Simultaneous EEG-fMRI:
from acquisition to
application.
Karen Mullinger
Sir Peter Mansfield Magnetic Resonance Centre,
School of Physics and Astronomy
University of Nottingham
Overview
• Introduction
• Aspects of getting good quality data
• Optimising experimental set-up
‒ General pointers
‒ Facilitating good:
 gradient artefact correction
 pulse artefact correction
‒ Summary
• Application
‒ Neurovascular coupling.
‒ Latest results (food for thought)
Why Simultaneous
EEG –fMRI?
• Very powerful spatiotemporal tool
• Same experimental environment
• Same attention and awareness
• Same brain activity
 Necessary when brain activity can’t be
predicted
fMRI
O1
100 µV
EEG
Gradie
-20
Voltage, µV
EEG Artefact Sources
Slice
Phase
Readout
B
2000
1000
0
-1000 Artefact (GA): Switching of the
1. Gradient
-2000
gradient fields, causes large changes in magnetic flux
47
Voltage
µV
µV
Gradient
mT
Strength,Voltage
C
inducing electrical
signals
within
the
EEG.
46
A
45
EPI
Block
20
44
D
200
0
Slice
Phase
Readout
100
-20
0
Voltage
µV
Voltage,
µV
1 EPI cycle,
540s
Average slice Artefact
2000
200
1000
B
E
0
100
-1000
-2000
0
470
20
40
Time, ms
60
80
100
C
EEG Artefact Sources
2. Pulse Artefact (PA): Precise source unclear but
linked to the cardiac cycle.
1) Pulsatile
blood flow
effects (Hall
effect).
2) Small head
nod
3) Scalp
expansion
The Result!
200µV
O1
200 µV
O2
200 µV
F7
200 µV
F8
200 µV
T7
200 µV
T8
200 µV
P7
200 µV
P8
200 µV
RWAV S128
RWAV
RWAV S128
RWAV
RWAV S128
RWAV
RWAV S128
RWAV
RWAV S128
RWAV
Good quality EEG data
Two aspects to EEG-fMRI:
‒ Experimental set-up and data collection
‒ Best post-processing methods
Good quality EEG data
Experimental set-up and data collection
O1
100 µV
General advice
‒ Low impedances of EEG channels
 Less noisy EEG signals
‒ Subject comfort and padding
Minimise movement → reduced artefacts
General advice: Motion
Aim:
• To investigate effect of motion artefacts on EEGBOLD correlates
Method:
• 4 subjects
• Standard 32 channel EEG recording.
• EEG data were recorded during Dual Echo EPI:
• 40 slices, 84×84 matrix, 3×3×4 mm3 voxels
• TR=3s TE1/TE2 =20/48ms
• Episodic memory task: required to move a cursor
with a roller-ball to respond.
Jansen, M. et al, NeuroImage 59, 261-270 (2012)
General advice: Motion
Analysis:
• EEG
–
–
–
–
Gradient (AAS) and Pulse (OBS) artefact correction
ICA to remove residual artefacts
Noisy channels removed
Filtered 4-8Hz (Theta band)
• fMRI
– Motion and physiological correction
– Echoes combined
– Regressors:
1.
2.
3.
Continuous theta regressor
Head motion (from motion parameters)
Artefacts remaining after correction (from visual inspection)
Jansen, M. et al, NeuroImage 59, 261-270 (2012)
General advice: Motion
Not
convolved
with HRF
Convolved
with HRF
Jansen, M. et al, NeuroImage 59, 261-270 (2012)
General advice: Motion
Task: Foot motion
Not
convolved
with HRF
Convolved
with HRF
CAREFUL how you interpret results!
Jansen, M. et al, NeuroImage 59, 261-270 (2012)
General advice
‒ Low impedances of EEG channels
 Less noisy EEG signals
‒ Subject comfort and padding
Minimise movement → reduced artefacts
‒ Isolate amplifiers/cables from scanner bed
Minimise vibration of equipment
General advice
80
Voltage density (V/Hz)
Voltage density (V/Hz)
100
80
7T, no scanning
60
40
20
60
0
0
10
20
30
40
Amplifier
suspended.
