Fatigue in Parkinson's disease: The contribution of cerebral metabolic changes
Bibliographic record
Abstract
Abstract Fatigue is a common and disabling non‐motor symptom in Parkinson's disease associated with a feeling of overwhelming lack of energy. The aim of this study was to identify the neural substrates that may contribute to the development of fatigue in Parkinson's disease. Twenty‐three Parkinson's disease patients meeting UK Brain Bank criteria for the diagnosis of idiopathic Parkinson's disease were recruited and completed the 2‐[18F]fluoro‐2‐deoxy‐D‐glucose (FDG)‐PET scan. The metabolic activities of Parkinson's disease patients with fatigue were compared to those without fatigue using statistical parametric mapping analysis. The Parkinson's disease group exhibiting higher level of fatigue showed anti‐correlated metabolic changes in cortical regions associated with the salience (i.e., right insular region) and default (i.e., bilateral posterior cingulate cortex) networks. The metabolic abnormalities detected in these brain regions displayed a significant correlation with level of fatigue and were associated with a disruption of the functional correlations with different cortical areas. These observations suggest that fatigue in Parkinson's disease may be the expression of metabolic abnormalities and impaired functional interactions between brain regions linked to the salience network and other neural networks. Hum Brain Mapp 38:283–292, 2017. © 2016 Wiley Periodicals, Inc.
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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) | 0.001 | 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".