Structural MRI Differences Between Parkinson’s Disease Motor Subtypes in Early-Stage: A Multicontrast Imaging Study
Bibliographic record
Abstract
ABSTRACT Background Parkinson’s disease (PD) is characterized by dopaminergic neuron degeneration, leading to motor and neuropsychological symptoms. PD is clinically divided into tremor-dominant (TD) and postural instability-gait disorder (PIGD) subtypes, which may differ in neuroanatomical changes. Neuroimaging explores these differences, enhancing understanding of PD heterogeneity. Objectives This study examines neuroanatomical differences between subtypes using MRI, focusing on subcortical volumes, cortical thickness, iron deposition, and white matter changes. Methods This cross-sectional study included 51 PD patients and controls. Participants underwent clinical assessments and MRI. Cortical and subcortical segmentation was automated using FreeSurfer, and quantitative susceptibility mapping was used to assess brain iron content. Diffusion-weighted MRI data were processed using Tractseg for tractometry analysis. Results The PD-TD group exhibited higher iron levels in the substantia nigra compared to healthy controls. Iron deposition in the thalamus correlated with MDS-UPDRS-part-III and PIGD scores. Tractometry showed differences in fractional anisotropy (FA) between PD-TD and PD-PIGD in the bilateral fronto-pontine tract (FPT). The PD-PIGD group had decreased FA in the middle cerebellar peduncle (MCP) compared to controls. FA in the left FPT correlated with tremor scores, while FA in the MCP correlated with PIGD scores. Conclusions This study highlights distinct neuroimaging signatures between PD motor subtypes. Elevated iron deposition in the substantia nigra is a shared feature, particularly in the TD subtype. Subtype-specific white matter changes, including reduced FA in the FPT and MCP, correlate with tremor and PIGD scores. These findings underscore the potential of neuroimaging biomarkers in unraveling PD heterogeneity and guiding tailored approaches.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".