Model-Based Closed-Loop Control of Thalamic Deep Brain Stimulation
Bibliographic record
Abstract
Abstract Closed-loop control of deep brain stimulation (DBS) is crucial for effective and automatic treatments of various neurological disorders like Parkinson’s disease (PD) and essential tremor (ET). Manual (open-loop) DBS programming solely based on clinical observations relies on neurologists’ expertise and patients’ experience. The continuous stimulation in open-loop DBS may decrease battery life and cause side effects. On the contrary, a closed-loop DBS system utilizes a feedback biomarker/signal to track worsening (or improving) patient’s symptoms and offers several advantages compared to open-loop DBS. Existing closed-loop DBS control systems do not incorporate physiological mechanisms underlying the DBS or symptoms, for example how DBS modulates dynamics of synaptic plasticity. In this work, we proposed a computational framework for development of a model-based DBS controller where a biophysically-reasonable model can describe the relationship between DBS and neural activity, and a polynomial-based approximation can estimate the relationship between the neural and behavioral activity. A controller is utilized in our model in a quasi-real-time manner to find DBS patterns that significantly reduce the worsening of symptoms. These DBS patterns can be tested clinically by predicting the effect of DBS before delivering it to the patient. We applied this framework to the problem of finding optimal DBS frequencies for essential tremor given EMG recordings solely. Building on our recent network model of ventral intermediate nuclei (Vim), the main surgical target of the tremor, in response to DBS, we developed a biophysically-reasonable simulation in which physiological mechanisms underlying Vim-DBS are linked to symptomatic changes in EMG signals. By utilizing a PID controller, we showed that a closed-loop system can track EMG signals and adjusts the stimulation frequency of Vim-DBS so that the power of EMG in [2, 200] Hz reaches a desired target. We demonstrated that our model-based closed-loop control system of Vim-DBS finds an appropriate DBS frequency that aligns well with clinical studies. Our model-based closed-loop system is adaptable to different control targets, highlighting its potential usability for different diseases and personalized systems.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".