P.083 Characterization of an arteriovenous malformation using 7T structural and functional imaging: A case report
Bibliographic record
Abstract
Background: Cerebral arteriovenous malformations (AVMs) are a type of vascular abnormality characterized by abnormal connections between arteries and veins without the normal interposed capillary bed. The gold standard for diagnosis is digital subtraction angiography (DSA). Functional MRI (fMRI), particularly with the increased sensitivity at ultra-high field (>=7T), may help to further characterize AVMs, but has not been performed in this population. Methods: We present a functional and structural neuroimaging analysis of an AVM at 7T. Resting-state fMRI was analyzed using independent components analysis (ICA) and compared to normal controls. Structural T1-weighted images were obtained at 1.5T and 7T. The patient also underwent DSA. Results: A 44 year-old, right handed man presented with a generalized tonic-clonic seizure. MRI at 1.5T and 7T revealed an AVM located in the pineal region measuring 3.2 cm. Multiple large feeder vessels were identified, and the AVM drained into the vein of Galen, clearly visualized on the 7T images. Functional imaging revealed an altered default mode network and ICA-identified vascular networks corresponding to the AVM. Conclusions: Imaging at 7T clearly delineates AVM structure. Functional connectivity is altered by the AVM. Vessel-specific independent components were identified that may be helpful for AVM characterization.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".