Brain Activity May Reveal Suicide Risk in Depressed Patients

Researchers have found that it may soon be possible to determine a depressed patient’s risk of suicide by analyzing the neural connections in their brain. The patterns of neural networks in people who have exhibited suicidal behavior differ from those who have not attempted suicide. This discovery could lead to new methods for suicide prevention and therapy.

Every year, tens of thousands of people take their own lives, most often driven by depression. Affective disorders are frequently accompanied by an increased risk of suicide, but it is not easy for medical professionals to recognize warning signs. “Currently, there are few ways to identify individuals at high risk for suicidal behavior,” explains study co-author Scott Langenecker from the University of Utah in Salt Lake City. “We have to rely on what patients tell us and the conclusions of their doctors, which is often not enough.”

It is known that illnesses like depression are linked to specific features of the nervous system, particularly the cognitive control system (the so-called salience network) and the default mode network. Langenecker and his colleagues, led by Jonathan Stange from the University of Illinois at Chicago, wondered whether the activity of these neural networks could reveal something about the risk of suicidal behavior.

To investigate, the researchers used functional magnetic resonance imaging (fMRI) to scan the brains of 212 young people. Among the participants, 18 had affective disorders and had previously attempted suicide. Another 60 participants had at least once thought about suicide. There were also 52 patients without such a history and 82 healthy individuals in the control group. At the time of the study, all patients were in remission and felt normal.

Using brain scans, the scientists analyzed which areas of the brain were active when patients were in a calm, relaxed state. They were especially interested in the functional connections within and between the previously identified networks. “This is the first study to try to understand the neural mechanisms underlying suicide,” says Stange.

The key finding: patients who had previously attempted suicide showed weakened connections within the cognitive control network, which is involved in problem-solving and regulating impulsivity. This network also had underdeveloped connections with other networks, including the default mode network.

“The results show that patients with affective disorders and a history of suicidal behavior display a characteristic pattern of network connections,” the researchers state. These patterns differ from those seen in patients who have had suicidal thoughts but have not attempted suicide.

In the future, new ways may emerge to identify patients at risk for suicide, and possibly even to treat them. “If we can better understand these connections, we could reduce the risk of suicide,” suggests Stange. However, further research is needed.

The number of participants who had attempted suicide in this study was small, so the researchers plan to include more such patients in future studies and observe them over a longer period of time.

The researchers also hope to find out what the brain looks like during an acute suicidal phase that requires intervention. “Ultimately, we want to use this information to prevent suicides,” Stange concludes.