Beyond Statistical Significance: Implications of Network Structure on Neuronal Activity
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{"title"=>"Beyond statistical significance: Implications of network structure on neuronal activity", "type"=>"journal", "authors"=>[{"first_name"=>"Ioannis", "last_name"=>"Vlachos", "scopus_author_id"=>"36946560600"}, {"first_name"=>"Ad", "last_name"=>"Aertsen", "scopus_author_id"=>"7004250093"}, {"first_name"=>"Arvind", "last_name"=>"Kumar", "scopus_author_id"=>"56016220000"}], "year"=>2012, "source"=>"PLoS Computational Biology", "identifiers"=>{"pui"=>"364329943", "isbn"=>"1553-7358", "issn"=>"1553734X", "doi"=>"10.1371/journal.pcbi.1002311", "scopus"=>"2-s2.0-84857473385", "pmid"=>"22291581", "sgr"=>"84857473385"}, "id"=>"cd25e4de-334a-34fa-9344-eb39b09eb23c", "abstract"=>"It is a common and good practice in experimental sciences to assess the statistical significance of measured outcomes. For this, the probability of obtaining the actual results is estimated under the assumption of an appropriately chosen null-hypothesis. If this probability is smaller than some threshold, the results are deemed statistically significant and the researchers are content in having revealed, within their own experimental domain, a “surprising” anomaly, possibly indicative of a hitherto hidden fragment of the underlying “ground-truth”. What is often neglected, though, is the actual importance of these experimental outcomes for understanding the system under investigation. We illustrate this point by giving practical and intuitive examples from the field of systems neuroscience. Specifically, we use the notion of embeddedness to quantify the impact of a neuron's activity on its downstream neurons in the network. We show that the network response strongly depends on the embeddedness of stimulated neurons and that embeddedness is a key determinant of the importance of neuronal activity on local and downstream processing. We extrapolate these results to other fields in which networks are used as a theoretical framework.", "link"=>"http://www.mendeley.com/research/beyond-statistical-significance-implications-network-structure-neuronal-activity", "reader_count"=>122, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>9, "Student > Doctoral Student"=>9, "Researcher"=>38, "Student > Ph. D. Student"=>26, "Student > Postgraduate"=>5, "Student > Master"=>13, "Other"=>3, "Student > Bachelor"=>5, "Lecturer"=>1, "Professor"=>11}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>9, "Student > Doctoral Student"=>9, "Researcher"=>38, "Student > Ph. D. Student"=>26, "Student > Postgraduate"=>5, "Student > Master"=>13, "Other"=>3, "Student > Bachelor"=>5, "Lecturer"=>1, "Professor"=>11}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Engineering"=>12, "Biochemistry, Genetics and Molecular Biology"=>1, "Mathematics"=>5, "Agricultural and Biological Sciences"=>47, "Medicine and Dentistry"=>3, "Neuroscience"=>18, "Philosophy"=>1, "Physics and Astronomy"=>6, "Psychology"=>9, "Computer Science"=>15, "Linguistics"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>12}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Neuroscience"=>{"Neuroscience"=>18}, "Physics and Astronomy"=>{"Physics and Astronomy"=>6}, "Psychology"=>{"Psychology"=>9}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>47}, "Computer Science"=>{"Computer Science"=>15}, "Linguistics"=>{"Linguistics"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Mathematics"=>{"Mathematics"=>5}, "Unspecified"=>{"Unspecified"=>4}, "Philosophy"=>{"Philosophy"=>1}}, "reader_count_by_country"=>{"United States"=>10, "United Kingdom"=>4, "Malaysia"=>1, "Portugal"=>1, "Switzerland"=>2, "Spain"=>1, "India"=>1, "Canada"=>2, "Sweden"=>2, "Brazil"=>1, "Israel"=>1, "France"=>3, "Germany"=>4}, "group_count"=>8}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/686868"], "description"=>"<p>(A) Rasterplot of excitatory (1–700) and inhibitory (701–900) neurons recorded in the simulation experiment. (B) Rows are sorted such that neurons with similar rate modulations appear together. Evidently, a subgroup of neurons fires action-potentials in a correlated manner during certain epochs in time (short black lines near bottom of the frame). (C) Schematic depiction of the underlying network from which neural activity was sampled. (D) The same network reorganized graphically using a force vector algorithm (cf. <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002311#s6\" target=\"_blank\">Methods</a>) to reveal its modular structure. Note that in this Gedankenexperiment the big subnetwork controls the arm movement.</p>", "links"=>[], "tags"=>["events", "modular"], "article_id"=>357369, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Ioannis Vlachos", "Ad Aertsen", "Arvind Kumar"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002311.