So you want to run
an MVPA experiment…
Lindsay Morgan
April 9, 2012
Overview
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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
• Larger # of conditions
– Similarity analyses
• Better estimate of the
response distribution
across exemplars
• Psychologically less
predictable
• Requires sequence
optimization (e.g.,
OptSeq, de Bruijn)
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
Pre-processing
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Pre-process each run separately
Slice time correction
Motion correction
Smoothing?
To Smooth or Not to Smooth?
Op de Beeck NeuroImage 2010
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
Data transformation so far…
Mur et al., Soc Cog Affective Neurosci, 2009
Ungrouped design
• 96 images
• Each image
presented 1x/run
• 3 comparisons
• Inanimate vs.
animate
• Face vs. body
• Natural vs.
artificial
Kriegeskorte et al., Frontiers Sys Neurosci, 2008
Betas or t values?
Misaki et al., NeuroImage, 2010
Pattern Normalization
Misaki et al., NeuroImage, 2010
Pattern Normalization
Misaki et al., NeuroImage, 2010
Data transformation so far…
Mur et al., Soc Cog Affective Neurosci, 2009
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
The Classifier
Misaki et al., NeuroImage, 2010
Which classifier should you use?
Misaki et al., NeuroImage, 2010
Data transformation complete!
Mur et al., Soc Cog Affective Neurosci, 2009
How to implement the classifier
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AFNI 3dsvm
Princeton MVPA toolbox
PyMVPA toolbox
LIBSVM toolbox
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