Methods for Dummies 201112 FIL MEEG Analysis Contrasts

Methods for Dummies 2011/12 - FIL M/EEG Analysis Contrasts, Inferences and Source Localisation 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 (1 st and 2 nd level analysis) • maybe a bit on other ways of looking at the data

real word trial Experimental Design 600 ms + 600 ms tim e spinster MEG data 600 ms + (imagine some technical specification about the MEG machine and the acquisition here… like number of sensors etc) Randomised presentation of non word trial + (i) 96 real words (spinster) (ii) 96 non-words (stinster) 600 ms e tim stinster 600 ms +

Aim: Steps (i) create an image of the data = MEG 1 st level analysis (ii) conduct statistical test = MEG 2 nd level analysis (iii) use SPM for source localisation + e tim Identify at what point in time and over what sensor area the greatest difference lies in the responses to words vs nonwords. real word trial 600 ms spinster 600 ms +

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 2 nd level analysis across subjects – (ii) allows within subject statistical tests + comparison across subjects and conditions (essentially looking at levels within conditions)

Averaging trials an illustration Stimulus/Event Onset X real words O nonwords

Averaging trials Averaging

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

Note: 275 sensors averaged ERFs Time bins may be as many as 1 per ms From Kilner & Friston, 2010)

A word on interpolation from 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 2 D 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. ” (p 4) In other words: You create a 2 D 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 (space × time) summary statistic image- Again! A B C Construction of a 3 D (space × time) data volume from sensor-space maps Creation of a single image= what happens in MEG 1 st level analysis from Litvak et al (2011)

Realistically, you might want to choose a particular time slot for your analysis and not look at all time points Example: if we were interested in the N 170 component, one could average the data between 150 and 190 milliseconds. 1 Words Nonwords 0 to reduce data that needs to be compared (and subsequently controlled with FWE)

Smoothing • Important step to take before 2 nd 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.

2 nd Level Analysis Given the contrast images from the 1 st level, we can now test for differences between conditions. 2 nd level contrast 2 nd level model = used in f. MRI 1 -1 SPM output: = second level + Voxel map, where each voxel contains one statistical value The associated p-value is adjusted for multiple comparisons

nd 2 2 sample t-test Design Matrix words Nonwords L E V E L

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 from Litvak et al (2011)

Summary- MEG in one slide • Similar to f. MRI analysis. The aim of the 1 st level is to compute contrast images that provide the input to the second level. • Difference: here we are not modelling the data at 1 st level, but simply forming images of data over time At the 1 st 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 3 D image that contains information on polarity over space and time to provide input to the 2 nd level Example: when within the N 170 component, is there a significant difference in polarity at any given 1 sensor between my 2 conditions (and which sensor areas are these)? Words 0 Nonwords

Transform data into frequency spectrum 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 Useful for evoked responses and induced responses: Not phase-locked to the stimulus onset – not revealed with classical averaging methods SPM uses the Morlet Wavelet Transform Wavelets: mathematical functions that can break a signal into different frequency components. [Tallon-Baudry et. al. 1999] 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 ? 1 p. T

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 - EPSP Layer IV radial tangential Lopez da. Silva, 2004

IIb Forward problem Forward solution How to model the surfaces i. e. the area between recording electrode and cortical generator? Skin, CSF, skull, brain Realistic shape – (BEM isotropic, FEM anisotropic) Plummer, 2008

Dipole Fitting Estimated position Measured data ? Estimated data

IIc Inverse problem Inverse solutions Discrete 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: 3 D Volume image for 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 also no correlations between distant sources (Beamformers)

f. MRI Oscillatory changes can be co-located with BOLD response MEG composite MEG + EEG…? These can also be usefully combined Singh et al. 2002

IId SPM source analysis 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

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, Mf. D) - Maro Machizawa / Himn Sabir (2008, Mf. D) - 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 SPM 8, ” 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 Mf. D talks Vladimir Litvak, Marta Garrido and Gareth Barnes

III The buttons in SPM : Graphical user interface for 3 D 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 template MRI

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 Recommend ation: Forward Model 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. 2008 a) 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 SPACE Maximal intensity projection (MIP)

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

5) Summarizing the results of inverse reconstruction as an image 3 D NIf. TI images allow GLM based statistical analysis (Random field theory)