Dorsal Column Spinal Cord Stimulation Attenuates Brain-Spine Connectivity through Locomotion- and Visuospatial-Specific Area Activation in Progressive Freezing of Gait
Bibliographic record
Abstract
INTRODUCTION: Freezing of gait (FOG) is a clinical phenomenon with major life impairments and significant reduction in quality of life for affected patients. FOG is a feature of Parkinson's disease and a hallmark of primary progressive FOG, currently reclassified as Progressive Supranuclear Palsy-progressive gait freezing (PSP-PGF). The pathophysiology of FOG and particularly PGF, which is a rare degenerative disorder with a progressive natural history of gait decline, is poorly understood. Mechanistically, changes in oscillatory activity and synchronization in frontal cortical regions, the basal ganglia, and the midbrain locomotor region have been reported, indicating that dysrhythmic oscillations and coherence could play a causal role in the pathophysiology of FOG. Deep brain stimulation and spinal cord stimulation (SCS) have been tested as therapeutic neuromodulation avenues for FOG with mixed outcomes. METHODS: We analyzed gait and balance in 3 patients with PSP-PGF who received percutaneous thoracic SCS and utilized magnetoencephalography (MEG), electroencephalography, and electromyography to evaluate functional connectivity between the brain and spine. RESULTS: Gait and balance did not worsen over a 13-month period. This observation was accompanied by decreased beta-band spectral power in the whole brain and particularly in the basal ganglia. This was accompanied by increased functional connectivity in and between the sensorimotor cortices, basal ganglia, temporal cortex, and cerebellum, and a surge in corticomuscular coherence when SCS was paired with visual cues. CONCLUSION: Our results suggest synergistic activity between brain and spinal circuits upon SCS for FOG in PGF, which may have implications for future brain-spine interfaces and closed-loop neuromodulation for patients with FOG.
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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) | 0.002 | 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".