SCIENTIFIC HUB

Follow the evidence.

SCIENTIFIC LITERATURE · MED

Identifying and validating subtypes of Parkinson's disease based on multimodal MRI data <i>via</i> hierarchical clustering analysis.

Front Hum Neurosci · 2022-07-29

DOI: 10.3389/fnhum.2022.919081PMID: 35966989

Open accessresearch-article; Journal ArticleOriginal source ↗

Research overview

The text below is the source article’s original abstract under a verified reuse license. cc by

ObjectiveWe 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.MethodsEighty-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.ResultsTwo 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.ConclusionHierarchical 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.

Scientific assessment

01

Research question

Assess the question within the original study’s scope.

02

Methods & sample

Check the source article for study design and sample details.

03

Key findings

An editorially approved findings summary is not yet published for this record.

04

Limitations

Generalizability, bias and clinical meaning require separate assessment.

Methods, samples and clinical conclusions are not inferred from bibliographic metadata.