Corticospinal tract mapping in children with ruptured arteriovenous malformations using functionally guided diffusion-tensor imaging
Bibliographic record
Abstract
Arteriovenous malformations (AVMs) can lead to distortion or reorganization of functional brain anatomy, making localization of eloquent white matter tracts challenging. To improve the accuracy of corticospinal tract (CST) mapping, recent studies have examined the use of functional imaging techniques to help localize cortical motor activations and use these as seed points to reconstruct CSTs using diffusion-tensor imaging (DTI). The authors examined the role of pretreatment functionally guided DTI CST mapping in 3 children with ruptured AVMs. In 2 patients, magnetoencephalography motor activations were adjacent to the nidus and/or hemorrhagic cavity. However, in 1 child, functional MRI motor activations were detected in both hemispheres, suggestive of partial transfer of cortical motor function. In all children, quantitative analysis showed that fractional anisotropy values and fiber density indices were reduced in the CSTs of the hemisphere harboring the AVM compared with the unaffected side. In 2 children, CST caliber was slightly diminished, corresponding to no motor deficit in 1 patient and a temporary motor deficit in the other. In contrast, 1 child demonstrated marked reduction and displacement of the CSTs, correlating with severe motor deficit. Preoperative motor tractography data were loaded onto the intraoperative neuronavigation platform to guide complete resection of the AVM in 2 cases without permanent neurological deficits. These preliminary results confirm the feasibility of CST mapping in children with ruptured AVMs using functionally guided DTI tractography. Prospective studies are needed to assess the full value of this technique in the risk stratification, prognosis, and multimodality management of pediatric AVMs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".