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Entropy
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Brain Connectivity and Information Theory
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Brain Connectivity and Information Theory

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: closed (31 July 2021)

Special Issue Editors

Dr. Daniel Takahashi

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Guest Editor
Brain Institute, Federal University of Rio Grande do Norte, Natal 59092-540, Brazil
Interests: connectivity analysis; causality; information theory; phase transition; animal communication
Dr. Demian Battaglia

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Guest Editor
Institut de Neurosciences des Systèmes, Aix-Marseille University, Inserm, INS, 13005 Marseille, France
Interests: computational and theoretical neuroscience; dynamics of functional connectivity; neural information routing and processing

Special Issue Information

Dear Colleagues,

Describing how different brain structures are related to each other has played a significant role in our understanding of brain functions. Not surprisingly, several brain connectivity measures were proposed in the literature with a priori little relationship with each other. This abundance of methods created a paradox in which, despite the number of studies exploring brain connectivity increasing considerably, most of them are not readily comparable. A general and common framework is necessary to fix this situation. Information theory is the natural candidate framework to unify the different connectivity measures and concepts introduced in the literature. This Special Issue explores how information theory can lead us to a comprehensive understanding of connectivity in the brain. We are particularly interested in studies showing the relationship between information theoretical quantities and well-known connectivity measures in the literature. Studies comparing different connectivity measures with an emphasis on information theory are also welcome.

Dr. Daniel Takahashi
Dr. Demian Battaglia
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • connectivity analysis
  • causality
  • effective connectivity
  • connectome
  • transfer entropy
  • granger causality

Published Papers (2 papers)

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Research

22 pages, 2775 KiB  
Article
Granger Causality among Graphs and Application to Functional Brain Connectivity in Autism Spectrum Disorder
by Adèle Helena Ribeiro, Maciel Calebe Vidal, João Ricardo Sato and André Fujita
Entropy 2021, 23(9), 1204; https://doi.org/10.3390/e23091204 - 13 Sep 2021
Cited by 4 | Viewed by 2233
Abstract
Graphs/networks have become a powerful analytical approach for data modeling. Besides, with the advances in sensor technology, dynamic time-evolving data have become more common. In this context, one point of interest is a better understanding of the information flow within and between networks. [...] Read more.
Graphs/networks have become a powerful analytical approach for data modeling. Besides, with the advances in sensor technology, dynamic time-evolving data have become more common. In this context, one point of interest is a better understanding of the information flow within and between networks. Thus, we aim to infer Granger causality (G-causality) between networks’ time series. In this case, the straightforward application of the well-established vector autoregressive model is not feasible. Consequently, we require a theoretical framework for modeling time-varying graphs. One possibility would be to consider a mathematical graph model with time-varying parameters (assumed to be random variables) that generates the network. Suppose we identify G-causality between the graph models’ parameters. In that case, we could use it to define a G-causality between graphs. Here, we show that even if the model is unknown, the spectral radius is a reasonable estimate of some random graph model parameters. We illustrate our proposal’s application to study the relationship between brain hemispheres of controls and children diagnosed with Autism Spectrum Disorder (ASD). We show that the G-causality intensity from the brain’s right to the left hemisphere is different between ASD and controls. Full article
(This article belongs to the Special Issue Brain Connectivity and Information Theory)
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15 pages, 2177 KiB  
Article
Frequency Domain Repercussions of Instantaneous Granger Causality
by Luiz A. Baccalá and Koichi Sameshima
Entropy 2021, 23(8), 1037; https://doi.org/10.3390/e23081037 - 12 Aug 2021
Cited by 6 | Viewed by 1805
Abstract
Using directed transfer function (DTF) and partial directed coherence (PDC) in the information version, this paper extends the theoretical framework to incorporate the instantaneous Granger causality (iGC) frequency domain description into a single unified perspective. We show that standard vector autoregressive models allow [...] Read more.
Using directed transfer function (DTF) and partial directed coherence (PDC) in the information version, this paper extends the theoretical framework to incorporate the instantaneous Granger causality (iGC) frequency domain description into a single unified perspective. We show that standard vector autoregressive models allow portraying iGC’s repercussions associated with Granger connectivity, where interactions mediated without delay between time series can be easily detected. Full article
(This article belongs to the Special Issue Brain Connectivity and Information Theory)
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