Spectral Signatures of Reorganised Brain Networks in Disorders of Consciousness
Publication Date
October 16, 2014
Journal
PLOS Computational Biology
Authors
Srivas Chennu, Paola Finoia, Evelyn Kamau, Judith Allanson, et al
Volume
10
Issue
10
Pages
e1003887
DOI
https://dx.plos.org/10.1371/journal.pcbi.1003887
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1003887
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/25329398
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4199497
Europe PMC
http://europepmc.org/abstract/MED/25329398
Web of Science
000344547900038
Scopus
84908343115
Mendeley
http://www.mendeley.com/research/spectral-signatures-reorganised-brain-networks-disorders-consciousness
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{"title"=>"Spectral Signatures of Reorganised Brain Networks in Disorders of Consciousness", "type"=>"journal", "authors"=>[{"first_name"=>"Srivas", "last_name"=>"Chennu", "scopus_author_id"=>"23975638800"}, {"first_name"=>"Paola", "last_name"=>"Finoia", "scopus_author_id"=>"23091071800"}, {"first_name"=>"Evelyn", "last_name"=>"Kamau", "scopus_author_id"=>"37261390700"}, {"first_name"=>"Judith", "last_name"=>"Allanson", "scopus_author_id"=>"57196970071"}, {"first_name"=>"Guy B.", "last_name"=>"Williams", "scopus_author_id"=>"7406079634"}, {"first_name"=>"Martin M.", "last_name"=>"Monti", "scopus_author_id"=>"18536758300"}, {"first_name"=>"Valdas", "last_name"=>"Noreika", "scopus_author_id"=>"23668497700"}, {"first_name"=>"Aurina", "last_name"=>"Arnatkeviciute", "scopus_author_id"=>"56193785800"}, {"first_name"=>"Andrés", "last_name"=>"Canales-Johnson", "scopus_author_id"=>"50861208900"}, {"first_name"=>"Francisco", "last_name"=>"Olivares", "scopus_author_id"=>"56397890900"}, {"first_name"=>"Daniela", "last_name"=>"Cabezas-Soto", "scopus_author_id"=>"56398433400"}, {"first_name"=>"David K.", "last_name"=>"Menon", "scopus_author_id"=>"7101687199"}, {"first_name"=>"John D.", "last_name"=>"Pickard", "scopus_author_id"=>"7103342237"}, {"first_name"=>"Adrian M.", "last_name"=>"Owen", "scopus_author_id"=>"7202052668"}, {"first_name"=>"Tristan A.", "last_name"=>"Bekinschtein", "scopus_author_id"=>"6506534134"}], "year"=>2014, "source"=>"PLoS Computational Biology", "identifiers"=>{"issn"=>"15537358", "scopus"=>"2-s2.0-84908343115", "pui"=>"600311063", "doi"=>"10.1371/journal.pcbi.1003887", "isbn"=>"10.1371/journal.pcbi.1003887", "sgr"=>"84908343115", "pmid"=>"25329398"}, "id"=>"7d464e07-c855-3e57-ac9a-e5fe6f2574b9", "abstract"=>"Theoretical advances in the science of consciousness have proposed that it is concomitant with balanced cortical integration and differentiation, enabled by efficient networks of information transfer across multiple scales. Here, we apply graph theory to compare key signatures of such networks in high-density electroencephalographic data from 32 patients with chronic disorders of consciousness, against normative data from healthy controls. Based on connectivity within canonical frequency bands, we found that patient networks had reduced local and global efficiency, and fewer hubs in the alpha band. We devised a novel topographical metric, termed modular span, which showed that the alpha network modules in patients were also spatially circumscribed, lacking the structured long-distance interactions commonly observed in the healthy controls. Importantly however, these differences between graph-theoretic metrics were partially reversed in delta and theta band networks, which were also significantly more similar to each other in patients than controls. Going further, we found that metrics of alpha network efficiency also correlated with the degree of behavioural awareness. Intriguingly, some patients in behaviourally unresponsive vegetative states who demonstrated evidence of covert awareness with functional neuroimaging stood out from this trend: they had alpha networks that were remarkably well preserved and similar to those observed in the controls. Taken together, our findings inform current understanding of disorders of consciousness by highlighting the distinctive brain networks that characterise them. In the significant minority of vegetative patients who follow commands in neuroimaging tests, they point to putative network mechanisms that could support cognitive function and consciousness despite profound behavioural impairment.", "link"=>"http://www.mendeley.com/research/spectral-signatures-reorganised-brain-networks-disorders-consciousness", "reader_count"=>242, "reader_count_by_academic_status"=>{"Unspecified"=>5, "Professor > Associate Professor"=>16, "Librarian"=>2, "Researcher"=>44, "Student > Doctoral Student"=>12, "Student > Ph. D. 