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Mapping Symptom Complexity in Dementia With Lewy Bodies: A Network Analysis Approach
Emanuela Pizzolla1, Wissal El Ouahidi2, Kurt Segers3
1Department of Computational Medicine and Neuropsychiatry, Faculty of Medicine, University of Mons, Mons, Belgium.
The Journal of Neuropsychiatry and Clinical Neurosciences
|January 26, 2026
Summary
Network analysis identified key symptoms like visual hallucinations and urinary incontinence in Dementia with Lewy bodies (DLB). This approach can improve DLB diagnosis and targeted interventions.
Area of Science:
- Neuroscience
- Medical Informatics
- Gerontology
Background:
- Dementia with Lewy bodies (DLB) is the second most common neurodegenerative dementia, often underdiagnosed due to varied symptoms.
- Network analysis offers a novel approach to understand complex symptom interrelationships in DLB.
Purpose of the Study:
- To apply network analysis to DLB patients to map symptom connections.
- Identify central symptoms and explore symptom clustering patterns for improved diagnostic precision.
Main Methods:
- Retrospective analysis of 107 DLB patients' clinical records.
- Constructed a symptom network using pairwise Markov random fields and LASSO regularization.
- Computed centrality indices, predictability, and community detection; assessed stability via bootstrap.
Main Results:
- The symptom network showed positive correlations; visual hallucinations and urinary incontinence had the highest centrality.
- Identified three symptom clusters: cognitive-perceptual, motor-perceptual, and autonomic-somatic.
- Network stability was moderately robust, with slight improvement after model revision.
Conclusions:
- Central symptoms like visual hallucinations and urinary incontinence are potential high-yield diagnostic targets for DLB.
- Symptom clustering patterns may represent distinct clinical dimensions in DLB.
- Network analysis shows promise for refining DLB diagnoses and guiding interventions.
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