Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional...

Area of Science:

• Neuroscience
• Psychiatry
• Cognitive Science

Background:

• Major depressive disorder (MDD) is associated with disrupted large-scale brain network communication, evident in resting-state functional connectivity (rsFC).
• Previous studies show variable results, hindering consistent identification of network dysfunction patterns in MDD.

Purpose of the Study:

• To conduct a meta-analysis of rsFC studies to identify consistent patterns of network dysfunction in MDD.
• To investigate brain network alterations in MDD using seed-based voxelwise rsFC data.

Main Methods:

• Systematic retrieval of seed-based voxelwise rsFC studies comparing MDD patients with healthy controls published before June 30, 2014.
• Inclusion of 27 datasets from 25 publications (556 MDD patients, 518 controls) for meta-analysis.
• Categorization of seed regions into functional networks and multilevel kernel density analysis to identify hyper/hypoconnectivity patterns.

Main Results:

• MDD exhibited hypoconnectivity within the frontoparietal network (cognitive control) and between frontoparietal and dorsal attention networks.
• MDD was associated with hyperconnectivity within the default network (self-referential thought) and between frontoparietal and default networks.
• Hypoconnectivity was observed between salience/emotion processing systems and midline cortical regions involved in top-down regulation.

Conclusions:

• Reduced frontoparietal connectivity and imbalanced network connectivity may underlie attentional biases towards internal thoughts in MDD.
• Altered connectivity in cognitive control and emotion processing networks may contribute to mood regulation deficits.
• These findings support a neurocognitive model of MDD, linking network dysfunction to cognitive and affective abnormalities.