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Student"=>59, "Student > Postgraduate"=>14, "Other"=>14, "Student > Master"=>45, "Student > Bachelor"=>18, "Lecturer"=>3, "Lecturer > Senior Lecturer"=>2, "Professor"=>8}, "reader_count_by_subject_area"=>{"Unspecified"=>16, "Agricultural and Biological Sciences"=>48, "Arts and Humanities"=>1, "Philosophy"=>2, "Computer Science"=>26, "Earth and Planetary Sciences"=>1, "Engineering"=>12, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>1, "Mathematics"=>3, "Medicine and Dentistry"=>24, "Neuroscience"=>44, "Sports and Recreations"=>1, "Physics and Astronomy"=>7, "Psychology"=>52, "Social Sciences"=>1, "Linguistics"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>24}, "Social Sciences"=>{"Social Sciences"=>1}, "Sports and Recreations"=>{"Sports and Recreations"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>7}, "Psychology"=>{"Psychology"=>52}, "Mathematics"=>{"Mathematics"=>3}, "Unspecified"=>{"Unspecified"=>16}, "Environmental Science"=>{"Environmental Science"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>1}, "Engineering"=>{"Engineering"=>12}, "Neuroscience"=>{"Neuroscience"=>44}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>48}, "Computer Science"=>{"Computer Science"=>26}, "Linguistics"=>{"Linguistics"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Philosophy"=>{"Philosophy"=>2}}, "reader_count_by_country"=>{"United States"=>9, "Uruguay"=>1, "Japan"=>1, "United Kingdom"=>5, "Switzerland"=>2, "Canada"=>2, "Czech Republic"=>2, "Netherlands"=>1, "Austria"=>1, "Luxembourg"=>1, "Finland"=>1, "Poland"=>3, "Denmark"=>1, "Italy"=>1, "France"=>2, "Chile"=>1, "Germany"=>3}, "group_count"=>7}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1719883"], "description"=>"<p>Demographic and assessment details of patients from whom EEG data was analysed.</p>", "links"=>[], "tags"=>["behavioural impairment", "consciousness", "neuroimaging tests", "Reorganised Brain Networks", "network modules", "information transfer", "theta band networks", "Spectral Signatures", "canonical frequency bands", "brain networks", "network efficiency", "behavioural awareness", "graph theory", "novel topographical", "electroencephalographic data", "network mechanisms", "32 patients", "patient networks"], "article_id"=>1206526, "categories"=>["Uncategorised"], "users"=>["Srivas Chennu", "Paola Finoia", "Evelyn Kamau", "Judith Allanson", "Guy B. Williams", "Martin M. Monti", "Valdas Noreika", "Aurina Arnatkeviciute", "Andrés Canales-Johnson", "Francisco Olivares", "Daniela Cabezas-Soto", "David K. Menon", "John D. Pickard", "Adrian M. Owen", "Tristan A. Bekinschtein"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003887.t001", "stats"=>{"downloads"=>2, "page_views"=>21, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Demographic_and_assessment_details_of_patients_from_whom_EEG_data_was_analysed_/1206526", "title"=>"Demographic and assessment details of patients from whom EEG data was analysed.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-10-16 02:48:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/1719874"], "description"=>"<p>Panels A–C plot colour maps of individual NMI between networks of patients and controls. Red triangles encompass NMI values indexing degree of similarity of structure within each group. Within-group NMI between healthy alpha networks was significantly higher than that in patients (Panel D). This pattern was reversed in the delta and theta bands, where network structure was more similar between patients than controls. Magenta boxes in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003887#pcbi-1003887-g004\" target=\"_blank\">Figure 4C</a> encompass between-group NMI in patients and healthy controls, correlated against their CRS-R scores in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003887#pcbi-1003887-g006\" target=\"_blank\">figure 6C</a>.