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/meg (wiki)

14 May 2012 | 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

4-ish-D space by (factorised) time

Alternative approaches: SnPM, PPM, etc.

Overview

Example Datasets

CTF Multimodal Face-Evoked Dataset | SPM8 Manual (Rik Henson)

CTF MEG/ActiveTwo EEG:275 MEG128 EEG3T fMRI (with nulls)1mm3 sMRI

2 sessions

Stimuli:~160 face trials/session~160 scrambled trials/session

Group: N=12 subjects(Henson et al, 2009 a,b,c)

Chapter 37 SPM8 Manual

Neuromag Faces Dataset | Dan Wakeman & Rik Henson @ CBU

Biomag 2010 Award-Winning Dataset!

Neuromag MEG & EEG Data:102 Magnetometers204 Planar Gradiometers70 EEGHEOG, VEOG, ECG1mm3 sMRI

6 sessions

Stimuli: faces & scrambled-face images (480 trials total)

Group: N=18(Henson et al, 2011)

400-600ms

800-1000ms

1700ms

Neuromag Lexical Decision Dataset | Taylor & Henson @ CBU

Neuromag Lexical Decision

Neuromag MEG Data:102 Magnetometers204 Planar GradiometersHEOG, VEOG, ECG1mm3 sMRI

4 sessions

Stimuli:words & pseudowords presented visually(480 trials total)

Group: N=18Taylor & Henson (submitted)

The Multiple Comparison Problem

Where is the effect?: The curse of too much data

The Multiple Comparison Problem

• The more comparisons we conduct, the more Type I errors (false positives) we will make when the Null Hypothesis is true.

* Must consider Familywise (vs. per-comparison) Error Rate

• 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)?

time

The Multiple Comparison Problem

• The more comparisons we conduct, the more Type I errors (false positives) we will make when the Null Hypothesis is true.

* Must consider Familywise (vs. per-comparison) Error Rate

• 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/at what frequency

is there an effectan effect in time/frequency (2D)?

time

frequency

• The more comparisons we conduct, the more Type I errors (false positives) we will make when the Null Hypothesis is true.

* Must consider Familywise (vs. per-comparison) Error Rate

• 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/where

is there an effectin sensor-topographyspace/time (3D)?

The Multiple Comparison Problem

x

y

time

• The more comparisons we conduct, the more Type I errors (false positives) we will make when the Null Hypothesis is true.

* Must consider Familywise (vs. per-comparison) Error Rate

• 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/where

is there an effectin source space/time (4-ish-D)?

The Multiple Comparison Problem

y

x

time

z

The Multiple Comparison Problem

• The more comparisons we conduct, the more Type I errors (false positives) we will make when the Null Hypothesis is true.

* Must consider Familywise (vs. per-comparison) Error Rate

• 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.

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).

* Takes smoothness of images into account

Worsley Et Al (1996). Human Brain Mapping, 4:58-73

GLM: Condition Effects after removing variance due to confounds

Each trial

Each trial-type (6)

beta_00* image volumes reflect (adjusted) condition effects

Confounds (4)

Henson et al., 2008, NImage

W/in Subject(1st-level) model-> one image per trial-> one covariate value

per trial

Also: Group Analysis (2nd-level)-> one image per subject

per condition-> one covariate value per

subject per condition

Sensor-space analyses

Fac

esS

cram

ble

d

Faces > Scrambled

Where is an effect in time-frequency space?

Kilner et al., 2005, Nsci LettersCTF Multimodal Faces

1 subject (1st-level analysis)1 MEG channel

Morlet wavelet projection

1 t-x-f image per trial

Where is an effect in time-frequency space?

Neuromag Faces

MEG (Mag)

Fac

es >

Scr

amb

led

EEG

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)

18 subjects1 channel(2nd-level analysis)

Where is an effect in sensor-time space?

3DTopography-x-Time Image volume (EEG)

1 subjectALL EEG channels

Each trial, Topographic projection (2D) for each time point (1D) = topo-time image volume (3D)

Faces vs Scrambled

CTF Multimodal Faces

x

y

tim

e

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 (sessions, subjects) - using Signal Space Separation (SSS; Taulu et al, 2005; Neuromag systems)

Taylor & Henson (2008) BiomagNeuromag Lexical Decision

Stats over 18 subjects, RMS of planar gradiometers

Improved: (i.e., more blobs, larger T values) w/ head-position transform

Other evidence: ICA ‘splitting’ with movement

Where is an effect in sensor-time space?

Analysis over subjects (2nd Level): Magnetometers: Pseudowords vs Words PPM (P>.95 effect>0)

Effect Size

18

-18

fT

Taylor & Henson (submitted)Neuromag Lexical Decision

Source-space analyses

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

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) - For each sensor type OR fused modalities

Write data to 3D image - in MNI space - smooth along 2D surface

Smooth by 3D Gaussian

Submit to GLM

Henson et al., 2007, NImage

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

Henson et al, 2011Neuromag Faces

MEGsensor fusion

E+MEGEEG

RESULTS: Faces > Scrambled

fMRI

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

with fMRI priorsEEG+MEG+fMRI

fMRI

RESULTS: Faces > Scrambled

with group optimisedfMRI priors

EEG+MEG+fMRI

Henson et al, 2011Neuromag Faces

meanstats

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 do effects emerge/disappear in source space (4-ish-D: time factorised)?

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 t-win)

Where and When do effects emerge/disappear in source space (4-ish-D)?

source timecourses

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: Cond x TW interactions

Where and When do effects emerge/disappear in source space (4-ish-D)?

Alternative Approaches

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 correction conservative

Distributions over subjects/voxels may not be Gaussian (e.g., if sparse, as in MSP)

Alternative Approaches

(Taylor & Henson, submitted; Biomag 2010)

Alternative Approaches

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

Alternative Approaches

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)

CTF Multimodal Faces

Future Directions

• Extend RFT to 2D cortical surfaces (+1D time)? (e.g., Pantazis et al., 2005, NImage)

• Novel approaches to controlling for multiple comparisons

(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; but see Barnes et al., 2011)

To detect spatiotemporal patterns in 3D images(e.g., Duzel, 2004, NImage; Kherif, 2003, NImage)

-- The end --

• Thanks for listening

• Acknowledgements:• Rik Henson (MRC CBU)• Dan Wakeman (MRC CBU / now at MGH)• Vladimir, Karl, and the FIL Methods Group

• More info:• http://imaging.mrc-cbu.cam.ac.uk/meg (wiki)

jason.taylor@mrc-cbu.cam.ac.uk