Deconstructing multivariate decoding for the study of brain function

By Martin N. Hebart, Chris L. Baker. bioRxiv, 2017

308 citations recorded by the source corpus.

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. Highlights We highlight two sources of confusion that affect the interpretation of multivariate decoding results One confusion arises from the dual use of multivariate decoding for predictions in real-world applications and for interpretation in terms of brain function The other confusion arises from the different statistical and conceptual frameworks underlying classical univariate analysis to multivariate decoding We highlight six differences between classical univariate analysis and multivariate decoding and differences in the interpretation of signal and noise These confusions are illustrated in four examples revealing assumptions and limitations of multivariate decoding for interpretation

Subfields: Cognitive Neuroscience, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/c059443263cc74f20f9237bbbd8e7c216530c52d

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In Cognitive Neuroscience

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  2. Michael C. Freund
  3. Todd S. Braver
  4. Matthew Botvinick
  5. J. Cohen
  6. Richard J. Haier
  7. Luiz Pessoa
  8. Michael J Frank

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