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SPM for EEG/MEG

SPM for EEG/MEG. Stefan Kiebe l. 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/

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SPM for EEG/MEG

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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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