Resting-state functional connectivity abnormalities in first-onset unmedicated depression.

Area of Science:

• Neuroscience
• Computational Psychiatry
• Network Science

Background:

• Depression is associated with brain abnormalities, but its whole-brain topological structure is not fully understood.
• Understanding these topological alterations is crucial for advancing depression diagnosis and treatment.

Purpose of the Study:

• To investigate the whole-brain topological structure in first-episode, unmedicated depression patients using resting-state functional MRI.
• To explore the utility of brain network metrics as features for machine learning-based depression classification.

Main Methods:

• Collected resting-state functional MRI data from 36 depression patients and 27 healthy controls.
• Constructed functional connectivity networks using the Automated Anatomical Labeling template and partial correlation.
• Analyzed network properties using complex network theory and applied an artificial neural network for classification.

Main Results:

• Both groups exhibited small-world network attributes, but depression patients showed a significantly shorter characteristic path length, indicating increased randomization.
• Abnormal node attributes were identified in cortical-striatal-pallidal-thalamic circuits, with the right hippocampus and right thalamus correlating with depression severity.
• Brain network metrics, particularly those with significant intergroup differences, proved effective in machine learning classification of depression.

Conclusions:

• Brain network topology is significantly altered in depression, characterized by increased randomization and specific regional abnormalities.
• Brain network metrics derived from functional MRI data are valuable features for machine learning applications in clinical depression diagnosis.