1. Simultaneous EEG and fMRI for the localisation of spontaneous alpha-rhythm J.C. de Munck S.I. Gonçalves P.J.W. Pouwels R. Schoonhoven J.P.A. Kuijer E.J.W. Van Someren P. Anderson N.M. Maurits J.M. Hoogduin R.M. Heethaar F.H. Lopes da Silva VU Medical Centre, Amsterdam AZG, Groningen Institute of Neurobiology, UvA, Amsterdam

2. Outline Introduction Methodology Results Discussion and Future Work

3. Introduction The alpha rhythm is the hallmark of the resting state, therefore related to all fMRI studies. Different types of alpha activity: posterior alpha, mu rhythm, midtemporal third rhythm. Many open questions related to the nature and origin of this type of activity still remain.

4. Introduction Source localisation with EEG Find Solution to the EEG Inverse Problem Source Model: Current Dipole Volume Conductor: Realistic Spherical specify

5. Introduction fMRI

6. Introduction 4 minutes Average alpha power time series

7. Introduction EEG/fMRI voxel intensity : each point represents intensity of voxel during one 3 second MRI - EPI scan 4 minutes Average alpha power time series possible with Simultaneous EEG/fMRI

8. The MR greatly disturbs the EEG signal Methodology Artifacts on the EEG 1 slice Gradient artifacts RF pulse artifact

9. Methodology Artifacts on the EEG There are also artefact that are related to the heart beat.

10. Methodology Artefacts on the EEG These artefacts can be removed by an averaging procedure.

11. Methodology: Induced alpha rhythm f (0-100Hz) t (0-600s) 10 Hz eyes open eyes closed eyes open eyes open eyes open eyes open eyes open eyes open eyes open eyes open eyes open eyes closed eyes closed eyes closed eyes closed eyes closed eyes closed eyes closed eyes closed eyes closed

12. Results Experiment description Data recorded from 8 healthy subjects (4 males, 4 females, mean age 34±8), 2 discarded. Subjects instructed to lie still inside the scanner, keeping the eyes closed. EEG acquired with MR compatible EEG amplifier (SD MRI, Micromed, Treviso, Italy) and cap with 19 Aq/AgCl electrodes positioned in 10/20 system, Bipolar montage. Functional images acquired on 1.5 T MR scanner (Magnetom Sonata, Siemens, Erlangen, Germany) using T2* weighted EPI (TR=3000ms) consisting of 24 transversal slices. High resolution MPRAGE sequence consisting of 160 slices to provide anatomical reference. For each subject, 400 volumes (in a total of 20 mins of data) were acquired per subject. For 3 subjects, data was acquired in two series of 10 mins each.

13. Results: Spontaneous alpha rhythm t (0-1200s) f (0-100 Hz) 10 Hz

14. Results - Subjects 1, 2 and 3 Subject 1 FDR=10 -7 all derivations Subject 2 FDR=0.05 0.1529  0.4000 all derivations Subject 3 FDR=0.05 0.2000  0.4000 all derivations

15. Results - Subject 4 Subject 4 spectrogram

16. Results - Subject 4 Subject 4 spectrogram

17. Results - Subject 4 Subject 4 FDR=0.2 -0.2706  -0.1647 P3-O1, P4-O2 Alpha period (darker blue) -0.3176  -0.2353 C3-P3, C4-P4 Beta period (lighter blue)

18. Results - Subject 5 Subject 5 FDR=0.05 -0.4000  -0.1529 C3-P3, C4-P4, T5-T3, T6-T4

19. Results - Subjects 5 and 6 Subject 5 FDR=0.05 -0.4000  -0.1529 C3-P3, C4-P4, T5-T3, T6-T4 Subject 6 FDR=0.05 0.2000  0.4000 C3-P3, C4-P4

20. Discussion and Future work Our results suggest that inter-subject variability is important and should be taken into account. (e.g. subjects 1, 5 and 6). Furthermore, the results show that even within one subject (e.g. subject 4), different states correspond to different correlation patterns. Since the resting state is the reference state in most fMRI studies, our results show that variability in resting state may be an important cause of the variability of fMRI results.

