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Record W2095196515 · doi:10.1089/brain.2011.0009

Synchrony of Two Brain Regions Predicts the Blood Oxygen Level Dependent Activity of a Third

2011· article· en· W2095196515 on OpenAlexaff
Young-Ah Rho, Viktor Jirsa

Bibliographic record

VenueBrain Connectivity · 2011
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsBaycrest Hospital
FundersJames S. McDonnell Foundation
KeywordsNeurophysiologyCoherence (philosophical gambling strategy)NeuroscienceBlood-oxygen-level dependentFunctional magnetic resonance imagingSynchronization (alternating current)ElectroencephalographyCoupling (piping)Coupling strengthSIGNAL (programming language)ElectrophysiologyNetwork dynamicsFunctional connectivityPhysicsBrain activity and meditationBrain mappingPsychologyComputer scienceMathematics

Abstract

fetched live from OpenAlex

Spontaneously emerging coherent fluctuations have been long observed in electrophysiological and functional magnetic resonance imaging studies. These dynamics have been identified in multiple brain areas in the 1-100 and < 0.1 Hz frequency ranges spanning neurophysiological oscillations and blood oxygen level dependent (BOLD) signals, respectively. In this article, we demonstrate that transient neural synchronization between two sites may lead to the emergence of ultra-slow frequency fluctuations in the BOLD signal at another (third) site. Starting with a network model comprised of three neural oscillators, we illustrate the critical role of time delay and coupling strength in generating these slow coherent fluctuations as a function of intermittently occurring neural coherence. When extending the network toward biologically realistic primate connectivity, we find that the BOLD activation patterns arise from neurophysiological coherence, especially among medial cortical areas. This finding demonstrates a network-level mechanism whereby the BOLD activity at a given region is critically influenced by the neuroelectric synchronization patterns of other regions in the network.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.083
GPT teacher head0.272
Teacher spread0.189 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations8
Published2011
Admission routes1
Has abstractyes

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