1,721,680 research outputs found
Replication Data for: Multi-dimensional partisanship shapes climate policy support and behaviors
No full textReplication Data for: Multi-dimensional partisanship shapes climate policy support and behavior
Replication Data for: Cross-national public acceptance of sustainable global supply chains policy instruments
No full textReplication materials for "Cross-national public acceptance of sustainable global supply chains policy instruments" in Nature Sustainabilit
Polarization of Climate and Environmental Attitudes in the United States, 1973-2022
No full textReplication data for Polarization of Climate and Environmental Attitudes
in the United States, 1973-202
Accounting for the Complex Hierarchical Topology of EEG Functional Connectivity in Network Binarisation
No full textHerein are the network adjacency matrices, scripts and MATLAB functions used to provide the results in "Accounting for the complex hierarchical topology of EEG functional connectivity in network binarisation". Research into binary network analysis of brain function faces a methodological challenge in selecting an appropriate threshold to binarise edge weights. For EEG, such binarisation should take into account the complex hierarchical structure found in functional connectivity. We explore the density range suitable for such structure and provide a comparison of state-of-the-art binarisation techniques, the recently proposed Cluster-Span Threshold (CST), minimum spanning trees, efficiency-cost optimisation and union of shortest path graphs, with arbitrary proportional thresholds and weighted networks. We test these techniques on weighted complex hierarchy models by contrasting model realisations with small parametric differences. We also test the robustness of these techniques to random and targeted topological attacks. We reveal that complex hierarchical topology requires a medium-density range binarisation solution, such as the CST which proves near maximal for distinguishing differences when compared with arbitrary proportional thresholding. Simulated results are validated with the analysis of three relevant EEG datasets: eyes open and closed resting states; visual short-term memory tasks; and resting state Alzheimer's disease with a healthy control group. The CST consistently outperforms other state-of-the-art binarisation methods for topological accuracy and robustness in both synthetic and real data. We provide insights into how the complex hierarchical structure of functional networks is best revealed in medium density ranges and how it safeguards against targeted attacks.All files are MATLAB files.
EyesOpenClosedData.mat contains the adjacency matrices of data processed as in the manuscript. The data comes from the Neurophysiological Biomarker Toolbox, available from www.nbtwiki.net. SpainADdata.mat contains the adjacency matrices of data processed as in the manuscript. The description of the signals can be found in Escudero J, Abasolo D, Hornero R, Espino P, Lopez M. Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy. Physiological Measurement. 2016;27(11):1091-1106. YHAshapeBindData.mat contains the adjacency matrices of data processed as in the manuscript. The signals are described in Smith K, Azami H, Escudero J, Parra MA, Starr JM. Comparison of network analysis approaches on EEG connectivity in beta during Visual Short-term Memory binding tasks. IEEE Proc. EMBC 2015. 2015;doi:10.1109/EMBC.2015.7318829. They were provided by Mario A. Parra, supported by the Alzheimer's Society, grant # AS-R42303 and MRC grant # MRC-R42552.
Scripts 1-5 provide the m-files for running the network analyses presented in the manuscript:
Script1_runComplexHierarchySimulations.m runs the simulations and computes results for the complex hierarchy models.
Script2_RunNetworkAttacks.m runs the network attack results.
Script3_RunEyesOpenClosedData runs analysis of the EyesOpenClosedData.mat file.
Script4_runVSTMdata runs analysis of the YHAshapeBindData.mat file.
Script5_runADdata runs analysis of the SpainADdata.mat file.
The other files pertain to MATLAB functions used to implement these scripts. Necessary citations can be found inside the function files.
randomHierarchy.m- computes weighted complex hierarchy models.
PhaseLagIndex_Stam.m- computes the phase lag index of MEG/EEG signals.
CST.m- computes the cluster-span threshold of a weighted adjacency matrix.
unionofshortestpaths.m- computes the union of shortest paths of a weighted adjacency matrix.
