Characterizing Unipolar and Bipolar Depression by Alterations in Inflammatory Mediators and the Prefrontal-Limbic...

Area of Science:

• Neuroscience
• Psychiatry
• Medical Imaging

Background:

• The prefrontal-limbic system is crucial for emotion processing in unipolar depression (UD) and bipolar depression (BD).
• Existing evidence primarily stems from task-fMRI, with limited structural network data differentiating UD and BD.
• Inflammatory and growth factors are implicated in mood disorders, but their distinct roles in UD vs. BD require further investigation.

Purpose of the Study:

• To investigate structural, large-scale network differences in the emotional circuits of UD and BD patients.
• To explore the association of inflammatory and growth factors with these network alterations.
• To identify distinct neurobiological markers for differentiating UD from BD.

Main Methods:

• Diffusion-weighted imaging (DWI) and serum cytokine sampling were performed on 31 BD patients, 37 UD patients, and 61 healthy controls (HCs).
• Graph theory and network-based statistics (NBS) were employed to analyze prefrontal-limbic network alterations.
• Logistic regression was used to identify predictive markers for distinguishing between UD and BD.

Main Results:

• Unipolar depression patients exhibited higher s-100β protein levels, greater right amygdala efficiency, and an intensified prefrontal-cingulate-amygdala subnetwork compared to bipolar depression patients.
• Specific network alterations (prefrontal-cingulate-amygdala subnetwork, right amygdala nodal efficiency) and inflammatory markers (IL-8, IL-17, s-100β) were identified as risk factors for UD.
• Binary logistic regression achieved high accuracy (AUC 0.949, sensitivity 0.875, specificity 0.938) in classifying UD vs. BD.

Conclusions:

• Significant structural network differences exist in the emotional circuits of unipolar and bipolar depression.
• Distinct neurobiological profiles, including inflammatory markers and network efficiency, differentiate UD from BD.
• These findings advance our understanding of the underlying pathophysiology and diagnostic potential for mood disorders.