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

Overview: SPM5 for EEG/MEG Statistical Parametric Mapping voxel-based approach Conventional analysis Localisation of effects Evoked responses and power Spatial forward modelling/ Source reconstruction Forward model important for source reconstruction and DCM Source reconstruction localises activity in brain space t-contrast: tests for single dimension in parameter space F-contrast: tests for multiple dimensions inference at first or second level (fixed or random effects) over conditions or groups: main effect, difference, interaction: average over time window parametric modulation with extrinsic variable power comparison in time-frequency domain Dynamic Causal Modelling Models ERP/ERF as network activity. Explains differences between evoked responses as modulation of connectivity.

EEG and MEG EEG MEG - ~1929 (Hans Berger) - Neurophysiologists In the same way like SPM for fMRI Use SPM2‘s methods! However, model needs to be adapted to make proper inference Comparison with fMRI analysis to aid illustration Also, conventional model (to be shown) in the same framework - ~1929 (Hans Berger) - Neurophysiologists From 10-20 clinical system to 64, 127, 256 sensors - Potential V: ~10 µV - ~1968 (David Cohen) - Physicists From ~ 30 to more than 150 sensors - Magnetic field B: ~10-13 T

MEG@FIL 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. In the same way like SPM for fMRI Use SPM2‘s methods! However, model needs to be adapted to make proper inference Comparison with fMRI analysis to aid illustration Also, conventional model (to be shown) in the same framework

MEG data Example: MEG study of finger somatotopy [Meunier 2001] 400 stimulations of each finger right In the same way like SPM for fMRI Use SPM2‘s methods! However, model needs to be adapted to make proper inference Comparison with fMRI analysis to aid illustration Also, conventional model (to be shown) in the same framework ~ 50 ms left Index f Little f

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

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

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

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

How does SPM/EEG work? Preprocessing Projection SPM5-stats SPM{t} SPM{F} Control of FWE Raw M/EEG data 2D - scalp mass-univariate analysis Single trials Epoching Artefacts Filtering Averaging, etc. In the same way like SPM for fMRI Use SPM2‘s methods! However, model needs to be adapted to make proper inference Comparison with fMRI analysis to aid illustration Also, conventional model (to be shown) in the same framework 3D-source space

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

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

Summary statistics approach Example: difference between trial types Contrast: average between 150 and 190 ms 2nd level contrast -1 1 . . . = = + Identity matrix second level first level

Gaussian Random Fields t59 Control of Family-wise error Worsley et al., Human Brain Mapping, 1996 p = 0.05 Cluster Gaussian 10mm FWHM (2mm pixels) Search volume

Summary Conventional preprocessing in sensor space. After preprocessing, convert to voxel-space. Adjustment of p-values! Analysis of power or time data. t-contrast: tests for single dimension in parameter space F-contrast: tests for multiple dimensions inference at first or second level (fixed or random effects) over conditions or groups: main effect, difference, interaction: average over time window parametric modulation with extrinsic variable power comparison in time-frequency domain SPM needed to get to the DCM bit. Cool source reconstruction.