274 research outputs found
A statistical model for brain networks inferred from large-scale electrophysiological signals
Network science has been extensively developed to characterize structural
properties of complex systems, including brain networks inferred from
neuroimaging data. As a result of the inference process, networks estimated
from experimentally obtained biological data, represent one instance of a
larger number of realizations with similar intrinsic topology. A modeling
approach is therefore needed to support statistical inference on the bottom-up
local connectivity mechanisms influencing the formation of the estimated brain
networks. We adopted a statistical model based on exponential random graphs
(ERGM) to reproduce brain networks, or connectomes, estimated by spectral
coherence between high-density electroencephalographic (EEG) signals. We
validated this approach in a dataset of 108 healthy subjects during eyes-open
(EO) and eyes-closed (EC) resting-state conditions. Results showed that the
tendency to form triangles and stars, reflecting clustering and node
centrality, better explained the global properties of the EEG connectomes as
compared to other combinations of graph metrics. Synthetic networks generated
by this model configuration replicated the characteristic differences found in
brain networks, with EO eliciting significantly higher segregation in the alpha
frequency band (8-13 Hz) as compared to EC. Furthermore, the fitted ERGM
parameter values provided complementary information showing that clustering
connections are significantly more represented from EC to EO in the alpha
range, but also in the beta band (14-29 Hz), which is known to play a crucial
role in cortical processing of visual input and externally oriented attention.
These findings support the current view of the brain functional segregation and
integration in terms of modules and hubs, and provide a statistical approach to
extract new information on the (re)organizational mechanisms in healthy and
diseased brains.Comment: Due to the limitation "The abstract field cannot be longer than 1,920
characters", the abstract appearing here is slightly shorter than that in the
PDF fil
Statistical models of complex brain networks: a maximum entropy approach
The brain is a highly complex system. Most of such complexity stems from the
intermingled connections between its parts, which give rise to rich dynamics
and to the emergence of high-level cognitive functions. Disentangling the
underlying network structure is crucial to understand the brain functioning
under both healthy and pathological conditions. Yet, analyzing brain networks
is challenging, in part because their structure represents only one possible
realization of a generative stochastic process which is in general unknown.
Having a formal way to cope with such intrinsic variability is therefore
central for the characterization of brain network properties. Addressing this
issue entails the development of appropriate tools mostly adapted from network
science and statistics. Here, we focus on a particular class of maximum entropy
models for networks, i.e. exponential random graph models (ERGMs), as a
parsimonious approach to identify the local connection mechanisms behind
observed global network structure. Efforts are reviewed on the quest for basic
organizational properties of human brain networks, as well as on the
identification of predictive biomarkers of neurological diseases such as
stroke. We conclude with a discussion on how emerging results and tools from
statistical graph modeling, associated with forthcoming improvements in
experimental data acquisition, could lead to a finer probabilistic description
of complex systems in network neuroscience.Comment: 34 pages, 8 figure
The exploration-exploitation paradigm for networked biological systems
The stochastic exploration of the configuration space and the exploitation of
functional states underlie many biological processes. The evolutionary dynamics
stands out as a remarkable example. Here, we introduce a novel formalism that
mimics evolution and encodes a general exploration-exploitation dynamics for
biological networks. We apply it to the brain wiring problem, focusing on the
maturation of that of the nematode C. elegans. We demonstrate that a
parsimonious maxent description of the adult brain combined with our framework
is able to track down the entire developmental trajectory
Graph analysis of functional brain networks: practical issues in translational neuroscience
The brain can be regarded as a network: a connected system where nodes, or
units, represent different specialized regions and links, or connections,
represent communication pathways. From a functional perspective communication
is coded by temporal dependence between the activities of different brain
areas. In the last decade, the abstract representation of the brain as a graph
has allowed to visualize functional brain networks and describe their
non-trivial topological properties in a compact and objective way. Nowadays,
the use of graph analysis in translational neuroscience has become essential to
quantify brain dysfunctions in terms of aberrant reconfiguration of functional
brain networks. Despite its evident impact, graph analysis of functional brain
networks is not a simple toolbox that can be blindly applied to brain signals.
On the one hand, it requires a know-how of all the methodological steps of the
processing pipeline that manipulates the input brain signals and extract the
functional network properties. On the other hand, a knowledge of the neural
phenomenon under study is required to perform physiological-relevant analysis.
