1. How To Do Multivariate Pattern Analysis

2. What is MVPA?
Animations from Meyer & Kaplan (in press), Journal of Visualized Experiments

3. Univariate vs. multivariate analysis of
Not significant
Average
Average V1 V2 V3 V4 V5 V6
Univariate vs. multivariate analysis of
fMRI data
Individually not significant…
but significant when considered
in conjunction! — V1 V2 V3 V4 V5 V6

4. Multivariate pattern analysis:
Fusiform face area
Significant?
Univariate analysis:
Multivariate pattern analysis:
Reverse inference
The direction in which the correlation between the perceptual stimuli and brain activity is mapped does not matter from a statistical point of view.
Primary visual cortex V1 V2 V3 V4 V5 V6
Multivariate pattern
analysis:
Predictable?

5. Training trials Classifier Testing trials ? Testing trials Stimulus v
V1 activity
pattern
Stimulus v v v v v v v v v v v v
Classifier
Performance: 75%
V1 activity
pattern
Testing trials
Classifier guess ?
V1 activity
pattern v
Stimulus
Testing trials

6. Cross-validation paradigm:
Runs 1 2 3 4 5 6 7 8
Cross-validation steps 1
Performance 1
Overall
performance 2
Performance 2 3
Performance 3 4
Performance 4 5
Performance 5 6
Performance 6 7
Performance 7 8
Performance 8
Training run
Testing run

7. What do I need to do MVPA?
An fMRI experiment with an appropriate design
Almost any modern computer
PyMVPA software

8. Experiment design
As many trials as possible to train the classifier

9. Experiment design As many trials as possible to train the classifier
Clear BOLD pattern resulting from each trial TR TR TR TR TR TR TR TR TR TR TR TR TR TR TR TR TR TR TR TR TR

10. Sparse temporal sampling
Video clip TR

11. What is the input to the classifier?TR TR TR TR TR TR TR TR TR TR TR TR
TASK A
TASK B
Raw fMRI data

12. What is the input to the classifier?TR TR TR TR TR TR TR TR TR TR TR TR
TASK A
TASK B
Raw fMRI data
Averaged fMRI data
AVG
AVG

13. What is the input to the classifier?TR TR TR TR TR TR TR TR TR TR TR TR
TASK A
TASK B
Raw fMRI data
Averaged fMRI data
beta values from a GLM analysis

14. Data pre-processing Motion-correction Smoothing (?)
FSL
Motion-correction
Smoothing (?)
Trend removal / high pass filter
Z-scoring
PyMVPA

15. Gathering Your Tools

16. Why PyMVPA Alternative toolbox: Princeton MVPA toolbox
PyMVPA is free and open-source, does not require Matlab
Well-maintained and flexible
Python is great once you get used to it
I know how to use it

17. Python An interpreted, modern programming language
Produces very clear, easy to read code
Object-oriented
Extensive scientific computing modules available for python (scipy, nipy, etc.)

18. Brief python demo
Python command line
Python scripting
iPython

19. Gathering Your Tools Install PyMVPA: http://www.pymvpa.org
Version .4x versus .6x
Linux: Very easy, just type one command
Windows:
Mac: Instructions on website will only install .4x. To install .6x follow my guide:

20. Gather Your Ingredients

21. Ingredients 1 4-dimensional functional data file, motion-corrected
This should be all of your data from one subject. If you did multiple scans, concatenate them into one single 4D file, all motion corrected to the same volume

22. Sample preprocessing script

23. Ingredients 1 4-dimensional functional data file, motion-corrected
This should be all of your data from one subject. If you did multiple scans, concatenate them into one single 4D file, all motion corrected to the same volume
1 text file which contains “attributes”:
Column 1 labels each volume with a “target” category
Column 2 labels each volume with a “chunk”, e.g. scan
1 Mask file in the functional space

24. Terminology
VOLUMES
dog
violin
cow
vase
VOXELS

25. Terminology
SAMPLES
TARGETS
dog
violin
cow
vase
FEATURES
CHUNK

26. Sample attributes file
Sparse design
Block design

27. Getting started with pymvpa
Start python or ipython
Import the pymvpa module and explore it

28. Sample dataset
Subject saw nine different 5-second video clips: dog, cow, rooster, violin, piano, bass, vase, chainsaw, coins
One single volume acquired 7 seconds after the start of the clip
Eight scans, each stimulus seen 3 times in each scan (24 times across the experiment)

29. Getting started with pymvpa
Read in your attributes
Create a dataset

30. Mappers Mappers transform data samples
Dataset automatically maps data from 4D to 2D
Many mappings are reversible

31. Data pre-processing steps
Detrending
Z-scoring

32. Partitioners Used to split the data into training set and testing set
HalfPartitioner()
OddEvenPartitioner()
NFoldPartitioner()

33. Choosing a classifier algorithm
Nearest neighbor
 Support Vector Machine (SVM)
 Linear Discriminant Analysis (LDA)
 Gaussian Naive Bayes (GNB)
 Sparse Multinomial Linear Regression (SMLR)
...

34. Choosing a classifier algorithm

35. Support Vector Machine
Draws a hyperplane to separate the categories, maximizing the margin between classes

36. Support Vector Machine

37. Support Vector Machine
Draws a hyperplane to separate the categories, maximizing the margin between classes
Works quickly with on large feature sets (lots of voxels)
Common in fMRI pattern learning literature
Binary classifier
Linear version chosen (very little advantage to nonlinear SVM with lots of features and few stimuli)

38. Misaki et al, 2010, NeuroImage

39. Choosing a classifier algorithm

40. Setting up cross-validation

41. Go!

42. Results
Show confusion matrix:
Plot confusion matrix:

43. Significance testing Binomial test Permutation testing
Voxel sensitivity maps

44. Significance testing
Binomial test

45. Significance testing
Binomial test

46. Significance testing Permutation testing
• Generate a null distribution by randomly permuting pattern labels

47. Searchlight analysis

48. Searchlight analysis

49. To study on your own
Temporal exploration: averaging, temporal searchlights
Sensitivity maps
Permutation testing