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)
Cambridge, UK
| (wiki)
09 May 2011 | London | Thanks to Rik Henson and Dan CBU 1

2. Overview The SPM approach to M/EEG Statistics:
A mass-univariate statistical approach to inference regarding effects in space/time/frequency (using replications across trials or subjects).
In Sensor Space:
2D Time-Frequency
3D Topography-by-Time
In Source Space:
3D Time-Frequency contrast images
SPM vs SnPM vs PPM vs FDR
Other issues & Future directions 2

3. The Multiple Comparison Problem
Random Field Theory (RFT) is a method for correcting for multiple statistical comparisons with N-dimensional spaces (for parametric statistics, e.g., Z-, T-, F- statistics).
Note these comparisons are often made implicitly, e.g., by viewing (“eye- balling”) data before selecting a time-window or set of channels for statistical analysis! RFT correction depends on the search volume.
-> When is there an effect in time, e.g., GFP (1D)?
-> When/What frequency is there an effect in time and frequency (2D)?
-> When/Where is there an effect in sensor topography space and time (3D)?
-> When/Where is there an effect in source space and time (4-ish D)?
Worsley Et Al (1996). Human Brain Mapping, 4:58-73

4. CTF Multimodal Faces Dataset | SPM8 Manual (Rik Henson)
CTF MEG & EEG Data:
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. Neuromag Faces Dataset | Dan Wakeman & Rik Henson @ CBU
Neuromag MEG & EEG Data:
70 EEG
102 Magnetometers
204 Planar Gradiometers
H&VEOG
ECG
1mm3 sMRI
6 sessions
faces & scrambled-face images (480 trials)
Group:
N=18
Biomag 2010 Award-Winning Dataset!

6. Neuromag Lexical Decision Dataset | Taylor & Henson @ CBU
Neuromag MEG Data:
102 Magnetometers
204 Planar Gradiometers
H&VEOG
ECG
1mm3 sMRI
4 sessions
words & pseudowords presented visually (480 trials)
Group:
N=18
Taylor & Henson, submitted

7. 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
CTF Multimodal Faces

8. 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)
Neuromag Faces

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

10. GLM: Removing variance due to confounds
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

11. 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
Neuromag Lexical Decision

12. 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)
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

13. Where is an effect in source space (3D)?
EEG
MEG
E+MEG A
Neuromag Faces

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)
Sensors
Mags
Grads
Over
Participants
Over
Voxels
Neuromag Lexical Decision

15. 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 (fused sensors)
MSP
IID
Over
Participants
Over
Voxels
Neuromag Lexical Decision

16. 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:
CTF Multimodal Faces

17. 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)
CTF Multimodal Faces

18. 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 FWE 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
CTF Multimodal Faces

19. Where and When so effects emerge/disappear in source space (3D)?
Condition x Time-window
Interactions
Factorising time allows you to infer (rather than simply describe) when effects emerge or disappear.
We've used a data-driven method (hierarchical cluster analysis) in sensor space to define time-windows of interest for source-space analyses.
* high-dimensional distance between topography vectors
* cut the cluster tree where the solution is stable over longest range of distances
Taylor & Henson, submitted
Neuromag Lexical Decision

20. Where and When so effects emerge/disappear in source space (3D)?
Condition x Time-window
Interactions
Factorising time allows you to infer (rather than simply describe) when effects emerge or disappear.
We've used a data-driven method (hierarchical cluster analysis) in sensor space to define time-windows of interest for source-space analyses.
* estimate sources in each sub-time-window
* submit to GLM with conditions & time-windows as factors (here: Cond effects per TWin)
Taylor & Henson, submitted
Neuromag Lexical Decision

21. Where and When so effects emerge/disappear in source space (3D)?
Condition x Time-window
Interactions
Factorising time allows you to infer (rather than simply describe) when effects emerge or disappear.
We've used a data-driven method (hierarchical cluster analysis) in sensor space to define time-windows of interest for source-space analyses.
* estimate sources in each sub-time-window
* submit to GLM with conditions & time-windows as factors (here: Cond x TW interactions)
Taylor & Henson, submitted
Neuromag Lexical Decision

22. Future Directions Extend RFT to 2D cortical surfaces (+1D time)?
(e.g., Pantazis et al., 2005, NImage)
Novel approaches to the multiple comparison problem (e.g., 'unique extrema' in sensors: Barnes et al., 2011, NImage)
Go multivariate?
To identify linear combinations of spatial (sensor or source) effects in time and space
(e.g., Carbonnell, 2004, NImage)
To detect spatiotemporal patterns in 3D images
(e.g., Duzel, 2004, NImage; Kherif, 2003, NImage)

23. - The End -
Thanks!