Comparison of resting-state functional connectivity in marmosets with tracer-based cellular connectivity
Bibliographic record
Abstract
Resting-state functional MRI (RS-fMRI) is widely used to assess how strongly different brain areas are connected. However, this connection obtained by RS-fMRI, which is called functional connectivity (FC), simply refers to the correlation of blood oxygen level-dependent (BOLD) signals across time it has yet to be quantified how accurately FC reflects cellular connectivity (CC). In this study, we elucidated this relationship using RS-fMRI and quantitative tracer data in marmosets. In addition, we also elucidated the effects of distance between two brain regions on the relationship between FC and CC across seed region. To calculate FC, we used full correlation approach that is considered to reflect not only direct (monosynaptic connections) but also indirect pathways (polysynaptic connections). Our main findings are that: (1) overall FC obtained by RS-fMRI was highly correlated with tracer-based CC, but correlation coefficients varied remarkably across seed regions; (2) the strength of FC decreased with increase in the distance between two regions; (3) correlation coefficients between FC and CC after regressing out the effects of the distance between two regions still varied across seed regions, but some regions have strong correlations. These findings suggest that although FC reflects the strength of monosynaptic pathways, it is strongly affected by the distance between regions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".