Bibliographic record
Abstract
The production of a brain activity map from data acquired with a volunteer or patient and magnetic resonance imaging (MRI) requires a fairly wide range of interdisciplinary knowledge and techniques. Producing brain activity maps from functional MRI (fMRI) data requires knowledge and techniques from cognitive neuropsychology, physics, engineering and mathematics, particularly statistics. The process typically begins with a question in cognitive psychology that can be answered, at least in part, by combining the knowledge obtained from brain activity maps with previous knowledge of the function of specific regions of the brain. The previous knowledge of regional brain function generally has its origin in lesion studies where disease or injury has removed a brain region, and its function, from the brain's owner. Such lesion-based knowledge has firmly established the principle of functional segregation in the brain, where specific regions are responsible for specific functions. The use of fMRI to produce activation maps allows specific questions on functional segregation to be posed and investigated without risk to the person being studied. The brain is also known to be a very complex system in which several regions, working cooperatively, are required for some tasks. This cooperation among regions is known as functional integration and may be studied using fMRI techniques that lead to connectivity maps . Methods for producing activation and connectivity maps are reviewed here with the goal of providing a complete overview of all data processing currently available to produce the brain activity maps from raw fMRI data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.312 | 0.193 |
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".