1. Exploring Magnetoencephalography (MEG) Data Acquisition and Analysis Techniques Rosalia F. Tungaraza, Ph.D. Anthony Kelly, B.A. Ajay Niranjan, M.D., MBA July 22, 2010 1

2. 2 What does MEG Measure?

3. Data Acquisition and Analysis Steps 3 Acquisition Coregistration Preprocessing Averaging Localization

4. Data Acquisition and Analysis Steps 4 Fitting isotrack points to structural MRI Fitting a sphere Fitting isotrack points to structural MRI Fitting a sphere Acquisition Coregistration Preprocessing Averaging Localization

5. Data Acquisition and Analysis Steps 5 Temporal Filtering Bandpass 0.5 – 40Hz Maxwell’s Filtering Signal Source Projection (SSP) Temporal Filtering Bandpass 0.5 – 40Hz Maxwell’s Filtering Signal Source Projection (SSP) Acquisition Coregistration Preprocessing Averaging Localization

6. Raw Continuous Data Time frame: 10000 ms 6

7. Elekta's Maxwell Filtering Method 7

8. After Maxwell Filtering Time frame: 10000 ms 8

9. Is the Filtering Process Worth it? 9

10. 10 Reduction in Covariance of Magnetometer Signals

11. Data Acquisition and Analysis Steps 11 Selecting a time region and baseline Averaging by condition Selecting a time region and baseline Averaging by condition Acquisition Coregistration Preprocessing Averaging Localization

12. Retrieving Trials 12 Baseline: 200ms 1000ms :Time-frame

13. Median Nerve Stimulation ● Random 10ms long electrical stimulation of same voltage ● Purpose: ● localizer ● explore effect of number of trials on results 13

14. Median Nerve Stimulation Average 14 180 Trials

15. Hearing Your Voice in Real Time Purpose: ● explore limitations of MEG (jaw movement, head motion, activates many simultaneous brain regions e.t.c.) 15 + house … + water - Press a button - Read word out loud + book

16. Hearing Your Voice in Real Time Stimulation Average 16 - Complex task: elicits response from multiple brain regions - Variations in subject’s response 60 Trials

17. Data Acquisition and Analysis Steps 17 Dipole Fitting Acquisition Coregistration Preprocessing Averaging Localization

18. 18 Source Localization ? Forward Problem Inverse Problem Solvable! Always an estimate

19. 19 ? Dipole Fitting Technique 1.Pick a subset of sensors (ROI) 2.Select a time point 3.Dipole fit estimates magnetic field given the two parameters PROCEDURE Estimated SignalsMeasured Signals 4.

20. 20 Goodness-of-fit a measure that shows how much the estimated signal matches the measured signal Confidence Volumethe volume within which the dipole fitting method is confident the dipole exists Measures of Quality

21. 21 22ms52ms83ms 7 sensors 42 sensors 92 sensors 99.7% 99.2%98.2% 84.6% 97.6% 85.8% 84.6% 97.6%85.8% Median Nerve Dipole Fitting Results

22. 22 Hearing Your Voice in Real Time Dipole Fitting Results Fits dipole in improbable locations (single dipole insufficient) Needs a different source localization technique Brain Stem Post-central Gyrus Face Area 61.0%80.3% 82.2%

23. 23 Summary We have explored the following steps in MEG image acquisition and analysis: Acquisition of the MEG signals Coregistration of MEG isotrack data with structural MRI Preprocessing of the MEG signals Averaging the preprocessed signals Source localization of the averaged waveforms We found both strengths and weaknesses, which the user must take into account before making inferences from their analyzed data.

24. Acknowledgements ● MEG – Erika Laing – Anna Haridis – Dr. Ajay Niranjan ● MNTP – Drs. Seong-Gi Kim and Bill Eddy – Tomika Cohen and Rebecca Clark ● All other MNTP participants 24