Large-Scale, High-Resolution Multielectrode-Array Recording Depicts Functional Network Differences of Cortical and Hippocampal Cultures
Events
Loading … Spinner

Mendeley | Further Information

{"title"=>"Large-scale, high-resolution multielectrode-array recording depicts functional network differences of cortical and hippocampal cultures", "type"=>"journal", "authors"=>[{"first_name"=>"Shinya", "last_name"=>"Ito", "scopus_author_id"=>"55232547900"}, {"first_name"=>"Fang Chin", "last_name"=>"Yeh", "scopus_author_id"=>"36118374600"}, {"first_name"=>"Emma", "last_name"=>"Hiolski", "scopus_author_id"=>"55203653500"}, {"first_name"=>"Przemyslaw", "last_name"=>"Rydygier", "scopus_author_id"=>"35224039700"}, {"first_name"=>"Deborah E.", "last_name"=>"Gunning", "scopus_author_id"=>"8633178900"}, {"first_name"=>"Pawel", "last_name"=>"Hottowy", "scopus_author_id"=>"6507358095"}, {"first_name"=>"Nicholas", "last_name"=>"Timme", "scopus_author_id"=>"26655160200"}, {"first_name"=>"Alan M.", "last_name"=>"Litke", "scopus_author_id"=>"7004529719"}, {"first_name"=>"John M.", "last_name"=>"Beggs", "scopus_author_id"=>"8067817100"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84921712967", "pui"=>"373772084", "doi"=>"10.1371/journal.pone.0105324", "isbn"=>"1932-6203", "sgr"=>"84921712967", "pmid"=>"25126851"}, "id"=>"4f7ad1e8-7288-3c5a-b66a-7eca13b89028", "abstract"=>"Understanding the detailed circuitry of functioning neuronal networks is one of the major goals of neuroscience. Recent improvements in neuronal recording techniques have made it possible to record the spiking activity from hundreds of neurons simultaneously with sub-millisecond temporal resolution. Here we used a 512-channel multielectrode array system to record the activity from hundreds of neurons in organotypic cultures of cortico-hippocampal brain slices from mice. To probe the network structure, we employed a wavelet transform of the cross-correlogram to categorize the functional connectivity in different frequency ranges. With this method we directly compare, for the first time, in any preparation, the neuronal network structures of cortex and hippocampus, on the scale of hundreds of neurons, with sub-millisecond time resolution. Among the three frequency ranges that we investigated, the lower two frequency ranges (gamma (30-80 Hz) and beta (12-30 Hz) range) showed similar network structure between cortex and hippocampus, but there were many significant differences between these structures in the high frequency range (100-1000 Hz). The high frequency networks in cortex showed short tailed degree-distributions, shorter decay length of connectivity density, smaller clustering coefficients, and positive assortativity. Our results suggest that our method can characterize frequency dependent differences of network architecture from different brain regions. Crucially, because these differences between brain regions require millisecond temporal scales to be observed and characterized, these results underscore the importance of high temporal resolution recordings for the understanding of functional networks in neuronal systems.", "link"=>"http://www.mendeley.com/research/largescale-highresolution-multielectrodearray-recording-depicts-functional-network-differences-corti", "reader_count"=>64, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Student > Doctoral Student"=>3, "Researcher"=>13, "Student > Ph. D. Student"=>19, "Student > Postgraduate"=>5, "Student > Master"=>5, "Other"=>2, "Student > Bachelor"=>6, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Student > Doctoral Student"=>3, "Researcher"=>13, "Student > Ph. D. Student"=>19, "Student > Postgraduate"=>5, "Student > Master"=>5, "Other"=>2, "Student > Bachelor"=>6, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>4}, "reader_count_by_subject_area"=>{"Engineering"=>10, "Unspecified"=>2, "Mathematics"=>2, "Agricultural and Biological Sciences"=>22, "Medicine and Dentistry"=>1, "Neuroscience"=>14, "Physics and Astronomy"=>10, "Chemistry"=>1, "Computer Science"=>2}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>10}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Neuroscience"=>{"Neuroscience"=>14}, "Chemistry"=>{"Chemistry"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>10}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>22}, "Computer Science"=>{"Computer Science"=>2}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>2}}, "reader_count_by_country"=>{"Netherlands"=>1, "Japan"=>3, "Germany"=>3, "Spain"=>2}, "group_count"=>3}

Scopus | Further Information

