Where Are We with Deep Brain Stimulation? A Review of Scientific Publications and Ongoing Research
Bibliographic record
Abstract
BACKGROUND: Deep brain stimulation (DBS) is a neuromodulatory technique that delivers adjustable electrical stimuli to brain targets to relieve symptoms associated with dysregulated neural circuitry. Over the last several decades, DBS has been applied to a number of conditions, including motor, pain, mood, and cognitive disorders. An assessment of the body of work in this field is warranted to determine where we have been, define the current state of the field, and chart a path toward the future. OBJECTIVE: The aim of the study was to assess the state of DBS-related research by analyzing the DBS literature as well as active studies sponsored by the National Institutes of Health (NIH) or German Research Foundation (Deutsche Forschungsgemeinschaft [DFG]). METHODS: Peer-reviewed DBS publications were extracted from PubMed. Active NIH-funded DBS projects were extracted from the RePORT database and active DFG projects from the German Research Foundation database. Records were analyzed using custom-developed algorithms to generate a detailed overview of past and present DBS-related research. Specifically, records were categorized by publication year, journal, language, country of origin, contributing authors, disorder, brain target, study design, and topic. Expected project duration and costs were also provided for active studies. RESULTS: In total, 8,974 publications, 172 active NIH-funded projects, and 34 active DFG projects were identified. Records spanned 52 different disorders across 31 distinct brain targets and showed a recent shift toward studies examining conditions other than movement disorders. Most published works involved human research (80.6% of published studies), of which 10.2% were identified as clinical trials. Increasingly, studies focused on imaging or electrophysiological changes associated with DBS (69.8% NIH-active and 70.6% DFG-active vs. 25.8% published) or developing new stimulation techniques and adaptive technologies (37.8% NIH-active and 17.6% DFG-active vs. 6.5% published). CONCLUSIONS: This overview of past and present DBS-related studies provides insight into the status of DBS research and what we can anticipate in the future concerning new indications, improved/novel target selection and stimulation paradigms, closed-loop technology, and a better understanding of the mechanisms of action of DBS.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.023 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".