The study used resting-state magnetoencephalography, or MEG, to measure the timing of neuronal activity with greater temporal resolution than blood-oxygen-level-dependent functional magnetic resonance imaging. Participants’ MEG signals were projected onto 400 brain parcels defined by a publicly available atlas, and the researchers calculated parcel-to-parcel connectivity within three canonical networks: the default mode network, executive control network and salience network. This approach allowed the analysis to move beyond a single score for an entire network and test whether particular regional connections behaved differently during acute risk. The researchers described the work as a way to examine both relatively stable vulnerability and state-dependent changes associated with a recent crisis.

Participants came from the National Institute of Mental Health’s Neurobiology of Suicide protocol and were divided into four clinically defined groups. The high-risk group included eight people who had attempted suicide or experienced suicidal ideation with intent to act within the previous two weeks. Twenty-six participants formed a lower-risk group with a history of suicide attempts but no attempt or ideation during the past year. A clinical-control group of 22 had anxiety or mood disorders but no history of suicide attempts or ideation, while 21 minimal-risk participants had no personal or family history of psychiatric disorders or suicidal behavior. The final sample included 47 women and 30 men, with a mean age of 38.41 years.

Groups did not differ significantly in age, sex or ethnicity, but they did differ in mood and anxiety symptoms, medication history and suicidal-ideation scores. The high-risk group had substantially higher current ideation scores than every comparison group and also had greater depression and anxiety severity. A multivariate analytical method called DIABLO—Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies—integrated 2,628 default mode connectivity features, 1,225 executive control features and 1,176 salience features. A discrimination design, selected because the network measures were highly correlated, significantly separated the groups at p = 0.018, whereas an alternative sparse partial least-squares design did not at p = 0.319.

Follow-up analyses indicated that the high-risk group generally had reduced within-network connectivity across all three systems. Reduced default mode connectivity involved the dorsolateral prefrontal cortex in pairs with the retrosplenial or medial prefrontal cortex and with a combined precuneus and posterior cingulate region. Lower executive control connectivity appeared among frontal and parietal areas, including the precuneus, while weaker salience connections joined medial or lateral frontal regions with sensory, insular and parietal areas. One exception was stronger connectivity between the lateral prefrontal cortex and parietal operculum than in the clinical-control group. These were group-level associations, and the analysis identified influential features rather than demonstrating that any single connection could independently predict an individual’s risk.

The relationship between recent ideation and connectivity was more specific than the overall group differences. Among participants in the high-risk group, stronger default mode connectivity between the precuneus or posterior cingulate cortex and the dorsolateral prefrontal cortex, and between the dorsolateral and medial prefrontal cortices, was associated with more severe current suicidal thoughts. The researchers reported interaction effects for these relationships, along with a negative interaction for another posterior default mode connection. In the salience network, stronger connections between the medial frontal and medial parietal cortices and between the parietal operculum and lateral prefrontal cortex were likewise associated with greater ideation severity in the recent-crisis group.

By contrast, executive control connectivity between the precuneus and posterior medial prefrontal cortex showed an inverse association with recent ideation in the high-risk group relative to lower-risk and minimal-risk participants. The investigators used Bayesian linear mixed models to accommodate non-normal data and the limited sample. Models were run with four Markov chain Monte Carlo chains and 5,000 iterations, with additional iterations used when convergence was inadequate. They reported convergence with R-hat below 1.1 and generally acceptable effective sample sizes. These models describe associations with ideation severity; they did not prospectively predict whether a participant would attempt suicide or validate the signatures in an independent cohort.

The results suggest that a recent suicidal crisis is associated with a reorganization of coordinated activity spanning cognitive control, internal thought and detection of salient stimuli. The default mode and salience relationships were generally positive with ideation severity, while the executive control relationship was negative, demonstrating that regions grouped within the same broad network can contribute in different directions. The authors suggested that stronger default mode coupling may reflect intensified self-referential and emotional processing, weaker precuneus–posterior medial prefrontal coupling may relate to impaired cognitive integration, and stronger salience coupling may reflect active emotion regulation or amplified processing of emotionally relevant information. These interpretations were drawn from prior literature rather than tested directly in the present experiment, which did not manipulate death-related cues or measure the cognitive processes proposed as explanations.

Sensitivity analyses complicated the most straightforward interpretation. When depression severity, anxiety severity and medication history were included as covariates, several executive control and salience associations were attenuated, whereas most selected default mode features remained comparatively robust. Because the high-risk group had more severe mood and anxiety symptoms and a more extensive medication history than the comparison groups, the neural patterns cannot yet be cleanly attributed to suicidal crisis alone. Participants were also medication-free or receiving clinically indicated prescriptions, and the sample included both major depressive disorder and bipolar disorder, introducing additional variability. The timing between clinical assessments and MEG collection varied modestly and was longer for some high-risk participants because of scheduling constraints.

Major limitations were the small high-risk sample, the absence of an external validation dataset and possible sensitivity to the chosen brain-parcellation scheme. The group categories also captured different temporal states—recent crisis, historical risk, clinical control and minimal risk—rather than tracking the same individuals through changing levels of risk. The results therefore support the hypothesis that acute suicidal states have electrophysiological signatures that differ from historical vulnerability, but they do not show that those signatures are unique to suicide, are stable over time or can be used in routine screening. Larger longitudinal studies, external replication and samples with closely matched symptom severity and medication histories will be needed to determine whether the network connections retain their value as objective biomarkers of acute risk.

Subject of Research: Magnetoencephalography assessment of brain-network connectivity in recent suicidal crisis and historical suicide risk

Article Title: Brain network-driven predictive neural markers for suicide risk

Article References: Lee, Y., Gilbert, J. R., Chin Fatt, C. R., Stout, J. D., Waldman, L. R., Lamontagne, S. J., Zarate, C. A., Jr., & Ballard, E. D. (2026). Brain network-driven predictive neural markers for suicide risk. Frontiers in Psychiatry, 17, Article 1950068. https://doi.org/10.3389/fpsyt.2026.1950068

Image Credits: AI Generated

DOI: 10.3389/fpsyt.2026.1950068

Keywords: suicide risk, magnetoencephalography, suicidal ideation, brain connectivity, default mode network, executive control network, salience network, DIABLO, electrophysiology, predictive biomarkers, Brain, network-driven