Glucose metabolism in small subcortical structures in Parkinson’s disease
Bibliographic record
Abstract
OBJECTIVES: Evidence from experimental animal models of Parkinson's disease (PD) suggests a characteristic pattern of metabolic perturbation in discrete, very small basal ganglia structures. These structures are generally too small to allow valid investigation by conventional positron emission tomography (PET) cameras. However, the high-resolution research tomograph (HRRT) PET system has a resolution of 2 mm, sufficient for the investigation of important structures such as the pallidum and thalamic subnuclei. MATERIALS AND METHODS: Using the HRRT, we performed [(18)F]-fluorodeoxyglucose (FDG) scans on 21 patients with PD and 11 age-matched controls. We employed three types of normalization: white matter, global mean, and data-driven normalization. We performed volume-of-interest analyses of small subcortical gray matter structures. Voxel-based comparisons were performed to investigate the extent of cortical hypometabolism. RESULTS: The most significant level of relative subcortical hypermetabolism was detected in the external pallidum (GPe), irrespective of normalization strategy. Hypermetabolism was suggested also in the internal pallidum, thalamic subnuclei, and the putamen. Widespread cortical hypometabolism was seen in a pattern very similar to previously reported patterns in patients with PD. CONCLUSION: The presence and extent of subcortical hypermetabolism in PD is dependent on type of normalization. However, the present findings suggest that PD, in addition to widespread cortical hypometabolism, is probably characterized by true hypermetabolism in the GPe. This finding was predicted by the animal 2-deoxyglucose autoradiography literature, in which high-magnitude hypermetabolism was also most robustly detected in the GPe.
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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 | 0.001 | 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".