{"@_fa"=>"true", "link"=>[{"@_fa"=>"true", "@ref"=>"self", "@href"=>"https://api.elsevier.com/content/abstract/scopus_id/84921712967"}, {"@_fa"=>"true", "@ref"=>"author-affiliation", "@href"=>"https://api.elsevier.com/content/abstract/scopus_id/84921712967?field=author,affiliation"}, {"@_fa"=>"true", "@ref"=>"scopus", "@href"=>"https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84921712967&origin=inward"}, {"@_fa"=>"true", "@ref"=>"scopus-citedby", "@href"=>"https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=84921712967&origin=inward"}], "prism:url"=>"https://api.elsevier.com/content/abstract/scopus_id/84921712967", "dc:identifier"=>"SCOPUS_ID:84921712967", "eid"=>"2-s2.0-84921712967", "dc:title"=>"Large-scale, high-resolution multielectrode-array recording depicts functional network differences of cortical and hippocampal cultures", "dc:creator"=>"Ito S.", "prism:publicationName"=>"PLoS ONE", "prism:eIssn"=>"19326203", "prism:volume"=>"9", "prism:issueIdentifier"=>"8", "prism:pageRange"=>nil, "prism:coverDate"=>"2014-08-15", "prism:coverDisplayDate"=>"15 August 2014", "prism:doi"=>"10.1371/journal.pone.0105324", "citedby-count"=>"14", "affiliation"=>[{"@_fa"=>"true", "affilname"=>"Santa Cruz Institute for Particle Physics", "affiliation-city"=>"Santa Cruz", "affiliation-country"=>"United States"}, {"@_fa"=>"true", "affilname"=>"Indiana University", "affiliation-city"=>"Bloomington", "affiliation-country"=>"United States"}], "pubmed-id"=>"25126851", "prism:aggregationType"=>"Journal", "subtype"=>"ar", "subtypeDescription"=>"Article", "article-number"=>"e105324", "source-id"=>"10600153309", "openaccess"=>"1", "openaccessFlag"=>true}

Facebook

  • {"url"=>"http%3A%2F%2Fjournals.plos.org%2Fplosone%2Farticle%3Fid%3D10.1371%252Fjournal.pone.0105324", "share_count"=>0, "like_count"=>0, "comment_count"=>0, "click_count"=>0, "total_count"=>0}

Counter

  • {"month"=>"8", "year"=>"2014", "pdf_views"=>"66", "xml_views"=>"15", "html_views"=>"363"}
  • {"month"=>"9", "year"=>"2014", "pdf_views"=>"37", "xml_views"=>"1", "html_views"=>"131"}
  • {"month"=>"10", "year"=>"2014", "pdf_views"=>"16", "xml_views"=>"2", "html_views"=>"71"}
  • {"month"=>"11", "year"=>"2014", "pdf_views"=>"15", "xml_views"=>"1", "html_views"=>"81"}
  • {"month"=>"12", "year"=>"2014", "pdf_views"=>"34", "xml_views"=>"2", "html_views"=>"56"}
  • {"month"=>"1", "year"=>"2015", "pdf_views"=>"16", "xml_views"=>"0", "html_views"=>"52"}
  • {"month"=>"2", "year"=>"2015", "pdf_views"=>"10", "xml_views"=>"0", "html_views"=>"45"}
  • {"month"=>"3", "year"=>"2015", "pdf_views"=>"9", "xml_views"=>"0", "html_views"=>"42"}
  • {"month"=>"4", "year"=>"2015", "pdf_views"=>"11", "xml_views"=>"1", "html_views"=>"28"}
  • {"month"=>"5", "year"=>"2015", "pdf_views"=>"11", "xml_views"=>"0", "html_views"=>"36"}
  • {"month"=>"6", "year"=>"2015", "pdf_views"=>"11", "xml_views"=>"1", "html_views"=>"201"}
  • {"month"=>"7", "year"=>"2015", "pdf_views"=>"6", "xml_views"=>"0", "html_views"=>"56"}
  • {"month"=>"8", "year"=>"2015", "pdf_views"=>"10", "xml_views"=>"1", "html_views"=>"47"}
  • {"month"=>"9", "year"=>"2015", "pdf_views"=>"14", "xml_views"=>"1", "html_views"=>"33"}
  • {"month"=>"10", "year"=>"2015", "pdf_views"=>"18", "xml_views"=>"1", "html_views"=>"35"}
  • {"month"=>"11", "year"=>"2015", "pdf_views"=>"26", "xml_views"=>"0", "html_views"=>"41"}
  • {"month"=>"12", "year"=>"2015", "pdf_views"=>"7", "xml_views"=>"0", "html_views"=>"40"}
  • {"month"=>"1", "year"=>"2016", "pdf_views"=>"12", "xml_views"=>"0", "html_views"=>"41"}
  • {"month"=>"2", "year"=>"2016", "pdf_views"=>"12", "xml_views"=>"0", "html_views"=>"32"}
  • {"month"=>"3", "year"=>"2016", "pdf_views"=>"4", "xml_views"=>"0", "html_views"=>"27"}
  • {"month"=>"4", "year"=>"2016", "pdf_views"=>"10", "xml_views"=>"0", "html_views"=>"48"}
  • {"month"=>"5", "year"=>"2016", "pdf_views"=>"12", "xml_views"=>"0", "html_views"=>"22"}
  • {"month"=>"6", "year"=>"2016", "pdf_views"=>"33", "xml_views"=>"0", "html_views"=>"58"}
  • {"month"=>"7", "year"=>"2016", "pdf_views"=>"6", "xml_views"=>"0", "html_views"=>"29"}
  • {"month"=>"8", "year"=>"2016", "pdf_views"=>"10", "xml_views"=>"0", "html_views"=>"28"}
  • {"month"=>"9", "year"=>"2016", "pdf_views"=>"13", "xml_views"=>"0", "html_views"=>"37"}
  • {"month"=>"10", "year"=>"2016", "pdf_views"=>"9", "xml_views"=>"0", "html_views"=>"28"}
  • {"month"=>"11", "year"=>"2016", "pdf_views"=>"13", "xml_views"=>"0", "html_views"=>"28"}
  • {"month"=>"12", "year"=>"2016", "pdf_views"=>"15", "xml_views"=>"1", "html_views"=>"35"}
  • {"month"=>"1", "year"=>"2017", "pdf_views"=>"14", "xml_views"=>"1", "html_views"=>"28"}
  • {"month"=>"2", "year"=>"2017", "pdf_views"=>"46", "xml_views"=>"0", "html_views"=>"34"}
  • {"month"=>"3", "year"=>"2017", "pdf_views"=>"91", "xml_views"=>"0", "html_views"=>"36"}
  • {"month"=>"4", "year"=>"2017", "pdf_views"=>"85", "xml_views"=>"1", "html_views"=>"41"}
  • {"month"=>"5", "year"=>"2017", "pdf_views"=>"15", "xml_views"=>"0", "html_views"=>"34"}
  • {"month"=>"6", "year"=>"2017", "pdf_views"=>"12", "xml_views"=>"0", "html_views"=>"38"}
  • {"month"=>"7", "year"=>"2017", "pdf_views"=>"16", "xml_views"=>"2", "html_views"=>"25"}
