Attentional modulation of stimulus-synchronized BOLD oscillations in the human visual cortex
Bibliographic record
Abstract
Visual cortical neurons synchronize their firing rates to periodic visual stimuli. EEG is commonly used to study directed attention by frequency-tagging brain responses to multiple stimuli oscillating at different frequencies but is limited by its coarse spatial resolution. Here we leverage frequency-tagged fMRI (ft-fMRI) to study the influence of directed attention on the fine-grained spatiotemporal dynamics of competing stimulus-driven visual cortical oscillations. In this 7T fMRI experiment, participants (n=7) distributed their attention to simultaneously presented visual checkerboard stimuli oscillating at 0.125 Hz and 0.2 Hz. Our analysis revealed that distinct populations of visual cortical neurons exhibited either in-phase or anti-phase synchronization with the oscillating stimuli. The spatial topographies of these populations were highly replicable across scan sessions within participants, indicative of a fine-grained map of competitive feature-tuned responses (Dice's coefficient > 0.59; K-S test: p < 10-39). In accordance with this observation, we found that directed attention homogeneously increased the amplitude of anti-phase BOLD oscillations across the visual hierarchy, consistent with a distributed attention-driven suppressive field (Reynolds & Heeger, 2009). In contrast, attentional modulation of in-phase BOLD oscillations increased hierarchically from V1 to hV4, consistent with the effects of target enhancement reported in prior monkey electrophysiology (Moran & Desimone, 1985) and event-related fMRI work (Kastner et al., 1998). Finally, the strength of anti-phase (Wilcoxon test: p = 0.016), but not in-phase (p = 0.16), modulation predicted psychophysical correlates of attentional performance, further highlighting the mechanistic dissociation of attention-driven target enhancement and surround suppression. Together, our findings support the biased competition model of attention, which posits that attention modulates competing neural populations through concurrent mechanisms of enhancement and suppression. ft-fMRI extends the boundaries for research on the neural basis of biased competition in humans by providing a non-invasive method for distinguishing between concurrent stimulus-synchronized in-phase (enhancing) and anti-phase (suppressing) BOLD oscillations.
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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".