M/EEG Analysis Contrasts, Inferences

and Source Localisation

Methods for Dummies 2011/12 - FIL

Suz Prejawa

‘Ōiwi Parker Jones

Let’s get right into it…

• experimental design• a note on images and lots on image construction in SPM•The SPM processing pipeline (1st and 2nd level analysis)• maybe a bit on other ways of looking at the data

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spinster

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time

real word trial

time

600 ms

600 ms

600 ms

Experimental Design

MEG data(imagine some technical specification about the MEG machine and the acquisition here… like number of sensors etc)

Randomised presentation of (i) 96 real words (spinster) (ii) 96 non-words (stinster)

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spinster

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time

real word trial

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time

600 ms

600 ms

600 msstinster

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time

non word trial

Aim:Identify at what point in time andover what sensor area the greatestdifference lies in the responses towords vs nonwords.

Steps (i) create an image of the data

= MEG 1st level analysis(ii) conduct statistical test

= MEG 2nd level analysis(iii) use SPM for source localisation

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spinster

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time

real word trial

time

600 ms

600 ms

600 ms

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spinster

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time

real word trial

A word on images

• Essentially what you want is a single image file– So, sensor data needs to be converted in SPM– You have a choice

(i) to average across your trials to obtain 1 image file per condition (ii) to obtain 1 image file for each trial in every condition (epoch data)

– (i) for 2nd level analysis across subjects– (ii) allows within subject statistical tests + comparison

across subjects and conditions (essentially looking at levels within conditions)

Stimulus/EventOnset

X real wordsO nonwords

Averaging trialsan illustration

Averaging

Averaging trials

And then you do this… with, let’s say, averaged data

From Kilner & Friston, 2010)

Note:

275 sensors

averaged ERFs

Time bins may be as many as 1 per ms

A word on interpolationfrom Litvak et al (2011)

“… Data in the time domain are converted into an image by generating a scalp map for each time frame and stacking scalp maps over peristimulus time. Scalp maps are generated using the 2D sensor layout specified in the dataset and linear interpolation between sensors. The user is asked to specify the output dimensions of the interpolated scalp map. Typically, we suggest 64 pixels in each spatial direction.” (p4)

In other words:

You create a 2D space by flattening the sensor locations and interpolating between them to create an image of M*M pixels

(where M=number of channels)

Construction of a 3D (space × space × time) data volume from sensor-space maps

Creation of a single image= what happens in MEG 1st level analysis

A

B

C

Construction of (space × space × time) summary statistic image- Again!

from Litvak et al (2011)

Example: if we were interested in the N170 component, one could average the data between 150 and 190 milliseconds.

0

1

Words

Nonwords

Realistically, you might want to choose a particular time slot for your analysis and not look at all time points

to reduce data that needs to be compared (and subsequently controlled with FWE)

Smoothing

• Important step to take before 2nd level analysis (In SPM, use smooth images function in the drop down other menu)

• Used to adjust images so that they better conform to the assumptions of random field theory

• Necessary for taking into consideration spatial and temporal variability between subjects

• General guiding principle: Let smoothing kernel match the data feature you need to enhance. Try to smooth the images with different kernels and see what looks best.

2nd Level Analysis

Given the contrast images from the 1st level, we can now

test for differences between conditions.

1Tc = 2X

2

+ 2

second levelsecond level

1 -1

2nd level contrast 2nd level model = used in fMRI

SPM output:

Voxel map, where each voxel contains one

statistical value

The associated p-value is adjusted for multiple

comparisons

2 sample t-testDesign Matrix

2nd

L

E

V

E

L

words

Non-words

from Litvak et al (2011)

And that is what you end up with…

This is not the brain (but sensor space and time)

showing where (in the scalp space) there is an effect (in polarity) between the 2 conditions and when

after it has been controlled for multiple comparisons

Summary- MEG in one slide

At the 1st level, we select periods or time points in peri-stimulous time that we would like to analyse.

Time is treated as an experimental factor and we create a 3D image that

contains information on polarity over space and time

to provide input to the 2nd level

0

1

•Similar to fMRI analysis. The aim of the 1st level is to compute contrast images that provide the input to the second level.

•Difference: here we are not modelling the data at 1st level, but simply forming images of data over time

Words

Nonwords

Example: when within the N170 component, is there a significant difference in polarity at any given sensor between my 2 conditions (and which sensor areas are these)?

Transform data into frequency spectrum

-100 -50 0 50 100 150 200 250 300 350 400

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ms

fem

to T

MRO33 (200)

-100 0 100 200 300 400

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ms

Hz

MRO33 (200)

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Ideal for induced responses i.e. responses not phase locked to the stimulus onset

Different methods but SPM uses the Morlet Waveform Transform (mathematical functions which breaks a signal into different components)

Trade off between time resolution and frequency resolution

Comparing frequency bands

between 2 conditions

Time-Frequency analysis

Transform data into time-frequency domain

Not phase-locked to the stimulus onset – not revealed with classical averaging methods

[Tallon-Baudry et. al. 1999]

Useful for evoked responses and induced responses:

SPM uses the Morlet Wavelet Transform

Wavelets: mathematical functions that can break a signal into different frequency components.