21. EEG ECG fMRI EEG2 Correlation pattern Spectrogram The analysis of simultaneous EEG-ECG-fMRI data is quit complex. Discussion and Future work

22. There is a correlation between BOLD and the heartbeat signal... Discussion and Future work Heart beat Volumes 3 s.

23. There is a correlation between BOLD and the heartbeat signal... Discussion and Future work Heart beat Volumes 3 s.

24. There are many fMRI points that correlate well with the heart beats. Therefore the heart beat should be accounted for in the correlation analysis. Discussion and Future work

25. EEG ECG fMRI EEG2 Correlation pattern Spectrogram But in the future it will be even more complex... MRI Source model SSP Discussion and Future work

26. The complexity of the problem put demands on the software for the data analysis: High performance Good visualisation tool Efforts to keep track of raw data, intermediate results and end product.

27. Results - Subjects 5 and 6

28. Results - Temporal modulation of the regressor Subject 2 Subject 5 disc. sub

29. Results - Temporal modulation of the regressor (all derivations) N is the number of samples; P i is the power value at time sample i; is the average power.

30. Results - Temporal modulation of the regressor and within subject variation

31. Discussion and Conclusions Results suggest that the resting state is not comparable amongst subjects and sometimes, not even within one subject. As the resting state plays an important role in fMRI analysis where the paradigms are of the type “rest-task”, the abovementioned variability should be considered when questioning how comparable are fMRI results from different subjects. The question raised previously could be ultimately addressed by recording the simultaneous EEG and using the average alpha power time series as a distractor in the fMRI analysis.

32. Future work Technical improvements - Signal Space Projection methods; - Dipole fitting on simultaneous EEG; - Non-linear correlation measures; - Variability of hemodynamic response. Scientific questions - Can the abovementioned findings be confirmed in a more systematic study? - Does the alpha rhythm variability decrease when the state of the subject is more well defined? - What is the relation between the first and second harmonics of the alpha rhythm?

33. Methodology False Detection Rate (FDR) In this procedure, where N null hypothesis are being tested simultaneously, the goal is to control the goal of FDR (Benjamin and Hochberg (1995)): where E(.) stands for the expected value; F is the number of false detections; T is the number of true detections; FDR = 0 if T+F=0.

34. Methodology False Detection Rate (FDR) 1. Select desired FDR bound q; 2. Order p-values from smallest to largest p 1  p 2  ….  p N ; 3. Determine largest i such as: 4. Declare voxels v(1) to v(i) as active.

35. Technical requirements Safety issues E.g. Presence of metal wires that can act as antennas; Existence of wire loops generating induced currents;

36. Technical requirements Hardware solutions Degradation of MR signal: RF contamination, ferromagnetic materials Shielding of EEG system Use of appropriate materials EEG artifact caused by the MR DC amplifiers of large dynamic range and high resolution (22/24 bits) High sampling frequency (> 1 KHz) Safety: limitation of induced currents and closed loops current limiting resistors close to electrodes use of carbon wires careful wire placement avoiding loops fiber optic connection between subject + EEG Amp. and the remaining system.

37. First Experiments BioSemi (24 bits, 16 KHz) Signals after removing average over slices and volumes Raw signals Remaining RF pulse

38. First Experiments Linear interpolation of remaining RF artifact Unfiltered data

39. First Experiments Average Ballist. Art. Corrected (black) vs. uncorrected (gray) data

40. Methodology Artifacts on the EEG Ballistocardiogram artifact on the EEG (time locked to the ECG) wire displacement due to pulsate vessel movement Hall Effect v F-F- F+F+ B VHVH

41. Methodology Artifacts on the MR RF contamination of the MR signal by the EEG hardware.

42. Methodology Artifacts on the MR Degradation of the MR signal by the presence of ferromagnetic materials.