Deconstructing multivariate decoding for the study of brain function.
Gespeichert in:
| Titel: | Deconstructing multivariate decoding for the study of brain function. |
|---|---|
| Autoren: | Hebart MN; Section on Learning and Plasticity, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA. Electronic address: martin.hebart@nih.gov., Baker CI; Section on Learning and Plasticity, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA. |
| Quelle: | NeuroImage [Neuroimage] 2018 Oct 15; Vol. 180 (Pt A), pp. 4-18. Date of Electronic Publication: 2017 Aug 04. |
| Publikationsart: | Journal Article; Research Support, N.I.H., Intramural; Research Support, Non-U.S. Gov't; Review |
| Sprache: | English |
| Info zur Zeitschrift: | Publisher: Academic Press Country of Publication: United States NLM ID: 9215515 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1095-9572 (Electronic) Linking ISSN: 10538119 NLM ISO Abbreviation: Neuroimage Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Orlando, FL : Academic Press, c1992- |
| MeSH-Schlagworte: | Multivariate Analysis*, Brain/*physiology , Brain Mapping/*methods , Image Processing, Computer-Assisted/*methods, Humans |
| 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. (Copyright © 2017 Elsevier Inc. All rights reserved.) |
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| Grant Information: | Z01 MH002909 United States ImNIH Intramural NIH HHS; ZIA MH002909 United States ImNIH Intramural NIH HHS |
| Contributed Indexing: | Keywords: Decoding; Encoding; Multivariate analysis; Multivariate decoding; Multivariate pattern analysis; Prediction; fMRI |
| Entry Date(s): | Date Created: 20170808 Date Completed: 20190201 Latest Revision: 20250530 |
| Update Code: | 20250530 |
| PubMed Central ID: | PMC5797513 |
| DOI: | 10.1016/j.neuroimage.2017.08.005 |
| PMID: | 28782682 |
| Datenbank: | MEDLINE |
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Nájsť tento článok vo Web of Science | 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.<br /> (Copyright © 2017 Elsevier Inc. All rights reserved.) |
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| ISSN: | 1095-9572 |
| DOI: | 10.1016/j.neuroimage.2017.08.005 |