1. Statistical Analysis of M/EEG Sensor- and Source-Level Data
Practical aspects of…
Statistical Analysis of M/EEG Sensor- and Source-Level Data
Jason Taylor
MRC Cognition and Brain Sciences Unit (CBU)
Cambridge Centre for Ageing and Neuroscience (CamCAN)
19 January 2011 | Brussels | Almost entirely stolen from Rik Henson

2. Overview
A mass-univariate statistical approach to localising effects in space/time/frequency (using replications across trials/subjects)…
Sensor Space:
2D Time-Frequency (within-subject)
3D Topography-by-Time (within-subject)
3D Topography-by-Time (between-subjects)
Source Space:
3D Time-Frequency contrast images
SPM vs SnPM vs PPM vs FDR
Other issues & Future directions

3. Random Field Theory (RFT)
RFT is a method for correcting for multiple statistical comparisons with N-dimensional spaces (for parametric statistics, e.g., Z-, T-, F- statistics)…
-> When is there an effect in time, eg GFP (1D)?
-> Where is there an effect in time-frequency space (2D)?
-> Where is there an effect in time-sensor space (3D)?
-> Where is there an effect in time-source space (4D)?
Worsley Et Al (1996). Human Brain Mapping, 4:58-73

4. “Multimodal Dataset” in SPM8 Manual (Rik Henson)
Single subject:
128 EEG
275 MEG
3T fMRI (with nulls)
1mm3 sMRI
2 sessions
~160 face trials and ~160 scrambled trials per session
Group:
N=12 subjects, as in Henson et al, 2009 a,b,c)
Chapter 33 SPM8 Manual

5. “CBU Neuromag Dataset” (Dan Wakeman & Rik Henson)
Single subject:
70 EEG
102 Magnetometers
204 Planar Gradiometers
H&VEOG
ECG
1mm3 sMRI
6 sessions
faces & scrambled-face images
Group:
N=18
Biomag 2010 Award-Winning Dataset!

6. Where is an effect in time-frequency space?
Single MEG channel
Mean over trials of Morlet wavelet projection (i.e., induced + evoked)
Write t x f image per trial
Compute SPM, RFT correct
Faces > Scrambled
Faces
Scrambled
Kilner et al., 2005, Nsci Letters
Multimodal Dataset

7. Where is an effect in time-frequency space?
Single channel
-Effect over
subjects
EEG
MEG (Mag) ms
8-18Hz
Faces > Scrambled
Freq (Hz)
Freq (Hz)
Time (ms)
Time (ms)
CBU Neuromag Dataset

8. Where is an effect in sensor-time space?
Topography-x-Time Image (EEG)
Stats (F): Faces vs Scrambled
(single subject, over trials)
Multimodal Dataset

9. Where is an effect in sensor-time space?
Each trial-type (6)
Confounds (4)
Each trial
W/in Subject
(1st-level) model
beta_00* images reflect mean (adjusted) 3D topo-time volume for each condition
Henson et al., 2008, NImage

10. Where is an effect in sensor-time space?
Analysis over subjects (2nd Level)
NOTE for MEG: Complicated by variability in head-position
SOLUTION: Virtual transformation to same position in sessions, subjects
Without transformation to Device Space
With transformation to Device Space
Stats over 18 subjects, RMS of planar gradiometers
Improved (i.e., more blobs,
larger T values) w/ transform
Taylor & Henson (2008) Biomag

11. Where is an effect in source space (3D)?
Source Analysis over N=12 subjects, MEG: 102 mags, MSP, evoked, RMS of source energy, smoothed 12-mm kernel
STEPS:
Estimate evoked/induced energy (RMS) at each dipole for a certain time-frequency window of interest
(e.g., ms, 8-18 Hz)
For each condition (Faces, Scrambled images)
Smooth along 2D surface
Write data to 3D image (in MNI space)
Smooth by 3D gaussian
Analysis Mask
Note sparseness of MSP inversions….
Henson et al., 2007, NImage

12. Where is an effect in source space (3D)?
EEG
MEG
E+MEG A
CBU Neuromag Dataset

13. Where is an effect in source space (3D)?
Classical SPM approach
Caveats:
Inverse operator induces long-range error correlations (e.g., similar gain vectors from non-adjacent dipoles with similar orientation), making RFT conservative
Need a cortical mask, else activity ‘smoothed’ outside
Distributions over subjects may not be Gaussian (e.g., if sparse, as in MSP)
(Taylor & Henson, submitted; Biomag 2010)
Sources
MSP
IID
Over
Participants
Over
Voxels

14. Where is an effect in source space (3D)?
Classical SPM approach
Caveats:
Inverse operator induces long-range error correlations (e.g., similar gain vectors from non-adjacent dipoles with similar orientation), making RFT conservative
Need a cortical mask, else activity ‘smoothed’ outside
Distributions over subjects may not be Gaussian (e.g., if sparse, as in MSP)
(Taylor & Henson, submitted; Biomag 2010)
Sources
Mags
Grads (RMS)
Over
Participants
Over
Voxels

15. Where is an effect in source space (3D)?
Non-Parametric Approach (SnPM)
Robust to non-Gaussian distributions
Less conservative than RFT when dfs<20
Caveats:
No idea of effect size (e.g., for power, future expts)
Exchangeability difficult for more complex designs
(Taylor & Henson, Biomag 2010)
p<.05 FWE
SnPM Toolbox by Holmes & Nichols:
Multimodal Dataset

16. Where is an effect in source space (3D)?
Posterior Probability Maps (PPMs)
Bayesian Inference
No need for RFT (no MCP)
Threshold on posterior probabilty of an effect greater than some size
Can show effect size after thresholding
Caveats:
Assume Gaussian distribution (e.g., of mean over voxels), sometimes not met (ok for IID…?)
(Taylor & Henson, submitted; Biomag 2010)
p>.95 (γ>1SD)
Multimodal Dataset

17. Where is an effect in source space (3D)?
Topological FDR correction
Choose an uncorrected threshold (e.g., p<.001) to define topologial features (e.g., peak and cluster size)
Topological FDR actually produces higher corrected p-values (i.e., fewer suprathreshol voxels) than FEW in the data used here
Caveat:
If sources are constrained to a gray matter cortical surface, are topological features as meaningful?
(Taylor & Henson, Biomag 2010)
SPM
p<.001 unc
Multimodal Dataset

18. Future Directions Extend RFT to 2D cortical surfaces (+1D time)?
(e.g., Pantazis et al., 2005, Nimage)
Go multivariate?
To localise linear combinations of spatial (sensor or source) effects in time
(e.g., Carbonnell, 2004, NImage; Barnes & Litvak, Biomag 2010)
To detect spatiotemporal patterns in 3D images
(e.g., Duzel, 2004, NImage; Kherif, 2003, NImage)

19. - The End -
Thanks!