So you want to run an MVPA experiment…

Lindsay MorganApril 9, 2012

Overview

• Study Design• Preprocessing• Pattern Estimation• Voxel Selection• Classifier

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)

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

• 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

Mur et al., Soc Cog Affective Neurosci, 2009

Data transformation so far…

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

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

• 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