multivariate pattern analysis

Multivariate Pattern AnalysisJohn ClitheroDuke University01.13.10

Overview•MVPA and fMRI

•Examples in the Literature

•PyMVPA Example

Motivation for MVPA in fMRI•Complements univariate approaches that

investigate the involvement of regions in a specific mental activity.▫Investigates the representational content of regions.▫Distributed responses and representations.

•Model-free vs model-based analysis.•Machine learning is concerned with developing

algorithms that automatically learn with experience.▫For each example, learn to predict the value

of its label (can be one of two classes, yes/no).

MVPA and fMRI data

Norman et al. (2006)

Orientation Selectivity in Visual Cortex

Kamitani and Tong (2005)

Unconscious Determinants of Free Decisions

Soon et al. (2008)

Grey Matter Density and Psychotic Illness

Sun et al. (2009)

Pattern classification analysis achieved 86.1% accuracy in discriminating patients from controls using leave-one-out cross-validation.

Performance Evaluation• Main goal is accurate

prediction.• How to estimate true error

rate?• Use the entire training data

to select classifier and estimate the error rate▫ Final model will normally overfit

the training data.• Use N-fold Cross-Validation

▫ use N-1 folds for training and the remaining one for testing.

▫ Estimate of true error rate is then an average of N error rates.

What exactly goes into the classifier?Features for the classifier will be M voxels.

•Time-compressed preprocessed BOLD signal (average of 2 or 3 consecutive TRs).

•Single-trial beta estimates from a GLM.

•Same voxels but at different timepoints.

•Structural information (e.g., GMD).

Feature selection can be done in many ways.

•Mean activation level.•Activation differences between classes.•Consistent behavior.•Filter: rank all voxels, pick the best M.•Recursive: Rank, pick best P%. Repeat.•Searchlights.

What exactly goes into the classifier?

Clithero et al. (2009)

Processes of Economic Valuation

Shown is the increase (red) or decrease (blue) in CV performance when local information from one ROI combined with local information from another.

Clithero et al. (Submitted)

What does it mean to find common neural patterns within individuals versus across individuals?

Comparing Within- and Cross-Participant Models

Clithero et al. (Submitted)

Clithero et al. (Submitted)

Predictive Spatial Patterns and Individual Behavior

Dissimilarity Analysis

Kriegeskorte et al. (2008)

Is there an elegant way to compare activity patterns of experimental conditions?

Kriegeskorte et al. (2008)

Connecting Research Branches

Odor quality coding and categorization

Howard et al. (2009)

Common steps in MVPA studies•Preprocessing •Picking a feature space •Making examples •Feature selection •Training/testing•Correlation/similarity•Reporting results

PyMVPA Terminology

Hanke et al. (2009)

An (Almost) Complete Analysis in PyMVPA

You don’t need to know a lot of Python…

…to do use PyMVPA.

• PyMVPA is freely available and (sometimes) easy to install.• MVPA is generally very computationally intensive, but

PyMVPA has optimized many of its scripts.▫ Analyses can take hours or even days using a single processor.▫ Computing clusters make life better if you want to do large-scale or

whole-brain MVPA.

• What you need from Python that is probably not already installed on your linux box:

Getting your data into PyMVPA

Classifier (Lots of Options)• k-Nearest Neighbor• Ridge Regression• Penalized Logistic Regression• Sparse Multinomial Logistic Regression• Support Vector Machines

Feature Spaces and Selection•PyMVPA has built-in functions for

▫Searchlight feature selection.▫Recursive feature selection.

•To run searchlight analyses for all voxels in your mask:

Sensitivity Analysis

Confusion Matrices and Generalization

LaConte (2009)

Statistics•Multiple Comparisons (e.g., # of

searchlights)•Binomial Tests•Bootstrap estimates of accuracy variance

▫How dependent is accuracy/model on the presence of specific examples?

•Permutation tests ▫How likely is the process to bias results

optimistically in the presence of no information in the example labels?

PyMVPA is very flexible…

Hanke et al. (2009)

Relevant Packages• SciPy (http://www.scipy.org/)

• Pynifti (http://niftilib.sourceforge.net/pynifti/)

• PyMVPA (http://www.pymvpa.org/)

• LIBSVM (http://www.csie.ntu.edu.tw/~cjlin/libsvm/)

• MVPA Toolbox (http://www.csbmb.princeton.edu/mvpa/)

• 3DSVM (http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dsvm.html)

(Possibly) Helpful Readings• Norman et al. (2006). Beyond mind-reading: multi-voxel

pattern analysis of fMRI data. Trends in Cognitive Sciences, 10: 424-430.

• Haynes and Rees (2006). Decoding mental states from brain activity in humans. Nature Reviews Neuroscience, 7: 523-34.

• Mitchell et al. (2004). Learning to decode cognitive states from brain images. Machine Learning, 57:145-175.

• Mur et al. (2009). Representational content with pattern-information fMRI: an introductory guide. Social Cognitive and Affective Neuroscience, 4: 101-109.

• Kriegeskorte et al. (2009). Representational similarity analysis – connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 2: 1-28.

• Kriegeskorte et al. (2009). Circular analysis in systems neuroscience: the dangers of double dipping. Nature Neuroscience, 12: 535-540.

• Hanke et al. (2009). PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics, 7: 37-53.

Acknowledgments• Scott Huettel• McKell Carter• David Smith

• Chris Coutlee• Anne Harsch• Ed McLaurin• O’Dhaniel Mullette-

Gillman• Brandi Newell• Allison Scott• Adrienne Taren• Vinod Venkatraman• Amy Winecoff• Richard Yaxley