24,545 research outputs found
Development of modularity in the neural activity of children's brains
We study how modularity of the human brain changes as children develop into
adults. Theory suggests that modularity can enhance the response function of a
networked system subject to changing external stimuli. Thus, greater cognitive
performance might be achieved for more modular neural activity, and modularity
might likely increase as children develop. The value of modularity calculated
from fMRI data is observed to increase during childhood development and peak in
young adulthood. Head motion is deconvolved from the fMRI data, and it is shown
that the dependence of modularity on age is independent of the magnitude of
head motion. A model is presented to illustrate how modularity can provide
greater cognitive performance at short times, i.e.\ task switching. A fitness
function is extracted from the model. Quasispecies theory is used to predict
how the average modularity evolves with age, illustrating the increase of
modularity during development from children to adults that arises from
selection for rapid cognitive function in young adults. Experiments exploring
the effect of modularity on cognitive performance are suggested. Modularity may
be a potential biomarker for injury, rehabilitation, or disease.Comment: 29 pages, 11 figure
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
The envirome and the connectome: exploring the structural noise in the human brain associated with socioeconomic deprivation
Complex cognitive functions are widely recognized to be the result of a number of brain regions working together as large-scale networks. Recently, complex network analysis has been used to characterize various structural properties of the large scale network organization of the brain. For example, the human brain has been found to have a modular architecture i.e. regions within the network form communities (modules) with more connections between regions within the community compared to regions outside it. The aim of this study was to examine the modular and overlapping modular architecture of the brain networks using complex network analysis. We also examined the association between neighborhood level deprivation and brain network structure – modularity and grey nodes. We compared network structure derived from anatomical MRI scans of 42 middle-aged neurologically healthy men from the least (LD) and the most deprived (MD) neighborhoods of Glasgow with their corresponding random networks. Cortical morphological covariance networks were constructed from the cortical thickness derived from the MRI scans of the brain. For a given modularity threshold, networks derived from the MD group showed similar number of modules compared to their corresponding random networks, while networks derived from the LD group had more modules compared to their corresponding random networks. The MD group also had fewer grey nodes – a measure of overlapping modular structure. These results suggest that apparent structural difference in brain networks may be driven by differences in cortical thicknesses between groups. This demonstrates a structural organization that is consistent with a system that is less robust and less efficient in information processing. These findings provide some evidence of the relationship between socioeconomic deprivation and brain network topology
Resting-State Functional Connectivity in Late-Life Depression: Higher Global Connectivity and More Long Distance Connections
Functional magnetic resonance imaging recordings in the resting-state (RS)
from the human brain are characterized by spontaneous low-frequency
fluctuations in the blood oxygenation level dependent signal that reveal
functional connectivity (FC) via their spatial synchronicity. This RS study
applied network analysis to compare FC between late-life depression (LLD)
patients and control subjects. Raw cross-correlation matrices (CM) for LLD were
characterized by higher FC. We analyzed the small-world (SW) and modular
organization of these networks consisting of 110 nodes each as well as the
connectivity patterns of individual nodes of the basal ganglia. Topological
network measures showed no significant differences between groups. The
composition of top hubs was similar between LLD and control subjects, however
in the LLD group posterior medial-parietal regions were more highly connected
compared to controls. In LLD, a number of brain regions showed connections with
more distant neighbors leading to an increase of the average Euclidean distance
between connected regions compared to controls. In addition, right caudate
nucleus connectivity was more diffuse in LLD. In summary, LLD was associated
with overall increased FC strength and changes in the average distance between
connected nodes, but did not lead to global changes in SW or modular
organization
Complex networks: new trends for the analysis of brain connectivity
Today, the human brain can be studied as a whole. Electroencephalography,
magnetoencephalography, or functional magnetic resonance imaging techniques
provide functional connectivity patterns between different brain areas, and
during different pathological and cognitive neuro-dynamical states. In this
Tutorial we review novel complex networks approaches to unveil how brain
networks can efficiently manage local processing and global integration for the
transfer of information, while being at the same time capable of adapting to
satisfy changing neural demands.Comment: Tutorial paper to appear in the Int. J. Bif. Chao
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