Computational analysis of two novel deep brain stimulation pulsing patterns on a thalamocortical network model of Parkinson’s disease
Bibliographic record
Abstract
Deep brain stimulation (DBS) at high frequencies has revolutionized efforts to alleviate Parkinson's disease symptoms for approximately 30 years. Since then, there has been vast investigation into the mechanisms of action of DBS. Recently, synaptic suppression was found to play a pivotal role in the fundamental mechanisms underlying DBS. Based on this understanding, researchers introduced two novel DBS pulsing strategies that use a minimal number of stimuli. In contrast to conventional DBS (cDBS) pulsing, which employs continuous high-frequency pulses (>100 Hz), the two novel methods incorporate changes in pulsing frequency and on/off pulsing periods. In this computational study, we investigated the network effects of these two suggested patterns using an updated version of a biophysically realistic thalamocortical network model of DBS. Both suggested pulsing patterns significantly reduced the exaggerated beta power (∼13 Hz-30 Hz oscillations) in the motor cortex, with careful consideration of the intensity of the stimulating pulses. In addition, they significantly reduced the level of network synchronization. We compared these findings with the effects of 20 and 130 Hz cDBS on our network model and did not observe effects contrary to those of 130 Hz cDBS. The two suggested patterns, which were computationally successful in reproducing known DBS network effects, could potentially increase the battery life of DBS device and reduce the microlesion effect associated with long-term cDBS pulsing. These outcomes, however, require confirmation in further studies.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".