</p>", "links"=>[], "tags"=>["behavioural impairment", "consciousness", "neuroimaging tests", "Reorganised Brain Networks", "network modules", "information transfer", "theta band networks", "Spectral Signatures", "canonical frequency bands", "brain networks", "network efficiency", "behavioural awareness", "graph theory", "novel topographical", "electroencephalographic data", "network mechanisms", "32 patients", "patient networks"], "article_id"=>1206517, "categories"=>["Uncategorised"], "users"=>["Srivas Chennu", "Paola Finoia", "Evelyn Kamau", "Judith Allanson", "Guy B. Williams", "Martin M. Monti", "Valdas Noreika", "Aurina Arnatkeviciute", "Andrés Canales-Johnson", "Francisco Olivares", "Daniela Cabezas-Soto", "David K. Menon", "John D. Pickard", "Adrian M. Owen", "Tristan A. Bekinschtein"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003887.g004", "stats"=>{"downloads"=>2, "page_views"=>23, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Normalised_mutual_information_between_brain_networks_/1206517", "title"=>"Normalised mutual information between brain networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-10-16 02:48:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/1719873"], "description"=>"<p>Panels plot group-wise graph-theoretic metrics averaged over all connection densities considered. Clustering of alpha band networks in controls was significantly higher in controls than patients (Panel A), while characteristic path length was lower (Panel B). There were no significant differences in modularity between patients and controls in any frequency band (Panel C). SD of participation coefficients in control networks in the alpha band was significantly greater than patients, indicating the presence of diversely connected inter-modular hubs (Panel D). Differences in clustering and participation coefficient were markedly reversed in the delta and theta bands. Error bars indicate SE of the mean. All p-values were corrected for multiple comparisons.</p>", "links"=>[], "tags"=>["behavioural impairment", "consciousness", "neuroimaging tests", "Reorganised Brain Networks", "network modules", "information transfer", "theta band networks", "Spectral Signatures", "canonical frequency bands", "brain networks", "network efficiency", "behavioural awareness", "graph theory", "novel topographical", "electroencephalographic data", "network mechanisms", "32 patients", "patient networks"], "article_id"=>1206516, "categories"=>["Uncategorised"], "users"=>["Srivas Chennu", "Paola Finoia", "Evelyn Kamau", "Judith Allanson", "Guy B. Williams", "Martin M. Monti", "Valdas Noreika", "Aurina Arnatkeviciute", "Andrés Canales-Johnson", "Francisco Olivares", "Daniela Cabezas-Soto", "David K. Menon", "John D. Pickard", "Adrian M. Owen", "Tristan A. Bekinschtein"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003887.g003", "stats"=>{"downloads"=>0, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Graph_theoretic_topology_metrics_of_brain_networks_/1206516", "title"=>"Graph-theoretic topology metrics of brain networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-10-16 02:48:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/1719882"], "description"=>"<p>Cross-spectral density between pairs of channels was estimated using the dwPLI measure. Resulting symmetric connectivity matrices were thresholded before the estimation of graph-theoretic metrics. In the connectivity matrix shown (bottom left), the threshold has been set to plot top 30% of strongest connections. In the topograph (bottom middle), modules heuristically identified by the Louvain algorithm are indicated by colour, and inter-modular edges are plotted in black.</p>", "links"=>[], "tags"=>["behavioural impairment", "consciousness", "neuroimaging tests", "Reorganised Brain Networks", "network modules", "information transfer", "theta band networks", "Spectral Signatures", "canonical frequency bands", "brain networks", "network efficiency", "behavioural awareness", "graph theory", "novel topographical", "electroencephalographic data", "network mechanisms", "32 patients", "patient networks"], "article_id"=>1206525, "categories"=>["Uncategorised"], "users"=>["Srivas Chennu", "Paola Finoia", "Evelyn Kamau", "Judith Allanson", "Guy B. Williams", "Martin M. Monti", "Valdas Noreika", "Aurina Arnatkeviciute", "Andrés Canales-Johnson", "Francisco Olivares", "Daniela Cabezas-Soto", "David K. Menon", "John D. Pickard", "Adrian M. Owen", "Tristan