Neurobiology of placebo effect in Parkinson's disease: What we have learned and where we are going
Bibliographic record
Abstract
The placebo effect is a phenomenon produced when an inert substance administered like a regular treatment improves the clinical outcome. Parkinson's disease (PD) is one of the main clinical disorders for which the placebo response rates are high. The first evidence of the neurobiological mechanisms underlying the placebo effect in PD stems from 2001, when de la Fuente-Fernandez and colleagues demonstrated that a placebo injection led to the release of dopamine in the striatal nuclei of PD measured with positron emission tomography technology. Since then, several studies have been conducted to investigate the neurobiological underpinnings of placebo responses. This article presents a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Of an initial yield of 143 papers, 19 were included. The lessons learned from these studies are threefold: (i) motor improvement is dependent on the activation of the entire nigrostriatal pathway induced by dopamine release in the dorsal striatum; (ii) the magnitude of placebo-induced effects is modulated by an expectancy of improvement, which is in turn related to the release of dopamine within the ventral striatum; (iii) the functioning of the neural pathways underlying the placebo response can be tuned by prior exposure and learning strategies. In conclusion, although the neural network underlying the placebo effect in PD has been largely confirmed and accepted, what remains to be established is how, when, and where the expectation of reward (mediated by the ventral striatum) interacts with the primary motor system (mediated by the dorsal striatum) to induce clinical improvement in motor symptoms. © 2018 International Parkinson and Movement Disorder Society.
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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 | 0.035 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".