A growing amount of research is shedding light on the neurological processes that drive suicidal ideation (SI), providing hope for more effective therapies. A recent study conducted by Madhukar Trivedi, M.D., Professor of Psychiatry and Director of the Centre for Depression Research and Clinical Care at UT Southwestern, demonstrates the promise of electroencephalography (EEG) as a low-cost, accessible method for detecting brain dysfunction associated with SI. The findings, published in the Journal of Psychiatric Research, have the potential to revolutionise how clinicians estimate suicide risk, particularly in young people suffering from depressed or bipolar disorders. 

Suicide is a major public health issue, particularly among youth and young adults. Suicide is the third greatest cause of death among youth aged 10 to 19, and the second leading cause among those aged 20 to 34, according to the Centres for Disease Control and Prevention (CDC). The surge in suicide rates, particularly among adolescents, highlights the importance of more precise, early detection approaches. 

Psychiatric tests and self-reporting, historically used to determine suicide risk, are subjective and have limitations. However, breakthroughs in neuroimaging techniques, notably functional MRI (fMRI), have revealed that abnormalities in the brain's neural networks, such as the default mode network (DMN), are associated with suicidal ideation. "The challenge is that while fMRI has provided significant insights, it is prohibitively expensive for routine clinical use," Dr. Trivedi says. This expensive barrier has resulted in a lack of accessible, objective instruments for assessing suicide risk. 

Dr. Trivedi's team sought an alternative and resorted to EEG, a far less expensive brain-scanning tool. The study included 111 participants aged 10 to 26, all of whom had a history of or were currently diagnosed with depression or bipolar illness. They used resting-state EEG data to discover brain connection patterns related to suicidal thoughts. The researchers discovered that participants who scored high on suicidal thoughts in the Concise Health Risk Tracking self-report measure had significant dysfunction in the DMN. 

"EEG offers a cost-effective and practical way to assess neurobiological markers of suicide risk," says Cherise R. Chin Fatt, Ph.D., an Assistant Professor of Psychiatry and study author. By analysing EEG connection patterns, notably in the alpha, beta, and theta frequency bands, the researchers were able to narrow down hundreds of connectivity pairs to those most closely associated with SI. "This study gives us preliminary evidence that EEG can reliably detect brain network disruptions linked to suicidal thoughts," Dr. Trivedi says. 

The study's significant findings included the involvement of the default mode network (DMN), a network associated with self-referential thinking and mind wandering. Disruptions in this network have been associated with severe depressive disorder and other mental health issues. "What we are seeing is a consistent pattern of DMN dysfunction in individuals experiencing active suicidal ideation," says Russell Toll, Ph.D., another study researcher. The correlation between DMN dysfunction and SI suggests a potential neurobiological marker for evaluation using EEG.

Despite these encouraging results, the use of EEG to quantify SI is still in its early stages. Jane A. Foster, Ph.D., the study's co-author, advises that further research is necessary to validate these findings across larger, more diverse populations. Nonetheless, the ramifications are considerable. EEG, which is currently widely available and less expensive than other neuroimaging technologies, may provide clinicians with a more objective method of identifying at-risk individuals early on, allowing for prompt intervention. 

As teenage suicide rates rise, there is an increasing demand for more reliable and accessible diagnostic methods. The UT Southwestern study provides a clearer picture of the brain's function in suicidal ideation, giving hope that neurobiological indicators, such as those detected by EEG, will eventually play an important role in suicide prevention efforts.