A. Bekinschtein"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003887.g007", "stats"=>{"downloads"=>2, "page_views"=>21, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Data_processing_pipeline_for_graph_theoretical_analysis_/1206525", "title"=>"Data processing pipeline for graph-theoretical analysis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-10-16 02:48:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/1719867"], "description"=>"<p>Panels A–C depict mean channel-wise power spectra in controls, MCS and VS patients. Dashed lines indicated the boundaries of the canonical frequency bands: delta, theta, alpha, beta and gamma. Panel D plots the % contribution of power in each band to the total power in each channel, averaged across channels within each group. Panels E and F depict the opposite trends in power contributions from the delta and alpha bands of patients, as functions of their CRS-R scores. Patients P1, P2 and P3 are highlighted for comparison to plots in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003887#pcbi-1003887-g006\" target=\"_blank\">figure 6</a>.</p>", "links"=>[], "tags"=>["behavioural impairment", "consciousness", "neuroimaging tests", "Reorganised Brain Networks", "network modules", "information transfer", "theta band networks", "Spectral Signatures", "canonical frequency bands", "brain networks", "network efficiency", "behavioural awareness", "graph theory", "novel topographical", "electroencephalographic data", "network mechanisms", "32 patients", "patient networks"], "article_id"=>1206510, "categories"=>["Uncategorised"], "users"=>["Srivas Chennu", "Paola Finoia", "Evelyn Kamau", "Judith Allanson", "Guy B. Williams", "Martin M. Monti", "Valdas Noreika", "Aurina Arnatkeviciute", "Andrés Canales-Johnson", "Francisco Olivares", "Daniela Cabezas-Soto", "David K. Menon", "John D. Pickard", "Adrian M. Owen", "Tristan A. Bekinschtein"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003887.g001", "stats"=>{"downloads"=>0, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Band_wise_power_contributions_in_healthy_controls_and_patients_/1206510", "title"=>"Band-wise power contributions in healthy controls and patients.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-10-16 02:48:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/1719881"], "description"=>"<p>Panels A–D plot correlations between graph-theoretic metrics of alpha networks and behavioural CRS-R scores of individual patients. Red and blue circles indicate VS and MCS patients respectively. Filled circles indicate patients who followed command with fMRI tennis imagery. Robust linear regressions indicated by solid lines included all patients, whereas those indicated by dashed lines only included MCS patients. All metrics improved alongside progressive increase in CRS-R scores of MCS patients. Panels E, F and G plot alpha band networks of representative VS patients P1, P2 and P3, respectively. All 3 patients had the same CRS-R score, but only P3 showed evidence of command following. Compared to P1 and P2 (panels E and F), P3 also had remarkably well-preserved alpha network structure (panel G). Highlighted circles in panels A–D demonstrate that graph-theoretic metrics of P3's alpha network were exceptional outliers amongst the patient group, much more so than P3's delta/alpha power (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003887#pcbi-1003887-g001\" target=\"_blank\">figures 1E and F</a>).</p>", "links"=>[], "tags"=>["behavioural impairment", "consciousness", "neuroimaging tests", "Reorganised Brain Networks", "network modules", "information transfer", "theta band networks", "Spectral Signatures", "canonical frequency bands", "brain networks", "network efficiency", "behavioural awareness", "graph theory", "novel topographical", "electroencephalographic data", "network mechanisms", "32 patients", "patient networks"], "article_id"=>1206524, "categories"=>["Uncategorised"], "users"=>["Srivas Chennu", "Paola Finoia", "Evelyn Kamau", "Judith Allanson", "Guy B. Williams", "Martin M. Monti", "Valdas Noreika", "Aurina Arnatkeviciute", "Andrés Canales-Johnson", "Francisco Olivares", "Daniela Cabezas-Soto", "David K. Menon", "John D. Pickard", "Adrian M. Owen", "Tristan A. Bekinschtein"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003887.g006", "stats"=>{"downloads"=>3, "page_views"=>75, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Graph_theoretic_metrics_as_predictors_of_CRS_R_scores_/1206524", "title"=>"Graph-theoretic metrics as predictors of CRS-R scores.