Gamma-band cortical functional network abnormalities in late-life depression with suicidal ideation: insights from...

Late-life depression (LLD) is the major risk factor for elderly suicide, and suicidal ideation (SI) is a crucial stage for prevention. However, LLD are less likely openly express SI. Gamma oscillations, closely linked to cognition and mental processes, may contribute to the pathophysiology of LLD and suicidal behavior through their dysregulation within large-scale brain networks. The aim of our research was to investigate the cortical functional networks in the gamma band to better understand the neurobiological mechanisms underlying SI in LLD.

Electroencephalography (EEG) was recorded from 30 LLD with SI (LLD-SI), 32 LLD without SI (LLD-NSI), and 34 normal controls. We applied source-level graph theory based on functional connectivity in gamma band and utilized machine learning to differentiate between LLD-SI and LLD-NSI groups using network features.

Significant diminished gamma functional connectivity, particularly involving the orbitofrontal cortex, was observed in both subtypes of the LLD group. In graph theory analysis, LLD-SI showed decreased average clustering coefficient (p < 0.001) and characteristic path length (p = 0.021), along with increased global efficiency (p = 0.015) compared to LLD-NSI. Compared to NC, LLD-SI also demonstrated reduced average clustering coefficient (p = 0.004), characteristic path length (p = 0.004), and higher global efficiency (p = 0.004). We also found several nodal metrics, which suggested potential hubs related to SI. The graph theorical method effectively distinguished SI in LLD, with an accuracy of 69.35%, sensitivity of 73.33%, and specificity of 65.63% based on gamma-band network features.

The sample sizes are relatively small. Higher-density EEG systems and interventional study designs should be included in future research. Future studies should incorporate external validation datasets to confirm the clinical utility of the proposed classification framework.

Our research provides valuable insights into the brain connectome in gamma band of SI in LLD. Gamma-band network indices may serve as potential biomarkers for detecting SI and offer frequency-specific targets for neuromodulation in suicide prevention and treatment strategies for LLD patients.