Dynamic Network Drivers of Seizure Generation, Propagation and Termination in Human Neocortical Epilepsy
Publication Date
December 17, 2015
Journal
PLOS Computational Biology
Authors
Ankit N. Khambhati, Kathryn A. Davis, Brian S. Oommen, Stephanie H. Chen, et al
Volume
11
Issue
12
Pages
e1004608
DOI
https://dx.plos.org/10.1371/journal.pcbi.1004608
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004608
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/26680762
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4682976
Europe PMC
http://europepmc.org/abstract/MED/26680762
Web of Science
000368521900020
Scopus
84953305036
Mendeley
http://www.mendeley.com/research/dynamic-network-drivers-seizure-generation-propagation-termination-human-neocortical-epilepsy
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Mendeley | Further Information

{"title"=>"Dynamic Network Drivers of Seizure Generation, Propagation and Termination in Human Neocortical Epilepsy", "type"=>"journal", "authors"=>[{"first_name"=>"Ankit N.", "last_name"=>"Khambhati", "scopus_author_id"=>"55355500100"}, {"first_name"=>"Kathryn A.", "last_name"=>"Davis", "scopus_author_id"=>"28767557800"}, {"first_name"=>"Brian S.", "last_name"=>"Oommen", "scopus_author_id"=>"57197650260"}, {"first_name"=>"Stephanie H.", "last_name"=>"Chen", "scopus_author_id"=>"56924559200"}, {"first_name"=>"Timothy H.", "last_name"=>"Lucas", "scopus_author_id"=>"7101667991"}, {"first_name"=>"Brian", "last_name"=>"Litt", "scopus_author_id"=>"7003317462"}, {"first_name"=>"Danielle S.", "last_name"=>"Bassett", "scopus_author_id"=>"15047396100"}], "year"=>2015, "source"=>"PLoS Computational Biology", "identifiers"=>{"sgr"=>"84953305036", "doi"=>"10.1371/journal.pcbi.1004608", "pmid"=>"18056803", "issn"=>"15537358", "arxiv"=>"1407.5105", "scopus"=>"2-s2.0-84953305036", "pui"=>"607623880"}, "id"=>"c94c73fc-3168-3369-b62e-b1642610ecd5", "abstract"=>"The epileptic network is characterized by pathologic, seizure-generating ‘foci’ embedded in a web of structural and functional connections. Clinically, seizure foci are considered optimal targets for surgery. However, poor surgical outcome suggests a complex relationship between foci and the surrounding network that drives seizure dynamics.We developed a novel technique to objectively track seizure states fromdynamic functional networks con- structed from intracranial recordings. Each dynamical state captures unique patterns of net- work connections that indicate synchronized and desynchronized hubs of neural populations. Our approach suggests that seizures are generatedwhen synchronous relationships near foci work in tandemwith rapidly changing desynchronous relationships fromthe surrounding epileptic network. As seizures progress, topographical and geometrical changes in network connectivity strengthen and tighten synchronous connectivity near foci—amechanismthat may aid seizure termination.Collectively, our observations implicate distributed cortical structures in seizure generation, propagation and termination, and may have practical significance in determiningwhich circuits tomodulate with implantable devices.", "link"=>"http://www.mendeley.com/research/dynamic-network-drivers-seizure-generation-propagation-termination-human-neocortical-epilepsy", "reader_count"=>98, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>12, "Researcher"=>23, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>29, "Student > Postgraduate"=>4, "Other"=>4, "Student > Master"=>5, "Student > Bachelor"=>6, "Lecturer"=>3, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>12, "Researcher"=>23, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>29, "Student > Postgraduate"=>4, "Other"=>4, "Student > Master"=>5, "Student > Bachelor"=>6, "Lecturer"=>3, "Professor"=>4}, "reader_count_by_subject_area"=>{"Unspecified"=>10, "Engineering"=>15, "Biochemistry, Genetics and Molecular Biology"=>1, "Mathematics"=>2, "Agricultural and Biological Sciences"=>15, "Medicine and Dentistry"=>21, "Neuroscience"=>22, "Business, Management and Accounting"=>1, "Physics and Astronomy"=>4, "Chemistry"=>1, "Psychology"=>2, "Computer Science"=>4}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>15}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>21}, "Neuroscience"=>{"Neuroscience"=>22}, "Chemistry"=>{"Chemistry"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>4}, "Psychology"=>{"Psychology"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>15}, "Computer Science"=>{"Computer Science"=>4}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>10}}, "reader_count_by_country"=>{"United States"=>3, "Brazil"=>1, "United Kingdom"=>1, "Switzerland"=>2}, "group_count"=>15}

