Connectivity-Based Subtyping of De Novo Parkinson Disease: Biomarkers, Medication Effects and Longitudinal Progression
Bibliographic record
Abstract
Parkinson's disease (PD) is characterized by divergent clinical symptoms and prognosis, suggesting the presence of distinct subtypes. Identifying these subtypes is crucial for understanding the underlying pathophysiology, predicting disease progression, and developing personalized treatments. In this study, we propose a connectivity-based subtyping approach, which measures each patient's deviation from the reference structural covariance networks established in healthy controls. Using data from the Parkinson's Progression Markers Initiative, we identified two distinct subtypes of de novo PD patients: 248 patients with typical cortical-striato-thalamic dysfunctions and 41 patients showing weakened dorsal raphe nucleus (DRN)-to-cortical/striatal projections. The proposed subtyping approach demonstrated high stability in terms of random sampling of healthy or diseased population and longitudinal prediction at follow-up visits, outperforming the traditional motor phenotypes. Compared to the typical PD, patients with the DRN-predominant subtype were characterized by less server motor symptoms at baseline and distinct imaging biomarkers, including larger striatal volumes, higher concentration of cerebrospinal fluid amyloid-β and amyloid-β/t(p)-tau ratio. Subtype-specific associations and drug effects were identified that the DRN subtype exhibited more pronounced medication effects on motor symptoms, potentially regulated by DRN serotonergic modulation through striatal dopaminergic neurons. The DRN serotonergic inputs also regulated non-motor symptoms, the aggregation of CSF biomarkers and the conversion to more severe disease states. Our findings suggest that the DRN-predominant subtype represents a unique clinical and biological phenotype of PD characterized by an enhanced response to anti-parkinsonian treatment, more favorable prognosis and slower progression of dopamine depletion. This study may contribute to clinical practice of precision medicine, early invention and individualized treatments in PD and other neurodegenerative diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".