Frequency-tagged fMRI: A platform for fine-grained spatiotemporal analysis of cortical function
Bibliographic record
Abstract
Abstract Frequency tagging with functional MRI (ft-fMRI) enables precise mapping of neural dynamics by synchronizing oscillatory stimuli to stimulus-driven blood-oxygen-level-dependent (BOLD) responses. We developed and validated a dual-frequency tagging protocol to dissociate fundamental, multiplexed, and nonlinear intermodulation frequency responses across the human visual cortex at high spatial resolution. Using 3T and 7T fMRI, we reliably detected frequency-tagged BOLD responses at the level of individual vertices, revealing fine-grained cortical topographies and robust temporal synchronization to driving frequencies. Multiplexed responses, encoding multiple frequencies simultaneously, and nonlinear intermodulation components, were spatially dissociable and exhibited reproducible dynamics within and across experimental sessions. These findings establish ft-fMRI as a powerful tool for investigating fine-grained cortical computations, previously inaccessible to traditional fMRI. By bridging the spatiotemporal resolution gap between electrophysiology and fMRI, ft-fMRI provides a versatile platform for studying perception, attention, and multisensory integration in health and disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".