Evidence map›Paper›PMID 22330428›Full record

ArticleNeural systems & circuits2011

Functional connectivity in a rhythmic inhibitory circuit using Granger causality.

Tilman Kispersky, Gabrielle J Gutierrez, Eve Marder

Abstract read
In one paragraph

Article in Neural systems & circuits, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

21 citing papers in PubMed.

  1. Article
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  7. A study of problems encountered in Granger causality analysis from a neuroscience perspective.Proceedings of the National Academy of Sciences of the United States of America · 2017
    Article
  8. Analysis of complex neural circuits with nonlinear multidimensional hidden state models.Proceedings of the National Academy of Sciences of the United States of America · 2016
    Article
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  13. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Tilman Kispersky415 South Street, Biology Department and Volen Center, MS 013, Brandeis University, Waltham, MA 02454-9110, USA. tilman@brandeis.edu.
Gabrielle J Gutierrez
Eve Marder

Funding

Quantitative Neuroscience: Tools for Bridging Levels of AnalysisT32NS007292 · NINDS · BRANDEIS UNIVERSITY · PI GINA G TURRIGIANO · 1986 to 2026
$12.7M
Variability and Compensation in Reliable Neuronal NetworksR01MH046742 · NIMH · BRANDEIS UNIVERSITY · PI MARDER, EVE E · 1990 to 2025
$7.8M
NIMH NIH HHS R01 MH046742NINDS NIH HHS T32 NS007292
6 · The paper itself

Abstract

backgroundUnderstanding circuit function would be greatly facilitated by methods that allow the simultaneous estimation of the functional strengths of all of the synapses in the network during ongoing network activity. Towards that end, we used Granger causality analysis on electrical recordings from the pyloric network of the crab Cancer borealis, a small rhythmic circuit with known connectivity, and known neuronal intrinsic properties.

resultsGranger causality analysis reported a causal relationship where there is no anatomical correlate because of the strong oscillatory behavior of the pyloric circuit. Additionally, we failed to find a direct relationship between synaptic strength and Granger causality in a set of pyloric circuit models.

conclusionsWe conclude that the lack of a relationship between synaptic strength and functional connectivity occurs because Granger causality essentially collapses the direct contribution of the synapse with the intrinsic properties of the postsynaptic neuron. We suggest that the richness of the dynamical properties of most biological neurons complicates the simple interpretation of the results of functional connectivity analyses using Granger causality.

Identifiers

PMID22330428
PMCPMC3314404

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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.