Functional correlates of pallidal stimulation for Parkinson's disease
Bibliographic record
Abstract
We measured regional cerebral blood flow with H2 15O and positron emission tomography (PET) scanning at rest and during a motor task to study the mechanism of motor improvement induced by deep brain stimulation of the internal globus pallidus in Parkinson's disease. Six right-handed patients with Parkinson's disease were scanned while performing a predictable paced sequence of reaching movements and while observing the same screen displays and tones. PET studies were performed ON and OFF stimulation in a medication-free state. Internal globus pallidus deep brain stimulation improved off-state United Parkinson's Disease Rating Scale motor ratings (37%, p < 0.002) and reduced timing errors (movement onset time, 55%, p < 0.01) as well as spatial errors (10%, p < 0.02). Concurrent regional cerebral blood flow recordings revealed a significant enhancement of motor activation responses in the left sensorimotor cortex (Brodmann area [BA] 4), bilaterally in the supplementary motor area (BA 6), and in the right anterior cingulate cortex (BA 24/32). Significant correlations were evident between the improvement in motor performance and the regional cerebral blood flow changes mediated by stimulation. With internal globus pallidus deep brain stimulation, improved movement initiation correlated with regional cerebral blood flow increases in the left sensorimotor cortex and ventrolateral thalamus and in the contralateral cerebellum. By contrast, improved spatial accuracy correlated with regional cerebral blood flow increases in both cerebellar hemispheres and in the left sensorimotor cortex. These results suggest that internal globus pallidus deep brain stimulation may selectively improve different aspects of motor performance. Multiple, overlapping neural pathways may be modulated by this intervention.
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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".