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-10-16 02:48:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/1719876"], "description"=>"<p>Panels A–C plot group-wise averaged dwPLI values as a function of the Euclidean distance between pairs of EEG channels. Inset in Panel C plots a histogram of these inter-channel distances. Controls had stronger long-range connectivity in the alpha band, whereas patients had stronger local connectivity. Topographical distances spanned by alpha network modules, as measured by modular span, were significantly greater in controls (panel D). No differences were observed in modular span in other frequency bands.</p>", "links"=>[], "tags"=>["behavioural impairment", "consciousness", "neuroimaging tests", "Reorganised Brain Networks", "network modules", "information transfer", "theta band networks", "Spectral Signatures", "canonical frequency bands", "brain networks", "network efficiency", "behavioural awareness", "graph theory", "novel topographical", "electroencephalographic data", "network mechanisms", "32 patients", "patient networks"], "article_id"=>1206519, "categories"=>["Uncategorised"], "users"=>["Srivas Chennu", "Paola Finoia", "Evelyn Kamau", "Judith Allanson", "Guy B. Williams", "Martin M. Monti", "Valdas Noreika", "Aurina Arnatkeviciute", "Andrés Canales-Johnson", "Francisco Olivares", "Daniela Cabezas-Soto", "David K. Menon", "John D. Pickard", "Adrian M. Owen", "Tristan A. Bekinschtein"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003887.g005", "stats"=>{"downloads"=>0, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Topographical_embedding_of_brain_networks_using_modular_span_/1206519", "title"=>"Topographical embedding of brain networks using modular span.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-10-16 02:48:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/1719900", "https://ndownloader.figshare.com/files/1719901", "https://ndownloader.figshare.com/files/1719902", "https://ndownloader.figshare.com/files/1719903"], "description"=>"<div><p>Theoretical advances in the science of consciousness have proposed that it is concomitant with balanced cortical integration and differentiation, enabled by efficient networks of information transfer across multiple scales. Here, we apply graph theory to compare key signatures of such networks in high-density electroencephalographic data from 32 patients with chronic disorders of consciousness, against normative data from healthy controls. Based on connectivity within canonical frequency bands, we found that patient networks had reduced local and global efficiency, and fewer hubs in the alpha band. We devised a novel topographical metric, termed modular span, which showed that the alpha network modules in patients were also spatially circumscribed, lacking the structured long-distance interactions commonly observed in the healthy controls. Importantly however, these differences between graph-theoretic metrics were partially reversed in delta and theta band networks, which were also significantly more similar to each other in patients than controls. Going further, we found that metrics of alpha network efficiency also correlated with the degree of behavioural awareness. Intriguingly, some patients in behaviourally unresponsive vegetative states who demonstrated evidence of covert awareness with functional neuroimaging stood out from this trend: they had alpha networks that were remarkably well preserved and similar to those observed in the controls. Taken together, our findings inform current understanding of disorders of consciousness by highlighting the distinctive brain networks that characterise them. In the significant minority of vegetative patients who follow commands in neuroimaging tests, they point to putative network mechanisms that could support cognitive function and consciousness despite profound behavioural impairment.