CrossRef

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2610667"], "description"=>"<p>(<b>a</b>) Example clustering of time windows to network states during a single pre-seizure epoch demonstrating rapid network reconfiguration. State assignments are overlaid on ECoG signals. Red traces correspond to seizure onset nodes. (<b>b</b>) Example clustering of time windows to network states during associated seizure epoch (from EEC to Termination) demonstrating slower network reconfiguration. (<b>c</b>) Network flexibility—or average rate of network state transitions—during pre-seizure and seizure epochs. The epileptic network displayed significantly more geometric reconfigurations during pre-seizure epochs than in seizure epochs (<i>N</i> = 88, <i>p</i> = 2.2 × 10<sup>−16</sup>). (<b>d</b>) Size-ordered distribution of total fractional duration of the 6 longest network states from each epoch (<i>PS</i> (<i>S</i>) indicates states of pre-seizure (seizure) epochs). All epochs are normalized to have duration of 1. We retain the first 3 network states of each epoch (<i>PS</i><sub>0</sub>, <i>PS</i><sub>1</sub>, <i>PS</i><sub>2</sub>, <i>S</i><sub>0</sub>, <i>S</i><sub>1</sub>, <i>S</i><sub>2</sub>) for the remaining analysis.</p>", "links"=>[], "tags"=>["network connections", "foci work", "novel technique", "seizures progress", "seizure generation", "drives seizure dynamics", "seizure foci", "intracranial recordings", "seizure termination", "Human Neocortical Epilepsy", "track seizure states", "network connectivity", "desynchronized hubs", "desynchronous relationships", "Dynamic Network Drivers"], "article_id"=>1626224, "categories"=>["Biological Sciences"], "users"=>["Ankit N. Khambhati", "Kathryn A. Davis", "Brian S. Oommen", "Stephanie H. Chen", "Timothy H. Lucas", "Brian Litt", "Danielle S. Bassett"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004608.g003", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distinct_dynamical_states_of_epileptic_networks_/1626224", "title"=>"Distinct dynamical states of epileptic networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-12-18 11:22:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/2610668"], "description"=>"<p>(<b>a</b>) Functional connection density during pre-seizure (<i>PS</i>) and seizure (<i>S</i>) network states. We average connection strengths over all time windows within each network state (<i>N</i> = 80). We found significant increase in connection density from all <i>PS</i> to any <i>S</i> network state, and significantly greater connection density during <i>S</i><sub>2</sub> and <i>S</i><sub>1</sub> compared to <i>S</i><sub>0</sub>. (<b>b</b>) Connection type index indicating strong or weak connection dominance during <i>PS</i> and <i>S</i> network states (<i>N</i> = 80). We found significant change from weak type dominance during <i>PS</i> to quasi weak-strong type dominance during <i>S</i><sub>0</sub> and strong type dominance during <i>S</i><sub>1</sub> and <i>S</i><sub>2</sub>.</p>", "links"=>[], "tags"=>["network connections", "foci work", "novel technique", "seizures progress", "seizure generation", "drives seizure dynamics", "seizure foci", "intracranial recordings", "seizure termination", "Human Neocortical Epilepsy", "track seizure states", "network connectivity", "desynchronized hubs", "desynchronous relationships", "Dynamic Network Drivers"], "article_id"=>1626225, "categories"=>["Biological Sciences"], "users"=>["Ankit N. Khambhati", "Kathryn A. Davis", "Brian S. Oommen", "Stephanie H. Chen", "Timothy H. Lucas", "Brian Litt", "Danielle S. Bassett"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004608.g004", "stats"=>{"downloads"=>2, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Changes_in_global_connectivity_of_epileptic_networks_/1626225", "title"=>"Changes in global connectivity of epileptic networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-12-18 11:21:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2610670"], "description"=>"<p>(<b>a</b>) Example of network geography with preserved 2-D spatial relationships between nodes for categories of strongest and weakest connections in upper and lower 5% of connection strength distribution; Connection colors indicate weak (blue) and strong (red); the clinically-determined seizure onset sensors are shown in red. (<b>b</b>) Connection strength within 3 geographic connection types during <i>PS</i> and <i>S</i> network states (<i>N</i> = 50). During <i>PS</i> and <i>S</i>, we observed significantly stronger connections amongst SOZ-SOZ regions than OUT-OUT and SOZ-OUT regions. During <i>S</i>, we observed significant increase in SOZ-SOZ connections as seizures initiate and progress. (<b>c</b>) ROC AUC compared to 95% bootstrapped confidence intervals using connection strength to predict SOZ-SOZ connections during <i>PS</i> and <i>S</i> network states (<i>N</i> = 50). The synchronized <i>S</i><sub>2</sub> state yielded the best performance, while the desynchronized <i>PS</i><sub>0</sub> state yielded the worst performance.