Brain network-driven predictive neural markers for suicide risk.

Abstract

Suicide is a significant public health challenge, underscoring the need for temporally precise and objective biomarkers to identify acute suicide risk. One potential biomarker is electrophysiological brain connectivity measured by magnetoencephalography (MEG). This study used publicly available atlases to estimate parcel-level within-network connectivity. The multi-omics statistical method was applied to assess whether canonical networks-including the default mode (DMN), executive control (ECN), and salience (SN) networks-could differentiate individuals experiencing a recent suicidal crisis (high risk) from those with a history of suicide attempts but not in the past year (lower risk), clinical controls with depression but no history of suicide risk (clinical controls), and individuals with no personal or family history of either psychiatric disorder or suicidal behavior (minimal risk). The high risk group exhibited significantly weaker functional connectivity within specific regions of these networks (<i>ps</i>&#xa0;&lt;.05). Further analyses revealed that recent suicidal ideation scores were positively associated with functional DMN connectivity (e.g., between the precuneus/posterior cingulate cortex and dorsal/medial prefrontal cortex) and SN connectivity (e.g., between the medial frontal cortex and medial parietal cortex and between the parietal operculum and lateral prefrontal cortex) (<i>ps</i>&#xa0;&lt;.05). Conversely, functional ECN connectivity (between the medial/posterior prefrontal cortex and precuneus) was negatively associated with recent suicidal ideation (<i>ps</i>&#xa0;&lt;.05). These findings highlight critical brain regions and network interactions, advancing our understanding of the neural mechanisms underlying suicide risk. Leveraging electrophysiological connectivity and innovative tools may help develop targeted interventions and early identification strategies for those at heightened risk of suicide. www.clinicaltrials.gov, identifier NCT02543983.