Magnetic Resonance Imaging of Marmoset Monkeys
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
The use of the common marmoset monkey (Callithrix jacchus) for neuroscientific research has grown markedly in the last decade. Magnetic resonance imaging (MRI) has played a significant role in establishing the extent of comparability of marmoset brain architecture with the human brain and brains of other preclinical species (eg, macaques and rodents). As a non-invasive technique, MRI allows for the flexible acquisition of the same sequences across different species in vivo, including imaging of whole-brain functional topologies not possible with more invasive techniques. Being one of the smallest New World primates, the marmoset may be an ideal nonhuman primate species to study with MRI. As primates, marmosets have an elaborated frontal cortex with features analogous to the human brain, while also having a small enough body size to fit into powerful small-bore MRI systems typically employed for rodent imaging; these systems offer superior signal strength and resolution. Further, marmosets have a rich behavioral repertoire uniquely paired with a lissencephalic cortex (like rodents). This smooth cortical surface lends itself well to MRI and also other invasive methodologies. With the advent of transgenic modification techniques, marmosets have gained significant traction as a powerful complement to canonical mammalian modelling species. Marmosets are poised to make major contributions to preclinical investigations of the pathophysiology of human brain disorders as well as more basic mechanistic explorations of the brain. The goal of this article is to provide an overview of the practical aspects of implementing MRI and fMRI in marmosets (both under anesthesia and fully awake) and discuss the development of resources recently made available for marmoset imaging.
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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 | 0.000 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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