a very short introduction to multivariate pattern analysis (mvpa) … · 2014. 11. 10. · a very...
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A very short introduction to multivariate patternanalysis (MVPA) for neuroscience
Michael Hanke & Yaroslav Halchenko
University of Magdeburg, GermanyDartmouth College, USA
Giessen 2014
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SPM via GLM (“Encoding model”)
p(brain activity|behavior)
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SPM via GLM (“Encoding model”)
?ResearchQuestion
ExperimentDesign
Stimuli
GLMGLMGLM
HypothesisTesting
GLM
SPM
??
?
?
? ?
p(brain activity|behavior)
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That’s not enough
Eric Kandel in Principles of Neuroscience
“The task of neural science is to explain behavior in terms ofthe activities of the brain.”
p(brain activity|behavior) 6= p(behavior|brain activity)
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Approach: Meta analysis and Bayes’ theorem
Yarkoni et al., Nature Methods, 2011; http://neurosynth.orgH2 (Dartmouth; Magdeburg) MVPA Intro Giessen 2014 4 / 16
Why multivariate methods?
Haufe et al., 2014, NeuroImageH2 (Dartmouth; Magdeburg) MVPA Intro Giessen 2014 5 / 16
Why multivariate methods?
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Why multivariate methods?
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Pioneering work: visual objects
Haxby et al., Science, 2001H2 (Dartmouth; Magdeburg) MVPA Intro Giessen 2014 7 / 16
k-Nearest Neighbours
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k-Nearest Neighbours
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k-Nearest Neighbours
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k-Nearest Neighbours
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MVPA approach: Reverse the flow!
p(behavior|brain activity)H2 (Dartmouth; Magdeburg) MVPA Intro Giessen 2014 9 / 16
Which classifier?
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Decision models – linear problem
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Decision models – non-linear problem
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Model appropriateness
James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning:with Applications in R. Springer Texts in Statistics. Springer. (free PDF copy)
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Data representation – classification
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Enough said. . . (for now)
Let’s see how we can do this. . .
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References
James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning: with Applications in R.Springer Texts in Statistics. Springer. (free PDF copy).
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