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Alexandre Gramfort http://alexandre.gramfort.net http://scikit-learn.org “Lire dans les pensées avec Scikit-Learn” “Mind Reading with Scikit-Learn” Paris Machine Learning Meetup - Sept. 2013
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Paris machine learning meetup 17 Sept. 2013

Jan 27, 2015

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Brief intro to machine learning for mining functional MRI data
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Page 1: Paris machine learning meetup 17 Sept. 2013

Alexandre Gramforthttp://alexandre.gramfort.net

http://scikit-learn.org

“Lire dans les pensées avec Scikit-Learn”

“Mind Reading with Scikit-Learn”

Paris Machine Learning Meetup - Sept. 2013

Page 2: Paris machine learning meetup 17 Sept. 2013

Alexandre Gramfort Mind Reading with the Scikit-Learn

Basics of Functional MRI (fMRI)

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Oxy. Hb

Deoxy. Hb

Neurons

3D volumes(1 every 1 or 2s)

High spatialresolution

(vox ⋍ 2mm)

Scanner

NuclearMagnetic

Resonance

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Page 3: Paris machine learning meetup 17 Sept. 2013

courtesy of Gael Varoquauxhttp://www.youtube.com/watch?v=uhCF-zlk0jY

Page 4: Paris machine learning meetup 17 Sept. 2013

Alexandre Gramfort Mind Reading with the Scikit-Learn

Learning from fMRI

4

Image,sound, task

fMRI volumes

Challenge: Learn and Predict from the fMRI data

scanningMachine Learningstim

Any variable:healthy?

Page 5: Paris machine learning meetup 17 Sept. 2013

Alexandre Gramfort Mind Reading with the Scikit-Learn

Result from Miyawaki et al. Neuron 2008

5

http://www.youtube.com/watch?v=h1Gu1YSoDaY

Page 6: Paris machine learning meetup 17 Sept. 2013

Alexandre Gramfort Mind Reading with the Scikit-Learn

Result from Miyawaki et al. Neuron 2008

6

• Some details about the data:

• 2h of scanning

• 1 image for 12s then 12s of rest

• 800MB of raw data (200MB compressed)

• 5,000 good voxels

Page 7: Paris machine learning meetup 17 Sept. 2013

Alexandre Gramfort Mind Reading with the Scikit-Learn

Result from Nishimoto et al. 2011

7

http://www.youtube.com/watch?v=nsjDnYxJ0bo

Page 8: Paris machine learning meetup 17 Sept. 2013

Alexandre Gramfort Mind Reading with the Scikit-Learn

Result from Nishimoto et al. 2011

8

• Some details about the data:

• 30GB of stimuli (15 frames/s in .png for 3h)

• about 4,000 volumes

• about 10GB of raw data

• 30,000 “good” voxels

• > 3h in the scanner

Page 9: Paris machine learning meetup 17 Sept. 2013

Alexandre Gramfort Mind Reading with the Scikit-Learn

Classification example with fMRI

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The objective is to be ableto predict

given an fMRI volume

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objective: Predict given y = {�1, 1} x 2 Rp

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Patient Controlsvs.Faces Housesvs.

... ...vs.1 -1vs.

Page 10: Paris machine learning meetup 17 Sept. 2013

Demo onHaxby et al. Science 2001

Challenge: Predict the object category viewed

Sample stimuli:

Face House Chair Shoe

Page 11: Paris machine learning meetup 17 Sept. 2013

Alexandre Gramfort Mind Reading with the Scikit-Learn

Miyawaki et al. 2008 with Scikit-Learn

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< 250 Lines of codes