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Overview Contrast in fMRI v contrast in MEG 2D interpolation 1 st level 2 nd level Which buttons? Other clever things with SPM for MEG Things to bear in.

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Presentation on theme: "Overview Contrast in fMRI v contrast in MEG 2D interpolation 1 st level 2 nd level Which buttons? Other clever things with SPM for MEG Things to bear in."— Presentation transcript:

1 Overview Contrast in fMRI v contrast in MEG 2D interpolation 1 st level 2 nd level Which buttons? Other clever things with SPM for MEG Things to bear in mind

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3 What is first level analysis? Compute contrast images for input to 2 nd level In fMRI involves –Specifying design matrix –Estimating parameters

4 time Input for design matrices MRI MEG Epoched Averaged Each condition separate Impossible to model Down time included No averaging Conditions in series

5 1 st level analysis Interpolate sensor data over scalp space Average across specific time window Not actually modelling No error estimation Average over time window Could use other functions here time 00 1

6 2D interpolation Interpolates sensor data onto scalp map select preprocessed mat-file choose 64 dimensions interpolate channels Produces an average.img file for each trial type

7 1 st Level Add directory containing 2D-interpolated data

8 1 st Level Select 1.mat file to give SPM the dimensions of your data

9 1 st Level Specify time-window of interest Averages over this time window

10 At the end of 1 st level Average_con_001.img for each trial type = average at time window No 1 st level differentiation between conditions time 00 1

11 2 nd level analysis

12 Subject 1Subject 2 Condition 1 Condition 2 2 nd level analysis Slowest changing factor first Needs contrasts before can be estimated To weight contrasts At 2 nd level Imcalc spm_eeg_weight_epochs.m

13 Contrast vector = [ ] 2 nd level analysis

14 T = 249! 2 nd level analysis

15 Other clever things with SPM for MEG Time-frequency analysis Convert to 3D (time) Make contrasts in source space – not yet possible

16 Time-frequency decomposition Transform data into frequency spectrum Different methods filtering Fourier transform Wavelet transform – localised in time and frequency

17 Continuous wavelet transform Time series x(t) is convolved with a function – the mother wavelet ψ(t) Quantifies similarity between signal and wavelet function at scale s and translation τ * Morlet wavelet (real part)

18 Convert to 3D (time) Adds time as an extra dimension select 2D interpolated.img file time

19 Contrasts in source space Uses structural MRI to create mesh of cortical surface Estimates cortical source for MEG signal using Forward computation Inverse solution (more detail next week) Use source-localised images as input for spm

20 Things to bear in mind Projection onto voxel space –Scalp maps alone not very meaningful –3D source localisation subject to inverse problem More inter-subject variability Less modelling at 1 st level Prone to false negatives

21 So why use SPM for MEG/EEG? Classical ERP analysis Time frequency Time as a dimension Source localisation DCM Integration of M-EEG with fMRI

22 References S. J. Kiebel: 10 November ppt-slides on ERP analysis at S.J. Kiebel and K.J. Friston. Statistical Parametric Mapping for Event-Related Potentials I: Generic Considerations. NeuroImage, 22(2): , Todd, C. Handy (ed.) Event-Related Potentials: A Methods Handbook. MIT SPM5 Manual FIL Methods Group.


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