Towards “synaptomic” deep brain stimulation: Electrophysiological targeting of basal ganglia pathways
Bibliographic record
Abstract
Parkinson's disease (PD) is characterized by both motor and non-motor symptoms.While deep brain stimulation (DBS) is widely used for motor symptoms, its effects on cognitive deficits and its ability to adapt to realtime motor symptom fluctuations remain underexplored.We present results demonstrating how neural oscillations in motor and non-motor networks can inform targeted treatments for PD symptoms.Based on findings that subthalamic nucleus (STN) theta oscillations play a role in cognitive processing, we first examined the effects of theta-frequency DBS in the STN on working memory, the most common distinct cognitive deficit in PD.In a cohort of 20 PD patients, we applied DBS at theta, beta, low, and high gamma frequencies in a randomized, blinded manner during a computerized task.Bilateral STN-DBS at the theta frequency improved working memory performance, with no effect on motor function.This improvement was frequency-and task-specific, linked to increased structural connectivity between the STN and the right middle frontal gyrus, a region involved in cognitive control.These findings highlight the potential of theta-frequency STN-DBS as a targeted intervention for cognitive symptoms in PD.Second, we demonstrate the promise of adaptive DBS, guided by neural oscillations, in reducing motor symptom burden.Conventional DBS lacks responsiveness to fluctuating clinical and neural states.In a small clinical trial, we report results from a blinded, randomized application of chronic adaptive DBS in real-world settings.Using a sensing-enabled DBS device, we identified stimulation-entrained gamma oscillations in the STN or motor cortex as optimal biomarkers of dopaminergic states.By adjusting stimulation based on these neural signals in real-time, adaptive DBS halved motor symptom duration and improved quality of life compared to standard-of-care DBS during normal daily life.These findings underscore the potential of personalized adaptive neurostimulation in PD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".