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Source localization MfD 2010, 17 th Feb 2010 Diana Omigie and Stjepana Kovac

Source localization MfD 2010, 17 th Feb 2010 Diana Omigie and Stjepana Kovac.

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Source localization MfD 2010, 17 th Feb 2010 Diana Omigie and Stjepana Kovac

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  1. Source localization MfD 2010, 17th Feb 2010 Diana Omigie and Stjepana Kovac

  2. Source localization:I Aim / ApplicationII Theorya) What is recorded (EEG / MEG) b) Forward problem Forward solutions c) Inverse problem Inverse solutions d) Inverse solutions: discrete vs. distributedIII The buttons in SPM

  3. I Aim To find a focus of brain activity by analysing the electrical activity recorded from surface electrodes (EEG) or SQUID (Superconductive Quantum Interference Device; MEG)

  4. I Application:- focal epilepsy:spikes seizures- evoked potentials:auditory evoked potentialssomatosensory evoked potentialscognitive event related potentials-

  5. IIa What is recorded - EPSP Layer IV radial tangential Lopez daSilva, 2004

  6. IIb Forward problem Forward solution How to model the surfaces i.e. the area between recording electrode and cortical generator? Skin, CSF, skull, brain Realistic shape – (BEM isotropic, FEM anisotropic) Plummer, 2008

  7. + - + - IIc Inverse problem Inverse solutions • Discrete: • Equivalent current dipole • Distributed (differ in side constraint): • Minimum norm • (Halmalainen & Ilmoniemi 1984) • LORETA (Pascual-Marqui, 1994) • MSP – multiple sparse priors (Friston, 2008) • ...........

  8. IIc Inverse problem Inverse solutions

  9. SPM source analysis Two aspects of source analysis are original in SPM: • Based on Bayesian formalism: generic inversion it can incorporate and estimate the relevance of multiple constraints (data driven relevance estimation – Baysian model comparison) • The subjects specific anatomy incorporated in the generative model of the data

  10. III The buttons in SPM :Graphical user interface for 3D source localisation

  11. III EEG/MEG imaging pipeline 0) Load the file • Source space modeling • Data co-registration • Forward computation • Inverse reconstruction • Summarizing the results of the inverse reconstruction as an image

  12. 0) Load the file

  13. 1) Source space modeling MRI – individual head meshes (boundaries of different head compartments) based on the subject’s structural scan Template – SPM’s template head model based on the MNI brain template MRI

  14. 1) Source space modeling • Select mesh size: • - coarse • normal • fine

  15. 2) Data co-registration Fiducials – landmark based coregistration Surface matching Co-register

  16. 2) Data co-registration Methods to co-register • “select” from default locations • “type” MNI coordinates directory • “click” manually each fiducial point from MRI images

  17. 3) Forward computation Recommendation: Single shell for MEG BEM for EEG Forward Model

  18. 3) Forward computation

  19. 4) Inverse reconstruction Imaging VB-ECD Beamforming Invert

  20. 4) Inverse reconstruction Default – click “Standard”: • “MSP” method will be used. MSP : Multiple Sparse Priors (Friston et al. 2008a) Alternatives: • GS (greedy search: default): • iteratively add constraints (priors) • ARD (automatic relevance determination): • iteratively remove irrelevant constraints • COH (coherence): • LORETA-like smooth prior …

  21. 4) Inverse reconstruction TIME Time course of the region with maximal activity SPACE Maximal intensity projection (MIP)

  22. 5) Summarizing the results of inverse reconstruction as an image ? Timewindow of interest (ms peri-stimulus time) ? Frequency band of interest (default 0) ? Evoked/ induced inversion applied either to each trial (induced) and then averaged or inversion applied to the averaged trials (evoked) Window

  23. 5) Summarizing the results of inverse reconstruction as an image 3D NIfTI images allow GLM based statistical analysis (Random field theory)

  24. Sources • indicated under figures- Stavroula Kousta / Martin Chadwick (2007, MfD)- Maro Machizawa / Himn Sabir (2008, MfD) • - SPM 8 manual • BESA tutorials (http://www.besa.de), M. Scherg

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