  • {"month"=>"8", "year"=>"2017", "pdf_views"=>"15", "xml_views"=>"1", "html_views"=>"23"}
  • {"month"=>"9", "year"=>"2017", "pdf_views"=>"36", "xml_views"=>"1", "html_views"=>"30"}
  • {"month"=>"10", "year"=>"2017", "pdf_views"=>"20", "xml_views"=>"2", "html_views"=>"49"}
  • {"month"=>"11", "year"=>"2017", "pdf_views"=>"15", "xml_views"=>"0", "html_views"=>"71"}
  • {"month"=>"12", "year"=>"2017", "pdf_views"=>"25", "xml_views"=>"3", "html_views"=>"41"}
  • {"month"=>"1", "year"=>"2018", "pdf_views"=>"18", "xml_views"=>"0", "html_views"=>"33"}
  • {"month"=>"2", "year"=>"2018", "pdf_views"=>"34", "xml_views"=>"1", "html_views"=>"21"}
  • {"month"=>"3", "year"=>"2018", "pdf_views"=>"27", "xml_views"=>"2", "html_views"=>"14"}
  • {"month"=>"4", "year"=>"2018", "pdf_views"=>"40", "xml_views"=>"0", "html_views"=>"28"}
  • {"month"=>"5", "year"=>"2018", "pdf_views"=>"44", "xml_views"=>"1", "html_views"=>"26"}
  • {"month"=>"6", "year"=>"2018", "pdf_views"=>"14", "xml_views"=>"1", "html_views"=>"28"}
  • {"month"=>"7", "year"=>"2018", "pdf_views"=>"1", "xml_views"=>"3", "html_views"=>"14"}
  • {"month"=>"8", "year"=>"2018", "pdf_views"=>"17", "xml_views"=>"1", "html_views"=>"29"}
  • {"month"=>"9", "year"=>"2018", "pdf_views"=>"11", "xml_views"=>"0", "html_views"=>"29"}
  • {"month"=>"10", "year"=>"2018", "pdf_views"=>"13", "xml_views"=>"1", "html_views"=>"25"}
  • {"month"=>"11", "year"=>"2018", "pdf_views"=>"11", "xml_views"=>"0", "html_views"=>"24"}
  • {"month"=>"12", "year"=>"2018", "pdf_views"=>"9", "xml_views"=>"0", "html_views"=>"17"}
  • {"month"=>"1", "year"=>"2019", "pdf_views"=>"5", "xml_views"=>"1", "html_views"=>"19"}
  • {"month"=>"2", "year"=>"2019", "pdf_views"=>"14", "xml_views"=>"0", "html_views"=>"22"}
  • {"month"=>"3", "year"=>"2019", "pdf_views"=>"14", "xml_views"=>"0", "html_views"=>"22"}
  • {"month"=>"4", "year"=>"2019", "pdf_views"=>"11", "xml_views"=>"0", "html_views"=>"24"}
  • {"month"=>"5", "year"=>"2019", "pdf_views"=>"17", "xml_views"=>"0", "html_views"=>"20"}

Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1638076"], "description"=>"<p><i>k</i> is the degree, <i>N</i> is the size of the network (the number of neurons; <i>N</i> = 100, in our networks). <i>p</i> is the connectivity density (<i>p</i> = 0.01 for <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105324#pone-0105324-g007\" target=\"_blank\">Figure 7</a>).</p>", "links"=>[], "tags"=>["brain regions", "hz", "network structure", "hundred", "frequency ranges", "neuron", "cortex"], "article_id"=>1140928, "categories"=>["Biological Sciences"], "users"=>["Shinya Ito", "Fang-Chin Yeh", "Emma Hiolski", "Przemyslaw Rydygier", "Deborah E. Gunning", "Pawel Hottowy", "Nicholas Timme", "Alan M. Litke", "John M. Beggs"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0105324.t003", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Probability_density_functions_for_the_model_degree_distributions_/1140928", "title"=>"Probability density functions for the model degree distributions.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-08-15 03:46:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/1638074"], "description"=>"<p>A: Various network measures were determined for networks in different frequency ranges as a function of connectivity density. Error bars indicate SEM of 22 and 16 data sets in cortex and hippocampus, respectively. <i>Number of disconnected neurons:</i> These are the number of neurons with zero input and output degrees, in subsampled networks of 100 neurons. Both in cortex and hippocampus, high-frequency networks had a smaller number of disconnected neurons. The high-frequency cortical networks had a significantly smaller number than the hippocampal networks. The number of disconnected nodes may affect other network measures through changing the effective size of the networks (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105324#s3\" target=\"_blank\">Results</a>). <i>Global clustering coefficient:</i> All the frequency ranges in hippocampus showed similar values. Note that the high-frequency cortical networks had lower values than all the other networks. <i>Network efficiency:</i> There were no differences with p<0.01 significance in this measure (see B). The values grew monotonically with connectivity density. <i>Assortativity:</i> Hippocampal networks showed slightly negative values in all the frequency ranges. The low frequency networks showed lower assortativity. In cortex, the values were significantly different in each frequency range. The high-frequency networks showed positive assortativity unlike all the other networks. B: Results of significance tests (two tailed Student’s t-test) of network measures at 1% connectivity density. The tests were done among different scales in the same structure, and different structures at the same scales (black lines). One star represents p<0.05 significance, and two stars represent p<0.01 significance. Note that significant differences were observed in different scales in cortex, and between cortex and hippocampus especially at the high frequency range, but fewer significant differences were observed between the different scales in hippocampus.