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@article{chavez_node_2013,
title = {Node Accessibility in Cortical Networks during Motor Tasks},
volume = {11},
issn = {1559-0089},
doi = {10.1007/s12021-013-9185-2},
abstract = {Recent findings suggest that the preparation and execution of voluntary self-paced movements are accompanied by the coordination of the oscillatory activities of distributed brain regions. Here, we use electroencephalographic source imaging methods to estimate the cortical movement-related oscillatory activity during finger extension movements. Then, we apply network theory to investigate changes (expressed as differences from the baseline) in the connectivity structure of cortical networks related to the preparation and execution of the movement. We compute the topological accessibility of different cortical areas, measuring how well an area can be reached by the rest of the network. Analysis of cortical networks reveals specific agglomerates of cortical sources that become less accessible during the preparation and the execution of the finger movements. The observed changes neither could be explained by other measures based on geodesics or on multiple paths, nor by power changes in the cortical oscillations.},
timestamp = {2017-03-21T14:57:38Z},
langid = {english},
number = {3},
journaltitle = {Neuroinformatics},
shortjournal = {Neuroinformatics},
author = {Chavez, Mario and De Vico Fallani, Fabrizio and Valencia, Miguel and Artieda, Julio and Mattia, Donatella and Latora, Vito and Babiloni, Fabio},
date = {2013-07},
pages = {355--366},
keywords = {Adult,Brain Mapping,Cerebral cortex,Electroencephalography,Evoked Potentials; Motor,Female,Humans,Male,Models; Neurological,Movement,Nerve Net,Young Adult},
eprinttype = {pmid},
eprint = {23712897}
}
@article{de_vico_fallani_topological_2017,
title = {A {{Topological Criterion}} for {{Filtering Information}} in {{Complex Brain Networks}}},
volume = {13},
issn = {1553-7358},
doi = {10.1371/journal.pcbi.1005305},
abstract = {In many biological systems, the network of interactions between the elements can only be inferred from experimental measurements. In neuroscience, non-invasive imaging tools are extensively used to derive either structural or functional brain networks in-vivo. As a result of the inference process, we obtain a matrix of values corresponding to a fully connected and weighted network. To turn this into a useful sparse network, thresholding is typically adopted to cancel a percentage of the weakest connections. The structural properties of the resulting network depend on how much of the inferred connectivity is eventually retained. However, how to objectively fix this threshold is still an open issue. We introduce a criterion, the efficiency cost optimization (ECO), to select a threshold based on the optimization of the trade-off between the efficiency of a network and its wiring cost. We prove analytically and we confirm through numerical simulations that the connection density maximizing this trade-off emphasizes the intrinsic properties of a given network, while preserving its sparsity. Moreover, this density threshold can be determined a-priori, since the number of connections to filter only depends on the network size according to a power-law. We validate this result on several brain networks, from micro- to macro-scales, obtained with different imaging modalities. Finally, we test the potential of ECO in discriminating brain states with respect to alternative filtering methods. ECO advances our ability to analyze and compare biological networks, inferred from experimental data, in a fast and principled way.},
timestamp = {2017-03-21T14:58:05Z},
langid = {english},
number = {1},
journaltitle = {PLoS computational biology},
shortjournal = {PLoS Comput. Biol.},
author = {De Vico Fallani, Fabrizio and Latora, Vito and Chavez, Mario},
date = {2017-01},
pages = {e1005305},
eprinttype = {pmid},
eprint = {28076353},
pmcid = {PMC5268647}
}
@article{battiston_structural_2014-1,
title = {Structural Measures for Multiplex Networks},
volume = {89},
issn = {1550-2376},
doi = {10.1103/PhysRevE.89.032804},
abstract = {Many real-world complex systems consist of a set of elementary units connected by relationships of different kinds. All such systems are better described in terms of multiplex networks, where the links at each layer represent a different type of interaction between the same set of nodes rather than in terms of (single-layer) networks. In this paper we present a general framework to describe and study multiplex networks, whose links are either unweighted or weighted. In particular, we propose a series of measures to characterize the multiplexicity of the systems in terms of (i) basic node and link properties such as the node degree, and the edge overlap and reinforcement, (ii) local properties such as the clustering coefficient and the transitivity, and (iii) global properties related to the navigability of the multiplex across the different layers. The measures we introduce are validated on a genuinely multiplex data set of Indonesian terrorists, where information among 78 individuals are recorded with respect to mutual trust, common operations, exchanged communications, and business relationships.},
timestamp = {2017-03-21T14:58:29Z},
langid = {english},
number = {3},
journaltitle = {Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics},
shortjournal = {Phys Rev E Stat Nonlin Soft Matter Phys},
author = {Battiston, Federico and Nicosia, Vincenzo and Latora, Vito},
date = {2014-03},
pages = {032804},
eprinttype = {pmid},
eprint = {24730896}
}
@article{de_vico_fallani_redundancy_2012,
title = {Redundancy in Functional Brain Connectivity from Eeg Recordings},
volume = {22},
issn = {0218-1274},
url = {http://www.worldscientific.com/doi/abs/10.1142/S0218127412501581},
doi = {10.1142/S0218127412501581},
abstract = {The concept of redundancy is a critical resource of the brain enhancing the resilience to neural damages and dysfunctions. In the present work, we propose a graph-based methodology to investigate the connectivity redundancy in brain networks. By taking into account all the possible paths between pairs of nodes, we considered three complementary indexes, characterizing the network redundancy (i) at the global level, i.e. the scalar redundancy (ii) across different path lengths, i.e. the vectorial redundancy (iii) between node pairs, i.e. the matricial redundancy. We used this procedure to investigate the functional connectivity estimated from a dataset of high-density EEG signals in a group of healthy subjects during a no-task resting state. The statistical comparison with a benchmark dataset of random networks, having the same number of nodes and links of the EEG nets, revealed a significant (p $<$ 0.05) difference for all the three indexes. In particular, the redundancy in the EEG networks, for each frequency band, appears radically higher than random graphs, thus revealing a natural tendency of the brain to present multiple parallel interactions between different specialized areas. Notably, the matricial redundancy showed a high (p $<$ 0.05) redundancy between the scalp sensors over the parieto-occipital areas in the Alpha range of EEG oscillations (7.5–12.5 Hz), which is known to be the most responsive channel during resting state conditions.},
timestamp = {2017-03-21T14:59:22Z},
issue = {07},
journaltitle = {International Journal of Bifurcation and Chaos},
shortjournal = {Int. J. Bifurcation Chaos},
author = {De Vico Fallani, Fabrizio and Toppi, Jlenia and Di Lanzo, Claudia and Vecchiato, Giovanni and Astolfi, Laura and Borghini, Gianluca and Mattia, Donatella and Cincotti, Febo and Babiloni, Fabio},
urldate = {2017-03-21},
date = {2012-07-01},
pages = {1250158},
file = {Full Text PDF:/Users/jeremy.guillon/Library/Application Support/Zotero/Profiles/n4011ych.default/zotero/storage/22MAF57D/De Vico Fallani et al. - 2012 - Redundancy in functional brain connectivity from e.pdf:application/pdf;Snapshot:/Users/jeremy.guillon/Library/Application Support/Zotero/Profiles/n4011ych.default/zotero/storage/UCGWEDFV/S0218127412501581.html:text/html}
}