Non-normalized individual analysis of statistical parametric mapping for clinical fMRI
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it