The transform is a convolution

The Power and Phase Angle can be computed from the wavelet coefficients:

Source localization:I Aim / Application

II Theory

a) What is recorded (EEG / MEG)

b) Forward problem Forward solutions

c) Inverse problem Inverse solutions

d) Inverse solutions: discrete vs. distributed

I Aim

To find a focus of brain activity by analysing the electrical

activity recorded from surface electrodes (EEG) or SQUID

(Superconductive Quantum Interference Device; MEG)

Where does the data come from ?

1pT1s

I Application:

- focal epilepsy:

spikes

- evoked potentials:

auditory evoked potentials

somatosensory evoked potentials

cognitive event related potentials

- induced responses:

alpha/beta/gamma oscillations Fries et al., 2008. (also see Barnes et al., 2004 )

IIa What is recorded

Lopez daSilva, 2004

EPSP

-

Layer IV

radial

tangential

IIb Forward problem Forward solutionHow to model the surfaces i.e. the area between

recording electrode and cortical generator?

Plummer, 2008Realistic shape – (BEM isotropic, FEM anisotropic)

Skin, CSF, skull, brain

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-13

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Estimated dataEstimated position

Measured data

Dipole Fitting

?

IIc Inverse problem Inverse solutionsDiscrete source analysis Distributed source analysis

Current dipole represents an extended brain area

Each current dipole represents one small brain segment

Number of sources < number of sensors Number of sources >> number of sensors

The leadfieldmatrix has more rows (number of sensors) than colums (number of sources)

The leadfieldmatrix has more colums (number of sources) than rows (number of sensors)

Result:Source model and source waveforms

Result: 3D Volume imagefor each timepoint

Useful priors for cinema audiences

• Things further from the camera appear smaller• People are about the same size• Planes are much bigger than people

Useful priors for MEG analysis

• At any given time only a small number of sources are active (Dipole fitting)

• All sources are active but overall their energy is minimized (Minimum norm)

• As above but there are also no correlations between distant sources (Beamformers)

Singh et al. 2002

ME

G c

ompo

site

fMR

IOscillatory changes can be co-located with BOLD response

MEG + EEG…?

These can also be usefully combined

Two aspects of source analysis are *original* in SPM:

- Based on Bayesian formalism: generic inversion it can incorporate and estimate the relevance of multiple constraints (data driven relevance estimation – Baysian model comparison)

- The subjects specific anatomy incorporated in the generative model of the data

SPM source analysis

IId

Conclusions/If you only remember one three things!

• MEG/EEG inverse problem can be solved…if you have some prior knowledge.

• SPM lets you test between priors in a Bayesian framework, and incorporate subject specific anatomy.

• Really exciting part is the millisecond temporal resolution we can now exploit.

Thanks!

- Gareth Barnes

- Vladimir Litvak

- Marta Garrido

- Calvin

- Hobbes

- And…

you, of course!

Sources – Part 2

- Look under figures

- Gareth Barnes’s “The M/EEG inverse problem and

solutions” lecture

- Stavroula Kousta / Martin Chadwick (2007, MfD)

- Maro Machizawa / Himn Sabir (2008, MfD)

- SPM 8 manual

- BESA tutorials (http://www.besa.de), M. Scherg

- Dipole Simulator (http://www.besa.de/updates/tools/)

References/ source of information for part 1

Vladimir Litvak, Jérémie Mattout, Stefan Kiebel, et al., “EEG and MEG Data Analysis in SPM8,” Computational Intelligence and Neuroscience, vol. 2011, Article ID 852961, 32 pages, 2011. doi:10.1155/2011/852961

Kilner J, Friston K. (2010) Topological inference for EEG and MEG. Annals of Applied Statistics, 4(3): 1272–1290.

Previous MEG MfD talks

Vladimir Litvak, Marta Garrido and Gareth Barnes

III The buttons in SPM :Graphical user interface for 3D source localisation

III EEG/MEG imaging pipeline

0) Load the file

1) Source space modeling

2) Data co-registration

3) Forward computation

4) Inverse reconstruction

5) Summarizing the results of the inverse reconstruction as an image

0) Load the file

1) Source space modeling

MRI

template

1) Source space modeling

Select mesh size:

- coarse

- normal

- fine

2) Data co-registration

Co-register

2) Data co-registration

Methods to co-register

– “select” from default locations

– “type” MNI coordinates directory

– “click” manually each fiducial point

from MRI images

3) Forward computation

Forward Model

Recommend

ation:

Single shell

for MEG

BEM for

EEG

3) Forward computation

4) Inverse reconstruction

Imaging

VB-ECD

Beamforming

4) Inverse reconstruction

Default – click “Standard”:• “MSP” method will be used. MSP : Multiple Sparse Priors (Friston

et al. 2008a)Alternatives:• GS (greedy search: default):

– iteratively add constraints (priors)• ARD (automatic relevance determination):

– iteratively remove irrelevant constraints• COH (coherence):

– LORETA-like smooth prior …

4) Inverse reconstruction

TIME Time course of the region with maximal activity

SPACEMaximal intensity projection (MIP)

5) Summarizing the results of inverse reconstruction as an image

Window

5) Summarizing the results of inverse reconstruction as an image

3D NIfTI images

allow GLM

based statistical

analysis

(Random field

theory)