Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/17691
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dc.contributor.authorIvanoska, Ilinkaen_US
dc.contributor.authorZanin, Massimilianoen_US
dc.contributor.authorGüntekin, Baharen_US
dc.contributor.authorYener, Görseven_US
dc.contributor.authorLoncar-Turukalo, Tatjanaen_US
dc.contributor.authorSveljo, Oliveraen_US
dc.contributor.authorPapo, Daviden_US
dc.contributor.authorJakovljevic, Niksaen_US
dc.date.accessioned2022-05-19T08:01:24Z-
dc.date.available2022-05-19T08:01:24Z-
dc.date.issued2021-04-12-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/17691-
dc.description.abstractAnatomical and dynamical connectivity are essential to healthy brain function. However, quantifying variations in connectivity across conditions or between patient populations and appraising their functional significance are highly nontrivial tasks. Here we show that link ranking differences induce specific geometries in a convenient auxiliary space that are often easily recognisable at mere eye inspection. Link ranking can also provide fast and reliable criteria for network reconstruction parameters for which no theoretical guideline has been proposed.en_US
dc.publisherSpringer USen_US
dc.relation.ispartofNeuroinformaticsen_US
dc.subjectFunctional brain connectivity · Complex networks · Link difference ranking · Alzheimer’s disease · Schizophreniaen_US
dc.titleA fast transform for brain connectivity difference evaluationen_US
dc.typeJournal Articleen_US
item.grantfulltextopen-
item.fulltextWith Fulltext-
crisitem.author.deptFaculty of Computer Science and Engineering-
Appears in Collections:Faculty of Computer Science and Engineering: Journal Articles
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