Determination of effective brain connectivity from functional connectivity with application to resting state connectivities

Robinson, P. A., Sarkar, S., Pandejee, Grishma Mehta and Henderson, J. A. (2014) Determination of effective brain connectivity from functional connectivity with application to resting state connectivities. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 90 1: 012707-1-012707-6. doi:10.1103/PhysRevE.90.012707

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Author Robinson, P. A.
Sarkar, S.
Pandejee, Grishma Mehta
Henderson, J. A.
Title Determination of effective brain connectivity from functional connectivity with application to resting state connectivities
Journal name Physical Review E - Statistical, Nonlinear, and Soft Matter Physics   Check publisher's open access policy
ISSN 1539-3755
1550-2376
Publication date 2014-07-30
Sub-type Article (original research)
DOI 10.1103/PhysRevE.90.012707
Open Access Status File (Publisher version)
Volume 90
Issue 1
Start page 012707-1
End page 012707-6
Total pages 7
Place of publication College Park, MD United States
Publisher American Physical Society
Collection year 2015
Language eng
Formatted abstract
Neural field theory insights are used to derive effective brain connectivity matrices from the functional connectivity matrix defined by activity covariances. The symmetric case is exactly solved for a resting state system driven by white noise, in which strengths of connections, often termed effective connectivities, are inferred from functional data; these include strengths of connections that are underestimated or not detected by anatomical imaging. Proximity to criticality is calculated and found to be consistent with estimates obtainable from other methods. Links between anatomical, effective, and functional connectivity and resting state activity are quantified, with applicability to other complex networks. Proof-of-principle results are illustrated using published experimental data on anatomical connectivity and resting state functional connectivity. In particular, it is shown that functional connection matrices can be used to uncover the existence and strength of connections that are missed from anatomical connection matrices, including interhemispheric connections that are difficult to track with techniques such as diffusion spectrum imaging.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Non HERDC
School of Information Technology and Electrical Engineering Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 5 times in Thomson Reuters Web of Science Article | Citations
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Created: Mon, 18 Aug 2014, 16:58:22 EST by James Henderson on behalf of School of Information Technol and Elec Engineering