20
0
Amplifier on
the scanner bore
0
50
100
150
Frequency, (Hz)
200
250
Mullinger, K.J. et al, MRI 26(7), 968-977 (2008)
General advice
‒ Low impedances of EEG channels
Less noisy EEG signals
‒ Subject comfort and padding
Minimise movement → reduced artefacts
‒ Isolate amplifiers/cables from scanner bed
Minimise vibration of equipment
‒ Turn cyrocooler compression pumps off
Minimise noise sources
General advice
50
15
7T, no scanning
Voltage density (V/Hz)
10
40
5
30
0
Everything on
0
50
100
150
Cryopumps off..
20
10
0
0
50
100
150
Frequency, (Hz)
200
250
...and room lights,
gradient and patient
airflow
Mullinger, K.J. et al, MRI 26(7), 968-977 (2008)
Gradient artefact
Average Artefact
Subtraction (AAS)
Fp1
2000 µV
Fp2
Fp1
2000 µV
2000 µV
O1
Fp2
2000 µV
2000 µV
O2
O1
2000 µV
2000 µV
T7
O2
2000 µV
2000 µV
T8
T7
2000 µV
R128
RWAV
S 59
RWAV
R128
RWAV
RWAV
RWAV
R128
RWAV
RWAV
R128RWAV
2000 µV
RWAV R128
RWAV
T8
Allen, P.J. et al. NeuroImage
12, 230-239 (2000)
2000 µV
R128
RWAV
S 59
RWAV
R128
RWAV
RWAV
RWAV
R128
RWAV
RWAV
R128RWAV
RWAV R128
RWAV
Artefact Correction
requirements
AAS Requires:
– Artefact to be highly repeatable across cycles
– Precisely recording the artefact waveform and the
beginning of each volume.
– These requirements must be closely adhered to as the
unfiltered GA is at least 10,000 times larger than an
evoked response
Residual artefacts are problematic
Precise sampling
–Acquire EEG data at 5kHz
–Ensure your slice TR is a
multiple of the scanner clock
period (i.e. 200μs)
WARNING:
–TR entered into console is
not always the TR outputted
due to rounding issues!!
–Philips System for
equidistant EPI: TR
*Need clinical science agreement for this
Calculator*
Precise sampling
– Synchronise the MR Scanner and EEG clocks
using the output from the MR scanner.
 Philips system: use the 10MHz output from the MR
scanner clock to drive the EEG clock
Mandelkow, H. et al, NeuroImage 32(3)1120-1126 (2006)
Mullinger, K.J. et al, JMRI 27(3): p. 607-616 (2008)
Grad
Phase
Readout
-20
B
EPI
Block
1 EPI cycle,
540s
Voltage µV
0
Slice
Phase
Readout
Average slice artifact
B
1000
0
-1000
-2000
47
Voltage µV
180 dynamics, 20 slices, 3 subjects
46
45
TR = 2s, synchronised
D
200
100
TR = 2s, not synchronised
0
C
46
Results from electrode F7 for a
45
single subject
44
Standard Deviation associated
with average slice artifact
C
44
Voltage µV
2000
1000
0
47
20
-20
2000
-1000
-2000
Mullinger, K.J. et al, JMRI 27(3): 607-616 (2008)D
Voltage µV
Voltage, µV
Gradient Strength, mT
A
Voltage, µV
Experimental Results
E
200
100
TR = 2.0001s, synchronised
0
0
20
40
Time, ms
60
80
100
Minimising GA amplitude
• Why?
– Prevent channel saturation
– Allow higher EEG recording bandwidth
– Improve artefact correction
• How?
– Position subjects 4cm in foot direction
(naision at isocentre = 0cm). Approximately
at Fp1&2.
Yan, W.X., et al. NeuroImage 46(2):459-471. (2009)
Mullinger, K.J. et al, NeuroImage, 54(3):1942-1950 (2011)
Optimal Position: standard
fMRI
Aim:
• Compare GA produced by a multi-slice EPI sequence at
standard and optimal subject positions.
Method:
• 6 subjects
• Experiments were carried out with the nasion at:
 iso-centre
 optimal (+4 cm) z-offset
• Standard 32 channel EEG recording, 250 Hz low pass filter.
