Amygdala network dysfunction in late-life depression phenotypes: Relationships with symptom dimensions.

Area of Science:

• Neuroscience
• Psychiatry
• Gerontology

Background:

• The amygdala is vital for emotional processing and implicated in late-life depression (LLD).
• Understanding amygdala network function in LLD phenotypes, specifically LLD alone versus LLD with mild cognitive impairment (LLD-MCI), is crucial but currently unknown.
• Investigating these differences can clarify LLD pathophysiology and heterogeneity.

Purpose of the Study:

• To investigate amygdala functional connectivity (FC) differences between LLD alone, LLD-MCI, and healthy controls.
• To examine the relationship between amygdala network dysfunction and specific symptom dimensions in LLD phenotypes.
• To elucidate the neural underpinnings distinguishing LLD clinical presentations.

Main Methods:

• A resting-state functional connectivity magnetic resonance imaging (R-fcMRI) study.
• Seed-based voxelwise analysis of amygdala FC in 63 elderly participants (LLD [n=22], LLD-MCI [n=15], healthy controls [n=26]).
• Correlation analysis between amygdala FC abnormalities and symptom severity/executive functioning.

Main Results:

• LLD-only participants exhibited increased FC in posterior default mode/vermis and decreased FC in fronto-parietal/salience/temporal areas compared to controls.
• LLD-MCI participants showed decreased FC in default mode/cognitive control/salience/visual areas and increased FC in lateral parietal cortex versus controls.
• LLD-MCI group displayed reduced FC in occipital/posterior default mode areas compared to the LLD-only group, with distinct FC abnormalities correlating with symptom severity and executive function.

Conclusions:

• Distinct amygdala FC abnormalities characterize LLD-only and LLD-MCI phenotypes.
• These network dysfunctions are associated with specific symptom dimensions (depression, anxiety) and executive functioning deficits.
• Amygdala FC impairments may serve as a biomarker to differentiate LLD phenotypes and explain clinical heterogeneity.