Electrophysiologic Validation of Diffusion Tensor Imaging Tractography during Deep Brain Stimulation Surgery
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Diffusion tensor imaging fiber tractography-assisted planning of deep brain stimulation is an emerging technology. We investigated its accuracy by using electrophysiology under clinical conditions. We hypothesized that a level of concordance between electrophysiology and DTI fiber tractography can be reached, comparable with published modeling approaches for deep brain stimulation surgery. MATERIALS AND METHODS: Eleven patients underwent subthalamic nucleus deep brain stimulation. DTI scans and high-resolution T1- and T2-weighted MR imaging was performed at 3T. Corticospinal tracts were traced. We studied electrode positions and current amplitudes that elicited corticospinal tract effects during the operation to determine relative corticospinal tract distance. Postoperatively, 3D deep brain stimulation electrode contact locations and stimulation patterns were applied for the same corticospinal tract distance estimation. RESULTS: Intraoperative electrophysiologic (n = 40) clinical effects in 11 patients were detected. The mean intraoperative electrophysiologic corticospinal tract distance was 3.0 ± 0.6 mm; the mean image-derived corticospinal tract distance (DTI fiber tractography) was 3.0 ± 1.3 mm. The 95% limits of agreement were ±2.4 mm. Postoperative electrophysiology (n = 44) corticospinal tract activation effects were encountered in 9 patients; 39 were further evaluated. Mean electrophysiologic corticospinal tract distance was 3.7 ± 0.7 mm; for DTI fiber tractography, it was 3.2 ± 1.9 mm. The 95% limits of agreement were ±2.5 mm. CONCLUSIONS: DTI fiber tractography depicted the medial corticospinal tract border with proved concordance. Although the overall range of measurements was relatively small and variance was high, we believe that further use of DTI fiber tractography to assist deep brain stimulation procedures is advisable if inherent limitations are respected. These results confirm our previously published electric field simulation studies.
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| 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".