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Typicality of Functional Connectivity robustly captures motion artifacts in rs-fMRI across datasets, atlases and preprocessing pipelines
View ORCID ProfileJakub Kopal, View ORCID ProfileAnna Pidnebesna, View ORCID ProfileDavid Tomeček, Jaroslav Tintěra, View ORCID ProfileJaroslav Hlinka
doi: https://doi.org/10.1101/2020.03.06.980193
Jakub Kopal
aInstitute of Computer Science, The Czech Academy of Sciences, Prague, Czech Republic
bDepartment of Computing and Control Engineering, University of Chemistry and Technology, Prague, Czech Republic
dCentre de Recherche Cerveau et Cognition, Universit Paul Sabatier, Toulouse, France
Anna Pidnebesna
aInstitute of Computer Science, The Czech Academy of Sciences, Prague, Czech Republic
cNational Institute of Mental Health, Klecany, Czech Republic
eFaculty of Electrical Engineering, Czech Technical University, Prague, Czech Republic
David Tomeček
cNational Institute of Mental Health, Klecany, Czech Republic
eFaculty of Electrical Engineering, Czech Technical University, Prague, Czech Republic
Jaroslav Tintěra
cNational Institute of Mental Health, Klecany, Czech Republic
fInstitute for Clinical and Experimental Medicine, Prague, Czech Republic
Jaroslav Hlinka
aInstitute of Computer Science, The Czech Academy of Sciences, Prague, Czech Republic
cNational Institute of Mental Health, Klecany, Czech Republic

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Posted March 06, 2020.
Typicality of Functional Connectivity robustly captures motion artifacts in rs-fMRI across datasets, atlases and preprocessing pipelines
Jakub Kopal, Anna Pidnebesna, David Tomeček, Jaroslav Tintěra, Jaroslav Hlinka
bioRxiv 2020.03.06.980193; doi: https://doi.org/10.1101/2020.03.06.980193
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