The aim of this review is to provide practical indications to make sense of
brain network analysis and contrast counterproductive attitudes
Non-parametric resampling of random walks for spectral network clustering
Parametric resampling schemes have been recently introduced in complex
network analysis with the aim of assessing the statistical significance of
graph clustering and the robustness of community partitions. We propose here a
method to replicate structural features of complex networks based on the
non-parametric resampling of the transition matrix associated with an unbiased
random walk on the graph. We test this bootstrapping technique on synthetic and
real-world modular networks and we show that the ensemble of replicates
obtained through resampling can be used to improve the performance of standard
spectral algorithms for community detection.Comment: 5 pages, 2 figure
Human brain distinctiveness based on EEG spectral coherence connectivity
The use of EEG biometrics, for the purpose of automatic people recognition,
has received increasing attention in the recent years. Most of current analysis
rely on the extraction of features characterizing the activity of single brain
regions, like power-spectrum estimates, thus neglecting possible temporal
dependencies between the generated EEG signals. However, important
physiological information can be extracted from the way different brain regions
are functionally coupled. In this study, we propose a novel approach that fuses
spectral coherencebased connectivity between different brain regions as a
possibly viable biometric feature. The proposed approach is tested on a large
dataset of subjects (N=108) during eyes-closed (EC) and eyes-open (EO) resting
state conditions. The obtained recognition performances show that using brain
connectivity leads to higher distinctiveness with respect to power-spectrum
measurements, in both the experimental conditions. Notably, a 100% recognition
accuracy is obtained in EC and EO when integrating functional connectivity
between regions in the frontal lobe, while a lower 97.41% is obtained in EC
(96.26% in EO) when fusing power spectrum information from centro-parietal
regions. Taken together, these results suggest that functional connectivity
patterns represent effective features for improving EEG-based biometric
systems.Comment: Key words: EEG, Resting state, Biometrics, Spectral coherence, Match
score fusio
Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data
The recent development of genetically encoded calcium indicators enables
monitoring in vivo the activity of neuronal populations. Most analysis of these
calcium transients relies on linear regression analysis based on the sensory
stimulus applied or the behavior observed. To estimate the basic properties of
the functional neural circuitry, we propose a network-based approach based on
calcium imaging recorded at single cell resolution. Differently from previous
analysis based on cross-correlation, we used Granger-causality estimates to
infer activity propagation between the activities of different neurons. The
resulting functional networks were then modeled as directed graphs and
characterized in terms of connectivity and node centralities. We applied our
approach to calcium transients recorded at low frequency (4 Hz) in ventral
neurons of the zebrafish spinal cord at the embryonic stage when spontaneous
coiling of the tail occurs. Our analysis on population calcium imaging data
revealed a strong ipsilateral connectivity and a characteristic hierarchical
organization of the network hubs that supported established propagation of
activity from rostral to caudal spinal cord. Our method could be used for
detecting functional defects in neuronal circuitry during development and
pathological conditions
Green compressive sampling reconstruction in IoT networks
In this paper, we address the problem of green Compressed Sensing (CS) reconstruction within Internet of Things (IoT) networks, both in terms of computing architecture and reconstruction algorithms. The approach is novel since, unlike most of the literature dealing with energy efficient gathering of the CS measurements, we focus on the energy efficiency of the signal reconstruction stage given the CS measurements. As a first novel contribution, we present an analysis of the energy consumption within the IoT network under two computing architectures. In the first one, reconstruction takes place within the IoT network and the reconstructed data are encoded and transmitted out of the IoT network; in the second one, all the CS measurements are forwarded to off-network devices for reconstruction and storage, i.e., reconstruction is off-loaded. Our analysis shows that the two architectures significantly differ in terms of consumed energy, and it outlines a theoretically motivated criterion to select a green CS reconstruction computing architecture. Specifically, we present a suitable decision function to determine which architecture outperforms the other in terms of energy efficiency. The presented decision function depends on a few IoT network features, such as the network size, the sink connectivity, and other systems’ parameters. As a second novel contribution, we show how to overcome classical performance comparison of different CS reconstruction algorithms usually carried out w.r.t. the achieved accuracy. Specifically, we consider the consumed energy and analyze the energy vs. accuracy trade-off. The herein presented approach, jointly considering signal processing and IoT network issues, is a relevant contribution for designing green compressive sampling architectures in IoT networks
Editorial: Bridging Scales and Levels
Network neuroscience strives to understand the networks of the brain on all spatiotemporal scales and levels of observation. Current experimental and theoretical capabilities are beginning to facilitate a more holistic perspective, uniting these networks. This focus feature, “Bridging Scales and Levels,” aims to document current research and looks to future progress towards this vision
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