Preoperative functional magnetic resonance imaging assessment of higher-order cognitive function in patients undergoing surgery for brain tumors
Bibliographic record
Abstract
OBJECT: Resection of brain tumors has been shown to increase patient survival. The extent of the possible resection, however, depends on whether the tumor has invaded brain regions important for motor, sensory, or cognitive processes and whether the brain tissue surrounding the tumor maintains its functional role. The goal of the present study was to develop new pre- and intraoperative tools to specifically assess the function of the rostral part of the dorsal premotor cortex (PMdr) in 4 patients with brain tumors close to this region. METHODS: Using functional magnetic resonance (fMR) imaging and a task developed to assess accurate selection between competing responses based on conditional rules, the authors preoperatively assessed the function of the PMdr in 4 patients with brain tumors close to this region. In 1 patient, the authors developed an intraoperative procedure to assess performance on the task during the tumor resection. RESULTS: Preoperative fMR imaging data showed specific activity increases in the vicinity of the tumors, that is, in the PMdr. As confirmed by postoperative structural MR imaging, the extent of the tumor resection was optimal and the functional region within the PMdr was preserved. Furthermore, patients exhibited no postoperative deficits during task performance, demonstrating that the function was preserved. Intraoperative behavioral results demonstrated that the cognitive processes underlying performance on the task remained intact throughout the tumor resection. CONCLUSIONS: These findings suggest that preoperative fMR imaging, together with intraoperative behavioral evaluation, may be a useful paradigm to assist neurosurgeons in preserving cognitive function in patients with brain tumors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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".