TY - JOUR
T1 - Linking brain-wide multivoxel activation patterns to behaviour
T2 - Examples from language and math
AU - Raizada, Rajeev D.S.
AU - Tsao, Feng Ming
AU - Liu, Huei Mei
AU - Holloway, Ian D.
AU - Ansari, Daniel
AU - Kuhl, Patricia K.
N1 - Funding Information:
The authors would like to thank Tanzeem Choudhury, Nikolaus Kriegeskorte and Russ Poldrack for helpful comments on the manuscript. Kuhl and Raizada were supported by a National Science Foundation (NSF) Science of Learning Center grant (NSF 0354453) to the University of Washington Learning in Informal and Formal Environments (LIFE) Center. Tsao and Liu were supported by grants from The National Science Council of Taiwan. Ansari and Holloway were supported by grants from the NSF Science of Learning Center Program (SBE-0354400), the Natural Sciences and Engineering Council of Canada, Canada Foundation for Innovation (CFI), the Ontario Ministry for Research and Innovation (MRI) and the Canada Research Chairs Program.
PY - 2010/5
Y1 - 2010/5
N2 - A key goal of cognitive neuroscience is to find simple and direct connections between brain and behaviour. However, fMRI analysis typically involves choices between many possible options, with each choice potentially biasing any brain-behaviour correlations that emerge. Standard methods of fMRI analysis assess each voxel individually, but then face the problem of selection bias when combining those voxels into a region-of-interest, or ROI. Multivariate pattern-based fMRI analysis methods use classifiers to analyse multiple voxels together, but can also introduce selection bias via data-reduction steps as feature selection of voxels, pre-selecting activated regions, or principal components analysis. We show here that strong brain-behaviour links can be revealed without any voxel selection or data reduction, using just plain linear regression as a classifier applied to the whole brain at once, i.e. treating each entire brain volume as a single multi-voxel pattern. The brain-behaviour correlations emerged despite the fact that the classifier was not provided with any information at all about subjects' behaviour, but instead was given only the neural data and its condition-labels. Surprisingly, more powerful classifiers such as a linear SVM and regularised logistic regression produce very similar results. We discuss some possible reasons why the very simple brain-wide linear regression model is able to find correlations with behaviour that are as strong as those obtained on the one hand from a specific ROI and on the other hand from more complex classifiers. In a manner which is unencumbered by arbitrary choices, our approach offers a method for investigating connections between brain and behaviour which is simple, rigorous and direct.
AB - A key goal of cognitive neuroscience is to find simple and direct connections between brain and behaviour. However, fMRI analysis typically involves choices between many possible options, with each choice potentially biasing any brain-behaviour correlations that emerge. Standard methods of fMRI analysis assess each voxel individually, but then face the problem of selection bias when combining those voxels into a region-of-interest, or ROI. Multivariate pattern-based fMRI analysis methods use classifiers to analyse multiple voxels together, but can also introduce selection bias via data-reduction steps as feature selection of voxels, pre-selecting activated regions, or principal components analysis. We show here that strong brain-behaviour links can be revealed without any voxel selection or data reduction, using just plain linear regression as a classifier applied to the whole brain at once, i.e. treating each entire brain volume as a single multi-voxel pattern. The brain-behaviour correlations emerged despite the fact that the classifier was not provided with any information at all about subjects' behaviour, but instead was given only the neural data and its condition-labels. Surprisingly, more powerful classifiers such as a linear SVM and regularised logistic regression produce very similar results. We discuss some possible reasons why the very simple brain-wide linear regression model is able to find correlations with behaviour that are as strong as those obtained on the one hand from a specific ROI and on the other hand from more complex classifiers. In a manner which is unencumbered by arbitrary choices, our approach offers a method for investigating connections between brain and behaviour which is simple, rigorous and direct.
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U2 - 10.1016/j.neuroimage.2010.01.080
DO - 10.1016/j.neuroimage.2010.01.080
M3 - Article
C2 - 20132896
AN - SCOPUS:77950535667
SN - 1053-8119
VL - 51
SP - 462
EP - 471
JO - NeuroImage
JF - NeuroImage
IS - 1
ER -