Transfer Entropy Reconstruction and Labeling of Neuronal Connections from Simulated Calcium Imaging
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{"title"=>"Transfer entropy reconstruction and labeling of neuronal connections from simulated calcium imaging", "type"=>"journal", "authors"=>[{"first_name"=>"Javier G.", "last_name"=>"Orlandi", "scopus_author_id"=>"26659171200"}, {"first_name"=>"Olav", "last_name"=>"Stetter", "scopus_author_id"=>"55356650900"}, {"first_name"=>"Jordi", "last_name"=>"Soriano", "scopus_author_id"=>"15048698300"}, {"first_name"=>"Theo", "last_name"=>"Geisel", "scopus_author_id"=>"7006784868"}, {"first_name"=>"Demian", "last_name"=>"Battaglia", "scopus_author_id"=>"7006321888"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "arxiv"=>"1309.4287", "scopus"=>"2-s2.0-84902590291", "sgr"=>"84902590291", "pui"=>"373337417", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "pmid"=>"24905689", "doi"=>"10.1371/journal.pone.0098842"}, "id"=>"1a862764-5ba1-3bb9-b79c-a396a4aeea0e", "abstract"=>"Neuronal dynamics are fundamentally constrained by the underlying structural network architecture, yet much of the details of this synaptic connectivity are still unknown even in neuronal cultures in vitro. Here we extend a previous approach based on information theory, the Generalized Transfer Entropy, to the reconstruction of connectivity of simulated neuronal networks of both excitatory and inhibitory neurons. We show that, due to the model-free nature of the developed measure, both kinds of connections can be reliably inferred if the average firing rate between synchronous burst events exceeds a small minimum frequency. Furthermore, we suggest, based on systematic simulations, that even lower spontaneous inter- burst rates could be raised to meet the requirements of our reconstruction algorithm by applying a weak spatially homogeneous stimulation to the entire network. By combining multiple recordings of the same in silico network before and after pharmacologically blocking inhibitory synaptic transmission, we show then how it becomes possible to infer with high confidence the excitatory or inhibitory nature of each individual neuron.", "link"=>"http://www.mendeley.com/research/transfer-entropy-reconstruction-labeling-neuronal-connections-simulated-calcium-imaging", "reader_count"=>87, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Researcher"=>15, "Student > Doctoral Student"=>8, "Student > Ph. D. Student"=>30, "Student > Postgraduate"=>5, "Student > Master"=>11, "Other"=>2, "Student > Bachelor"=>8, "Lecturer > Senior Lecturer"=>2, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Researcher"=>15, "Student > Doctoral Student"=>8, "Student > Ph. D. Student"=>30, "Student > Postgraduate"=>5, "Student > Master"=>11, "Other"=>2, "Student > Bachelor"=>8, "Lecturer > Senior Lecturer"=>2, "Professor"=>3}, "reader_count_by_subject_area"=>{"Engineering"=>15, "Unspecified"=>2, "Biochemistry, Genetics and Molecular Biology"=>1, "Mathematics"=>7, "Agricultural and Biological Sciences"=>28, "Medicine and Dentistry"=>2, "Neuroscience"=>10, "Physics and Astronomy"=>18, "Computer Science"=>4}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>15}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Neuroscience"=>{"Neuroscience"=>10}, "Physics and Astronomy"=>{"Physics and Astronomy"=>18}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>28}, "Computer Science"=>{"Computer Science"=>4}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Mathematics"=>{"Mathematics"=>7}, "Unspecified"=>{"Unspecified"=>2}}, "reader_count_by_country"=>{"Hungary"=>1, "United States"=>3, "China"=>1, "United Kingdom"=>2, "Italy"=>1, "Germany"=>2, "Spain"=>2}, "group_count"=>4}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1525102"], "description"=>"<p><b>A</b> ROC curves for the reconstruction of a network with both excitatory and inhibitory connections active, supposing to know <i>a priori</i> information about neuronal type. GTE is first applied to the “E+I” data. Next, following Dale's principle and exploiting the available information on neuronal type, links are classified according to their excitatory (red) or inhibitory (blue) nature. <b>B</b> ROC curves for the best possible identification of excitatory and inhibitory connections, when information on neuronal type is unaccessible. Excitatory links (red) are identified by adding together the Transfer Entropy scores of simulations run in “E–only” and “E+I” conditions, and later thresholding them. Inhibitory links (blue) are identified by computing the difference in Transfer Entropy scores between the runs with inhibition present and blocked. Inset: fraction of excitatory and inhibitory neurons correctly identified from these ROC curves. Results were not significantly different from random guess (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0098842#s4\" target=\"_blank\">Methods</a>). All the results were averaged over different network realizations. The shaded areas in the main plots, as well as the error bars in the inset, correspond to 95% confidence intervals.</p>", "links"=>[], "tags"=>["anatomy", "nervous system", "neuroanatomy", "Connectomics", "cell biology", "Cellular types", "Animal cells", "neurons", "Computational biology", "computational neuroscience", "Circuit models", "neuroscience", "Developmental neuroscience", "Neural circuit formation", "neuroimaging", "Calcium imaging", "neural networks", "Computer modeling", "physics", "Interdisciplinary physics"], "article_id"=>1049328, "categories"=>["Biological Sciences"], "users"=>["Javier G. Orlandi", "Olav Stetter", "Jordi Soriano", "Theo Geisel", "Demian Battaglia"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0098842.g003", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Optimal_network_reconstruction_/1049328", "title"=>"Optimal network reconstruction.