A method for chronic and semi-chronic microelectrode array implantation in deep brain structures using image guided neuronavigation
Bibliographic record
Abstract
Abstract Precise targeting of deep brain structures in humans and large animal models has been a challenge for neuroscientists. Conventional protocols used in animal models typically require large access chambers which are prone to infection and involve assembly and implantation of complex microdrives for semi-chronic applications. Here we present a methodology for improving targeting of subcortical structures in large animals such as macaque monkeys, using image guided neuronavigation. Design of custom cranial caps allowed for incorporation of stable fiducial markers, required for increased targeting accuracy in neuronavigation procedures, resulting in an average targeting error of 1.6 mm over three implantations. Incorporation of anchor bolt chambers, commonly used in human neurosurgery, provided a minimally invasive entrance to the brain parenchyma, allowing for chronic recordings. By leveraging existing 3D printing technology, we fabricated an anchor bolt-mounted microdrive for semi-chronic applications. Our protocol leverages commercially available tools for implantation, decreases the risk of infection and complications of open craniotomies, and improves the accuracy and precision of chronic electrode implantations targeting deep brain structures in large animal models.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".