Patient-specific multi-modal modeling uncovers neurotransmitter receptor involvement in motor and non-motor axes of Parkinson’s disease
Bibliographic record
Abstract
Abstract Multi-systemic neurodegeneration in Parkinson’s disease (PD) is increasingly acknowledged, involving several neurotransmitter systems beyond the classical dopaminergic circuit and resulting in heterogeneous motor and non-motor symptoms. Nevertheless, the mechanistic basis of neuropathological and symptomatic heterogeneity remains unclear. Here, we use patient-specific generative brain modeling to identify neurotransmitter receptor-mediated mechanisms involved in PD progression. Combining receptor maps with longitudinal neuroimaging (PPMI data), we detect a diverse set of receptors influencing gray matter atrophy, microstructural degeneration, and dendrite loss in PD. Importantly, identified receptor mechanisms correlate with symptomatic variability along two distinct axes, representing motor/psychomotor symptoms with large GABAergic contributions, and cholinergically-driven visuospatial dysfunction. Furthermore, we map cortical and subcortical regions where receptors exert significant influence on neurodegeneration. Our work constitutes the first personalized causal model linking the progression of multi-factorial brain reorganization in PD across spatial scales, including molecular systems, accumulation of neuropathology in macroscopic brain regions, and clinical phenotypes.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".