LISA improves statistical analysis for fMRI

LISA improves statistical analysis for fMRI

Functional magnetic resonance imaging (fMRI) is a powerful technique for measuring human brain activity, but the statistical analysis of fMRI data can be difficult. Here, the authors introduce a new fMRI analysis tool, LISA, which provides increased statistical power compared to existing techniques.

Bibliographic Details
Main Authors: Gabriele Lohmann, Johannes Stelzer, Eric Lacosse, Vinod J. Kumar, Karsten Mueller, Esther Kuehn, Wolfgang Grodd, Klaus Scheffler
Format: Article
Language:English
Published: Nature Publishing Group 2018-10-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-018-06304-z
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spelling doaj-7f07805dbd994058bbc72fc35d55f56c2021-05-11T09:32:00ZengNature Publishing GroupNature Communications2041-17232018-10-01911910.1038/s41467-018-06304-zLISA improves statistical analysis for fMRIGabriele Lohmann0Johannes Stelzer1Eric Lacosse2Vinod J. Kumar3Karsten Mueller4Esther Kuehn5Wolfgang Grodd6Klaus Scheffler7Department of Biomedical Magnetic Resonance Imaging, University Hospital TübingenDepartment of Biomedical Magnetic Resonance Imaging, University Hospital TübingenMagnetic Resonance Centre, Max-Planck-Institute for Biological CyberneticsMagnetic Resonance Centre, Max-Planck-Institute for Biological CyberneticsMethods & Development Group Nuclear Magnetic Resonance, Max-Planck-Institute for Human Cognitive and Brain SciencesGerman Center for Neurodegenerative Diseases (DZNE)Magnetic Resonance Centre, Max-Planck-Institute for Biological CyberneticsDepartment of Biomedical Magnetic Resonance Imaging, University Hospital TübingenFunctional magnetic resonance imaging (fMRI) is a powerful technique for measuring human brain activity, but the statistical analysis of fMRI data can be difficult. Here, the authors introduce a new fMRI analysis tool, LISA, which provides increased statistical power compared to existing techniques.https://doi.org/10.1038/s41467-018-06304-z
collection DOAJ
language English
format Article
sources DOAJ
author Gabriele Lohmann
Johannes Stelzer
Eric Lacosse
Vinod J. Kumar
Karsten Mueller
Esther Kuehn
Wolfgang Grodd
Klaus Scheffler
spellingShingle Gabriele Lohmann
Johannes Stelzer
Eric Lacosse
Vinod J. Kumar
Karsten Mueller
Esther Kuehn
Wolfgang Grodd
Klaus Scheffler
LISA improves statistical analysis for fMRI
Nature Communications
author_facet Gabriele Lohmann
Johannes Stelzer
Eric Lacosse
Vinod J. Kumar
Karsten Mueller
Esther Kuehn
Wolfgang Grodd
Klaus Scheffler
author_sort Gabriele Lohmann
title LISA improves statistical analysis for fMRI
title_short LISA improves statistical analysis for fMRI
title_full LISA improves statistical analysis for fMRI
title_fullStr LISA improves statistical analysis for fMRI
title_full_unstemmed LISA improves statistical analysis for fMRI
title_sort lisa improves statistical analysis for fmri
publisher Nature Publishing Group
series Nature Communications
issn 2041-1723
publishDate 2018-10-01
description Functional magnetic resonance imaging (fMRI) is a powerful technique for measuring human brain activity, but the statistical analysis of fMRI data can be difficult. Here, the authors introduce a new fMRI analysis tool, LISA, which provides increased statistical power compared to existing techniques.
url https://doi.org/10.1038/s41467-018-06304-z
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AT johannesstelzer lisaimprovesstatisticalanalysisforfmri
AT ericlacosse lisaimprovesstatisticalanalysisforfmri
AT vinodjkumar lisaimprovesstatisticalanalysisforfmri
AT karstenmueller lisaimprovesstatisticalanalysisforfmri
AT estherkuehn lisaimprovesstatisticalanalysisforfmri
AT wolfganggrodd lisaimprovesstatisticalanalysisforfmri
AT klausscheffler lisaimprovesstatisticalanalysisforfmri
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