Emerging Techniques for the Personalization of Deep Brain Stimulation Programming
Bibliographic record
Abstract
The success of deep brain stimulation (DBS) relies on applying carefully titrated therapeutic stimulation at specific targets. Once implanted, the electrical stimulation parameters at each electrode contact can be modified. Iteratively adjusting the stimulation parameters enables testing for the optimal stimulation settings. Due to the large parameter space, the currently employed empirical testing of individual parameters based on acute clinical response is not sustainable. Within the constraints of short clinical visits, optimization is particularly challenging when clinical features lack immediate feedback, as seen in DBS for dystonia and depression and with the cognitive and axial side effects of DBS for Parkinson's disease. A personalized approach to stimulation parameter selection is desirable as the increasing complexity of modern DBS devices also expands the number of available parameters. This review describes three emerging imaging and electrophysiological methods of personalizing DBS programming. Normative connectome-base stimulation utilizes large datasets of normal or disease-matched connectivity imaging. The stimulation location for an individual patient can then be varied to engage regions associated with optimal connectivity. Electrophysiology-guided open- and closed-loop stimulation capitalizes on the electrophysiological recording capabilities of modern implanted devices to individualize stimulation parameters based on biomarkers of success or symptom onset. Finally, individual functional MRI (fMRI)-based approaches use fMRI during active stimulation to identify parameters resulting in characteristic patterns of functional engagement associated with long-term treatment response. Each method provides different but complementary information, and maximizing treatment efficacy likely requires a combined approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".