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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Estimating complex cortical networks via surface recordings-A critical note

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Author(s):
Antiqueira, Lucas [1] ; Rodrigues, Francisco A. [2] ; van Wijk, Bernadette C. M. [3] ; Costa, Luciano da F. [1] ; Daffertshofer, Andreas [3]
Total Authors: 5
Affiliation:
[1] Univ Sao Paulo, Inst Fis Sao Carlos, Grp Computacao Interdisciplinar, BR-13560970 Sao Carlos, SP - Brazil
[2] Univ Sao Paulo, Inst Ciencias Matemat & Computacao, Dept Matemat Aplicada & Estat, BR-13560970 Sao Carlos, SP - Brazil
[3] Vrije Univ Amsterdam, Res Inst MOVE, NL-1081 BT Amsterdam - Netherlands
Total Affiliations: 3
Document type: Journal article
Source: NeuroImage; v. 53, n. 2, p. 439-449, NOV 1 2010.
Web of Science Citations: 23
Abstract

We discuss potential caveats when estimating topologies of 3D brain networks from surface recordings. It is virtually impossible to record activity from all single neurons in the brain and one has to rely on techniques that measure average activity at sparsely located (non-invasive) recording sites Effects of this spatial sampling in relation to structural network measures like centrality and assortativity were analyzed using multivariate classifiers A simplified model of 3D brain connectivity incorporating both short- and long-range connections served for testing. To mimic M/EEG recordings we sampled this model via non-overlapping regions and weighted nodes and connections according to their proximity to the recording sites We used various complex network models for reference and tried to classify sampled versions of the ``brain-like{''} network as one of these archetypes It was found that sampled networks may substantially deviate in topology from the respective original networks for small sample sizes For experimental studies this may imply that surface recordings can yield network structures that might not agree with its generating 3D network. (C) 2010 Elsevier Inc All rights reserved (AU)

FAPESP's process: 05/00587-5 - Mesh (graph) modeling and techniques of pattern recognition: structure, dynamics and applications
Grantee:Roberto Marcondes Cesar Junior
Support Opportunities: Research Projects - Thematic Grants