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Part I: EEG and MEG Inverse dipole fit and Deviation scan approaches

Part I: EEG and MEG Inverse dipole fit and Deviation scan approaches. 20.05.2014 Carsten Wolters. [Wolters, Vorlesungsskriptum , Chapter 8.1] . EEG/MEG inverse dipole fit approaches. [Wolters, Vorlesungsskriptum , Chapter 8.1] . EEG/MEG inverse dipole fit approaches.

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Part I: EEG and MEG Inverse dipole fit and Deviation scan approaches

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  1. Part I: EEG and MEG Inverse dipole fit and Deviation scan approaches 20.05.2014 Carsten Wolters

  2. [Wolters, Vorlesungsskriptum, Chapter 8.1] EEG/MEG inversedipole fit approaches

  3. [Wolters, Vorlesungsskriptum, Chapter 8.1] EEG/MEG inversedipole fit approaches

  4. [Wolters, Vorlesungsskriptum, Chapter 8.1] EEG/MEG inversedipole fit and deviationscanapproaches

  5. [Wolters, Vorlesungsskriptum, Chapter 8.1] EEG/MEG inversedipole fit and deviationscanapproaches

  6. [Wolters, Vorlesungsskriptum, Chapter 8.3.1] EEG/MEG inversedipole fit approaches

  7. [Wolters, Vorlesungsskriptum, Chapter 8.3.1] EEG/MEG inversedipole fit approaches

  8. [Wolters, Vorlesungsskriptum, see subsection “Optimization” in Chapter 4.5.1] Reminder: Nelder-Meadoptimizationfor image registration: Thecostfunction

  9. [Wolters, Vorlesungsskriptum, see subsection “Optimization” in Chapter 4.5.1] • Reminder: Nelder-Meadoptimizationfor image registration: Theoptimizationalgorithm

  10. [Wolters, Vorlesungsskriptum, see subsection “Optimization” in Chapter 4.5.1] • Reminder: Nelder-Meadoptimizationfor image registration: Theoptimizationalgorithm

  11. [Wolters, Vorlesungsskriptum, see subsection “Optimization” in Chapter 4.5.1] • Reminder: Nelder-Meadoptimizationfor image registration: Theoptimizationalgorithm

  12. [Wolters, Vorlesungsskriptum, Chapter 8.3.1] EEG/MEG inversedipole fit approaches

  13. [Wolters, Vorlesungsskriptum, Chapter 8.3.2] Global optimizationusingSimulatedAnnealing (SA)

  14. [Wolters, Vorlesungsskriptum, Chapter 8.3.2] • Global optimizationusingSimulatedAnnealing (SA)

  15. [Wolters, Vorlesungsskriptum, Chapter 8.3.2] Global optimizationusingSimulatedAnnealing (SA)

  16. [Wolters, Vorlesungsskriptum, Chapter 8.3.2] Global optimizationusingSimulatedAnnealing (SA)

  17. [Wolters, Vorlesungsskriptum, Chapter 8.3.3] Dipole fit and deviationscanapproaches: Determination of the linear parameters

  18. [Wolters, Vorlesungsskriptum, Chapter 8.3.3] • Dipole fit and deviationscanapproaches: Determination of the linear parameters

  19. [Wolters, Vorlesungsskriptum, Chapter 8.3.3] • Dipole fit and deviationscanapproaches: Determination of the linear parameters

  20. [Wolters, Vorlesungsskriptum, Chapter 8.3.3] • Dipole fit and deviationscanapproaches: Determination of the linear parameters

  21. [Wolters, Vorlesungsskriptum, Chapter 8.3.3] • Dipole fit and deviationscanapproaches: Determination of the linear parameters

  22. [Wolters, Vorlesungsskriptum, Chapter 8.3.3] • Dipole fit and deviationscanapproaches: Determination of the linear parameters

  23. [Wolters, Vorlesungsskriptum, Chapter 8.3.3] [Wolters, Beckmann, Rienäcker, Buchner, Brain Topography, 1999] • Dipole fit and deviationscanapproaches: Determination of the linear parameters

  24. [Wolters, Vorlesungsskriptum, Chapter 8.3.3] [Wolters, Beckmann, Rienäcker, Buchner, Brain Topography, 1999] • Dipole fit and deviationscanapproaches: Determination of the linear parameters

  25. [Wolters, Vorlesungsskriptum, Chapter 8.3.3] [Wolters, Beckmann, Rienäcker, Buchner, Brain Topography, 1999] • Dipole fit and deviationscanapproaches: Determination of the linear parameters

  26. [Wolters, Vorlesungsskriptum, Chapter 8.3.3] [Wolters, Beckmann, Rienäcker, Buchner, Brain Topography, 1999] • Dipole fit and deviationscanapproaches: Determination of the linear parameters

  27. [Wolters, Vorlesungsskriptum, Chapter 8.3.3] [Wolters, Beckmann, Rienäcker, Buchner, Brain Topography, 1999] • Dipole fit and deviationscanapproaches: Determination of the linear parameters

  28. [Aydin, Vorwerk, Küpper, Heers, Kugel, Galka, Hamid, Wellmer, Kellinghaus, Rampp, Wolters, Plos One, 2014] Single Dipole Deviation Scan (SDDS) and calibration

  29. T1, T2, DTI 27 minutes! • Medianus nerve stimulation7 minutes! • Skull Conductivity • High interindividual variance • EEG very sensitive • MEG farless sensitive • Sub-averages: SNR (>3) Aydin et al., 2014,PLoS ONE

  30. Segmentation White Matter Gray Matter CSF Outer skull T1 MRI Skin Segmented MRI Spongiosa T2 MRI Inner skull FAST BETSURF FLIRT MATLAB

  31. Headmodels

  32. InclusionofAnisotropy T2 MRI b0- Warp-Field Susceptibility Corrected DW-MRI White Matter Gray Matter b0+ EC Corrected D1,…D20 Diffusion Tensors FA D1,…D20 Conductivity Tensors FLIRT FAIR (Ruthotto et al, 2012) DTIFIT MATLAB

  33. Headmodelanisotropy

  34. Geometrically adapted 6 compartment hexahedral finite element head model Geometrically adapted SimBio-VGRID Mesh SimBio Forward

  35. Headmodels

  36. Use 22ms component of Somatosensory Evoked Potentials (SEP) and Fields (SEF) EEG MEG MEG EEG

  37. Calibrationprocedureforcombined EEG/MEG sourcelocalization

  38. Calibrationprocedureforcombined EEG/MEG sourcelocalization

  39. Calibrationprocedureforcombined EEG/MEG sourcelocalization

  40. SEP/SEF localizationdifferencesforthe different headmodelsfrom Table 1

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