Functional neuroimaging in Parkinson's disease
Bibliographic record
Abstract
INTRODUCTION: Functional neuroimaging techniques have greatly contributed to improving our understanding of Parkinson's disease (PD) neurodegeneration and related compensatory mechanisms. AREAS COVERED: In this paper, the authors analyze the role of functional neuroimaging as a diagnostic tool in PD and review functional neuroimaging studies on PD progression and compensatory adaptations. Through this, the article provides the reader with sensible approaches for the use of functional neuroimaging in the diagnosis of PD. The reader is also provided with knowledge on the time course of nigrostriatal dopamine dysfunction in PD as well as an overview of the potential beneficial and deleterious effects of increased dopamine turnover. Finally, the reader is provided with a critical discussion of the differential effects of levodopa and dopamine agonists on presynaptic dopamine markers and the implications for the interpretation of clinical trials. EXPERT OPINION: Functional neuroimaging probably plays a limited role in the diagnosis of PD. Parkinson's disease pathology leads to an exponential decline in nigrostriatal dopamine function and a compensatory increase in dopamine turnover, which may help delay symptom onset. On the negative side, increased dopamine turnover contributes to the development of treatment-related motor complications. Presynaptic markers of dopamine function are subject to regulatory changes, compromising the direct interpretation of neuroimaging results in trials of neuroprotective therapies for PD.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".