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Abnormal brain functional networks in systemic lupus erythematosus: a graph theory, network-based statistic and
Yifan Yang1, Ru Bai1, Shuang Liu1
1Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming 650032, China.
Brain Communications
|April 10, 2025
Summary
Systemic lupus erythematosus (SLE) patients show altered brain functional networks, with reduced global connectivity and a specific subnet showing decreased connections. Machine learning effectively classified SLE patients based on these network differences.
Area of Science:
- Neuroscience
- Medical Imaging
- Systems Biology
Background:
- Brain functional network impairments in systemic lupus erythematosus (SLE) are not fully understood.
- Investigating topological alterations in brain networks is crucial for understanding SLE pathophysiology.
Purpose of the Study:
- To investigate brain functional network topological alterations in SLE patients.
- To apply machine learning for classifying SLE patients versus healthy controls using neuroimaging data.
Main Methods:
- Resting-state functional MRI data from 127 SLE patients and 102 healthy controls were analyzed.
- Brain functional networks were constructed using automated anatomical labeling and Pearson correlation.
- Network-based statistics and support vector machine were employed for group comparison and classification.
Main Results:
- SLE patients exhibited significantly lower normalized clustering coefficient and small-world index.
- A 12-node, 11-edge subnetwork showed significantly reduced connectivity in SLE patients (P = 0.024).
- Machine learning models successfully classified SLE from controls, identifying key subnetworks.
Conclusions:
- Systemic lupus erythematosus patients display suboptimal global brain functional connectivity network topology.
- A specific brain subnetwork with reduced connectivity is characteristic of SLE.
- Neuroimaging-based network analysis holds potential for SLE diagnosis and understanding its neurological impact.
