Non-normalized individual analysis of statistical parametric mapping\nfor clinical fMRI
Bibliographic record
Abstract
Background : Pre-operative evaluation to localize function within the\ncerebral cortices is essential before brain surgery. Blood oxygenation\nlevel-dependent functional magnetic resonance imaging (fMRI) has been\nused for this purpose.\tAims : To obtain clearer and more\nunderstandable functional images. Patients and Methods : Ten patients\nwith brain tumors underwent fMRI including hand-gripping and word\ngeneration tasks. The statistical parametric mapping (SPM) approach was\nused for subsequent analysis to localize the motor or language\nfunctions. SPM includes image pre-processing, statistical computation,\nand significance testing. In order to demonstrate a spatial\nrelationship between the lesions and a functioning area in the\nindividual structural MR images, normalization to the Montreal\nNeurological Institute coordinates was intentionally not performed. \nResults : In seven cases out of 10, the patient's motor area was\nclearly visualized. Language areas were also demonstrated in seven\ncases.\tConclusions : We conclude that application of SPM (version 8)\nanalysis to non-normalized individual data for the purpose of\nperforming pre-operative fMRI is a useful method for investigation of\nfunctional localization.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.028 | 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".