Static and dynamic connectomics differentiate between depressed patients with and without suicidal ideation

Area of Science:

• Neuroscience
• Psychiatry
• Computational Biology

Background:

• Suicidal ideation (SI) is linked to neural circuit dysfunction.
• The role of the brain's dynamic properties (dynome) in differentiating SI within depression remains unclear.

Purpose of the Study:

• To investigate differences in static and dynamic brain connectomics between depressed patients with and without SI.
• To assess the potential of connectomic features as diagnostic and predictive markers for SI severity.

Main Methods:

• Resting-state functional magnetic resonance imaging (fMRI) was used in depressed patients with SI (n=48), without SI (NSI, n=30), and healthy controls (HC, n=30).
• Static and dynamic connectomics were constructed using full-length time-series correlations and sliding window approaches, respectively.
• Temporal variability of dynamic connectomics was quantified via variance of topological properties.

Main Results:

• Both static and dynamic connectomic properties differentiated between SI and NSI groups.
• SI patients showed decreased static connectomic properties and increased dynamic connectomic properties compared to HC and NSI.
• Combined static and dynamic connectomic properties achieved 75% accuracy in differentiating SI from NSI and predicted SI severity (r=0.55).

Conclusions:

• Combining static and dynamic connectomic analyses provides a robust method for differentiating suicidal ideation in depression.
• These findings suggest that integrated brain connectome and dynome analysis could serve as a potential neuromarker for SI diagnosis and prediction.