Transcript
Page 1: Event-related Synthetic Aperture Magnetometry (SAMerf)

Event-related Synthetic Aperture Magnetometry

(SAMerf)

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Outline

• Review of traditional SAM

• Introduction to SAMerf

• Cheyne et al. motor experiment

• Sliding window SAMerf

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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

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SAM analysis of an n-back working memory task

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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

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Virtual Channels

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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)

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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

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Cheyne et al. motor experimentSpatiotemporal 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.

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SAMerf processing stream

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Movement-related fields

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Single subject

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Mean of 8 subjects

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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

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Low frequency component High frequency component

5 Clicks – 250ms ISI 4-6 seconds between click trains

Stimuli

Five Click Auditory Experiment

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Time-Frequency analysis using the Stockwell Transform

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Sliding window SAM with 50ms windows and 25ms steps Gamma-band: 25-50Hz

left

right

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Right IFG

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Summary

• Use traditional SAM to find power changes in frequency bands

• Use SAMerf to localize evoked fields and phase-locked activity

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Thanks!


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