Imaging Cerebral Glucose Metabolism during Dual‐Task Walking in Patients with Parkinson's disease
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Gait impairment is a hallmark of Parkinson's disease (PD). Natural walking involves more cognitive demand than treadmill walking or in-laboratory walking tests because patients have to actively work on navigation and top-down cognitive control which taxes cognitive reserve in the prefrontal cortex. To mimic the prefrontal engagement occurring with natural walking in a controlled and safe environment, dual-task (DT) treadmill walking has been developed. In this study, we tested the feasibility of imaging DT walking-related changes in brain glucose metabolism in patients with PD. METHODS: Fifteen patients with PD were scanned with fluorodeoxyglucose (FDG) positron emission tomography. Five patients performed DT walking, and 10 patients were rested during the FDG uptake period. First, the images were contrasted between the groups. Second, the walking-related brain glucose metabolism was inspected at the individual level. RESULTS: Consistently increased glucose metabolism was identified in DT walking versus rest in the primary visual/sensorimotor areas, thalamus, superior colliculus, and cerebellum. In individual level analysis, patients with less progressed disease (n = 3) showed prefrontal activity during DT walking while patients with more progressed disease (n = 2) did not. CONCLUSION: This study confirms the feasibility of imaging glucose metabolism during DT walking in patients with PD. We also report that during DT walking, there is a lesser degree of prefrontal engagement in the patients with more progressed disease compared to those with less progressed disease, implying increased degrees of frontal dysfunction with PD progression.
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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 | 0.000 |
| 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".