A novel combinational approach of microstimulation and bioluminescence imaging to study the mechanisms of action of cerebral electrical stimulation in mice
Bibliographic record
Abstract
Key points We have developed a unique prototype to perform brain stimulation in mice. This system presents a number of advantages and new developments: 1) all stimulation parameters can be adjusted, 2) both positive and negative current pulses can be generated, guaranteeing electrically balanced stimulation regimen, 3) which can be produced with both low and high impedance electrodes, 4) the developed electrodes ensure localized stimulation and 5) can be used to stimulate and/or record brain potential and 6) in vivo recording of electric pulses allows the detection of defective electrodes (wire breakage or short circuits). This new micro‐stimulator device further allows simultaneous live bioluminescence imaging of the mouse brain, enabling real time assessment of the impact of stimulation on cerebral tissue. The use of this novel tool in various transgenic mouse models of disease opens up a whole new range of possibilities in better understanding brain stimulation. Abstract Deep brain stimulation (DBS) is used to treat a number of neurological conditions and is currently being tested to intervene in neuropsychiatric conditions. However, a better understanding of how it works would ensure that side effects could be minimized and benefits optimized. We have thus developed a unique device to perform brain stimulation (BS) in mice and to address fundamental issues related to this methodology in the pre‐clinical setting. This new microstimulator prototype was specifically designed to allow simultaneous live bioluminescence imaging of the mouse brain, allowing real time assessment of the impact of stimulation on cerebral tissue. We validated the authenticity of this tool in vivo by analysing the expression of toll‐like receptor 2 (TLR2), corresponding to the microglial response, in the stimulated brain regions of TLR2‐fluc‐GFP transgenic mice, which we further corroborated with post‐mortem analyses in these animals as well as in human brains of patients who underwent DBS to treat their Parkinson's disease. In the present study, we report on the development of the first BS device that allows for simultaneous live in vivo imaging in mice. This tool opens up a whole new range of possibilities that allow a better understanding of BS and how to optimize its effects through its use in murine models of disease.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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".