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FIAR: an R package for analyzing functional integration in the brain

Author
Organization
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The integrative neuroscience of behavioral control (Neuroscience)
Abstract
Functional integration in the brain refers to distributed interactions among functionally segregated regions. Investigation of effective connectivity in brain networks, i.e, the directed causal influence that one brain region exerts over another region, is being increasingly recognized as an important tool for understanding brain function in neuroimaging studies. Methods for identifying intrinsic relationships among elements in a network are increasingly in demand. Over the last few decades several techniques such as Bayesian networks, Granger causality, and dynamic causal models have been developed to identify causal relations in dynamic systems. At the same time, established techniques such as structural equation modeling (SEM) are being modified and extended in order to reveal underlying interactions in imaging data. In the R package FIAR, which stands for Functional Integration Analysis in R, we have implemented many of the latest techniques for analyzing brain networks based on functional magnetic resonance imaging (fMRI) data. The package can be used to analyze experimental data, but also to simulate data under certain models.
Keywords
functional magnetic resonance imaging, Granger causality, VARIABILITY, MODEL, CONNECTIVITY, FMRI, TIME-SERIES, GRANGER CAUSALITY, BOLD HEMODYNAMIC-RESPONSES, dynamic causal modeling, functional integration, structural equation modeling

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Citation

Please use this url to cite or link to this publication:

Chicago
Roelstraete, Bjorn, and Yves Rosseel. 2011. “FIAR: An R Package for Analyzing Functional Integration in the Brain.” Journal of Statistical Software 44 (13): 1–32.
APA
Roelstraete, B., & Rosseel, Y. (2011). FIAR: an R package for analyzing functional integration in the brain. JOURNAL OF STATISTICAL SOFTWARE, 44(13), 1–32.
Vancouver
1.
Roelstraete B, Rosseel Y. FIAR: an R package for analyzing functional integration in the brain. JOURNAL OF STATISTICAL SOFTWARE. 2011;44(13):1–32.
MLA
Roelstraete, Bjorn, and Yves Rosseel. “FIAR: An R Package for Analyzing Functional Integration in the Brain.” JOURNAL OF STATISTICAL SOFTWARE 44.13 (2011): 1–32. Print.
@article{2129302,
  abstract     = {Functional integration in the brain refers to distributed interactions among functionally segregated regions. Investigation of effective connectivity in brain networks, i.e, the directed causal influence that one brain region exerts over another region, is being increasingly recognized as an important tool for understanding brain function in neuroimaging studies. Methods for identifying intrinsic relationships among elements in a network are increasingly in demand. Over the last few decades several techniques such as Bayesian networks, Granger causality, and dynamic causal models have been developed to identify causal relations in dynamic systems. At the same time, established techniques such as structural equation modeling (SEM) are being modified and extended in order to reveal underlying interactions in imaging data. In the R package FIAR, which stands for Functional Integration Analysis in R, we have implemented many of the latest techniques for analyzing brain networks based on functional magnetic resonance imaging (fMRI) data. The package can be used to analyze experimental data, but also to simulate data under certain models.},
  author       = {Roelstraete, Bjorn and Rosseel, Yves},
  issn         = {1548-7660},
  journal      = {JOURNAL OF STATISTICAL SOFTWARE},
  language     = {eng},
  number       = {13},
  pages        = {1--32},
  title        = {FIAR: an R package for analyzing functional integration in the brain},
  volume       = {44},
  year         = {2011},
}

Web of Science
Times cited: