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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".