so you want to run an mvpa experiment…
DESCRIPTION
So you want to run an MVPA experiment…. Lindsay Morgan April 9, 2012. Overview. Study Design Preprocessing Pattern Estimation Voxel Selection Classifier. Study Design. Blocked design Smaller # of conditions Better estimate of the average response pattern. Event Related Design - PowerPoint PPT PresentationTRANSCRIPT
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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