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http://hdl.handle.net/20.500.12188/17691
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ivanoska, Ilinka | en_US |
dc.contributor.author | Zanin, Massimiliano | en_US |
dc.contributor.author | Güntekin, Bahar | en_US |
dc.contributor.author | Yener, Görsev | en_US |
dc.contributor.author | Loncar-Turukalo, Tatjana | en_US |
dc.contributor.author | Sveljo, Olivera | en_US |
dc.contributor.author | Papo, David | en_US |
dc.contributor.author | Jakovljevic, Niksa | en_US |
dc.date.accessioned | 2022-05-19T08:01:24Z | - |
dc.date.available | 2022-05-19T08:01:24Z | - |
dc.date.issued | 2021-04-12 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.12188/17691 | - |
dc.description.abstract | Anatomical 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.publisher | Springer US | en_US |
dc.relation.ispartof | Neuroinformatics | en_US |
dc.subject | Functional brain connectivity · Complex networks · Link difference ranking · Alzheimer’s disease · Schizophrenia | en_US |
dc.title | A fast transform for brain connectivity difference evaluation | en_US |
dc.type | Journal Article | en_US |
item.fulltext | With Fulltext | - |
item.grantfulltext | open | - |
crisitem.author.dept | Faculty of Computer Science and Engineering | - |
Appears in Collections: | Faculty of Computer Science and Engineering: Journal Articles |
Files in This Item:
File | Опис | Size | Format | |
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Zanin2021_Article_AFastTransformForBrainConnecti.pdf | 1.78 MB | Adobe PDF | View/Open |
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