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

jason.taylor@mrc-cbu.cam.ac.uk | http://imaging.mrc-cbu.cam.ac.uk (wiki)

09 May 2011 | London | Thanks to Rik Henson and Dan Wakeman @ CBU

Practical aspects of…

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

Overview

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

CTF Multimodal Faces Dataset | SPM8 Manual (Rik Henson)

CTF MEG & EEG Data:128 EEG275 MEG3T 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

Neuromag Faces Dataset | Dan Wakeman & Rik Henson @ CBU

Biomag 2010 Award-Winning Dataset!

Neuromag MEG & EEG Data:70 EEG102 Magnetometers204 Planar GradiometersH&VEOGECG1mm3 sMRI

6 sessions

faces & scrambled-face images (480 trials)

Group: N=18

Neuromag Lexical Decision Dataset | Taylor & Henson @ CBU

Taylor & Henson, submitted

Neuromag MEG Data:102 Magnetometers204 Planar GradiometersH&VEOGECG1mm3 sMRI

4 sessions

words & pseudowords presented visually (480 trials)

Group: N=18

Fac

esS

cram

ble

d

Faces > Scrambled

Where is an effect in time-frequency space?

Kilner et al., 2005, Nsci LettersCTF Multimodal Faces

Single MEG channelMean over trials of Morlet wavelet projection (i.e., induced + evoked)-Write t x f image per trial-Compute SPM, RFT correct

Fac

es >

Scr

amb

led

Where is an effect in time-frequency space?

Neuromag Faces

EEG MEG (Mag)

100-220ms8-18Hz

Fre

q (H

z)

6

9

0

-500 +1000 Time (ms)

Fre

q (H

z)

6

9

0

-500 +1000 Time (ms)

Single channel-Effect over subjects

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

GLM: Removing variance due to confounds

Each trial

Each trial-type (6)

beta_00* images reflect mean (adjusted) 3D topo-time volume for each condition

Confounds (4)

Henson et al., 2008, NImage

W/in Subject(1st-level) model

Without transformation to Device Space

With transformation to Device Space

Where is an effect in sensor-time space?

Analysis over subjects (2nd Level)NOTE for MEG: Complicated by variability in head-positionSOLUTION: Virtual transformation to same position in sessions, subjects

Taylor & Henson (2008) BiomagNeuromag Lexical Decision

Stats over 18 subjects, RMS of planar gradiometers

*Improved* (i.e., more blobs, larger T values) w/ transform

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

Note sparseness of MSP inversions….

Analysis Mask

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)

Smooth along 2D surface

Write data to 3D image (in MNI space)

Smooth by 3D Gaussian

Henson et al., 2007, NImage

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

MEG E+MEGA EEG

Neuromag Faces

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

Sensors

Over Participants

Over Voxels

Mags Grads

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)

Neuromag Lexical Decision

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

Sources (fused sensors)

Over Participants

Over Voxels

MSP IID

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)

Neuromag Lexical Decision

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

p<.05 FWENon-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)

SnPM Toolbox by Holmes & Nichols:http://go.warwick.ac.uk/tenichols/software/snpm/

CTF Multimodal Faces

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

p>.95 (γ>1SD)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)

CTF Multimodal Faces

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

Taylor & Henson, submittedNeuromag Lexical Decision

Condition x Time-windowInteractions

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

Where and When so effects emerge/disappear in source space (3D)?

Taylor & Henson, submittedNeuromag Lexical Decision

Condition x Time-windowInteractions

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)

Where and When so effects emerge/disappear in source space (3D)?

Taylor & Henson, submittedNeuromag Lexical Decision

Condition x Time-windowInteractions

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)

Where and When so effects emerge/disappear in source space (3D)?

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)

- The End -

Thanks!