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Published byCaroline Warren Modified about 1 year ago

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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 mind

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

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

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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

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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

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1 st Level Add directory containing 2D-interpolated data

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

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

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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

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2 nd level analysis

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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

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Contrast vector = [ ] 2 nd level analysis

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T = 249! 2 nd level analysis

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

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

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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)

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

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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

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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

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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

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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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