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Statistical Analysis of M/EEG Sensor- and Source-Level Data Jason Taylor MRC Cognition and Brain Sciences Unit (CBU) PowerPoint Presentation
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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. Overview.

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slide1

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

overview
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

random field theory rft
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

multimodal dataset in spm8 manual rik henson
“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

cbu neuromag dataset dan wakeman rik henson
“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!

where is an effect in time frequency space
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

where is an effect in time frequency space7
Where is an effect in time-frequency space?

Single channel

-Effect over

subjects

EEG

MEG (Mag)

100-220ms

8-18Hz

Faces > Scrambled

6 90

6 90

Freq (Hz)

Freq (Hz)

-500 +1000

-500 +1000

Time (ms)

Time (ms)

CBU Neuromag Dataset

where is an effect in sensor time space
Where is an effect in sensor-time space?

Stats (F): Faces vs Scrambled

(single subject, over trials)

Topography-x-Time Image (EEG)

Multimodal Dataset

where is an effect in sensor time space9
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

slide10

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

slide11

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., 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

slide12

Where is an effect in source space (3D)?

MEG

E+MEG

EEG

A

CBU Neuromag Dataset

slide13

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

slide14

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

slide15

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:

http://go.warwick.ac.uk/tenichols/software/snpm/

Multimodal Dataset

slide16

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

slide17

Where is an effect in source space (3D)?

p<.001 unc

SPM

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)

Multimodal Dataset

future directions
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)