Simultaneous EEG and fMRI for the localisation of spontaneous alpha-rhythm

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

Outline • Introduction • Methodology • Results • Discussion and Future Work

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.

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

active active active active A rest rest rest rest voxel intensity R/P L 4 minutes : each point represents intensity of voxel during one 3 second MRI - EPI scan IntroductionfMRI

Average alpha power time series 4 minutes Introduction

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

Gradient artifacts RF pulse artifact 1 slice MethodologyArtifacts on the EEG The MR greatly disturbs the EEG signal

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

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

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

ResultsExperiment 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.

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

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

Results - Subject 4 Subject 4 spectrogram

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

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

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

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.

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

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

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

Discussion and Future work • 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.

Results - Subjects 5 and 6

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

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

Results - Temporal modulation of the regressor and within subject variation

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.

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?

MethodologyFalse 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.

1. Select desired FDR bound q; 2. Order p-values from smallest to largest p1p2 …. pN; 3. Determine largest i such as: 4. Declare voxels v(1) to v(i) as active. MethodologyFalse Detection Rate (FDR)

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

Technical requirementsHardware solutions Degradation of MR signal: RF contamination, ferromagnetic materials Safety: limitation of induced currents and closed loops EEG artifact caused by the MR • DC amplifiers of large dynamic range and high resolution (22/24 bits) • High sampling frequency (> 1 KHz) • 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. • Shielding of EEG system • Use of appropriate materials

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

Linear interpolation of remaining RF artifact First Experiments Unfiltered data

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

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

MethodologyArtifacts on the MR RF contamination of the MR signal by the EEG hardware.

MethodologyArtifacts on the MR Degradation of the MR signal by the presence of ferromagnetic materials.

Simultaneous EEG and fMRI for the localisation of spontaneous alpha-rhythm