Different Modular Organization Between Early Onset and Late Onset Depression: A Study Base on Granger Causality...

Area of Science:

• Neuroscience
• Brain Imaging
• Psychiatry

Background:

• Brain modular organization reflects neural activity patterns, which can differ in various disease states.
• Early-onset depression (EOD) and late-onset depression (LOD) share clinical symptoms but have distinct prognoses and treatments.
• Understanding the modular organization of EOD and LOD may elucidate their unique pathogenetic mechanisms.

Purpose of the Study:

• To investigate and compare the functional brain network modularity in patients with EOD and LOD.
• To identify potential differences in brain network organization that could explain varying clinical features and treatment responses.

Main Methods:

• Functional magnetic resonance imaging (fMRI) was performed on 82 patients (40 EOD, 42 LOD) and 90 healthy controls.
• Granger causality analysis was used to construct functional networks, followed by modularity, normalized mutual information (NMI), and Phi coefficient analyses.
• Cognitive functions including memory, executive function, and processing speed were assessed.

Main Results:

• Functional modularity analysis revealed significant differences among the three groups (EOD, LOD, controls) (P < 0.03).
• Normalized mutual information (NMI) analysis confirmed distinct network partitions between EOD and LOD (P < 0.02).
• The polar frontal cortex and dorsal stream visual cortex were identified as potentially specific nodes differentiating EOD and LOD modular assignments.

Conclusions:

• This study demonstrates significant differences in functional modularity and network partition between EOD and LOD within Granger causality networks.
• These findings support the hypothesis that EOD and LOD are associated with distinct underlying neuropathological changes.
• The identified differences in brain network organization may contribute to understanding the varied clinical trajectories and treatment outcomes in EOD and LOD.