# Dynamic Exploration of Resting-State Brain Attractors Altered in Major Depressive Disorder

## TL;DR

The study shows that brain activity patterns in people with depression differ from healthy individuals, suggesting new ways to understand and diagnose the condition.

## Contribution

The study introduces a novel method to analyze brain dynamics in depression, revealing specific network changes linked to the disorder.

## Key findings

- Patients with MDD showed reduced occupancy in the default mode network (DMN) compared to healthy controls.
- MDD patients exhibited increased occupancy in the occipito–parieto–temporal network, indicating compensatory brain dynamics.

## Abstract

Major depressive disorder (MDD) represents a heterogeneous condition lacking reliable neurobiological biomarkers and a mechanistic understanding. Time-resolved characterization of brain dynamics reveals that mental health is associated with a characteristic dynamical regime, exhibiting spontaneous switching between a repertoire of ghost attractor states forming resting-state networks. Analysing resting-state fMRI data from 848 patients with MDD and 794 healthy controls across 17 sites in China (REST-meta-MDD) using Leading Eigenvector Dynamics Analysis (LEiDA), we found patients with MDD exhibited significantly reduced default mode network (DMN) occupancy (p < 0.001; Hedges’ g = −0.51) and increased occipito–parieto–temporal state occupancy (p < 0.001; Hedges’ g = 0.42), suggesting compensatory dynamical rebalancing. These findings extend prior observations of DMN disruption in MDD, aligning with the emerging dynamical systems framework for mental health to advance the mechanistic understanding of MDD pathophysiology.