Cerebral blood flow changes induced by pedunculopontine nucleus stimulation in patients with advanced Parkinson's disease: A [<sup>15</sup>O] H<sub>2</sub>O PET study
Bibliographic record
Abstract
Patients with advanced Parkinson's disease (PD) develop disabling axial symptoms, including gait disturbances, freezing and postural instability poorly responsive to levodopa replacement therapy. The pedunculopontine nucleus (PPN) is involved in locomotion, control of posture, and behavioral states [i.e. wakefulness, rapid eye movement sleep]. Recent reports suggested that PPN modulation with deep brain stimulation (DBS) may be beneficial in the treatment of axial symptoms. However, the mechanisms underlying these effects are still unknown. We used [(15)O] H(2)O PET to investigate regional cerebral blood flow in three patients with advanced PD who underwent a new experimental surgical procedure with implantation of unilateral PPN-DBS. Patients were studied Off-medication with stimulator Off and On, both at rest and during a self-paced alternating motor task of the lower limbs. We used SPM2 for imaging data analysis, threshold P < 0.05 corrected at the cluster level. Stimulation induced significant regional cerebral blood flow increment in subcortical regions such as the thalamus (P < 0.006), cerebellum (P < 0.001), and midbrain region (P < 0.001) as well as different cortical areas involving medial sensorimotor cortex extending into caudal supplementary motor area (BA 4/6; P < 0.001). PPN-DBS in advanced PD resulted in blood flow and presumably neuronal activity changes in subcortical and cortical areas involved in balance and motor control, including the mesencephalic locomotor region (e.g. PPN) and closely interconnected structures within the cerebello-(rubro)-thalamo-cortical circuit. Whether these findings are associated with the DBS-PPN clinical effect remains to be proven. However, they suggest that PPN modulation may induce functional changes in neural networks associated with the control of lower limb movements.
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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 | 0.001 | 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".