Longitudinal prognosis of Parkinson’s outcomes using causal connectivity
Bibliographic record
Abstract
Despite the prevalence of Parkinson’s disease (PD), there are no clinically-accepted neuroimaging biomarkers to predict the trajectory of motor or cognitive decline or differentiate Parkinson’s disease from atypical progressive parkinsonian diseases. Since abnormal connectivity in the motor circuit and basal ganglia have been previously shown as early markers of neurodegeneration, we hypothesize that patterns of interregional connectivity could be useful to form patient-specific predictive models of disease state and of PD progression. We use fMRI data from subjects with Multiple System Atrophy (MSA), Progressive Supranuclear Palsy (PSP), idiopathic PD, and healthy controls to construct predictive models for motor and cognitive decline and differentiate between the four subgroups. Further, we identify the specific connections most informative for progression and diagnosis. When predicting the one-year progression in the MDS-UPDRS-III1* and Montreal Cognitive assessment (MoCA), we achieve new state-of-the-art mean absolute error performance. Additionally, the balanced accuracy we achieve in the diagnosis of PD, MSA, PSP, versus healthy controls surpasses that attained in most clinics, underscoring the relevance of the brain connectivity features. Our models reveal the connectivity between deep nuclei, motor regions, and the thalamus as the most important for prediction. Collectively these results demonstrate the potential of fMRI connectivity as a prognostic biomarker for PD and increase our understanding of this disease.
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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 | 0.002 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".