These Scans Show Two Different People Thinking About Death. What The Scans Tell Us Could Save Lives
Suicidal thoughts are difficult to diagnose. People who have these thoughts often don’t disclose them to other people, let alone healthcare professionals. Finding out whether people have these thoughts could lead to better interventions and targeted suicide prevention.
Scientists at Carnegie Mellon University and the University of Pittsburg have therefore attempted to identify suicidal thoughts as they occur in the brain. In a study published in Nature Human Behaviour, researchers scanned the brains of people who have suicidal thoughts. By studying how their brains light up upon hearing certain words (such as “death”), the researchers were able to train a machine-learning algorithm to spot certain activation patterns that indicate suicidal tendencies.
The researchers looked at 17 people between the ages of 18 and 30 who had reported suicidal ideation (thoughts of suicide or unusual preoccupation with suicide) to therapists. As a control, they also recruited 17 people who hadn’t had these thoughts.
They then scanned the paricipants’ brains using an fMRI machine whilst being shown a series of 30 words, both positive (e.g. “carefree” and “bliss”) and suicide-related concepts (“death”, “desperate”, “apathy”). They were also shown words such as “boredom”, “trouble”, and “cruelty”, which were seen as negative but not related to suicidal thoughts.
When the participants were shown the words that were related to suicide, the brains of the patients who had reported suicidal ideation lit up in a way that wasn’t seen in the control group. Of the words shown, “death” was the concept that showed the most striking difference between the two groups.
The resulting algorithm was able to identify people with suicidal thoughts with an astonishing 91 percent accuracy.
“What is central to this new study is that we can tell whether someone is considering suicide by the way that they are thinking about the death-related topics,” Marcel Just, co-lead of the study, said in a statement.
The machine-learning algorithm was even able to identify the nine people who had attempted suicide, rather than just thought about it, with 94 percent accuracy.
The authors hope that the research will lead to better treatment as well as better diagnosis.
“People with suicidal thoughts experience different emotions when they think about some of the test concepts,” Just said. “For example, the concept of ‘death’ evoked more shame and more sadness in the group that thought about suicide. This extra bit of understanding may suggest an avenue to treatment that attempts to change the emotional response to certain concepts.”
The researchers plan to test the approach on a larger sample size to see if it can be used to predict future suicidal behavior.
The hope is that it will “give clinicians in the future a way to identify, monitor and perhaps intervene with the altered and often distorted thinking that so often characterizes seriously suicidal individuals,” co-lead David Brent said.