P.201 Disruption of the Frontal Aslant Tract is Associated with Transient Aphasia and not Agraphia: A Neurosurgical Case Report
Bibliographic record
Abstract
Background: The frontal aslant tract (FAT) is a recently discovered white-matter tract connecting the medial superior frontal gyrus to the inferior frontal gyrus. There is increasing evidence for its importance in speech initiation and production. Despite this, there remains limited evidence demonstrating clinical outcomes when lesioning this tract. Methods: Medical records for the case were reviewed. Imaging was exported and tractography was performed using 3D Slicer. Results: A 58-year-old female presented with a focal seizure and imaging demonstrating a left frontal extra-axial lesion. She underwent a left frontal craniotomy for tumour debulking and biopsy. The final pathology was consistent with a diffuse large B-cell lymphoma. Postoperatively, she presented with expressive aphasia without agraphia. She was able to write out answers to questions she could not verbalize. We used tractography to provide evidence of postoperative disruption to her left FAT. At a 6-week clinical follow-up, her language deficits were clinically resolved. Conclusions: To our knowledge, this is the first reported case of aphasia without agraphia seen with disruption of the left FAT. Further elucidating clinical outcomes of disrupting the dominant FAT will aid in improved patient counselling, prognostication and neurosurgical planning. Further research is required to investigate the mechanisms underlying language recovery and handwriting.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".