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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