An open access resource for functional brain connectivity from fully awake marmosets
Bibliographic record
Abstract
The common marmoset (Callithrix jacchus) is quickly gaining traction as a premier neuroscientific model. However, considerable progress is still needed in understanding the functional and structural organization of the marmoset brain to rival that documented in longstanding preclinical model species, like mice, rats, and Old World primates. To accelerate such progress, we present the Marmoset Functional Brain Connectivity Resource (marmosetbrainconnectome.org), currently consisting of over 70 h of resting-state fMRI (RS-fMRI) data acquired at 500 µm isotropic resolution from 31 fully awake marmosets in a common stereotactic space. Three-dimensional functional connectivity (FC) maps for every cortical and subcortical gray matter voxel are stored online. Users can instantaneously view, manipulate, and download any whole-brain functional connectivity (FC) topology (at the subject- or group-level) along with the raw datasets and preprocessing code. Importantly, researchers can use this resource to test hypotheses about FC directly - with no additional analyses required - yielding whole-brain correlations for any gray matter voxel on demand. We demonstrate the resource's utility for presurgical planning and comparison with tracer-based neuronal connectivity as proof of concept. Complementing existing structural connectivity resources for the marmoset brain, the Marmoset Functional Brain Connectivity Resource affords users the distinct advantage of exploring the connectivity of any voxel in the marmoset brain, not limited to injection sites nor constrained by regional atlases. With the entire raw database (RS-fMRI and structural images) and preprocessing code openly available for download and use, we expect this resource to be broadly valuable to test novel hypotheses about the functional organization of the marmoset brain.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.142 | 0.051 |
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".