Brief intro to machine learning for mining functional MRI data

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

3. courtesy of GaelVaroquauxhttp://www.youtube.com/watch?v=uhCF-zlk0jY

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 fMRI
9
!!"#$%&'()*+,-#./
0123(%45678*###############################3(%45678*-#9:#;+*"#/:9:#
<=+))8>8&+?85*#@#748*&87=()
67+&(#5>#?'(#A4+8*#>(+?%4()#BC%=?8D+48+?(E
!"#$$%&'()*'+)#,-.
FG4(H8&?#%*)((*#B?()?E#8C+I(@######54
F<5C7+4(#74(H8&?(H#=+A(=#J8?'#?4%(#?+4I(?
K
L8D(*#+#?4+8*8*I#H+?+#)(?#@#7+84)
5>#B>(+?%4()-#=+A(=E-#(/'",#?'(#
&'+4+&?(48)?8&#5>#(+&'#&+?(I54,#8*#
?'(#>(+?%4(#)7+&(@
F.*#?'8)#&+)(#74(H8&?(H#M#?4%(#
FN(7(+?#>54#+==#)+C7=()
FOD(4+I(
!./
0123(%45678*###############################3(%45678*-#9:#;+*"#/:9:#
!"#$$%&'()*'+)#,-.
FG4(H8&?#%*)((*#B?()?E#8C+I(@######54
F<5C7+4(#74(H8&?(H#=+A(=#J8?'#?4%(#?+4I(?
F.*#?'8)#&+)(#74(H8&?(H#M#?4%(#
FN(7(+?#>54#+==#)+C7=()
FOD(4+I(
The 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

12. Alexandre Gramfort
alexandre.gramfort@telecom-paristech.fr
http://alexandre.gramfort.net
http://www.github.com/agramfort
@agramfort
Contact: