A novel wireless brain stimulation device for long-term use in freely moving mice
Bibliographic record
Abstract
Deep brain stimulation (DBS) has been used in clinical settings for many years despite a paucity of knowledge related to the anatomical and functional substrates that lead to benefits and/or side-effects in various disease contexts. In order to maximize the potential of this approach in humans, a better understanding of its mechanisms of action is absolutely necessary. However, the existing micro-stimulators available for pre-clinical models, are limited by the lack of relevant small size devices. This absence prevents sustained chronic stimulation and real time monitoring of animals during stimulation, parameters that are critical for comparison to clinical findings. We therefore sought to develop and refine a novel small wireless micro-stimulator as a means by which to study consequent behavioural to molecular changes in experimental animals. Building on previous work from our group, we refined our implantable micro-stimulator prototype, to be easily combined with intravital 2-photon imaging. Using our prototype we were able to replicate the well described clinical benefits on motor impairment in a mouse model of Parkinson's disease in addition to capturing microglia dynamics live during stimulation. We believe this new device represents a useful tool for performing pre-clinical studies as well as dissecting brain circuitry and function.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".