ECOfilter.m- computes the efficiency-cost optimisation threshold of a weighted adjacency matrix.
leafFraction.m- computes the leaf fraction of a binary minimum spanning tree.
FIRfiltersEOEC.mat- provides the alpha and beta filter coefficients for use in Script 3
[[SUPERSEDED - this dataset is replaced by a later version: https://doi.org/10.7488/ds/2109]] Accounting for the Complex Hierarchical Topology of EEG Functional Connectivity in Network Binarisation
No full text[[SUPERSEDED - this dataset is replaced by a later version: https://doi.org/10.7488/ds/2109]]
Research into binary network analysis of brain function faces a methodological challenge in selecting an appropriate threshold to binarise edge weights. For EEG, such binarisation should take into account the complex hierarchical structure found in functional connectivity. We explore the density range suitable for such structure and provide a comparison of state-of-the-art binarisation techniques, the recently proposed Cluster-Span Threshold (CST), minimum spanning trees and union of shortest path graphs, with arbitrary proportional thresholds and weighted networks. We test these techniques on weighted complex hierarchy models by contrasting model realisations with small parametric differences. We also test the robustness of these techniques to random and targeted topological attacks. We reveal that complex hierarchical topology requires a medium-density range binarisation solution, such as the CST which proves near maximal for distinguishing differences when compared with arbitrary proportional thresholding. Simulated results are validated with the analysis of three relevant EEG datasets: eyes open and closed resting states; visual short-term memory tasks; and resting state Alzheimer's disease with a healthy control group. The CST consistently outperforms other state-of-the-art binarisation methods for topological accuracy and robustness in both synthetic and real data. We provide insights into how the complex hierarchical structure of functional networks is best revealed in medium density ranges and how it safeguards against targeted attacks.
These EEG PLI connnectivity data sets are used in the analysis of our submitted manuscript: https://arxiv.org/abs/1610.06360.EyesOpenClosedData.mat contains the adjacency matrices of data processed as in the manuscript. The data comes from the Neurophysiological Biomarker Toolbox, available from www.nbtwiki.net.
SpainADdata.mat contains the adjacency matrices of data processed as in the manuscript. The description of the signals can be found in Escudero J, Abasolo D, Hornero R, Espino P, Lopez M. Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy. Physiological Measurement. 2016;27(11):1091-1106.
YHAshapeBindData.mat contains the adjacency matrices of data processed as in the manuscript. The signals are described in Smith K, Azami H, Escudero J, Parra MA, Starr JM. Comparison of network analysis approaches on EEG connectivity in beta during Visual Short-term Memory binding tasks. IEEE Proc. EMBC 2015. 2015;doi:10.1109/EMBC.2015.7318829. They were provided by Mario A. Parra, supported by the Alzheimer's Society, grant # AS-R42303 and MRC grant # MRC-R42552
The Complex Hierarchical Topology of EEG Functional Connectivity
No full textUnderstanding the complex hierarchical topology of functional brain networks is a key aspect of functional connectivity research. Such topics are obscured by the widespread use of sparse binary network models which are fundamentally different to the complete weighted networks derived from functional connectivity. We introduce two techniques to probe the hierarchical complexity of topologies. Firstly, a new metric to measure hierarchical complexity; secondly, a Weighted Complex Hierarchy (WCH) model. To thoroughly evaluate our techniques, we generalise sparse binary network archetypes to weighted forms and explore the main topological features of brain networks- integration, regularity and modularity- using curves over density. By controlling the parameters of our model, the highest complexity is found to arise between a random topology and a strict 'class-based' topology. Further, the model has equivalent complexity to EEG phase-lag networks at peak performance. Hierarchical complexity attains greater magnitude and range of differences between different networks than the previous commonly used complexity metric and our WCH model offers a much broader range of network topology than the standard scale-free and small-world models at a full range of densities. Our metric and model provide a rigorous characterisation of hierarchical complexity. Importantly, our framework shows a scale of complexity arising between 'all nodes are equal' topologies at one extreme and 'strict class-based' topologies at the other
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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