7,165 research outputs found
Hierarchical modularity in human brain functional networks
The idea that complex systems have a hierarchical modular organization
originates in the early 1960s and has recently attracted fresh support from
quantitative studies of large scale, real-life networks. Here we investigate
the hierarchical modular (or "modules-within-modules") decomposition of human
brain functional networks, measured using functional magnetic resonance imaging
(fMRI) in 18 healthy volunteers under no-task or resting conditions. We used a
customized template to extract networks with more than 1800 regional nodes, and
we applied a fast algorithm to identify nested modular structure at several
hierarchical levels. We used mutual information, 0 < I < 1, to estimate the
similarity of community structure of networks in different subjects, and to
identify the individual network that is most representative of the group.
Results show that human brain functional networks have a hierarchical modular
organization with a fair degree of similarity between subjects, I=0.63. The
largest 5 modules at the highest level of the hierarchy were medial occipital,
lateral occipital, central, parieto-frontal and fronto-temporal systems;
occipital modules demonstrated less sub-modular organization than modules
comprising regions of multimodal association cortex. Connector nodes and hubs,
with a key role in inter-modular connectivity, were also concentrated in
association cortical areas. We conclude that methods are available for
hierarchical modular decomposition of large numbers of high resolution brain
functional networks using computationally expedient algorithms. This could
enable future investigations of Simon's original hypothesis that hierarchy or
near-decomposability of physical symbol systems is a critical design feature
for their fast adaptivity to changing environmental conditions
Configuration model for correlation matrices preserving the node strength
Correlation matrices are a major type of multivariate data. To examine
properties of a given correlation matrix, a common practice is to compare the
same quantity between the original correlation matrix and reference correlation
matrices, such as those derived from random matrix theory, that partially
preserve properties of the original matrix. We propose a model to generate such
reference correlation and covariance matrices for the given matrix. Correlation
matrices are often analysed as networks, which are heterogeneous across nodes
in terms of the total connectivity to other nodes for each node. Given this
background, the present algorithm generates random networks that preserve the
expectation of total connectivity of each node to other nodes, akin to
configuration models for conventional networks. Our algorithm is derived from
the maximum entropy principle. We will apply the proposed algorithm to
measurement of clustering coefficients and community detection, both of which
require a null model to assess the statistical significance of the obtained
results.Comment: 8 figures, 4 table
Dynamic reconfiguration of human brain networks during learning
Human learning is a complex phenomenon requiring flexibility to adapt
existing brain function and precision in selecting new neurophysiological
activities to drive desired behavior. These two attributes -- flexibility and
selection -- must operate over multiple temporal scales as performance of a
skill changes from being slow and challenging to being fast and automatic. Such
selective adaptability is naturally provided by modular structure, which plays
a critical role in evolution, development, and optimal network function. Using
functional connectivity measurements of brain activity acquired from initial
training through mastery of a simple motor skill, we explore the role of
modularity in human learning by identifying dynamic changes of modular
organization spanning multiple temporal scales. Our results indicate that
flexibility, which we measure by the allegiance of nodes to modules, in one
experimental session predicts the relative amount of learning in a future
session. We also develop a general statistical framework for the identification
of modular architectures in evolving systems, which is broadly applicable to
disciplines where network adaptability is crucial to the understanding of
system performance.Comment: Main Text: 19 pages, 4 figures Supplementary Materials: 34 pages, 4
figures, 3 table
Fluctuations between high- and low-modularity topology in time-resolved functional connectivity
Modularity is an important topological attribute for functional brain
networks. Recent studies have reported that modularity of functional networks
varies not only across individuals being related to demographics and cognitive
performance, but also within individuals co-occurring with fluctuations in
network properties of functional connectivity, estimated over short time
intervals. However, characteristics of these time-resolved functional networks
during periods of high and low modularity have remained largely unexplored. In
this study we investigate spatiotemporal properties of time-resolved networks
in the high and low modularity periods during rest, with a particular focus on
their spatial connectivity patterns, temporal homogeneity and test-retest
reliability. We show that spatial connectivity patterns of time-resolved
networks in the high and low modularity periods are represented by increased
and decreased dissociation of the default mode network module from
task-positive network modules, respectively. We also find that the instances of
time-resolved functional connectivity sampled from within the high (low)
modularity period are relatively homogeneous (heterogeneous) over time,
indicating that during the low modularity period the default mode network
interacts with other networks in a variable manner. We confirmed that the
occurrence of the high and low modularity periods varies across individuals
with moderate inter-session test-retest reliability and that it is correlated
with previously-reported individual differences in the modularity of functional
connectivity estimated over longer timescales. Our findings illustrate how
time-resolved functional networks are spatiotemporally organized during periods
of high and low modularity, allowing one to trace individual differences in
long-timescale modularity to the variable occurrence of network configurations
at shorter timescales.Comment: Reorganized the paper; to appear in NeuroImage; arXiv abstract
shortened to fit within character limit
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