Non-normalized individual analysis of statistical parametric mapping for clinical fMRI
Bibliographic record
Abstract
BACKGROUND: Pre-operative evaluation to localize function within the cerebral cortices is essential before brain surgery. Blood oxygenation level-dependent functional magnetic resonance imaging (fMRI) has been used for this purpose. AIMS: To obtain clearer and more understandable functional images. PATIENTS AND METHODS: Ten patients with brain tumors underwent fMRI including hand-gripping and word generation tasks. The statistical parametric mapping (SPM) approach was used for subsequent analysis to localize the motor or language functions. SPM includes image pre-processing, statistical computation, and significance testing. In order to demonstrate a spatial relationship between the lesions and a functioning area in the individual structural MR images, normalization to the Montreal Neurological Institute coordinates was intentionally not performed. RESULTS: In seven cases out of 10, the patient's motor area was clearly visualized. Language areas were also demonstrated in seven cases. CONCLUSIONS: We conclude that application of SPM (version 8) analysis to non-normalized individual data for the purpose of performing pre-operative fMRI is a useful method for investigation of functional localization.
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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 | 0.018 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".