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Paris machine learning meetup 17 Sept. 2013

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This document discusses using machine learning and functional magnetic resonance imaging (fMRI) data to predict stimuli viewed by patients. Specifically, it summarizes research by Miyawaki et al. (2008) and Nishimoto et al. (2011) that used fMRI data to predict images viewed by patients, such as faces and houses, with over 50% accuracy. It also provides an example classification task using fMRI data to predict whether a patient viewed a face or house. The document states that this example prediction can be implemented in under 250 lines of code using Scikit-Learn machine learning library.

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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
Alexandre Gramfort Mind Reading with the Scikit-Learn Basics of Functional MRI (fMRI) 2 Oxy. Hb Deoxy. Hb Neurons 3D volumes (1 every 1 or 2s) High spatial resolution (vox ⋍ 2mm) Scanner Nuclear Magnetic Resonance
courtesy of GaelVaroquauxhttp://www.youtube.com/watch?v=uhCF-zlk0jY
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 scanning Machine Learningstim Any variable: healthy?
Alexandre Gramfort Mind Reading with the Scikit-Learn Result from Miyawaki et al. Neuron 2008 5 http://www.youtube.com/watch?v=h1Gu1YSoDaY
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
Alexandre Gramfort Mind Reading with the Scikit-Learn Result from Nishimoto et al. 2011 7 http://www.youtube.com/watch?v=nsjDnYxJ0bo
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
Alexandre Gramfort Mind Reading with the Scikit-Learn Classification example with f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he objective is to be able to predict given an fMRI volume !5678*###############################3(%45678*-#9:#;+*"#/:9:# !"#$$%&'()*'+)#,-. FG4(H8&?#%*)((*#B?()?E#8C+I(@######54 F<5C7+4(#74(H8&?(H#=+A(=#J8?'#?4%(#?+4I(? ?'(#>(+?%4(#)7+&(@ F.*#?'8)#&+)(#74(H8&?(H#M#?4%(# FN(7(+?#>54#+==#)+C7=() FOD(4+I( ie. objective: Predict giveny = { 1, 1} x 2 Rp y = { 1, 1} !+,-#./ 0123(%45678*###############################3(%45678*-#9:#;+*"#/:9:# !"#$$%&'()*'+)#,-. FG4(H8&?#%*)((*#B?()?E#8C+I(@######54 F<5C7+4(#74(H8&?(H#=+A(=#J8?'#?4%(#?+4I(? ?'(#>(+?%4(#)7+&(@ F.*#?'8)#&+)(#74(H8&?(H#M#?4%(# FN(7(+?#>54#+==#)+C7=() FOD(4+I( Patient Controlsvs. Faces Housesvs. ... ...vs. 1 -1vs.
Demo on Haxby et al. Science 2001 Challenge: Predict the object category viewed Sample stimuli: Face House Chair Shoe
Alexandre Gramfort Mind Reading with the Scikit-Learn Miyawaki et al. 2008 with Scikit-Learn 11 < 250 Lines of codes
Alexandre Gramfort alexandre.gramfort@telecom-paristech.fr http://alexandre.gramfort.net http://www.github.com/agramfort @agramfort Contact:

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Paris machine learning meetup 17 Sept. 2013

  • 1. 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
  • 2. Alexandre Gramfort Mind Reading with the Scikit-Learn Basics of Functional MRI (fMRI) 2 Oxy. Hb Deoxy. Hb Neurons 3D volumes (1 every 1 or 2s) High spatial resolution (vox ⋍ 2mm) Scanner Nuclear Magnetic Resonance
  • 4. 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 scanning Machine Learningstim Any variable: healthy?
  • 5. Alexandre Gramfort Mind Reading with the Scikit-Learn Result from Miyawaki et al. Neuron 2008 5 http://www.youtube.com/watch?v=h1Gu1YSoDaY
  • 6. 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
  • 7. Alexandre Gramfort Mind Reading with the Scikit-Learn Result from Nishimoto et al. 2011 7 http://www.youtube.com/watch?v=nsjDnYxJ0bo
  • 8. 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
  • 9. Alexandre Gramfort Mind Reading with the Scikit-Learn Classification example with f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he objective is to be able to predict given an fMRI volume !5678*###############################3(%45678*-#9:#;+*"#/:9:# !"#$$%&'()*'+)#,-. FG4(H8&?#%*)((*#B?()?E#8C+I(@######54 F<5C7+4(#74(H8&?(H#=+A(=#J8?'#?4%(#?+4I(? ?'(#>(+?%4(#)7+&(@ F.*#?'8)#&+)(#74(H8&?(H#M#?4%(# FN(7(+?#>54#+==#)+C7=() FOD(4+I( ie. objective: Predict giveny = { 1, 1} x 2 Rp y = { 1, 1} !+,-#./ 0123(%45678*###############################3(%45678*-#9:#;+*"#/:9:# !"#$$%&'()*'+)#,-. FG4(H8&?#%*)((*#B?()?E#8C+I(@######54 F<5C7+4(#74(H8&?(H#=+A(=#J8?'#?4%(#?+4I(? ?'(#>(+?%4(#)7+&(@ F.*#?'8)#&+)(#74(H8&?(H#M#?4%(# FN(7(+?#>54#+==#)+C7=() FOD(4+I( Patient Controlsvs. Faces Housesvs. ... ...vs. 1 -1vs.
  • 10. Demo on Haxby et al. Science 2001 Challenge: Predict the object category viewed Sample stimuli: Face House Chair Shoe
  • 11. Alexandre Gramfort Mind Reading with the Scikit-Learn Miyawaki et al. 2008 with Scikit-Learn 11 < 250 Lines of codes