• EEG data were recorded during standard EPI:
• 32 slices, 84×84 matrix, 3×3×4 mm3 voxels
• TR=2.5s TE =40ms; slice repetition frequency = 12.8 Hz
• Cued foot movement: 5s every 30s (total: 8 minutes):
cumulative head movements of <1 mm.
Optimal position: Results
RMS of average
artefact before
correction
Voltage (V)
2000
1500
• 40% average
reduction in RMS
over all channels
1000
500
Voltage (V)
0
0
150
10
20
30
40
Time(ms)
50
60
70
80
STD across slices
after correction
100
50
Isocentre
0
0
10
20
30
40
Time(ms)
Optimal position
50
60
70
80
• 36% reduction
in RMS at slice
harmonics after
correction
Pulse artefact
Pulse Artefact Correction
‒ Many methods of PA correction
• Average artefact subtraction (AAS)1
• Optimal basis sets (OBS)2
• Independent component analysis (ICA)3
‒ Varying levels of success reported
‒ Most require correctly identifying the QRS complex
within the ECG trace.
ECG
[1] Allen, P.J. et al, NeuroImage 8(3), 229-239 (1998)
[2] Srivastava, G. et al, NeuroImage 24, 50-60 (2005)
[3] Niazy, R.K. et al, NeuroImage 28, 720-737 (2005)
Pulse Artifact
20000
A
10000
5000
0
-5000
-10000
-15000
0
2.5
5
800
B
Voltage µV
600
400
200
0
-200
-400
-600
-800
(a.u.)
Problems:
• ECG is affected by
gradients as well.
• Sometimes hard to get a
good ECG trace.
• Trace is sometimes
saturated.
Voltage µV
15000
3500
3000
2500
2000
1500
0.0
2.5
5.0
C
20000
A
Voltage µV
15000
10000
5000
0
-5000
-10000
Solution on a Philips system*:
-15000
0
2.5
5
800
B
600
400
Voltage µV
• Use vector cardiogram (VCG)
from MR Scanner which is
unaffected by gradients1.
200
0
-200
-400
-800
Voltage (a.u.)
• R peak markers are also placed
automatically in the physlog file2
which can be used for pulse
artefact correction directly.
-600
3500
3000
2500
2000
1500
1000
500
0
-500
-1000
-1500
0.0
[2] Fischer et al. MRM, 42:361-370 (1999)
5.0
C
0.0
[1] Chia et al. JMRI, 12:678-688 (2000)
2.5
2.5
Time (s)
*Need research login to access physlog file
5.0
Results
• EEG trace from Tp10
averaged over all
cardiac cycles in 2
minute period.
Voltage, µV
50
0
-50
-100
-150
No correction
100
B
Voltage, µV
• Data gradient-corrected
and low-pass filtered at
70 Hz
A
100
50
0
-50
Using ECG markers
-100
C
40
Voltage, µV
• 0 time=R peak marker
from VCG
20
0
-20
-40
Mean
Standard Deviation
Using VCG markers
0
0.25
0.5
Time, s
0.75
Pulse Artefact
•
Precise source unclear but linked to the cardiac cycle.
• Variation between cardiac cycles
makes correction of difficult
• Problems increase with field
strength
• Need a greater understanding of
pulse artefact
Average pulse artefact
1) Pulsatile
R-peak
T7
blood flow
effects 2) Small head
T7
300
T7
200
300
100
200
0
100
-100
0
-200
nod
-100
3) Scalp
expansion
-300
-200
-100
-300
Debener, S. et al, Int. J. Psychophys, 2008, 67(3), p.189-199
0
100
200
300
400
500
Measuring the PA
constituents
• 6 subjects
• Recorded EEG data in 3T MR scanner
• 4 conditions:
1.
2.
3.
4.
Relaxed
Bite Bar and vacuum cushion (stop head nod)
Swimming cap (stop Hall effect)
2&3 (left with scalp expansion).
Yan, W.X., et al., HBM, 2010. 31(4): p. 604-620.
Mullinger, K.J. et al, #667 WTh HBM 2011. Quebec.
PA Experimental Results
100
Relax
60
40
0
0.2
0.4
0.6
C
80
Voltage (V)
80
100
Insulated
60
40
0
60
0
0.2
0.4
0.6
Time(s)
Restrained & D
insulated
0
0.2
0.4
Time(s)
Average RMS
0.6
EEGA
0
40
30
20
10
0
Relaxed
40
20
20
std
50
0
Time(s)
Voltage (V)
Mean RMS
40
20
0
B
60
20
100
Restrained
80
Voltage (V)
Voltage (V)
80
Amplitude
(uV)
(µV)
Amplitude
100
Restrained
Insulated
Condition
0
0.2
0.4
Time(s)
Subject RMS
0.6
Insulated &
Restrained
Summary
‒ SNR of EEG data inside the MR
scanner still lower than outside.
‒ Higher MR fields → increasing EEG
artefact problems.
‒ Experimental set-up is important.
Data Acquisition
Summary
‒ To improve gradient artefact correction:
 Chose TR and number of slices wisely
 Synchronise scanner clocks
 Optimally position the subject
‒ To improve pulse artefact correction:
 Use VCG to monitor cardiac trace
Application
Investigating origin of
Negative BOLD
• Negative BOLD Response (NBR): Regions where
there is a stimulus related decrease in BOLD signal.
• Reported in visual1, motor2 and somatosensory3
cortices.
From: [2] Stefanovic et al. Neuroimage 22;2004.
[1] Shmuel et al. Neuron 36(6);2002. [3] Kastrup et al. Neuroimage 41(4);2008.
Negative BOLD
• NBR origin unclear:
– Neuronal basis
– Haemodynamic artefact (blood steal)
• Invasive recordings in monkeys show a decrease
in local field potentials (LFP) and spiking activity in
regions of NBR, and suggest at least 60% of NBR
is neuronal in origin1.
 Clarification in humans is needed.
[1] Shmuel et al. Nat Neurosci. 9(4);2006.
Aim
To use simultaneous measurements of
BOLD, ASL and EEG to investigate the
relationship between natural fluctuations
in the NBR and somatosensory evoked
potentials (SEPs) during median nerve
stimulation (MNS)1
[1] Mullinger et al Proc. ISMRM #109; 2011
Method
Simultaneous EEG-fMRI:
– Philips Achieva 3T MR scanner; 8 channel SENSE head coil.
– 64 channel Brain Products EEG system.
Localiser: GE-EPI BOLD sequence used for planning.
Experiment:
– FAIR Double Acquisition Background Suppression1
sequence used for simultaneous BOLD and background
suppressed ASL data acquisition (TR=2.6s, TE=13/33ms
(ASL/BOLD), label delay=1400ms, 3x3x5mm3 voxels, 212mm FOV, SENSE
factor 2; background suppression TI1/TI2=340ms/560ms).
– Cardiac and respiration monitored.
– MR and EEG scanner clocks synchronised.
• EEG electrode positions digitised (Polhemus system,
Isotrack).
[1] Wesolowski et al. Proc. ISMRM, #6132;2009.
Paradigm
• 13 right handed subjects (8 males, 26±3 yrs)
• Stimulate median nerve of right wrist
• Amplitude: just above motor threshold to
cause thumb distension
• 2 Hz stimulation, 0.5ms pulses (Digitimer DS7A)
20 pulses
per block
10s
20s
40 blocks
10s
Analysis
EEG pre-processing
• Gradient and pulse artefact correction using average
artefact subtraction (Brain Vision Analyzer2)
• Data inspection:
– 3 subjects excluded due to gross (>3mm) or stimulus-locked
movement.
