Mapping white matter tracts with <scp>SEEG</scp> electrodes
Bibliographic record
Abstract
OBJECTIVE: Stereo-electroencephalography (SEEG) is designed to record gray matter (GM) activity for epileptogenic zone localization. SEEG electrodes, however, traverse white matter (WM) pathways that connect regions involved in seizure networks and cognition. We retrospectively evaluated WM tract sampling in patients with SEEG to identify pathways suitable for prospective WM stimulation studies and validated this in two patients. METHODS: This retrospective analysis included 86 individuals who underwent SEEG implantation for drug-resistant epilepsy between 2014 and 2020. Electrode contacts were localized using postoperative high-resolution computed tomography (CT), co-registered to preoperative 3T T1-weighted magnetic resonance imaging (MRI) and normalized to Montreal Neurological Institute (MNI) space. Tissue classification utilized the Harvard-Oxford atlas, whereas WM tract involvement was assessed using probabilistic WM atlases. Preoperative tractography with dissection of the optic radiation was performed and used for 50 Hz, image-guided SEEG stimulation of white matter. RESULTS: Among 86 patients (30 left, 40 right, 16 bilateral hemisphere implantations), 860 electrodes (6372 contacts) were implanted. Of 5853 intraparenchymal contacts (92%), 1826 (31%) were positioned exclusively within WM and 2554 (44%) at the GM/WM boundary. Patients had an average of 10 electrodes (74 contacts). Of intraparenchymal contacts, 4381 (75%) crossed WM pathways (average 21 tracts per patient). The most frequently sampled tracts were commissural fibers (corpus callosum: 100% of patients), followed by association fibers including the inferior fronto-occipital fasciculus (97.7%), superior longitudinal fasciculus II (96.5%), and cingulum (95.4%). In two separate patients, WM stimulation induced phosphenes at .5-1 mA in different distant electrodes with contacts in the optic radiation, which was reproducible and tract selective. SIGNIFICANCE: SEEG electrodes sample WM structures in approximately two-thirds of contacts, and association pathways demonstrate near-universal sampling, making them optimal candidates for systematic WM stimulation protocols. SEEG-WM stimulation with preoperative tractography was validated in two different patients.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".