</p>", "links"=>[], "tags"=>["brain regions", "hz", "network structure", "hundred", "frequency ranges", "neuron", "cortex"], "article_id"=>1140926, "categories"=>["Biological Sciences"], "users"=>["Shinya Ito", "Fang-Chin Yeh", "Emma Hiolski", "Przemyslaw Rydygier", "Deborah E. Gunning", "Pawel Hottowy", "Nicholas Timme", "Alan M. Litke", "John M. Beggs"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0105324.g008", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Graph_theoretic_measures_in_3_different_frequency_ranges_/1140926", "title"=>"Graph theoretic measures in 3 different frequency ranges.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-15 03:46:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/1638071"], "description"=>"<p>Connectivity maps of one of the tissues from cortex (A–C) and hippocampus (D–F) at three different frequency ranges: high frequency (A, D), gamma (B, E), and beta (C, F). The locations of the red circles indicate the estimated locations of the neurons. The size of a circle indicates the number of functional connections (degree) of the associated neuron with other neurons. Lines with a color gradient from blue to red indicate directed (delayed) connectivity that goes from the blue end to the red end; solid cyan lines indicate non-directed (non-delayed) connectivity. The threshold value for delayed connections was set to 1/4 of the Fourier period of the wavelet function (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105324#s2\" target=\"_blank\">Materials and Method</a>). Larger numbers of directed connections were observed in high frequency networks (A, D). One can see features such as the absence of high degree nodes and a shorter connectivity range in the high frequency cortex network (A), which are better quantified later.</p>", "links"=>[], "tags"=>["brain regions", "hz", "network structure", "hundred", "frequency ranges", "neuron", "cortex"], "article_id"=>1140923, "categories"=>["Biological Sciences"], "users"=>["Shinya Ito", "Fang-Chin Yeh", "Emma Hiolski", "Przemyslaw Rydygier", "Deborah E. Gunning", "Pawel Hottowy", "Nicholas Timme", "Alan M. Litke", "John M. Beggs"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0105324.g005", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_connectivity_maps_/1140923", "title"=>"Example connectivity maps.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-15 03:46:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/1638070"], "description"=>"<p>A: An example raster plot from one of the hippocampal tissues. There were events during which many of the neurons synchronously raise their firing rates (Red arrows, so called ‘network bursts’. See ‘Electrophysiological properties of the cultures’). These events typically lasted for seconds, and were observed in all of the hippocampal and cortical recordings. B: A representative extracellular spike waveform recorded with our system. The full width at half max was ∼0.3 ms. C: The firing rate distribution of the tissue in A.</p>", "links"=>[], "tags"=>["brain regions", "hz", "network structure", "hundred", "frequency ranges", "neuron", "cortex"], "article_id"=>1140922, "categories"=>["Biological Sciences"], "users"=>["Shinya Ito", "Fang-Chin Yeh", "Emma Hiolski", "Przemyslaw Rydygier", "Deborah E. Gunning", "Pawel Hottowy", "Nicholas Timme", "Alan M. Litke", "John M. Beggs"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0105324.g004", "stats"=>{"downloads"=>2, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Raster_plot_spike_waveform_and_firing_rate_distribution_of_a_representative_hippocampal_recording_/1140922", "title"=>"Raster plot, spike waveform, and firing rate distribution of a representative hippocampal recording.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-15 03:46:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/1638078"], "description"=>"<p>Parameters for wavelet transform.</p>", "links"=>[], "tags"=>["brain regions", "hz", "network structure", "hundred", "frequency ranges", "neuron", "cortex"], "article_id"=>1140930, "categories"=>["Biological Sciences"], "users"=>["Shinya Ito", "Fang-Chin Yeh", "Emma Hiolski", "Przemyslaw Rydygier", "Deborah E. Gunning", "Pawel Hottowy", "Nicholas Timme", "Alan M. Litke", "John M. Beggs"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0105324.t001", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Parameters_for_wavelet_transform_/1140930", "title"=>"Parameters for wavelet transform.