Blood Flow and Glucose Metabolism Dissociation in the Putamen Is Predictive of Levodopa Induced Dyskinesia in Parkinson's Disease Patients
Bibliographic record
Abstract
Background: The forefront treatment of Parkinson’s disease (PD) is Levodopa. When patients are treated with Levodopa cerebral blood flow is increased while cerebral metabolic rate is decreased in key subcortical regions including the putamen. This phenomena is especially pronounced in patients with Levodopa-induced dyskinesia (LID). Method: To study the effect of clinically-determined anti-parkinsonian medications, ten PD patients (5 with LID and 5 without LID) have been scanned with FDG-PET (a probe for glucose metabolism) and perfusion MRI (a probe for cerebral blood flow) both when they are ON and OFF medications. Patients additionally underwent resting state fMRI to detect changes in dopamine-mediated cortico-striatal connectivity. The degree of blood flow-glucose metabolism dissociation was quantified by comparing the FDG-PET and perfusion MRI. Results: A significant interaction effect (imaging modality × medication; blood flow-glucose metabolism dissociation) has been found in the putamen (p=0.023). Post-hoc analysis revealed that anti-parkinsonian medication consistently normalized the pathologically hyper-metabolic state of the putamen while mixed effects were observed in cerebral blood flow changes. This dissociation was especially predominant in patients with LID compared to the ones without. Unlike the prior study, this differentiation was not observed when cortico-striatal functional connectivity was assessed. Conclusion: We confirmed striatal neurovascular dissociation between FDG-PET and perfusion MRI in response to clinically determined anti-parkinsonian medication. We further proposed a novel analytical method to quantify the degree of dissociation in the putamen using only the ON condition scans, Putamen-to-thalamus Hyper-perfusion/hypo-metabolism Index (PHI), which may have a potential to be used as a biomarker for LID (correctly classifying 8 out 10 patients). For wider use of PHI, a larger validation study is warranted.
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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) | 0.002 | 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".