Patient-centered Transcriptomic and Multimodal Neuroimaging Determinants of Clinical Progression, Physical Activity and Treatment Needs in Parkinson’s Disease
Bibliographic record
Abstract
ABSTRACT Parkinson’s disease is a complex and multifactorial disorder, but how its biological and clinical complexity emerge from molecular to macroscopic brain interactions remains poorly understood. Here, we use a personalized multiscale generative brain model to characterize direct spatiotemporal links between genes and multimodal neuroimaging-derived biological factors in PD. We identified a set of genes modulating PD-associated longitudinal changes in dopamine transporter level, neuronal activity, dendrite density and tissue atrophy. Inter-individual heterogeneity in the gene-mediated biological mechanisms is associated with five distinct configurations of PD motor and non-motor symptoms. Although characterized by distinctive biological pathways, all the symptom configurations are associated with cell cycle processes. Notably, the protein-protein interaction networks underlying these configurations revealed distinct hub genes including MYC, CCNA2, CCDK1, SRC, STAT3 and PSMD4 . We also uncovered the biological mechanisms associated with physical activities performance in PD, and observed that leisure and work activities are principally related to neurotypical cholesterol homeostasis and inflammatory response processes, respectively. Finally, patient-tailored in silico gene perturbations revealed a set of putative disease-modifying drugs with potential to effectively treat PD, most of which are associated with dopamine reuptake and anti-inflammation. Our study constitutes the first self-contained multiscale approach providing comprehensive insights into the complex multifactorial pathogenesis of PD, unravelling key biological modulators of physical and clinical deterioration, and serving as a blueprint for optimum drug selection at personalized level.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".