</p></div>", "links"=>[], "tags"=>["behavioural impairment", "consciousness", "neuroimaging tests", "Reorganised Brain Networks", "network modules", "information transfer", "theta band networks", "Spectral Signatures", "canonical frequency bands", "brain networks", "network efficiency", "behavioural awareness", "graph theory", "novel topographical", "electroencephalographic data", "network mechanisms", "32 patients", "patient networks"], "article_id"=>1206529, "categories"=>["Uncategorised"], "users"=>["Srivas Chennu", "Paola Finoia", "Evelyn Kamau", "Judith Allanson", "Guy B. Williams", "Martin M. Monti", "Valdas Noreika", "Aurina Arnatkeviciute", "Andrés Canales-Johnson", "Francisco Olivares", "Daniela Cabezas-Soto", "David K. Menon", "John D. Pickard", "Adrian M. Owen", "Tristan A. Bekinschtein"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003887.s001", "https://dx.doi.org/10.1371/journal.pcbi.1003887.s002", "https://dx.doi.org/10.1371/journal.pcbi.1003887.s003", "https://dx.doi.org/10.1371/journal.pcbi.1003887.s004"], "stats"=>{"downloads"=>3, "page_views"=>25, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spectral_Signatures_of_Reorganised_Brain_Networks_in_Disorders_of_Consciousness_/1206529", "title"=>"Spectral Signatures of Reorganised Brain Networks in Disorders of Consciousness", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-10-16 02:48:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/1719871"], "description"=>"<p>Panels depict weighted connectivity networks averaged by group, for frequency bands of interest. In each network, the size of a node is proportional to its degree, and the thickness of an edge to its dwPLI weight. Modules identified by the Louvain algorithm are indicated by colour. For visual clarity, of the strongest 30% of edges, only the intra-modular edges are plotted. In the alpha band (panel C), healthy networks were characterised by a predominance of long-range frontoparietal modules. Patient alpha networks consisted of weaker, spatially localised modules. In contrast, patients had stronger structured connectivity within broadly synchronised modules in the delta and theta bands (panels A and B).</p>", "links"=>[], "tags"=>["behavioural impairment", "consciousness", "neuroimaging tests", "Reorganised Brain Networks", "network modules", "information transfer", "theta band networks", "Spectral Signatures", "canonical frequency bands", "brain networks", "network efficiency", "behavioural awareness", "graph theory", "novel topographical", "electroencephalographic data", "network mechanisms", "32 patients", "patient networks"], "article_id"=>1206514, "categories"=>["Uncategorised"], "users"=>["Srivas Chennu", "Paola Finoia", "Evelyn Kamau", "Judith Allanson", "Guy B. Williams", "Martin M. Monti", "Valdas Noreika", "Aurina Arnatkeviciute", "Andrés Canales-Johnson", "Francisco Olivares", "Daniela Cabezas-Soto", "David K. Menon", "John D. Pickard", "Adrian M. Owen", "Tristan A. Bekinschtein"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003887.g002", "stats"=>{"downloads"=>0, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Band_wise_connectivity_networks_in_healthy_controls_and_patients_/1206514", "title"=>"Band-wise connectivity networks in healthy controls and patients.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-10-16 02:48:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/1719884"], "description"=>"<p>CRS-R subscores of patients.</p>", "links"=>[], "tags"=>["behavioural impairment", "consciousness", "neuroimaging tests", "Reorganised Brain Networks", "network modules", "information transfer", "theta band networks", "Spectral Signatures", "canonical frequency bands", "brain networks", "network efficiency", "behavioural awareness", "graph theory", "novel topographical", "electroencephalographic data", "network mechanisms", "32 patients", "patient networks"], "article_id"=>1206527, "categories"=>["Uncategorised"], "users"=>["Srivas Chennu", "Paola Finoia", "Evelyn Kamau", "Judith Allanson", "Guy B. Williams", "Martin M. Monti", "Valdas Noreika", "Aurina Arnatkeviciute", "Andrés Canales-Johnson", "Francisco Olivares", "Daniela Cabezas-Soto", "David K. Menon", "John D. Pickard", "Adrian M. Owen", "Tristan A. Bekinschtein"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003887.t002", "stats"=>{"downloads"=>4, "page_views"=>26, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_CRS_R_subscores_of_patients_/1206527", "title"=>"CRS-R subscores of patients.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-10-16 02:48:51"}

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