Ai brain suicide

What, if suicides were easy to diagnose from outside? what if you could get inside someone’s head, to see when dark thoughts might turn to action? That is what scientist are attempting to do with the help of brain scans and artificial intelligence.

The researchers at Carnegie Mellon and the University of Pittsburgh examined how suicidal individuals think and feel differently about life and death, by looking at the figures of how their brains light up in a fMRI machine. Then, they edified a machine learning algorithm to separate those signals. Like a frontal lobe flare at the mention of the word “death,” for example. The computational classifier was capable to detect the suicidal ideators with more than 90 percent accuracy. Moreover, it was capable to differentiate people who had actually attempted self -harm from those who had only thought about it.

The study had a small sample size—34 subjects—so while the algorithm might excel at spotting particular blobs in this set of brains, it’s not obvious it would work as well in a broader population. Another dilemma that bedevils fMRI studies: Just because two things occur at the same time doesn’t prove one causes the other.

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 a 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 here?” For each word, the researchers recorded the subjects’ cerebral blood flow to find out which parts of their brains seemed to be at work.

Researchers at Florida State and Vanderbilt recently trained a machine learning algorithm on 3,250 electronic medical records for people who had attempted suicide sometime in the last 20 years. It identifies people not by their brain activity patterns, but by things like age, sex, prescriptions, and medical history. And it correctly predicts future suicide attempts about 85 percent of the time.