multivariate pattern analysis
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Multivariate Pattern Analysis. John Clithero Duke University 01.13.10. 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. - PowerPoint PPT PresentationTRANSCRIPT
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