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  LISA improves statistical analysis for fMRI

Lohmann, G., Stelzer, J., Lacosse, E., Kumar, V. J., Mueller, K., Kuehn, E., et al. (2018). LISA improves statistical analysis for fMRI. Nature Communications, 9: 4014. doi:10.1038/s41467-018-06304-z.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0002-53AA-1 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-9B0C-2
Genre: Journal Article

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 Creators:
Lohmann, Gabriele 1, 2, Author
Stelzer, Johannes 1, 2, Author
Lacosse, Eric 2, 3, Author
Kumar, Vinod J. 2, Author
Mueller, Karsten4, Author              
Kuehn, Esther5, 6, 7, Author              
Grodd , Wolfgang 2, Author
Scheffler , Klaus 1, 2, Author
Affiliations:
1Department of Biomedical Magnetic Resonance Imaging, University Hospital Tübingen, Germany, ou_persistent22              
2Department of High-field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, ou_persistent22              
3Max Planck Institute for Intelligent Systems, Tübingen, Germany, ou_persistent22              
4Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634558              
5Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
6German Center for Neurodegenerative Diseases, Magdeburg, Germany, ou_persistent22              
7Center for Behavioral Brain Sciences, Magdeburg, Germany, ou_persistent22              

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Free keywords: Computational neuroscience; Data processing; Neural circuits; Statistics
 Abstract: One of the principal goals in functional magnetic resonance imaging (fMRI) is the detection of local activation in the human brain. However, lack of statistical power and inflated false positive rates have recently been identified as major problems in this regard. Here, we propose a non-parametric and threshold-free framework called LISA to address this demand. It uses a non-linear filter for incorporating spatial context without sacrificing spatial precision. Multiple comparison correction is achieved by controlling the false discovery rate in the filtered maps. Compared to widely used other methods, it shows a boost in statistical power and allows to find small activation areas that have previously evaded detection. The spatial sensitivity of LISA makes it especially suitable for the analysis of high-resolution fMRI data acquired at ultrahigh field (≥7 Tesla).

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Language(s): eng - English
 Dates: 2018-07-202018-08-212018-10-01
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1038/s41467-018-06304-z
PMID: 30275541
PMC: PMC6167367
 Degree: -

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Project name : A Clinical Decision Support system based on Quantitative multimodal brain MRI for personalized treatment in neurological and psychiatric disorders / CDS-QUAMRI
Grant ID : 634541
Funding program : Horizon 2020
Funding organization : European Commission (EC)
Project name : -
Grant ID : 1U54MH091657
Funding program : -
Funding organization : National Institutes of Health (NIH)
Project name : -
Grant ID : -
Funding program : -
Funding organization : McDonnell Center for Systems Neuroscience at Washington University

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Title: Nature Communications
  Abbreviation : Nat. Commun.
Source Genre: Journal
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Publ. Info: London : Nature Publishing Group
Pages: - Volume / Issue: 9 Sequence Number: 4014 Start / End Page: - Identifier: ISSN: 2041-1723
CoNE: https://pure.mpg.de/cone/journals/resource/2041-1723