</p>", "links"=>[], "tags"=>["network connections", "foci work", "novel technique", "seizures progress", "seizure generation", "drives seizure dynamics", "seizure foci", "intracranial recordings", "seizure termination", "Human Neocortical Epilepsy", "track seizure states", "network connectivity", "desynchronized hubs", "desynchronous relationships", "Dynamic Network Drivers"], "article_id"=>1626227, "categories"=>["Biological Sciences"], "users"=>["Ankit N. Khambhati", "Kathryn A. Davis", "Brian S. Oommen", "Stephanie H. Chen", "Timothy H. Lucas", "Brian Litt", "Danielle S. Bassett"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004608.g005", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Regional_characteristics_of_network_geography_/1626227", "title"=>"Regional characteristics of network geography.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-12-18 11:22:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/2610671"], "description"=>"<p>(<b>a</b>) Average Spearman’s rank correlation coefficient between connection length and strength in <i>PS</i> and <i>S</i> epochs (<i>N</i> = 75). Physical connection lengths computed from regularly spaced nodes in electrode grid; connection lengths measured in millimeters. Stronger connections are consistently shorter than weaker connections. During <i>S</i>, there is significant topographical reorganization making weak connections longer and strong connections tighter.</p>", "links"=>[], "tags"=>["network connections", "foci work", "novel technique", "seizures progress", "seizure generation", "drives seizure dynamics", "seizure foci", "intracranial recordings", "seizure termination", "Human Neocortical Epilepsy", "track seizure states", "network connectivity", "desynchronized hubs", "desynchronous relationships", "Dynamic Network Drivers"], "article_id"=>1626228, "categories"=>["Biological Sciences"], "users"=>["Ankit N. Khambhati", "Kathryn A. Davis", "Brian S. Oommen", "Stephanie H. Chen", "Timothy H. Lucas", "Brian Litt", "Danielle S. Bassett"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004608.g006", "stats"=>{"downloads"=>2, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Topographical_characteristics_of_network_connectivity_/1626228", "title"=>"Topographical characteristics of network connectivity.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-12-18 11:21:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2610673"], "description"=>"<div><p>The epileptic network is characterized by pathologic, seizure-generating ‘foci’ embedded in a web of structural and functional connections. Clinically, seizure foci are considered optimal targets for surgery. However, poor surgical outcome suggests a complex relationship between foci and the surrounding network that drives seizure dynamics. We developed a novel technique to objectively track seizure states from dynamic functional networks constructed from intracranial recordings. Each dynamical state captures unique patterns of network connections that indicate synchronized and desynchronized hubs of neural populations. Our approach suggests that seizures are generated when synchronous relationships near foci work in tandem with rapidly changing desynchronous relationships from the surrounding epileptic network. As seizures progress, topographical and geometrical changes in network connectivity strengthen and tighten synchronous connectivity near foci—a mechanism that may aid seizure termination. Collectively, our observations implicate distributed cortical structures in seizure generation, propagation and termination, and may have practical significance in determining which circuits to modulate with implantable devices.