GR.3 Distinct longitudinal brain atrophy trajectories in parkinson’s disease clinical subtypes: insight towards precision medicine
Bibliographic record
Abstract
Background: Parkinson’s disease (PD) varies widely across individuals in terms of clinical manifestations and course of progression. We aimed to compare patterns of brain atrophy between PD clinical subtypes using longitudinally acquired brain MRIs. Methods: We used T1-weighted MRIs from Parkinson’s Progression Markers Initiative (PPMI) on 134 PD individuals and 60 healthy controls with at least two MRIs. Patients were classified into three clinical subtypes at de novo stage using validated subtyping criteria based on major motor and non-motor classifiers (early cognitive impairment, RBD, dysautonomia): mild-motor predominant (n=74), intermediate (n=44), and diffuse-malignant (n=16). Deformation-based morphometry (DBM) maps were calculated and mixed effect models were used to examine the interaction between PD subtypes and rate of atrophy across brain regions over time, controlling for sex and age at baseline. Results: Individuals with ‘diffuse malignant’ PD showed a significantly higher rate of atrophy across multiple brain regions, including lateral nucleus of the forebrain, precuneus, paracentral lobule, inferior temporal gyrus, fusiform gyrus, and lateral hemisphere of the cerebellum (FDR corrected p<0.05). Conclusions: We demonstrated an accelerated atrophy pattern within several brain regions in ‘diffuse malignant’ PD subtype. These findings suggest the presence of a more diffuse multidomain neurodegenerative process in a subgroup of people with PD, favoring the existence of diverse underlying pathophysiologies.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".