Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms, including cognitive impairment (CI) ranging from mild cognitive impairment (MCI) to Parkinson’s disease dementia (PDD). Growing evidence supports the gut-brain axis as playing an essential role in the pathophysiology of PD, suggesting promising applications for combining advanced neuroimaging techniques with microbiome profiling to accelerate biomarker discovery and therapeutic innovation. This study combines resting-state functional magnetic resonance imaging (rs-fMRI) and 16S rRNA sequencing of stool and saliva to identify biomarkers predictive of CI in PD.
A stepwise feature selection pipeline, incorporating ANOVA, random forest ranking, and partial correlation analysis, was applied to extract biologically meaningful features from rs-fMRI connectivity matrices and microbial taxa. Independent and joint machine learning models, including Random Forest, support vector machine, XGBoost, and logistic regression, were evaluated for their predictive performance. The joint model, integrating neuroimaging and microbiome features, outperformed modality-specific models in classifying HC, MCI, and PDD stages, achieving an accuracy of 88.9% and AUC of 97.2% with Random Forest. Key fMRI features involved the salience and default mode networks, while microbial biomarkers included taxa such as Faecalibacterium, Veillonella, and Streptococcus. Correlations between microbial taxa and fMRI features suggest potential gut-brain interactions influencing CI. For example, Faecalibacterium abundance was positively associated with connectivity in the salience network, while Veillonella showed links to executive function networks. These findings support the synergistic value of integrating multi-omics data for uncovering mechanisms underlying CI in PD.
This study demonstrates the utility of combining neuroimaging and microbiome data to enhance predictive performance and biological insight. The identified biomarkers may serve as a foundation for developing microbiome-targeted interventions and neuroimaging-guided strategies for managing cognitive decline in PD. Future work should focus on larger, longitudinal datasets and explainable AI approaches to further refine this integrative methodology.