English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  LISA improves statistical analysis for fMRI

Lohmann, G., Stelzer, J., Lacosse, E., Kumar, V., Mueller, K., Kuehn, E., et al. (2018). LISA improves statistical analysis for fMRI. Nature Communications, 9: 4014, pp. 1-9. doi:10.1038/s41467-018-06304-z.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/21.11116/0000-0002-4ED5-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-7553-D
Genre: Journal Article

Files

show Files

Locators

show
hide
Description:
-

Creators

show
hide
 Creators:
Lohmann, G1, 2, Author              
Stelzer, J1, 2, Author              
Lacosse, E1, 2, Author              
Kumar, V1, 2, Author              
Mueller, K, Author
Kuehn, E, Author
Grodd, W1, 2, Author              
Scheffler, K1, 2, Author              
Affiliations:
1Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

Content

show
hide
Free keywords: -
 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).

Details

show
hide
Language(s):
 Dates: 2018-10
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1038/s41467-018-06304-z
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Nature Communications
  Abbreviation : Nat. Commun.
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: London : Nature Publishing Group
Pages: - Volume / Issue: 9 Sequence Number: 4014 Start / End Page: 1 - 9 Identifier: ISSN: 2041-1723
CoNE: https://pure.mpg.de/cone/journals/resource/2041-1723