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Statistically_significant_activity_events_in_a_modular_network_/357369", "title"=>"Statistically significant activity events in a modular network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 09:25:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/687024"], "description"=>"<p>(A) Network response (PSTH) for identical stimulation of 30 different subpopulations of 250 neurons each (thin blue lines) in one example network. Observe that peak, onset, and rise times of responses of each subpopulation differ greatly. The thick blue lines depict the smallest and the biggest response, respectively. (B) Rasterplot of the network when the subpopulation of neurons with the lowest degree of embeddedness was stimulated. Light blue dots denote spikes from all neurons, dark blue dots those from stimulated ones. Inset: Magnified cut-out around 600 ms for neurons 4000–6000. Activation of weakly embedded neurons does not spread much in the network. (C) As in (B), but now the subpopulation with the highest average degree of embeddedness was stimulated, leading to a much bigger impact on the network activity. Activation of these strongly embedded neurons lead to a spreading of activity throughout the network. Moreover, feedforward inhibition suppressed the network activity entirely. (D) Response of all stimulated subpopulations (250 neurons each) and all networks pooled together (pale blue dots). On average, there was a positive correlation between out-degree and total network activity ( = 0.84). Two networks with small-world properties are highlighted (dark blue, light amber dots). The five random networks (filled gray triangles) did not exhibit high out-degree variance, and therefore the variance of their population response was small compared to that of the small-world networks. (E) Average correlation coefficient (sorted) between population response and degree of embeddedness as measured by out-degree and k-shell-out index. Both metrics had a high predictive power, with out-degree maintaining high prediction rates for most of the graphs. However, the predictive power of topology measures depended also on additional criteria (cf. main text and <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002311#pcbi-1002311-g003\" target=\"_blank\">Figure 3</a>).</p>", "links"=>[], "tags"=>["embeddedness"], "article_id"=>357521, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Ioannis Vlachos", "Ad Aertsen", "Arvind Kumar"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002311.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Structural_embeddedness_and_population_response_/357521", "title"=>"Structural embeddedness and population response.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 09:26:07"}
  • {"files"=>["https://ndownloader.figshare.com/files/687162"], "description"=>"<p>Example of a toy-network illustrating that the degree to which any given metric of neuron embeddedness predicts the neurons' impact on the population response may depend on single neuron properties. The small numbers next to each node indicate the corresponding k-shell-out index. (A,B) Neurons exhibited regular firing behavior. (A) A sufficiently strong input activating neuron 5 will yield propagation of activity to neurons 7–14. (B) If the same stimulus arrives in neuron 1, activity will only spread to neurons 2–4 and 6. In this case, the out-degree correctly predicts that the impact of neuron 5 is bigger than that of neuron 1. (C) Neurons exhibited bursting behavior. As previously, neuron 1 will activate neurons 2–4 and 6. However, the bursting response of these neurons may be sufficient to activate their post-synaptic targets as well, leading to spreading of activity over the entire network. Here, the impact of neuron 1 is clearly larger than that of neuron 5. This effect is not grasped by the widely used out-degree measure. However, higher-order network metrics, like the k-shell-out index, correctly assign a higher value to neuron 1, as compared to neuron 5. (D) Total network response in the three cases depicted in panels A–C. Note the higher impact of neuron 1 under some conditions (curve C), compared to that of neuron 5 (curve A).</p>", "links"=>[], "tags"=>["node", "properties", "higher-order"], "article_id"=>357657, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Ioannis Vlachos", "Ad Aertsen", "Arvind Kumar"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002311.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Interplay_between_node_properties_and_higher_order_network_features_/357657", "title"=>"Interplay between node properties and higher-order network features.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 09:26:50"}

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Relative Metric

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