil," "guilty"). The researchers also used previously mapped neural signatures that show the brain patterns of emotions like "shame" and "anger."
Five brain locations, along with six of the words, were found to be the best markers to distinguish the suicidal patients from the controls. Using just those locations and words, the researchers trained a machine-learning classifier that was able to correctly identify 15 of the 17 suicidal patients and 16 of 17 control subjects.
