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SPM for EEG/MEG: Source Reconstruction & Dynamic Causal Modelling Guide

Comprehensive overview of SPM5 for EEG/MEG, covering voxel-based analysis, source reconstruction, DCM, and statistical mapping. Learn the process of analyzing evoked responses and power in brain space. Master the application of dynamic causal modeling for network activity interpretation.

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SPM for EEG/MEG: Source Reconstruction & Dynamic Causal Modelling Guide

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

  2. 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 Dynamic Causal Modelling • Models ERP/ERF as network activity. • Explains differences between evoked responses as modulation of connectivity.

  3. EEG and MEG EEG MEG • - ~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

  4. 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.

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

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

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

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

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

  10. 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. 3D-source space

  11. 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

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

  13. 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

  14. Gaussian Random Fields t59 Control of Family-wise error Worsley et al., Human Brain Mapping, 1996 p = 0.05 Cluster Gaussian10mm FWHM (2mm pixels) Search volume

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

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