Graph theory approach for the structural-functional brain connectome of depression

Area of Science:

• Neuroscience
• Psychiatry
• Network Science

Background:

• Major depressive disorder (MDD) is a complex mental health condition with poorly understood neural underpinnings.
• Graph theory analysis of neuroimaging data offers a powerful framework to investigate brain network organization in psychiatric disorders.

Purpose of the Study:

• To review and synthesize findings from neuroimaging studies (2015-2020) using graph theory to examine brain network organization in MDD.
• To explore how network alterations in MDD vary with factors like age of onset, medication status, symptom severity, and treatment.

Main Methods:

• Systematic review of diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) studies in MDD.
• Application of graph theory metrics to analyze structural and functional brain connectivity networks.

Main Results:

• Resting-state functional connectivity networks in MDD exhibit diverse global organization based on onset age and medication status.
• MDD is associated with weaker intra-modular functional connections in default mode and limbic networks, and altered local graph metrics across several brain networks.
• Structural connectivity in late-life depression shows attenuated global organization compared to adult MDD, with local metrics influenced by symptom severity, stress, and treatment.

Conclusions:

• Brain connectome organization, both structural and functional, is significantly altered in MDD compared to healthy controls.
• Network alterations in MDD are heterogeneous, influenced by clinical and demographic factors.
• Further research is needed to link graph metrics to clinical features, cognitive styles, and treatment outcomes in MDD.