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-08-15 03:46:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/1638073"], "description"=>"<p>Average degree distribution of all frequency ranges for networks set at 1% connectivity density. The thin lines are the degree distributions of simulated model networks (scale-free, exponential, and random networks). The degree distribution of the scale-free network was achieved by randomly subsampling 100 nodes from a 1000 neuron scale-free network. Because of the subsampling effect, the result from the scale-free network does not appear as a straight line in this log-log plot (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105324#s2\" target=\"_blank\">Materials and Methods</a>). Error bars indicate the SEM of 25 and 22 data sets in hippocampus and cortex, respectively. A: Degree distributions of cortical networks. Gamma and beta networks had longer tails than a scale-free network, but the high-frequency network had a shorter tail than the other two. B: Degree distribution of hippocampal networks. All the frequency ranges had similar degree distributions, which had longer tails than a scale-free network.</p>", "links"=>[], "tags"=>["brain regions", "hz", "network structure", "hundred", "frequency ranges", "neuron", "cortex"], "article_id"=>1140925, "categories"=>["Biological Sciences"], "users"=>["Shinya Ito", "Fang-Chin Yeh", "Emma Hiolski", "Przemyslaw Rydygier", "Deborah E. Gunning", "Pawel Hottowy", "Nicholas Timme", "Alan M. Litke", "John M. Beggs"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0105324.g007", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Degree_distribution_/1140925", "title"=>"Degree distribution.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-15 03:46:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/1638072"], "description"=>"<p>A: Connectivity density as a function of distance. The distance was binned in 50 µm bins, and the connectivity density was evaluated at each distance. Error bars are SEM of all the cultures. Different colors represented different frequency ranges. Solid lines are exponential function fits to the data (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105324#s2\" target=\"_blank\">Materials and Methods</a>). In the legend, the decay lengths of the function fits are displayed. Inset is the same figure plotted in semi-log space. Exponential functions fit the data nicely, and give us the decay length λ. Consistent with other network properties, we observed differences in different frequency ranges in cortex, but not in hippocampus. B: Results of significance tests for the decay length λ. The tests were done among different scales in the same structure, and different structures at the same scales (black lines). The significance was evaluated from the standard deviations of λ, assuming a normal distribution. One star signifies p<0.05 significance; two stars are p<0.01 significance. While we saw no significant differences in hippocampus, the high frequency range in cortex was significantly different from the other two frequencies.</p>", "links"=>[], "tags"=>["brain regions", "hz", "network structure", "hundred", "frequency ranges", "neuron", "cortex"], "article_id"=>1140924, "categories"=>["Biological Sciences"], "users"=>["Shinya Ito", "Fang-Chin Yeh", "Emma Hiolski", "Przemyslaw Rydygier", "Deborah E. Gunning", "Pawel Hottowy", "Nicholas Timme", "Alan M. Litke", "John M. Beggs"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0105324.g006", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Decay_of_connectivity_density_over_distance_/1140924", "title"=>"Decay of connectivity density over distance.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-15 03:46:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/1638069"], "description"=>"<p>A–C: Six example cross-correlations and their wavelet transforms from each frequency range. Top of each panel is the cross-correlations of neurons. All these types of structures in the cross-correlation plots are observed both in cortex and hippocampus. The wavelet power spectrum of each pair is shown right below the cross-correlation. Hot colors indicate stronger power and cool colors indicate weaker power. White contours in the power spectrum indicate significance thresholds (See <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105324#s2\" target=\"_blank\">Materials and Methods</a>). Note the wide variety of shapes that can be observed in each frequency range. A: Examples from high frequency connectivity. Typically, a single peak in cross-correlation was observed. Multiple peaks and oscillatory shapes were observed less frequently. Many connections showed a peak offset from zero (also see D, E). B: Examples from gamma frequency connections. Single peaks and troughs, and oscillatory shapes were the most common. C: Examples from beta frequency connections. Single peaks and troughs, and