Three-dimensional electrophysiological database of motor responses obtained from the human internal capsule
Bibliographic record
Abstract
The somatotopic organization of the motor fibers within the posterior limb of the internal capsule (IC) in humans remains unclear. Several electrophysiological (EP) atlases created from stimulation during stereotactic neurosurgery have suggested that there is considerable overlap between representations of body parts. Overlap reported in these studies may have been due to the linear scaling methods applied to the data. In the current work, a three-dimensional EP database that accounts for inter-subject anatomical variability through the use of a nonlinear registration technique was developed. The somatotopic organization was explored at two levels of the IC using EP data acquired during stereotactic procedures on the ventrolateral nucleus of the thalamus and the subthalamic nucleus. This work has resulted in a functional atlas demonstrating the locations of body part representations at two levels of the IC that accounts for individual anatomical variability. These analyses suggest a face-anterior to leg-posterior somatotopic organization, with increasing overlap and less separation between adjacent representations at lower levels of the IC.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".