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-06 03:05:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/1525099"], "description"=>"<p><b>A</b> Top: Bright field and fluorescence images of a small region of a neuronal culture at day <i>in vitro</i> 12. Bright spots correspond to firing neurons. Bottom: Representative time traces of recorded fluorescence signals of 3 individual neurons. The numbers beside each trace identify the neurons on the images. Data shows, for the same neurons, the signal in recordings with only excitation active (“E”) and the signal with both excitation and inhibition active (“E+I”). <b>B</b> Population-averaged fluorescence signals in experiments (left) and simulations (right), illustrating the semi-quantitative matching between <i>in vitro</i> and <i>in silico</i> data. Top: excitatory-only traces (“E–only” data). For the experiments, inhibition was silenced through application of saturating concentrations of bicuculline. For the simulations, inhibitory synapses were silenced by setting their efficacy to zero. Bottom: traces for both excitation and inhibition active (“E+I” data). Network bursts appear as a fast increase of the fluorescence signal followed by a slow decay. Bursts are more frequent and display lower and more heterogeneous amplitudes in the presence of inhibitory connections. <b>C</b> Histogram of population-averaged fluorescence intensity for a 1 h recordings in experiments (left) and simulations (right). Data is shown in semilogarithmic scale for clarity. Red curves correspond to the “E–only” condition, and the blue curves to the “E+I” one.</p>", "links"=>[], "tags"=>["anatomy", "nervous system", "neuroanatomy", "Connectomics", "cell biology", "Cellular types", "Animal cells", "neurons", "Computational biology", "computational neuroscience", "Circuit models", "neuroscience", "Developmental neuroscience", "Neural circuit formation", "neuroimaging", "Calcium imaging", "neural networks", "Computer modeling", "physics", "Interdisciplinary physics"], "article_id"=>1049325, "categories"=>["Biological Sciences"], "users"=>["Javier G. Orlandi", "Olav Stetter", "Jordi Soriano", "Theo Geisel", "Demian Battaglia"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0098842.g001", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Neuronal_network_dynamics_/1049325", "title"=>"Neuronal network dynamics.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-06 03:05:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/1525104"], "description"=>"<p><b>A</b> and <b>B</b>, fraction of true positives from the reconstructions at the 5% false positive mark for the studied networks. “E–only” networks are shown in <b>A</b>; “E+I” networks in <b>B</b>. Inset: dependence of the spontaneous firing rate on the applied external drive, emulated here by increasing the rate of the background drive to the culture <i>in silico</i>. All the excitatory reconstructions reach a stable plateau in the reconstruction after removal of the instantaneous feedback term (IFT) correction (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0098842#s4\" target=\"_blank\">Methods</a>). The inhibitory reconstruction is accurate only for higher values of the external drive. <b>C</b> ROC curves extracted from <b>A</b> and <b>B</b> with an external stimulation of 4 Hz. Inset: fraction of excitatory and inhibitory neurons correctly identified from these reconstructions. Identification was statistically significant compared to random guessing. For excitatory neurons, (**); for inhibitory neurons, (***). <b>D</b> Example of an actual reconstruction after identification of neuronal type. Identified excitatory neurons are shown in red and inhibitory ones in blue. Incorrectly identified neurons are shown in grey. Correctly identified excitatory and inhibitory links are shown in red and blue, respectively, and wrongly identified links are shown in black. For clarity in the representation of the links, a threshold value lower than the optimal has been applied.</p>", "links"=>[], "tags"=>["anatomy", "nervous system", "neuroanatomy", "Connectomics", "cell biology", "Cellular types", "Animal cells", "neurons", "Computational biology", "computational neuroscience", "Circuit models", "neuroscience", "Developmental neuroscience", "Neural circuit formation", "neuroimaging", "Calcium imaging", "neural networks", "Computer modeling", "physics", "Interdisciplinary physics"], "article_id"=>1049330, "categories"=>["Biological Sciences"], "users"=>["Javier G. Orlandi", "Olav Stetter", "Jordi Soriano", "Theo Geisel", "Demian Battaglia"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0098842.g004", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Reconstruction_improvement_through_external_stimulation_/1049330", "title"=>"Reconstruction improvement through external stimulation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-06 03:05:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/1525101"], "description"=>"<p><b>A</b> Separation of the signal in two regimes according to the conditioning level (dotted line), a first one that encompasses the low activity events (red curves), and a second one that includes the bursting regimes only (blue). The same conditioning procedure is applied in both “E–only” networks (left) and in “E+I” ones (right). <b>B </b><i>Receiver Operating Characteristic</i> (ROC) curves quantify the accuracy of reconstruction and its sensitivity on conditioning. Functional networks are generated by including links with a calculated GTE score exceeding an arbitrary threshold. ROC curves plot then the fraction of true and false positives in the functional networks inferred for every possible threshold. For “E–only” networks (left) and “E+I” networks (right), the red curves show the goodness of the reconstruction after applying the conditioning procedure. Blue curves illustrate the reconstruction performance without conditioning. The ROC curves show that the conditioning procedure significantly improves reconstruction performance. ROC curves were averaged over different network realizations (95% confidence intervals shown).</p>", "links"=>[], "tags"=>["anatomy", "nervous system", "neuroanatomy", "Connectomics", "cell biology", "Cellular types", "Animal cells", "neurons", "Computational biology", "computational neuroscience", "Circuit models", "neuroscience", "Developmental neuroscience", "Neural circuit formation", "neuroimaging", "Calcium imaging", "neural networks", "Computer modeling", "physics", "Interdisciplinary physics"], "article_id"=>1049327, "categories"=>["Biological Sciences"], "users"=>["Javier G. Orlandi", "Olav Stetter", "Jordi Soriano", "Theo Geisel", "Demian Battaglia"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0098842.g002", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Signal_conditioning_/1049327", "title"=>"Signal conditioning.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-06 03:05:53"}

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