oscillatory shapes were found. Signs of inhibition on one of the sides also can be found (red arrows). D–G: The distribution of the delay and the frequency of the peaks of wavelet power spectra from cortex (D, F) and hippocampus (E, G) in scale 1 (D, E) and scale 2 (F, G). Examined frequency ranges were bounded by colored dashed lines. The right panel on each plot is the connectivity density at each frequency. Solid green curves indicate 1/4 of the Fourier period of the wavelet function. At this time delay, the primary peak of the wavelet function does not overlap with t = 0 (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105324#pone-0105324-g002\" target=\"_blank\">Figure 2A</a>). If the delay is larger than this value, the connection is considered as ‘directed (delayed)’. Broadly tuned clusters of connections were observed in both scale 1 and scale 2 (yellow arrows), which motivated us to set three frequency ranges: high frequency (100–1000 Hz), gamma (30–80 Hz) and beta (12–30 Hz). A peak in the theta band (4–12 Hz) was observed in the cortex (F, blue arrow), but not in hippocampus (G) (see the ‘Cross-correlations and wavelet transforms’ subsection).</p>", "links"=>[], "tags"=>["brain regions", "hz", "network structure", "hundred", "frequency ranges", "neuron", "cortex"], "article_id"=>1140921, "categories"=>["Biological Sciences"], "users"=>["Shinya Ito", "Fang-Chin Yeh", "Emma Hiolski", "Przemyslaw Rydygier", "Deborah E. Gunning", "Pawel Hottowy", "Nicholas Timme", "Alan M. Litke", "John M. Beggs"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0105324.g003", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Examples_of_cross_correlations_and_their_wavelet_transforms_/1140921", "title"=>"Examples of cross-correlations and their wavelet transforms.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-15 03:46:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/1638067"], "description"=>"<p>Photographs of cortico-hippocampal organotypic cultures. Tissues are grown on a filter paper. A: A bright field image of an example organotypic culture at DIV1. The hippocampal structure is visible without staining. Blue arrows indicate the location of markers. B: NeuN staining of the culture after data taking and tissue fixation at DIV16. There are missing neurons in CA3 as consistent with a previous report (Zimmer and Gähwiler, 1984), but the overall layer structure is well conserved. C: Overlaid photograph of A and B. Relative position is adjusted by aligning the two markers on the permeable filter paper. Positions and dimensions of the hippocampal structures are well conserved during the incubation period. D: Overlaid photograph of B, the outline of the array (yellow rectangle), and the estimated locations of the recorded neurons. Light blue circles are manually identified hippocampal neurons (see ‘Identification of hippocampal neurons’ subsection), and red circles are neurons recorded outside the hippocampal structure. Locations of the recorded neurons match with the granule cell layer and the cell body layer.</p>", "links"=>[], "tags"=>["brain regions", "hz", "network structure", "hundred", "frequency ranges", "neuron", "cortex"], "article_id"=>1140919, "categories"=>["Biological Sciences"], "users"=>["Shinya Ito", "Fang-Chin Yeh", "Emma Hiolski", "Przemyslaw Rydygier", "Deborah E. Gunning", "Pawel Hottowy", "Nicholas Timme", "Alan M. Litke", "John M. Beggs"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0105324.g001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Organotypic_culture_preparation_and_photo_overlay_/1140919", "title"=>"Organotypic culture preparation and photo overlay.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-15 03:46:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/1638068"], "description"=>"<p>A: An example of a complex Morlet function. The solid blue line is the real part; the dashed cyan line is the imaginary part. <i>T</i> is the Fourier period of the wavelet function. B: A zero-centered Gaussian peak with σ = 1 ms (black line) and the real part of the wavelet function that gives the maximum power (green line). The Fourier frequency and the delay time of the wavelet function were given by the peak in the wavelet power spectrum (C, arrowed green dot). C: Wavelet power spectrum of the Gaussian function defined in B. The wavelet power spectrum has a peak at time = 0 and frequency ∼116 Hz. This gives the approximate relation between the Fourier period and the Gaussian width: T ∼8.6σ.</p>", "links"=>[], "tags"=>["brain regions", "hz", "network structure", "hundred", "frequency ranges", "neuron", "cortex"], "article_id"=>1140920, "categories"=>["Biological Sciences"], "users"=>["Shinya Ito", "Fang-Chin Yeh", "Emma Hiolski", "Przemyslaw Rydygier", "Deborah E. Gunning", "Pawel Hottowy", "Nicholas Timme", "Alan M. Litke", "John M. Beggs"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0105324.g002", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_of_a_wavelet_function_and_peak_identification_/1140920", "title"=>"Example of a wavelet function and peak identification.