MétaCan
Menu
← Back to cohort
Record W4386258892 · doi:10.1167/jov.23.9.4777

Measuring competitive oscillatory activity in visual cortical populations using fMRI

2023· article· en· W4386258892 on OpenAlexaff
Reebal W. Rafeh, Geoffrey N. Ngo, Lyle Muller, Ravi S. Menon, Ali R. Khan, Taylor W. Schmitz, Marieke Mur

Bibliographic record

VenueJournal of Vision · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsWestern University
Fundersnot available
KeywordsFunctional magnetic resonance imagingVisual cortexReceptive fieldNeuroscienceStimulus (psychology)VoxelPhysicsElectroencephalographyVisual fieldPsychologyArtificial intelligencePattern recognition (psychology)Computer scienceCognitive psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.122
GPT teacher head0.362
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Vision→Same topicNeural dynamics and brain function→French-language works237,207→