Mapping Functional Homologies Between Human and Marmoset Brain Networks Using Movie-Driven Ultra-High Field fMRI
Bibliographic record
Abstract
Abstract Naturalistic stimuli, such as movies, offer a powerful tool for probing functional brain organization across species. Using movie-driven functional magnetic resonance imaging (md-fMRI), we recorded brain activity in humans and awake marmosets exposed to the same dynamic audiovisual stimulus. We applied tensor independent component analysis (tICA) to identify functional networks in each species, hierarchically clustered these components, and examined their within- and between-species temporal correlations to assess functional homologies. We found strong interspecies correspondence in core sensory networks, particularly those involved in visual and auditory processing, suggesting conserved mechanisms for sensory integration. In contrast, networks associated with higher-order cognition, including prefrontal and temporoparietal areas, were observed primarily in humans, highlighting species-specific specializations. These findings demonstrate the value of naturalistic paradigms and data-driven approaches in revealing both shared and divergent brain network architectures. By openly sharing our data and analysis pipelines, we aim to support future comparative studies and advance the marmoset as a model for investigating the evolutionary foundations of brain function.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".