Using a Deep Learning Approach for Model-based Control of Deep Brain Stimulation
Bibliographic record
Abstract
Abstract Deep brain stimulation (DBS) has been developed as a treatment method for various neurological disorders, including Parkinson’s disease, essential tremor and depression. Although DBS is effective, it often loses efficacy over sustained periods because a constant stimulation is applied without adapting to the patient’s current clinical state. In contrast, an adaptive closed-loop DBS system can offer more tailored stimulation in real-time based on a feedback biomarker. In early 2024, we developed a model-based DBS control framework that consists of three main functions: (1) a biophysically reasonable encoding model, (2) a simple decoding model, and (3) a controller. We used a polynomial fit function in the decoding model to approximate the neural-motor relationship, from DBS-induced Vim neural activity to muscle fiber electromyography (EMG). Despite promising results, the polynomial method is inaccurate in capturing the full representation of the neural-motor (EMG) function across different DBS frequencies. In this work, to capture the nonlinear intricate relationship between the neural and EMG patterns, we developed a one-dimensional convolutional neural network (1-D CNN) as a decoding model to predict the EMG signal directly from the DBS-induced Vim neural activity. The 1-D CNN network outputted a high R 2 value of 0.997 which significantly outperformed the polynomial method (R 2 = 0.277) and a deep learning approach based on long short-term memory (R 2 = 0.296). We anticipate that our work highlights the need for a data-driven approach that can reliably map neural activities to symptomatic signals like EMG for better adjusting DBS parameters.
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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 | 0.000 |
| 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".