Local Field Potential-Based Programming: A Proof-of-Concept Pilot Study
Bibliographic record
Abstract
OBJECTIVES: Programming deep brain stimulation (DBS) is still based on a trial-and-error approach, often becoming a time-consuming process for both treating physicians and patients. Several strategies have been proposed to streamline DBS programming, most of which are preliminary and mainly address Parkinson's disease, a condition readily responsive to DBS adjustments. In the present proof-of-principle pilot study, we successfully demonstrate that local field potential (LFP)-based programming can be an effective approach when used for DBS indications that have a delayed temporal onset of benefit. MATERIALS AND METHODS: A recently commercialized implantable pulse generator (IPG) with the capability to non-invasively and chronically stream live and/or record LFPs from a DBS electrode after implantation was used to program one pediatric patient with generalized dystonia and an adult with seizures refractory to multiple medications and vagal nerve stimulation. RESULTS: The IPG survey function detected a peak in the delta range (1.95 Hz) in the left globus pallidus of the first patient. This LFP was detected when recording in the brain area adjacent to contacts 9 and 10 and absent when recording from other areas. The chronic recording of the 1.95 Hz LFP with two sets of stimulation showed a greater power increase with the settings associated with a worsening of dystonia. Broadband LFP home recording of "absence seizure" and "focal/partial seizure" was used in the second patient and reviewer with the IPG "timeline" and "event" functions. The chronic recording of the 2.93 Hz and 8.79 Hz (spit sensing) showed a reduced power with the stimulation setting associated with seizure control. CONCLUSIONS: The approach presented in this pilot proof-of-concept study may inform and streamline the DBS programming for conditions requiring clinicians and patients to wait weeks before appreciating any clinical benefit. Prospective studies on larger samples of patients are warranted.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".