Inclusion of Attentional Networks in the Pre-Surgical Neuroimaging Assessment of a Large Deep Hemispheric Cavernous Malformation: An fMRI Case Report
Bibliographic record
Abstract
BACKGROUND AND IMPORTANCE: Functional magnetic resonance imaging (fMRI) is a noninvasive and reliable tool for mapping eloquent cortex in patients prior to brain surgery. Ensuring intact perceptual and cognitive processing is a key goal for neurosurgeons, and recent research has indicated the value of including attentional network processing in pre-surgical fMRI in order to help preserve such abilities, including reading, after surgery. CLINICAL PRESENTATION: We report a 42-year-old patient with a large cavernous malformation, near the left basal ganglia. The lesion measured 3.8 × 1.7 × 1.8 cm. In consultation with the patient and the multidisciplinary cerebrovascular team, the decision was made to offer the patient surgical resection. The surgical resection involved planned access via the left superior parietal lobule using stereotactic location. The patient declined an awake craniotomy; therefore, direct electrocortical stimulation (ECS) could not be used for intraoperative language localization in this case. Pre-surgical planning included fMRI localization of language, motor, sensory, and attentional processing. The key finding was that both reading and attention-processing tasks revealed consistent activation of the left superior parietal lobule, part of the attentional control network, and the site of the planned surgical access. Given this information, surgical access was adjusted to avoid interference with the attentional control network. The lesion was removed via the left inferior parietal lobule. The patient had no new neurologic deficits postoperatively but did develop mild neuropathic pain in the left hand. CONCLUSION: This case report supports recent research that indicates the value of including fMRI maps of attentional tasks along with traditional language-processing tasks in preoperative planning in patients undergoing neurosurgery procedures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".