– Noisy channels and/or blocks rejected
• Down-sampled: 600Hz
• Re-referenced: Average of non-noisy channels
• Filtered: 2-40 Hz
Analysis
EEG Beamformer1
Fitted2 basis set to
SEP for each block to
find peak-to-peak
P100-N140 amplitude
T-stat map:
active window: 0.01-0.16s
passive window: 0.3-0.45s
VE timecourse for single block
VE amplitude (nAm)
500
Averaged over
20 responses in
a block
0
-500
0
5
10
15
Time (s)
20
25
VE amplitude
amplitude (nAm)
VE
100
50
0
-50
30
[1] Brookes et al. NeuroImage 40(3);2008
[2] Mayhew et al. Clin. Neurophysiol. 117(6);2006
-100
-100
-50
-50 00
50
50 100
100 150
150 200
200 250
250 300
300 350
350 400
400
Time
(ms)
Time (ms)
Analysis
fMRI pre-processing
• Motion corrected (FLIRT, FSL)
• BOLD data physiologically corrected
(RETROICOR)
• Interpolated to effective TR=2.6s
• ASL: perfusion weighted image: Tag-Control
• BOLD image pairs averaged
• Normalised to MNI template
• Smoothed: 5mm FWHM kernel
Analysis
fMRI General Linear Models
0.06
0.05
Boxcar:
0.04

0.03
SEP
amplitude
modulator:
0.02
0.01
0
-0.01
0
20
40
60
80
100
120
• 2nd level fixed effects analysis on BOLD and ASL data
Timecourse for each region &
subject obtained; averaged
over subjects & blocks
Group ROI defined
for positive and
negative correlation.
5
7.22
7.2
BOLD: P<0.05 FWE
ASL: P<0.001 uncorr
BOLD signal
7.18
7.16
7.14
7.12
7.1
7.08
x 10
Results
BOLD
ASL
Positively correlated
with Boxcar
Negatively correlated
with Boxcar
Negatively correlated
with SEP amplitude
MNI peak co-ordinates
(-42,-20,50) Positive
(34,-16,46) Negative
(36,-18,50) SEP
• No positive correlation
amplitude of SEP and
fMRI in S1.
Results
Solid line = BOLD, Dashed line= ASL
Results
1
Isocontours of CMRO2 (Davis Model2)
20
15
 %BOLD
0.5
Constants: M = 7.2%1,
α = 0.38, β = 1.2
R = 0.9704, P<0.1*10-4
Gradient = 0.42
0
Coupling
ratio agrees
with Stefanovic3
-0.5
 %CMRO2
2
10
5
0
-5
-10
0
20
[1] Kastrup et-20
al., Neuroimage
41(4);2008.
 %CBF-15
[1] Davis
Kastrup
Neuroimage
[2]
et et
al.,al.,
PNAS,
95;1998
41(4);2008.
[3]
Stefanovic et al., NeuroImage
[2] Davis et al., PNAS, 95;1998
22;2004
40
-20
0
20
 %CBF
40
Discussion
• No positive correlation of fMRI and evoked
potentials in S11.
• Ipsilateral NBR cannot be explained by blood
steal2 as bilateral S1 regions are fed from
different vascular territories.
• CMRO2 shown in NBR region - suggests a
neuronal origin of the response.
[1] Klingner et al. Neuroimage 53(1); 2010
[2] Wade et al. Neuron 36(6);2002
Discussion
• Show for first time correlation between ipsilateral
S1 NBR and amplitude of concurrent EEG
evoked response from contralateral S1/M1.
 Agrees with area identified by Klingner where NBR is modulated
by intensity of MNS1.
• Suggest that NBR-SEP relationship arises
because NBR results from inhibition of task
irrelevant processing in ipsilateral S1, with
corresponding increase in excitability of
contralateral S1, as indexed by increasing SEP
amplitude.
[1] Klingner et al. Neuroimage 53(1); 2010
Why simultaneous
recordings....
• Trial by trial natural fluctuations in the evoked
response → simultaneous recordings are
essential.
• Can also study changes in oscillatory activity
and correlations with BOLD1 and also CBF........
Positively correlated
with Boxcar
Negatively correlated
with Boxcar
Negatively correlated
with Mu amplitude
p<0.05, FWE
[1] Mayhew et al, Proc ISMRM #1560, 2011
Why Simultaneous
recordings....food for thought
• Differences in oscillatory activity: providing
evidence of a neuronal origin of the poststimulus undershoot...
Acknowledgments
Colleagues
Professor Richard Bowtell
Dr Susan Francis
Winston Yan
Jade Havenhand
Dr Thomas White
Dr Marjie Jansen
Dr Elizabeth Liddle
Prof Peter Liddle
Birmingham University
Dr Stephen Mayhew
Dr Andrew Bagshaw
Industry
Robert Stormer (Brain Products)
Dr Matthew Clemence (Philips)
Funding
MRC
EPSRC
Mansfield Fellowships