Three-dimensional database of subcortical electrophysiology for image-guided stereotactic functional neurosurgery
Bibliographic record
Abstract
We present a method of constructing a database of intraoperatively observed human subcortical electrophysiology. In this approach, patient electrophysiological data are standardized using a multiparameter coding system, annotated to their respective magnetic resonance images (MRIs), and nonlinearly registered to a high-resolution MRI reference brain. Once registered, we are able to demonstrate clustering of like interpatient physiologic responses within the thalamus, globus pallidus, subthalamic nucleus, and adjacent structures. These data may in turn be registered to a three-dimensional patient MRI within our image-guided visualization program enabling prior to surgery the delineation of surgical targets, anatomy with high probability of containing specific cell types, and functional borders. The functional data were obtained from 88 patients (106 procedures) via microelectrode recording and electrical stimulation performed during stereotactic neurosurgery at the London Health Sciences Centre. Advantages of this method include the use of nonlinear registration to accommodate for interpatient anatomical variability and the avoidance of digitized versions of printed atlases of anatomy as a common database coordinate system. The resulting database is expandable, easily searched using a graphical user interface, and provides a visual representation of functional organization within the deep brain.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".