Statistical Analysis of M/EEG Sensor- and Source-Level Data

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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
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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

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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)
• -gt When is there an effect in time, eg GFP (1D)?
  
• -gt Where is there an effect in time-frequency
  space (2D)?
• -gt Where is there an effect in time-sensor space
  (3D)?
• -gt Where is there an effect in time-source space
  (4D)?

Worsley Et Al (1996). Human Brain Mapping, 458-73
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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
• N12 subjects, as in Henson et al, 2009 a,b,c)

Chapter 33 SPM8 Manual
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CBU Neuromag Dataset (Dan Wakeman Rik Henson)

• Single subject
• 70 EEG
• 102 Magnetometers
• 204 Planar Gradiometers
• HVEOG
• ECG
• 1mm3 sMRI
• 6 sessions
• faces scrambled-face images
• Group
• N18

Biomag 2010 Award-Winning Dataset!
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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 gt Scrambled
Faces
Scrambled
Kilner et al., 2005, Nsci Letters
Multimodal Dataset
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Where is an effect in time-frequency space?
Single channel -Effect over subjects
EEG
MEG (Mag)
100-220ms 8-18Hz
Faces gt Scrambled
6 90
6 90
Freq (Hz)
Freq (Hz)
-500
1000
-500
1000
Time (ms)
Time (ms)
CBU Neuromag Dataset
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Where is an effect in sensor-time space?
Stats (F) Faces vs Scrambled (single subject,
over trials)
Topography-x-Time Image (EEG)
Multimodal Dataset
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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
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Where is an effect in sensor-time space?
Analysis over subjects (2nd Level) NOTE for MEG
Complicated by variability in head-position SOLUTI
ON 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
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Where is an effect in source space (3D)?
Source Analysis over N12 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., 100-220ms, 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
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Where is an effect in source space (3D)?
MEG
EMEG
EEG
A
CBU Neuromag Dataset
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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
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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
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Where is an effect in source space (3D)?
Non-Parametric Approach (SnPM) Robust to
non-Gaussian distributions Less conservative
than RFT when dfslt20 Caveats No idea of effect
size (e.g., for power, future expts) Exchangeabil
ity difficult for more complex designs (Taylor
Henson, Biomag 2010)
plt.05 FWE
SnPM Toolbox by Holmes Nichols http//go.warwic
k.ac.uk/tenichols/software/snpm/
Multimodal Dataset
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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)
pgt.95 (?gt1SD)
Multimodal Dataset
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Where is an effect in source space (3D)?
plt.001 unc
SPM
Topological FDR correction Choose an uncorrected
threshold (e.g., plt.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)
Multimodal Dataset
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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)

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- The End -

• Thanks!