Altered gray matter structural covariance networks in postpartum depression: a graph theoretical analysis

Area of Science:

• Neuroscience
• Psychiatry
• Systems Biology

Background:

• Postpartum depression (PPD) is a significant global mental health issue.
• Current understanding of brain structural covariance network alterations in PPD is limited.
• This study explores the topographical organization of brain networks in PPD patients.

Purpose of the Study:

• To investigate alterations in brain structural covariance networks in patients with postpartum depression.
• To utilize graph theoretical analysis to characterize these network changes.
• To identify specific network properties and hub regions affected by PPD.

Main Methods:

• Acquired high-resolution 3D T1 structural images from 21 PPD patients and 18 healthy controls.
• Constructed whole-brain structural covariance networks using cortical thickness and Pearson correlation coefficients.
• Analyzed topological properties (small-worldness, efficiency, centrality) using graph theory and Network-based statistic (NBS).

Main Results:

• PPD and control groups exhibited small-worldness, more pronounced in PPD.
• PPD group showed increased local network efficiency.
• Identified distinct hub nodes and two disrupted subnetworks in PPD, involving cognitive-control, visual, and default mode networks.

Conclusions:

• Postpartum depression is associated with significant alterations in brain structural covariance network topology.
• Findings support the view of PPD as a systems-level disorder affecting brain organization.
• Characterization of network disruptions provides insights into the neurobiological underpinnings of PPD.