A novel 4D fMRI clustering technique to examine event-related spatiotemporal dynamics of face processing in naturalistic stimuli
Bibliographic record
Abstract
Abstract Cortical function is complex, nuanced, and involves information processing in a multimodal and dynamic world. However, previous functional magnetic resonance imaging (fMRI) research has generally characterized static activation differences between strictly controlled proxies of real-world stimuli that do not encapsulate the complexity of everyday multimodal experiences. Of primary importance to the field of neuroimaging is the development of techniques that distill complex spatiotemporal information into simple, behaviorally relevant representations of neural activation. Herein, we present a novel 4D spatiotemporal clustering method to examine dynamic neural activity associated with events (specifically the onset of human faces in audiovisual movies). Results from this study showed that 4D spatiotemporal clustering can extract clusters of fMRI activation over time that closely resemble the known spatiotemporal pattern of human face processing without the need to model a hemodynamic response function. Overall, this technique provides a new and exciting window into dynamic functional processing across both space and time using fMRI that has wide applications across the field of neuroscience.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".