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SPM for EEG/MEG Wellcome Dept. of Imaging Neuroscience University College London Stefan Kiebel

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Overview: SPM5 for EEG/MEG Statistical Parametric Mapping Spatial forward modelling/ Source reconstruction Spatial forward modelling/ Source reconstruction Dynamic Causal Modelling -voxel-based approach -Conventional analysis -Localisation of effects -Evoked responses and power -voxel-based approach -Conventional analysis -Localisation of effects -Evoked responses and power -Forward model important for source reconstruction and DCM -Source reconstruction localises activity in brain space -Forward model important for source reconstruction and DCM -Source reconstruction localises activity in brain space -Models ERP/ERF as network activity. -Explains differences between evoked responses as modulation of connectivity. -Models ERP/ERF as network activity. -Explains differences between evoked responses as modulation of connectivity.

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EEG and MEG MEG - ~1929 (Hans Berger) - Neurophysiologists - From clinical system to 64, 127, 256 sensors - Potential V: ~10 µV - ~1929 (Hans Berger) - Neurophysiologists - From clinical system to 64, 127, 256 sensors - Potential V: ~10 µV EEG - ~1968 (David Cohen) - Physicists - From ~ 30 to more than 150 sensors - Magnetic field B: ~ T - ~1968 (David Cohen) - Physicists - From ~ 30 to more than 150 sensors - Magnetic field B: ~ T

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275 sensor axial gradiometer MEG system supplied by VSM medtech. VSM medtech says Designed for unprecedented measurement accuracy, the combination of up to 275 optimum- length axial gradiometers and unique noise cancellation technology ensures the highest possible performance in some of today's most demanding urban magnetic environments.

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MEG data ~ 50 ms right left Index f Little f Example: MEG study of finger somatotopy 400 stimulations of each finger [Meunier 2001]

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average... single trials event-related potential/field (ERP/ERF) ERP/ERF

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Voxel spaces sensor data SPM 2D SPM 3D Single trial/evoked response

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Data (at each voxel) Single subject Trial type 1 Trial type i Trial type n... Multiple subjects Subject 1 Subject m Subject j...

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Time Intensity Time single voxel time series single voxel time series Mass univariate model specification model specification parameter estimation parameter estimation hypothesis statistic SPM

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How does SPM/EEG work? Raw M/EEG data Raw M/EEG data Single trials Epoching Artefacts Filtering Averaging, etc. Single trials Epoching Artefacts Filtering Averaging, etc. 2D - scalp mass-univariate analysis mass-univariate analysis SPM{t} SPM{F} Control of FWE SPM{t} SPM{F} Control of FWE Preprocessing Projection SPM5-stats 3D-source space 3D-source space

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SPM for M/EEG M/EEG data fMRI/ sMRI data Design matrices Time and time-frequency contrasts Corrected p-values Covariance constraints Preprocessing 2D- or 3D- M/EEG data 2 level hierarchical model SPM{t} SPM{F} SPM{t} SPM{F}

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Conventional analysis: example a1 a2 a3 a4 a5 a6 Example: difference in N170 component between trial types Example: difference in N170 component between trial types Average between 150 and 190 ms General linear model (here: 2-sample t-test) General linear model (here: 2-sample t-test) Trial type 2 Trial type 1 s1 s2 s3 s1 s2 s3 PST [ms]

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= = + first level second level Identity matrix Summary statistics approach Example: difference between trial types Example: difference between trial types Contrast: average between 150 and 190 ms nd level contrast...

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Gaussian Random Fields Search volume t 59 Gaussian 10mm FWHM (2mm pixels) p = 0.05 Cluster Control of Family-wise error Control of Family-wise error Worsley et al., Human Brain Mapping, 1996

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Summary Conventional preprocessing in sensor space. Adjustment of p-values! After preprocessing, convert to voxel-space. Analysis of power or time data. Cool source reconstruction. SPM needed to get to the DCM bit.

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