Longitudinal Functional Magnetic Resonance Imaging in Animal Models
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
Functional magnetic resonance imaging (fMRI) has had an essential role in furthering our understanding of brain physiology and function. fMRI techniques are nowadays widely applied in neuroscience research, as well as in translational and clinical studies. The use of animal models in fMRI studies has been fundamental in helping elucidate the mechanisms of cerebral blood-flow regulation, and in the exploration of basic neuroscience questions, such as the mechanisms of perception, behavior, and cognition. Because animals are inherently non-compliant, most fMRI performed to date have required the use of anesthesia, which interferes with brain function and compromises interpretability and applicability of results to our understanding of human brain function. An alternative approach that eliminates the need for anesthesia involves training the animal to tolerate physical restraint during the data acquisition. In the present chapter, we review these two different approaches to obtaining fMRI data from animal models, with a specific focus on the acquisition of longitudinal data from the same subjects.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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