Measuring competitive oscillatory activity in visual cortical populations using fMRI
Bibliographic record
Abstract
Brain oscillations reflect the synchronous periodic activity of neural populations. Oscillations can either be intrinsic to a neural system or can be driven by external stimulation. To better understand competitive processes in neural systems, electroencephalography (EEG) studies use the steady state visual evoked potential (SSVEP) to broadly drive competing oscillations in the visual system. Here we extend the SSVEP paradigm to a functional magnetic resonance imaging (fMRI) experiment to examine whether accelerated fMRI acquisition sequences can capture competing hemodynamic oscillations in localized visual populations. In this 3T fMRI experiment, participants detected target color changes in one visual field quadrant while two gratings were presented in the opposite quadrant. These gratings oscillated at 0.125 and 0.2 Hz (oscillations) or did not oscillate (control). Data were rapidly sampled (TR=300 ms; 2.5 mm isotropic) from a slab centered on the occipital lobe. Population receptive field mapping enabled the definition of the visuospatial preferences of individual voxels across the visual cortex. We localized voxels whose receptive fields overlapped with the stimulus location and responded to an oscillating stimulus in an independent experiment. We found enhanced oscillatory signatures, specifically, spectral density and signal periodicity, at our competing frequencies of 0.125 Hz and 0.2 Hz during the oscillations relative to the control condition. These results were validated in a complementary EEG experiment, indicating that the competitive hemodynamic oscillations measured by fMRI are driven by oscillatory neural activity. We demonstrate that SSVEP paradigms combined with accelerated fMRI sequences enable the examination of competitive oscillatory dynamics in visual populations. This protocol facilitates future investigations of the interaction between oscillatory activity and internal cognitive states with millimeter spatial precision.
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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".