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-15 03:46:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/1638077"], "description"=>"<p>Parameters for the connectivity analysis.</p>", "links"=>[], "tags"=>["brain regions", "hz", "network structure", "hundred", "frequency ranges", "neuron", "cortex"], "article_id"=>1140929, "categories"=>["Biological Sciences"], "users"=>["Shinya Ito", "Fang-Chin Yeh", "Emma Hiolski", "Przemyslaw Rydygier", "Deborah E. Gunning", "Pawel Hottowy", "Nicholas Timme", "Alan M. Litke", "John M. Beggs"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0105324.t002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Parameters_for_the_connectivity_analysis_/1140929", "title"=>"Parameters for the connectivity analysis.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-08-15 03:46:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/1638075"], "description"=>"<p>The same threshold values as <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105324#pone-0105324-g005\" target=\"_blank\">Figure 5</a> were applied to each connectivity map of the same data sets. Most of the connections in HFC and GFC were lost (A, B, D, E). Connections in the Beta frequency range (C, F) were different from the original connectivity maps (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105324#pone-0105324-g005\" target=\"_blank\">Figure 5C, F</a>).</p>", "links"=>[], "tags"=>["brain regions", "hz", "network structure", "hundred", "frequency ranges", "neuron", "cortex"], "article_id"=>1140927, "categories"=>["Biological Sciences"], "users"=>["Shinya Ito", "Fang-Chin Yeh", "Emma Hiolski", "Przemyslaw Rydygier", "Deborah E. Gunning", "Pawel Hottowy", "Nicholas Timme", "Alan M. Litke", "John M. Beggs"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0105324.g009", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Connectivity_maps_with_50_ms_jittering_/1140927", "title"=>"Connectivity maps with 50 ms jittering.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-15 03:46:10"}

PMC Usage Stats | Further Information

  • {"unique-ip"=>"14", "full-text"=>"15", "pdf"=>"9", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2015", "month"=>"4"}
  • {"unique-ip"=>"10", "full-text"=>"16", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2015", "month"=>"5"}
  • {"unique-ip"=>"15", "full-text"=>"18", "pdf"=>"5", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"8", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2015", "month"=>"6"}
  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2015", "month"=>"7"}
  • {"unique-ip"=>"14", "full-text"=>"15", "pdf"=>"9", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2015", "month"=>"3"}
  • {"unique-ip"=>"13", "full-text"=>"11", "pdf"=>"4", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"9", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2015", "month"=>"2"}
  • {"unique-ip"=>"14", "full-text"=>"14", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"7", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2015", "month"=>"8"}
  • {"unique-ip"=>"11", "full-text"=>"10", "pdf"=>"4", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2015", "month"=>"9"}
  • {"unique-ip"=>"19", "full-text"=>"20", "pdf"=>"7", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"4", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2015", "month"=>"10"}
  • {"unique-ip"=>"5", "full-text"=>"3", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"9", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2014", "month"=>"8"}
  • {"unique-ip"=>"22", "full-text"=>"13", "pdf"=>"10", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"8", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2014", "month"=>"9"}
  • {"unique-ip"=>"22", "full-text"=>"16", "pdf"=>"11", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"10", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2014", "month"=>"10"}
  • {"unique-ip"=>"7", "full-text"=>"6", "pdf"=>"4", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"2"}
  • {"unique-ip"=>"20", "full-text"=>"16", "pdf"=>"4", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"15", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2014", "month"=>"11"}
  • {"unique-ip"=>"12", "full-text"=>"10", "pdf"=>"7", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"4", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2014", "month"=>"12"}
  • {"unique-ip"=>"18", "full-text"=>"17", "pdf"=>"11", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"12", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2015", "month"=>"1"}
