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dc.contributor.author
Valizadeh, Seyedabolfazl
dc.contributor.author
Liem, Franziskus
dc.contributor.author
Mérillat, Susan
dc.contributor.author
Hänggi, Jürgen
dc.contributor.author
Jäncke, Lutz
dc.date.accessioned
2018-04-09T13:18:32Z
dc.date.available
2018-04-06T07:54:17Z
dc.date.available
2018-04-09T13:18:32Z
dc.date.issued
2018-04-04
dc.identifier.issn
2045-2322
dc.identifier.other
10.1038/s41598-018-23696-6
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/255775
dc.identifier.doi
10.3929/ethz-b-000255775
dc.description.abstract
We examined whether it is possible to identify individual subjects on the basis of brain anatomical features. For this, we analyzed a dataset comprising 191 subjects who were scanned three times over a period of two years. Based on FreeSurfer routines, we generated three datasets covering 148 anatomical regions (cortical thickness, area, volume). These three datasets were also combined to a dataset containing all of these three measures. In addition, we used a dataset comprising 11 composite anatomical measures for which we used larger brain regions (11LBR). These datasets were subjected to a linear discriminant analysis (LDA) and a weighted K-nearest neighbors approach (WKNN) to identify single subjects. For this, we randomly chose a data subset (training set) with which we calculated the individual identification. The obtained results were applied to the remaining sample (test data). In general, we obtained excellent identification results (reasonably good results were obtained for 11LBR using WKNN). Using different data manipulation techniques (adding white Gaussian noise to the test data and changing sample sizes) still revealed very good identification results, particularly for the LDA technique. Interestingly, using the small 11LBR dataset also revealed very good results indicating that the human brain is highly individual.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Nature Publishing Group
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Identification of individual subjects on the basis of their brain anatomical features
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2018-04-04
ethz.journal.title
Scientific Reports
ethz.journal.volume
8
en_US
ethz.journal.abbreviated
Sci Rep
ethz.pages.start
5611
en_US
ethz.size
9 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02070 - Dep. Gesundheitswiss. und Technologie / Dep. of Health Sciences and Technology::03654 - Riener, Robert / Riener, Robert
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02070 - Dep. Gesundheitswiss. und Technologie / Dep. of Health Sciences and Technology::03654 - Riener, Robert / Riener, Robert
en_US
ethz.date.deposited
2018-04-06T07:54:18Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2018-04-09T13:18:36Z
ethz.rosetta.lastUpdated
2021-02-14T23:12:42Z
ethz.rosetta.versionExported
true
ethz.COinS
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