So you want to run an MVPA experiment… Lindsay Morgan April 9, 2012.
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Transcript of So you want to run an MVPA experiment… Lindsay Morgan April 9, 2012.
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So you want to run an MVPA experiment…
Lindsay MorganApril 9, 2012
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Overview
• Study Design• Preprocessing• Pattern Estimation• Voxel Selection• Classifier
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Study DesignBlocked design
• Smaller # of conditions• Better estimate of the
average response pattern
Event Related Design• Larger # of conditions– Similarity analyses
• Better estimate of the response distribution across exemplars
• Psychologically less predictable
• Requires sequence optimization (e.g., OptSeq, de Bruijn)
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Study Design Suggestions
• Multiple runs– Independent data sets for training & testing– Many short runs preferable to a few long runs
(Coutanche & Thompson-Schill NeuroImage 2012)• Equal # of exemplars per stimulus class– Or use subsamples of more numerous class
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Pre-processing
• Pre-process each run separately• Slice time correction• Motion correction• Smoothing?
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To Smooth or Not to Smooth?
Op de Beeck NeuroImage 2010
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Pattern Estimation
Raw signal intensity values• Suitable for block or
slow event-related
Betas (parameter estimates) or t values
• Suitable for all designs• Derived from GLM– Accounts for overlap in
HRF– Can remove motion
effects and linear trends
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Mur et al., Soc Cog Affective Neurosci, 2009
Data transformation so far…
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Kriegeskorte et al., Frontiers Sys Neurosci, 2008
Ungrouped design• 96 images • Each image
presented 1x/run• 3 comparisons• Inanimate vs.
animate• Face vs. body• Natural vs.
artificial
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Betas or t values?
Misaki et al., NeuroImage, 2010
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Pattern Normalization
Misaki et al., NeuroImage, 2010
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Pattern Normalization
Misaki et al., NeuroImage, 2010
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Data transformation so far…
Mur et al., Soc Cog Affective Neurosci, 2009
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Voxel Selection
• Typically, performance decreases as the # of voxels increases
• Data must be independent of classifier– Anatomically-defined region– Functional localizer– Training set from your experimental data• E.g., ANOVA for all conditions at each voxel select top
N voxels
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The Classifier
Misaki et al., NeuroImage, 2010
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Which classifier should you use?
Misaki et al., NeuroImage, 2010
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Data transformation complete!
Mur et al., Soc Cog Affective Neurosci, 2009
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How to implement the classifier
• AFNI 3dsvm• Princeton MVPA toolbox• PyMVPA toolbox• LIBSVM toolbox
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General Conclusions
• Design your experiment to yield as many independent patterns as possible
• Estimate your patterns using t values (or z scores)
• Use a linear classifier