Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/22817
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dc.contributor.authorDetrez, Jan R.-
dc.contributor.authorVerstraelen, Peter-
dc.contributor.authorGebuis, Titia-
dc.contributor.authorVerschuuren, Marlies-
dc.contributor.authorKUIJLAARS, Jacobine-
dc.contributor.authorLanglois, Xavier-
dc.contributor.authorNuydens, Rony-
dc.contributor.authorTimmermans, Jean-Pierre-
dc.contributor.authorDe Vos, Winnok H.-
dc.date.accessioned2016-12-01T10:09:44Z-
dc.date.available2016-12-01T10:09:44Z-
dc.date.issued2016-
dc.identifier.citationAdvances in Anatomy Embryology and Cell Biology, 219, p. 123-148-
dc.identifier.issn0301-5556-
dc.identifier.urihttp://hdl.handle.net/1942/22817-
dc.description.abstractBrain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Amongst the neuronal structures that show morphological plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular communication and the associated calcium bursting behaviour. In vitro cultured neuronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardization of both image acquisition and image analysis, it has become possible to extract statistically relevant readouts from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies.-
dc.language.isoen-
dc.rights© Springer International Publishing Switzerland 2016-
dc.titleImage Informatics Strategies for Deciphering Neuronal Network Connectivity-
dc.typeJournal Contribution-
local.bibliographicCitation.authorsDe Vos, Winnok H.-
local.bibliographicCitation.authorsMunck, Sebastian-
local.bibliographicCitation.authorsTimmermans, Jean-Pierre-
dc.identifier.epage148-
dc.identifier.spage123-
dc.identifier.volume219-
local.bibliographicCitation.jcatA1-
local.type.refereedRefereed-
local.type.specifiedReview-
local.classdsPublValOverrule/author_version_not_expected-
dc.identifier.doi10.1007/978-3-319-28549-8_5-
dc.identifier.isi000385738300006-
item.fullcitationDetrez, Jan R.; Verstraelen, Peter; Gebuis, Titia; Verschuuren, Marlies; KUIJLAARS, Jacobine; Langlois, Xavier; Nuydens, Rony; Timmermans, Jean-Pierre & De Vos, Winnok H. (2016) Image Informatics Strategies for Deciphering Neuronal Network Connectivity. In: Advances in Anatomy Embryology and Cell Biology, 219, p. 123-148.-
item.contributorDetrez, Jan R.-
item.contributorVerstraelen, Peter-
item.contributorGebuis, Titia-
item.contributorVerschuuren, Marlies-
item.contributorKUIJLAARS, Jacobine-
item.contributorLanglois, Xavier-
item.contributorNuydens, Rony-
item.contributorTimmermans, Jean-Pierre-
item.contributorDe Vos, Winnok H.-
item.validationecoom 2017-
item.accessRightsRestricted Access-
item.fulltextWith Fulltext-
crisitem.journal.issn0301-5556-
crisitem.journal.eissn2192-7065-
Appears in Collections:Research publications
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