Evidence map›Paper›PMID 24204221›Full record

ArticlePLoS computational biology2013

Attention-dependent modulation of cortical taste circuits revealed by Granger causality with signal-dependent noise.

Qiang Luo, Tian Ge, Fabian Grabenhorst, Jianfeng Feng, Edmund T Rolls

Abstract read
In one paragraph

Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed.

  1. Trial
  2. Corticomuscular coupling study for post-stroke rehabilitation: a scoping review.Journal of neuroengineering and rehabilitation · 2025
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  8. The orbitofrontal cortex, food reward, body weight and obesity.Social cognitive and affective neuroscience · 2023
    Article
  9. Functional Connectivity of the Chemosenses: A Review.Frontiers in systems neuroscience · 2022
    Review
  10. Mind Causality: A Computational Neuroscience Approach.Frontiers in computational neuroscience · 2021
    Article
  11. Review
  12. Article
  13. Effective connectivity between Broca's area and amygdala as a mechanism of top-down control in worry.Clinical psychological science : a journal of the Association for Psychological Science · 2020
    Article
  14. Article
  15. Article
  16. Article
  17. Article
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

Qiang LuoShanghai Center for Mathematical Sciences, Fudan University, Shanghai, PR China ; College of Information Systems and Management, National University of Defense Technology, Hunan, PR China.
Tian Ge
Fabian Grabenhorst
Jianfeng Feng
Edmund T Rolls

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We show, for the first time, that in cortical areas, for example the insular, orbitofrontal, and lateral prefrontal cortex, there is signal-dependent noise in the fMRI blood-oxygen level dependent (BOLD) time series, with the variance of the noise increasing approximately linearly with the square of the signal. Classical Granger causal models are based on autoregressive models with time invariant covariance structure, and thus do not take this signal-dependent noise into account. To address this limitation, here we describe a Granger causal model with signal-dependent noise, and a novel, likelihood ratio test for causal inferences. We apply this approach to the data from an fMRI study to investigate the source of the top-down attentional control of taste intensity and taste pleasantness processing. The Granger causality with signal-dependent noise analysis reveals effects not identified by classical Granger causal analysis. In particular, there is a top-down effect from the posterior lateral prefrontal cortex to the insular taste cortex during attention to intensity but not to pleasantness, and there is a top-down effect from the anterior and posterior lateral prefrontal cortex to the orbitofrontal cortex during attention to pleasantness but not to intensity. In addition, there is stronger forward effective connectivity from the insular taste cortex to the orbitofrontal cortex during attention to pleasantness than during attention to intensity. These findings indicate the importance of explicitly modeling signal-dependent noise in functional neuroimaging, and reveal some of the processes involved in a biased activation theory of selective attention.

Indexed as

Models, StatisticalPrefrontal CortexSignal Processing, Computer-AssistedAdultCerebrovascular CirculationComputer SimulationFemaleHumansMagnetic Resonance ImagingMaleOxygenTasteYoung AdultOxygen

Identifiers

PMID24204221
PMCPMC3808464

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.