P.082 Neural Reorganization Following Compression of the Motor Cortex: An fMRI and DTI Case Report
Bibliographic record
Abstract
Background: Functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) are noninvasive and reliable tools for mapping eloquent cortex and white matter tracks prior to brain surgery. In this case, fMRI and DTI were used to inform the surgical approach in the resection of a deep cavernous malformation near the right lentiform nucleus. Post-surgery, the patient developed a fluid collection in the frontal cortex that applied pressure to M1, which led to reorganization of the motor cortex. Methods: The tasks included finger tapping, arm rubbing, and lip licking. All fMRI analyses were performed using BrainVoyager. Tensors were tracked from 20-direction diffusion MR images using DSIStudio. Results: An fMRI scan one-month pre-surgery revealed activation in M1 for the three tasks. A six-month follow-up scan revealed motor activation had been displaced by the fluid collection. A ten-month follow-up scan revealed that activation had shifted from its original location to more lateral and anterior regions. DTI revealed atrophy in the tracts through the insula, but increase in tracts through the lentiform nucleus. Conclusions: The results provide evidence that components of motor processing subserved by M1 can be taken over by adjacent regions, and that the rapid onset of pressure can lead to reorganization in a relatively short time period.
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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 | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".