Patients with suicidal ideation can be distinguished from non-suicidal individuals with high accuracy by applying machine-learning techniques to the representation of death- and life-related concepts in the brain, reports a paper published online this week in Nature Human Behaviour. This method can also distinguish between suicidal ideators who have made a suicide attempt from those who have not.
According to the World Health Organization, close to 800,000 people die by suicide every year. The assessment of suicide risk is among the most challenging problems facing mental health clinicians: suicidal patients frequently disguise their intention to commit suicide, while clinicians’ predictions of suicide risk have shown to be poor. Markers of suicide risk that do not rely on self-reports are therefore much needed.
Marcel Just, David Brent, and colleagues presented suicidal patients and control individuals undergoing functional magnetic resonance imaging (fMRI) scans with death- and life-related words. They found that neural activity in response to six of the words (death, cruelty, trouble, carefree, good and praise) and in five brain locations best discriminated between the suicidal patients and controls. The authors then trained a machine-learning algorithm to use this information to identify which participants were patients and which were controls. The algorithm correctly identified 15 of 17 patients as belonging to the suicide group and 16 of 17 healthy individuals as belonging to the control group. The authors went on to investigate just the suicidal patients, who were divided into two groups: those who had attempted suicide (nine participants) and those who had not (eight participants). The authors trained a new algorithm that correctly distinguished between suicide attempters and non-attempters in 16 out of 17 cases.
The study’s small sample size necessitates replication. However, as Barry Horwitz notes in an accompanying News & Views, if replicated and extended to other psychiatric populations, the method developed by Just and colleagues and similar functional neuroimaging methods have the potential to become a major medical tool for the diagnosis of neuropsychiatric disorders.