Striatal Dopaminergic Asymmetry as a marker of Brain-First and Body-First Subtypes in de novo Parkinson’s Disease
Bibliographic record
Abstract
Abstract Recently, the α-Synuclein Origin and Connectome (SOC) model of Parkinson’s disease (PD) has been proposed, which predicts a more malignant clinical subtype and symmetrical neurodegeneration in body-first compared to brain-first PD. Here, motor symptoms (MDS-UPDRS III), non-motor symptoms (NMSQ) and T1 MRI data of an incident de novo PD cohort, were compared between PD subjects with levels of putaminal dopaminergic asymmetry in the lowest tertile (PD-sym, n=41) and highest tertile (PD-asym, n=41), as measured by FDOPA-PET. PD-sym was associated with a higher burden of motor symptoms and non-motor symptoms with a probable neurological substrate caudally from the substantia nigra. Though overall brain volume was lower in PD-sym, no differences in the volumes and asymmetricity of specific brain regions could be found between PD-sym and PD-asym after adjusting for multiple testing. The more malignant clinical picture suggests an overrepresentation of body-first PD subjects in PD-sym according to the SOC-model. Also, lower overall brain volumes were found in PD-sym. However, structural MRI data might not be sufficient to assess regional differential degeneration between PD-sym and PD-asym in de novo PD. Additional imaging modalities and longitudinal follow-up could be required to support or reject the SOC-model.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".