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Event-related Synthetic Aperture Magnetometry (SAMerf)

Event-related Synthetic Aperture Magnetometry (SAMerf). Outline. Review of traditional SAM Introduction to SAMerf Cheyne et al. motor experiment Sliding window SAMerf. Review of traditional SAM. Estimates equivalent current dipole source power within specified frequency bands

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Event-related Synthetic Aperture Magnetometry (SAMerf)

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  1. Event-related Synthetic Aperture Magnetometry(SAMerf)

  2. Outline • Review of traditional SAM • Introduction to SAMerf • Cheyne et al. motor experiment • Sliding window SAMerf

  3. Review of traditional SAM • Estimates equivalent current dipole source power within specified frequency bands • Based on sensor covariance in time windows • Uses optimal spatial filters to estimate source power on a grid of voxels

  4. SAM analysis of an n-back working memory task

  5. How do we increase temporal resolution? • Sliding window SAM • Calculate SAM images for small overlapping time windows • Virtual channels • Use the SAM spatial filters to estimate time series

  6. Virtual Channels

  7. How can we increase signal-to-noise in a virtual channel? • Average - either in the temporal or frequency domain • Averaging in time will produce an evoked response (and ignore induced activity)

  8. Introduction to SAMerf • Traditional SAM is performed on time windows and frequency bands of interest • Virtual channels are created for each voxel • The virtual channels are averaged to generate an event-related response (ERF) • Amplitudes of the ERFs at small time windows are used to produce 3D maps

  9. Cheyne et al. motor experiment Spatiotemporal mapping of cortical activity accompanying voluntary movements using an event-related beamforming approach Douglas Cheyne, Leyla Bakhtazad, William Gaetz Neuromagnetic Imaging Laboratory, Hospital for Sick Children Research Institute, Toronto, Ontario, Canada Hum Brain Mapp. 2006 Mar;27(3):213-29.

  10. SAMerf processing stream

  11. Movement-related fields

  12. Single subject

  13. Mean of 8 subjects

  14. Sliding window SAMerf • Activity is averaged over a small time window • The averaging window is slid to observe temporal changes • Good for increasing signal-to-noise and characterizing high frequency bursts

  15. 5 Clicks – 250ms ISI 4-6 seconds between click trains Low frequency components Low frequency components Stimuli Five Click Auditory Experiment High frequency component Low frequency component

  16. Time-Frequency analysis using the Stockwell Transform

  17. Sliding window SAM with 50ms windows and 25ms steps Gamma-band: 25-50Hz left right

  18. Right IFG

  19. Summary • Use traditional SAM to find power changes in frequency bands • Use SAMerf to localize evoked fields and phase-locked activity

  20. Thanks!

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