Summary: Resting-state neuroimaging can reveal brain connectivity patterns associated with suicide risk. In young adults with mood disorders, those who have attempted suicide show reduced connectivity within the cognitive control network (CCN) and weakened connections between the CCN and the default mode network (DMN), compared with peers who have not attempted suicide.
Source: University of Illinois at Chicago
Researchers at the University of Illinois at Chicago and the University of Utah Health have identified differences in brain circuitry that may be linked to suicidal behavior among people with mood disorders. Published in Psychological Medicine, the study points to neural connectivity patterns that could help identify individuals at higher risk for suicide.
Suicide rates have been rising among young adults, particularly those affected by mood disorders such as major depression and bipolar disorder. Many people who die by suicide have had recent contact with health-care providers—often within 30 days—yet they may not present with obvious mood complaints at the time. Standard clinical screening and self-report measures are useful but imperfect, and better, objective tools are needed to identify those at greatest risk.
“Currently we rely heavily on self-report and clinician judgment,” said Scott Langenecker, professor of psychiatry at University of Utah Health and senior author on the study. “Those approaches help, but they do not capture the full picture.”
Prior research has described several large-scale brain networks relevant to mood and behavior: the cognitive control network (CCN), important for executive function, decision-making and impulse control; the salience and emotional network (SEN), involved in detecting and processing emotional and salient information; and the default mode network (DMN), associated with self-referential and internally focused thought. Much of the earlier work focused on depression itself rather than on suicide-related behavior specifically.
“This study is among the first to examine brain mechanisms that might be specifically related to suicide risk,” said Jonathan Stange, assistant professor of psychiatry at UIC and first author of the paper.
The team used resting-state functional magnetic resonance imaging (fMRI)—which records spontaneous brain activity while participants are awake and relaxed—to examine connectivity within and between these intrinsic networks. The analysis included 212 young adults from University of Illinois at Chicago and the University of Michigan.
Study participants were grouped as follows: young adults with mood disorders who had a history of a suicide attempt; those with mood disorders who had experienced suicidal ideation but had not attempted suicide; individuals with mood disorders without any history of suicidal ideation or attempts; and healthy comparison participants. All participants with mood disorders were in remission at the time of scanning.
Compared with other participants—including those with mood disorders who had experienced suicidal thoughts but never attempted—individuals with a prior suicide attempt showed reduced connectivity within the CCN and diminished connectivity between the CCN and the DMN. These circuits are linked to cognitive control, problem solving and impulsivity, functions that can influence the progression from suicidal thinking to suicidal behavior.
These connectivity differences may offer targets for future interventions. “If we can learn how to strengthen connectivity in these circuits, neuromodulatory or other treatments might ultimately reduce suicide risk,” Stange said.
The authors emphasize that these results are preliminary. The subgroup of participants with a history of suicide attempts was small (18 individuals), so findings require replication and validation in larger, prospective samples. It remains unclear whether the observed connectivity patterns reflect a distinct subtype of mood disorder associated with suicide risk, or rather a dimensional variation of risk across people with mood disorders. Because the scans were obtained while participants were in remission, the images may not reflect brain states during an acute suicidal crisis. The study design was retrospective rather than longitudinal.
A prospective, longitudinal design in which brain networks are measured at baseline and participants are followed over time would better identify which neural markers predict future suicidal thoughts or attempts and might guide the timing of clinical interventions.
“Our ultimate goal is prevention,” Stange said. “We want to use information from brain networks to identify people at heightened risk and to develop interventions that reduce the likelihood of suicide.”
The published paper is titled “Using Resting State Intrinsic Network Connectivity to Identify Suicide Risk in Mood Disorders.” Co-authors from University of Utah Health include Scott Langenecker, Stephanie Pocius and Robert C. Welsh. Co-authors from the University of Illinois at Chicago include Jonathan Stange, Kayla Kreutzer, Katie L. Bessette, Sophie R. DelDonno, Leah R. Kling, Runa Bhaumik and K. Luan Phan. Additional contributors include Lisanne M. Jenkins of Northwestern University and John G. Keilp of Columbia University.
Source:
University of Illinois at Chicago
Media Contact:
Jackie Carey – University of Illinois at Chicago
Image source:
The image is in the public domain.
Original research:
“Using resting-state intrinsic network connectivity to identify suicide risk in mood disorders,” Scott Langenecker et al., Psychological Medicine, doi: 10.1017/S0033291719002356. (Closed access)
Abstract
Using resting-state intrinsic network connectivity to identify suicide risk in mood disorders
Background
There is limited knowledge about neural substrates of suicide risk in mood disorders. Improving identification of biomarkers associated with a history of suicide-related behavior could enable more targeted treatments to reduce risk.
Methods
Participants included 18 young adults with mood disorders and a history of suicide-related behavior (past suicide attempt), 60 with mood disorders and a history of suicidal ideation but not attempts, 52 with mood disorders and no history of ideation or attempts, and 82 healthy comparison participants. Resting-state functional connectivity within and between intrinsic neural networks—the cognitive control network (CCN), salience and emotion network (SEN), and default mode network (DMN)—was compared across groups.
Results
Several fronto-parietal regions were identified in which individuals with a history of suicide-related behavior showed distinct connectivity patterns within the CCN and across CCN-SEN and CCN-DMN connections. Connectivity in some of these regions also distinguished the suicide-related behavior group on re-scanning 1–4 months later. Extracted measures classified group membership with good accuracy, sensitivity and specificity (79–88%).
Conclusions
These findings suggest that people with a history of suicide-related behavior in the context of mood disorders may exhibit reliable differences in intrinsic network connectivity, even compared with those who have mood disorders but no such history. Resting-state fMRI shows promise as a tool to identify subgroups of patients with mood disorders who may be at elevated risk for suicidal behavior.