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Identifying and validating subtypes of Parkinson's disease based on multimodal MRI data via hierarchical clustering
Kaiqiang Cao1, Huize Pang1, Hongmei Yu2
1Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China.
Objective:
We wished to explore Parkinson's disease (PD) subtypes by clustering analysis based on the multimodal magnetic resonance imaging (MRI) indices amplitude of low-frequency fluctuation (ALFF) and gray matter volume (GMV). Then, we analyzed the differences between PD subtypes.
Methods:
Eighty-six PD patients and 44 healthy controls (HCs) were recruited. We extracted ALFF and GMV according to the Anatomical Automatic Labeling (AAL) partition using Data Processing and Analysis for Brain Imaging (DPABI) software. The Ward linkage method was used for hierarchical clustering analysis. DPABI was employed to compare differences in ALFF and GMV between groups.
Results:
Two subtypes of PD were identified. The "diffuse malignant subtype" was characterized by reduced ALFF in the visual-related cortex and extensive reduction of GMV with severe impairment in motor function and cognitive function. The "mild subtype" was characterized by increased ALFF in the frontal lobe, temporal lobe, and sensorimotor cortex, and a slight decrease in GMV with mild impairment of motor function and cognitive function.
Conclusion:
Hierarchical clustering analysis based on multimodal MRI indices could be employed to identify two PD subtypes. These two PD subtypes showed different neurodegenerative patterns upon imaging.
Insights
Two Parkinson's disease (PD) subtypes were identified using magnetic resonance imaging (MRI) data. These subtypes, a "diffuse malignant" and a "mild" form, exhibit distinct patterns of brain changes and varying symptom severity.
Area of Science:
- Neuroimaging
- Neurology
- Data Science
Background:
- Parkinson's disease (PD) is a neurodegenerative disorder with heterogeneous clinical presentations.
- Understanding PD subtypes is crucial for targeted treatment strategies.
- Multimodal magnetic resonance imaging (MRI) offers insights into brain structure and function.
Purpose of the Study:
- To identify Parkinson's disease (PD) subtypes using clustering analysis.
- To investigate differences in brain imaging indices between identified PD subtypes.
- To correlate imaging findings with motor and cognitive function.
Main Methods:
- Recruited 86 PD patients and 44 healthy controls (HCs).
- Extracted amplitude of low-frequency fluctuation (ALFF) and gray matter volume (GMV) using DPABI software.
- Applied hierarchical clustering (Ward linkage) to multimodal MRI data.
Main Results:
- Identified two distinct PD subtypes: 'diffuse malignant' and 'mild'.
- The 'diffuse malignant' subtype showed reduced ALFF and GMV in visual and widespread areas, with severe motor/cognitive deficits.
- The 'mild' subtype exhibited increased ALFF in frontal/temporal lobes and slight GMV decrease, with mild impairments.
Conclusions:
- Hierarchical clustering of multimodal MRI indices effectively identifies PD subtypes.
- The identified subtypes demonstrate distinct neuroimaging patterns.
- These subtypes correlate with differential severity of motor and cognitive dysfunction in Parkinson's disease.