</p></div>", "links"=>[], "tags"=>["network connections", "foci work", "novel technique", "seizures progress", "seizure generation", "drives seizure dynamics", "seizure foci", "intracranial recordings", "seizure termination", "Human Neocortical Epilepsy", "track seizure states", "network connectivity", "desynchronized hubs", "desynchronous relationships", "Dynamic Network Drivers"], "article_id"=>1626230, "categories"=>["Biological Sciences"], "users"=>["Ankit N. Khambhati", "Kathryn A. Davis", "Brian S. Oommen", "Stephanie H. Chen", "Timothy H. Lucas", "Brian Litt", "Danielle S. Bassett"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004608", "stats"=>{"downloads"=>2, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Dynamic_Network_Drivers_of_Seizure_Generation_Propagation_and_Termination_in_Human_Neocortical_Epilepsy_/1626230", "title"=>"Dynamic Network Drivers of Seizure Generation, Propagation and Termination in Human Neocortical Epilepsy", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-12-18 11:21:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/2610653"], "description"=>"<p>(<b>a</b>) (<i><b>Top</b></i>) We create functional networks based on electrophysiology by windowing ECoG signals collected from patients with drug-resistant neocortical epilepsy implanted with intracranial electrodes into 1s time windows. Each sensor is represented as a network node, and weighted functional connectivity between sensors, interpreted as degree of synchrony, is represented as a network connection. (<i><b>Lower Right</b></i>) Functional connectivity is estimated by a magnitude normalized cross-correlation between sensor time series for each time window. (<i><b>Lower Left</b></i>) We study temporal dynamics of each unique connection in a network configuration matrix. (<b>b</b>) For each epileptic event, we estimate dynamic functional connectivity during the seizure and the pre-seizure epoch. A seizure epoch consists of time windows between seizure onset—as characterized by the earliest electrographic change (EEC) [<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004608#pcbi.1004608.ref024\" target=\"_blank\">24</a>]—and seizure termination. The associated pre-seizure epoch consists of an equal number of time windows as the seizure epoch and occurs immediately prior to the EEC.</p>", "links"=>[], "tags"=>["network connections", "foci work", "novel technique", "seizures progress", "seizure generation", "drives seizure dynamics", "seizure foci", "intracranial recordings", "seizure termination", "Human Neocortical Epilepsy", "track seizure states", "network connectivity", "desynchronized hubs", "desynchronous relationships", "Dynamic Network Drivers"], "article_id"=>1626220, "categories"=>["Biological Sciences"], "users"=>["Ankit N. Khambhati", "Kathryn A. Davis", "Brian S. Oommen", "Stephanie H. Chen", "Timothy H. Lucas", "Brian Litt", "Danielle S. Bassett"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004608.g001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Analysis_pipeline_for_dynamic_epileptic_networks_/1626220", "title"=>"Analysis pipeline for dynamic epileptic networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-12-18 11:21:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/2610654"], "description"=>"<p>(<b>a</b>) We estimate dynamic functional connectivity; colors represent arbitrary connection strengths ranging from strong to weak (red, gray, blue). (<b>b</b>) We track all unique functional connections over time using a configuration matrix, in which each vector represents the set of connection weights for a 1s time window. (<b>c</b>) We compute the similarity between the network geometries of each pair of time windows using a Pearson correlation coefficient. In the resultant configuration-similarity matrix, colors represent the magnitude of similarity and visually identified clusters are distinguished by colored, dashed lines (orange and green). (<b>d</b>) We optimize a modularity quality function to cluster the configuration vectors (and thus time windows) into communities. Each cluster or community contains time windows with similar network geometry; colors represent assignments of time windows to different network configuration communities (orange and green).</p>", "links"=>[], "tags"=>["network connections", "foci work", "novel technique", "seizures progress", "seizure generation", "drives seizure dynamics", "seizure foci", "intracranial recordings", "seizure termination", "Human Neocortical Epilepsy", "track seizure states", "network connectivity", "desynchronized hubs", "desynchronous relationships", "Dynamic Network Drivers"], "article_id"=>1626221, "categories"=>["Biological Sciences"], "users"=>["Ankit N. Khambhati", "Kathryn A. Davis", "Brian S. Oommen", "Stephanie H. Chen", "Timothy H. Lucas", "Brian Litt", "Danielle S. Bassett"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004608.g002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_for_identifying_network_configuration_states_/1626221", "title"=>"Schematic for identifying network configuration states.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-12-18 11:22:00"}

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{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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