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  Deconstructing multivariate decoding for the study of brain function

Hebart, M. N., & Baker, C. I. (2018). Deconstructing multivariate decoding for the study of brain function. NeuroImage, 180(Part A), 4-18. doi:10.1016/j.neuroimage.2017.08.005.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0005-20C9-4 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-586A-2
Genre: Journal Article

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 Creators:
Hebart, Martin N.1, Author              
Baker, Chris I.1, Author
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1External Organizations, ou_persistent22              

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Free keywords: Multivariate decoding; Multivariate analysis; Multivariate pattern analysis; Encoding; Decoding; fMRI; Prediction
 Abstract: Multivariate decoding methods were developed originally as tools to enable accurate predictions in real-world applications. The realization that these methods can also be employed to study brain function has led to their widespread adoption in the neurosciences. However, prior to the rise of multivariate decoding, the study of brain function was firmly embedded in a statistical philosophy grounded on univariate methods of data analysis. In this way, multivariate decoding for brain interpretation grew out of two established frameworks: multivariate decoding for predictions in real-world applications, and classical univariate analysis based on the study and interpretation of brain activation. We argue that this led to two confusions, one reflecting a mixture of multivariate decoding for prediction or interpretation, and the other a mixture of the conceptual and statistical philosophies underlying multivariate decoding and classical univariate analysis. Here we attempt to systematically disambiguate multivariate decoding for the study of brain function from the frameworks it grew out of. After elaborating these confusions and their consequences, we describe six, often unappreciated, differences between classical univariate analysis and multivariate decoding. We then focus on how the common interpretation of what is signal and noise changes in multivariate decoding. Finally, we use four examples to illustrate where these confusions may impact the interpretation of neuroimaging data. We conclude with a discussion of potential strategies to help resolve these confusions in interpreting multivariate decoding results, including the potential departure from multivariate decoding methods for the study of brain function.

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Language(s): eng - English
 Dates: 2017-07-282017-04-012017-08-012018-08-042018-10-15
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.neuroimage.2017.08.005
PMID: 28782682
PMC: PMC5797513
Other: Epub 2017
 Degree: -

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Project name : -
Grant ID : ZIA-MH-002909
Funding program : Intramural Research Program
Funding organization : National Institute of Mental Health (NIHM)
Project name : -
Grant ID : -
Funding program : Feodor-Lynen Fellowship
Funding organization : Alexander von Humboldt Foundation

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Title: NeuroImage
Source Genre: Journal
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Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 180 (Part A) Sequence Number: - Start / End Page: 4 - 18 Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166