  • {"unique-ip"=>"24", "full-text"=>"22", "pdf"=>"15", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2015", "month"=>"11"}
  • {"unique-ip"=>"37", "full-text"=>"16", "pdf"=>"30", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2015", "month"=>"12"}
  • {"unique-ip"=>"4", "full-text"=>"5", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"1"}
  • {"unique-ip"=>"12", "full-text"=>"14", "pdf"=>"6", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"3"}
  • {"unique-ip"=>"11", "full-text"=>"10", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"4"}
  • {"unique-ip"=>"6", "full-text"=>"10", "pdf"=>"5", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2016", "month"=>"5"}
  • {"unique-ip"=>"11", "full-text"=>"10", "pdf"=>"6", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"6"}
  • {"unique-ip"=>"6", "full-text"=>"8", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"6", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"7"}
  • {"unique-ip"=>"16", "full-text"=>"15", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"11", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"8"}
  • {"unique-ip"=>"8", "full-text"=>"11", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"9"}
  • {"unique-ip"=>"12", "full-text"=>"12", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"10"}
  • {"unique-ip"=>"13", "full-text"=>"15", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"11"}
  • {"unique-ip"=>"7", "full-text"=>"8", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"12"}
  • {"unique-ip"=>"12", "full-text"=>"10", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"1"}
  • {"unique-ip"=>"4", "full-text"=>"8", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"7", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"2"}
  • {"unique-ip"=>"6", "full-text"=>"6", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"3"}
  • {"unique-ip"=>"5", "full-text"=>"7", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"4"}
  • {"unique-ip"=>"19", "full-text"=>"22", "pdf"=>"4", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"5"}
  • {"unique-ip"=>"7", "full-text"=>"7", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"6"}
  • {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"7"}
  • {"unique-ip"=>"5", "full-text"=>"4", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"7", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"8"}
  • {"unique-ip"=>"30", "full-text"=>"28", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"9"}
  • {"unique-ip"=>"13", "full-text"=>"13", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"10"}
  • {"unique-ip"=>"4", "full-text"=>"4", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"11"}
  • {"unique-ip"=>"3", "full-text"=>"2", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"12"}
  • {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"1"}
  • {"unique-ip"=>"8", "full-text"=>"7", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"3"}
  • {"unique-ip"=>"8", "full-text"=>"9", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"1"}
  • {"unique-ip"=>"9", "full-text"=>"10", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"8", "full-text"=>"7", "pdf"=>"9", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"9"}
  • {"unique-ip"=>"11", "full-text"=>"11", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"4"}
  • {"unique-ip"=>"11", "full-text"=>"12", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"5"}
  • {"unique-ip"=>"12", "full-text"=>"13", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"6"}
  • {"unique-ip"=>"8", "full-text"=>"9", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"7"}
  • {"unique-ip"=>"13", "full-text"=>"12", "pdf"=>"7", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"8"}
  • {"unique-ip"=>"24", "full-text"=>"16", "pdf"=>"10", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"10"}
  • {"unique-ip"=>"10", "full-text"=>"15", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"12", "full-text"=>"12", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"6", "full-text"=>"4", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}

Relative Metric

{"start_date"=>"2014-01-01T00:00:00Z", "end_date"=>"2014-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences/Anatomy", "average_usage"=>[267]}, {"subject_area"=>"/Biology and life sciences/Cell biology", "average_usage"=>[286]}, {"subject_area"=>"/Biology and life sciences/Neuroscience", "average_usage"=>[289]}, {"subject_area"=>"/Computer and information sciences", "average_usage"=>[327, 511]}, {"subject_area"=>"/Computer and information sciences/Neural networks", "average_usage"=>[359, 536]}, {"subject_area"=>"/Engineering and technology/Equipment", "average_usage"=>[287, 444]}, {"subject_area"=>"/Engineering and technology/Signal processing", "average_usage"=>[276]}, {"subject_area"=>"/Medicine and health sciences", "average_usage"=>[285]}]}
Loading … Spinner
There are currently no alerts