Eric D. Kolaczyk

Qawi K. Telesford

Sean L. Simpson

Frontiers in Computational Neuroscience

PubMed

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Editorial: Complexity and emergence in brain network analyses

Sean L Simpson

Eric D Kolaczyk

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Complexity and emergence in brain network analyses

EDITORIAL
published: 02 June 2015
doi: 10.3389/fncom.2015.00065
Frontiers in Computational Neuroscience | www.frontiersin.org 1 June 2015 | Volume 9 | Article 65
Edited and reviewed by:
Misha Tsodyks,
Weizmann Institute of Science, Israel
*Correspondence:
Qawi K. Telesford,
qawitele@seas.upenn.edu
Received: 06 April 2015
Accepted: 17 May 2015
Published: 02 June 2015
Citation:
Telesford QK, Simpson SL and
Kolaczyk ED (2015) Editorial:
Complexity and emergence in brain
network analyses.
Front. Comput. Neurosci. 9:65.
doi: 10.3389/fncom.2015.00065
Editorial: Complexity and emergence
in brain network analyses
Qawi K. Telesford 1*, Sean L. Simpson 2 and Eric D. Kolaczyk 3
1Complex Systems Group, Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA, 2 Laboratory for
Complex Brain Networks, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem,
NC, USA, 3Department of Mathematics and Statistics, Boston University, Boston, MA, USA
Keywords: brain networks, complex systems, network dynamics, network analysis, group analysis, functional
imaging
Network science has become an invaluable tool for neuroimaging analysis and has advanced our
understanding of the brain’s complex structural and functional topology. Network science, an
interdisciplinary offshoot of graph theory, conceives of the brain as a system of nodes, representing
brain regions or voxels, and edges, representing the structural or functional connection between
these regions or voxels. Such a model is considered appealing in neuroscience as it describes a
system with various interacting regions that produces complex behaviors. This conception of the
brain has revealed that the brain exhibits “small-world” properties, namely that brain networks are
systems with regional specialization and efficient global information transfer. More importantly,
studies in brain networks have furthered our understanding of diseases and disorders affecting the
brain.
Assessing group differences in representative samples of brain networks has become increasingly
important, yet appropriate methodological developments have remained relatively sparse.
Additionally, there is increased interest in understanding dynamic changes, or how a network
changes over time. Currently, most network analysis tools are designed to analyze a static
representation, thus changes that occur over time are often overlooked or ignored. As the field
continues to grow, it is becoming increasingly important to develop methods for understanding
the complexity of these data. This research topic features selected contributions that explores
group analysis and dynamics in brain network studies including methodological and practical
applications employing various imaging modalities. This snapshot of the field represents the
growing interest to expand and validate new methods for use in the field of brain network
neuroscience.
“Defining nodes in complex brain networks” is a review article discussing the methodological
choices for defining nodes, particularly how the choice of atlas-based or voxel-based methods
affects the computed network (Stanley et al., 2013).
“Routing in the brain” is a commentary on network routing focusing on how current methods
and models may not be adequate for brain networks. Further exploration of the packet switching
network model may be more realistic and appropriate for the brain (Graham, 2014).
“A multimodal approach for determining brain networks by jointly modeling functional and
structural connectivity” is a methods article introducing a Bayesianmodel for estimating functional
networks, from functional magnetic resonance imaging (fMRI) data, leveraging complementary
structural diffusion tensor imaging (DTI) data (Xue et al., 2015).
“A permutation testing framework to compare groups of brain networks” is a methods article
describing a permutation testing framework to statistically compare groups of functional brain
networks while incorporating topological features inherent in each individual network (Simpson
et al., 2013).
Telesford et al. Brain network group and dynamic analysis
“Assessing dynamics, spatial scale, and uncertainty in task-
related brain network analyses” is a methods article describing
a statistical approach for computing uncertainty in static and
dynamic functional networks and aggregating network measures
in task-related electrocorticography (ECoG) data (Stephen et al.,
2014).
“Hierarchical vector auto-regressive models and their
applications to multi-subject effective connectivity” is a methods
article introducing a generalization of the vector auto-regressive
(VAR) model for comparing effective connectivity between
experimental conditions while accounting for between-subject
heterogeneity (Gorrostieta et al., 2013).
“Statistical network analysis for functional MRI: summary
networks and group comparisons” is a review article discussing
the construction of summary networks and how to test for
topological differences in groups of networks when these groups
also exhibit significant differences in density (Ginestet et al.,
2014).
“Detecting functional connectivity change points for single-
subject fMRI data” is a methods article that extends Dynamic
Connectivity Regression (DCR), a data-driven technique which
detects change points in functional connectivity between brain
regions to determine state changes over the course of an
experimental task, by introducing a novel algorithm aimed at
increasing the estimation of networks for individual subject data
(Cribben et al., 2013).
“The Laplacian spectrum of neural networks” is an original
research article examining the use of the Laplacian spectrum
in anatomic networks of the macaque, cat and Caenorhabditis
elegans. This method describes network structure at a systems
level, assessing systemic infrastructural properties of the entire
network as opposed to properties of specific nodes or connections
(De Lange et al., 2014).
“Quantifying network properties in multi-electrode
recordings: spatiotemporal characterization and inter-
trial variation of evoked gamma oscillations in mouse
somatosensory cortex in vitro” is an original research
article on network dynamics in multi-electrode array
data focusing on changes in the functional connectivity of
evoked gamma oscillations in cortical circuits (Carmeli et al.,
2013).
Acknowledgments
Research reported in this publication was supported by: the Air
Force Office of Scientific Research, under grant number AFOSR-
12RSL042 for EDK; the National Institute of Biomedical Imaging
and Bioengineering, under award number NBIB-K25EB012236
for SLS; and the National Institute on Alcohol Abuse and
Alcoholism, under award number NIAAA-F31AA019893 for
QKT.
References
Carmeli, C., Bonifazi, P., Robinson, H., and Small, M. (2013). Quantifying network
properties in multi-electrode recordings: spatiotemporal characterization and
inter-trial variation of evoked gamma oscillations in mouse somatosensory
cortex in vitro. Front. Comput. Neurosci. 7:134. doi: 10.3389/fncom.2013.00134
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Xue, W., Bowman, D., Pileggi, A. V., and Mayer, A. R. (2015). A
multimodal approach for determining brain networks by jointly modeling
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10.3389/fncom.2015.00022
Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Copyright © 2015 Telesford, Simpson and Kolaczyk. This is an open-access article
distributed under the terms of the Creative Commons Attribution License (CC
BY). The use, distribution or reproduction in other forums is permitted, provided
the original author(s) or licensor are credited and that the original publication
in this journal is cited, in accordance with accepted academic practice. No
use, distribution or reproduction is permitted which does not comply with these
terms.
Frontiers in Computational Neuroscience | www.frontiersin.org 2 June 2015 | Volume 9 | Article 65


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