Post on 22-Apr-2023
Magnetic Resonance Im
Simultaneous EEG–fMRI acquisition: how far is it from being a
standardized technique?
Girolamo Garreffaa,b, Marta Bianciardib,c,d, Gisela E. Hagbergc, Emiliano Macalusoc,
Maria Grazia Marciania,e, Bruno Maravigliab,d,*, Manuel Abbafatie, Marco Carnıb,d,
Ivo Brunif, Luigi Bianchia,c,e
aLaboratorio Neurofisiopatologia, IRCCS Fondazione S. Lucia, 00179 ROMA, ItalybEnrico Fermi Center, 00184 Rome, Italy
cLaboratorio Neuroimmagini, IRCCS Fondazione S. Lucia, 00179 ROMA, ItalydDip. Fisica, Universita di Roma bLa Sapienza,Q 00185 ROMA, Italy
eDip. Neuroscienze, Universita di Roma bTor Vergata,Q ItalyfEBNeuro S. p. A., 50127 Firenze, Italy
Received 29 October 2004; accepted 29 October 2004
Abstract
Simultaneous EEG–fMRI is a powerful tool to study spontaneous and evoked brain activity because of the complementary advantages of
the two techniques in terms of temporal and spatial resolution. In recent years, a significant number of scientific works have been published
on this subject. However, many technical problems related to the intrinsic incompatibility of EEG and MRI methods are still not fully solved.
Furthermore, simultaneous acquisition of EEG and event-related fMRI requires precise synchronization of all devices involved in the
experimental setup. Thus, timing issue must be carefully considered in order to avoid significant methodological errors.
The aim of the present work is to highlight and discuss some of technical and methodological open issues associated with the combined
use of EEG and fMRI. These issues are presented in the context of preliminary data regarding simultaneous acquisition of event-related
evoked potentials and BOLD images during a visual odd-ball paradigm.
D 2004 Elsevier Inc. All rights reserved.
Keywords: EEG; fMRI; Artefacts; Synchronization; Simultaneous acquisition
1. Introduction
The first EEG equipment designed for combined use
with MR was employed in 1993 [1] and the first clinical
fMRI applications were made in 1996 [2]. Ever since, a
great deal of work has been devoted to EEG–fMRI studies
because of the unique possibility to obtain measurements
with high spatial and temporal resolution simultaneously.
The technical problems that arise from the integration of the
two methods are many fold and can be classified in the
following categories: magnetic field-related effects (B0
effects); EEG artefacts induced by gradient switching and
radio-frequency (RF) pulses (MRI dynamic conditions); and
0730-725X/$ – see front matter D 2004 Elsevier Inc. All rights reserved.
doi:10.1016/j.mri.2004.10.013
* Corresponding author. Dipartimento di Fisica, Universita di Roma
bLa Sapienza,Q P.le Aldo Moro, 5 CAP: 00185 Rome, Italy. Tel.: +39
0649913473, +39 064454859; fax: +39 0649913484.
E-mail address: bruno.maraviglia@roma1.infn.it (B. Maraviglia).
timing issues related to protocols and devices (synchroni-
zation). Furthermore, other problems concern the electric
noise induced by the MR environment. These include
mechanically induced artefacts (MIA) like disturbances
from the magnet cold head pump and acoustic noise caused
by the operating gradient coils. These typically cause
vibrations on scanner structures and consequently electric
guitar-like pick-up effects on the wires and electrodes
placed in the magnetic field. Materials, geometrical and
electrical properties of the wiring and of the EEG electrodes
must thus be considered in order to optimize safety [3],
electrophysiological and MRI conditions.
In the present work we first describe these methodolog-
ical issues and then examine some experimental data,
discussing possible solutions. Furthermore, we report
preliminary results concerning simultaneous acquisition of
evoked potentials and BOLD imaging during event-related
visual stimulation.
aging 22 (2004) 1445–1455
G. Garreffa et al. / Magnetic Resonance Imaging 22 (2004) 1445–14551446
2. Outstanding issues for simultaneous
EEG–fMRI acquisition
2.1. Artefacts in EEG recordings
2.1.1. MRI pulse sequence artefact
MRI pulse sequence artefact (PSA) consists in strong
perturbations of EEG signals due to the time-varying
magnetic field gradients. Ives et al. [1] and Huang-Hellinger
et al. [4] described the induction of electromotive forces
related to wire loops perpendicular to the direction of the
field gradient. These forces are proportional to the residual
cross-sectional area of wires loop and to the slew rate of
system gradients. The gradients switch and the RF pulses
induce a critical electric noise that obscures all the EEG
traces. A detailed analysis of PSA, based on a priori
knowledge of MRI sequence parameters, can be used to
plan any artefact-removing procedure. The estimation of the
MRI sequence parameters in the raw EEG data can help to
develop analytical expressions of the artefact. For this
purpose the EEG data require acquisition at high sampling
rate. The analytical definition of PSA can in turn be used as
a reference (template) for filtering [5,6].
2.1.2. Quantum noise of the analogue-to-digital converter
The PSA is a signal that has a larger dynamic range
(usually several millivolts) than the EEG signal (tens to
hundreds of microvolts). This implies that the parameters
for EEG acquisition during combined EEG–fMRI have to
be different from those utilized outside the MR scanner in
order to avoid saturation of the analogue-to-digital con-
verters (ADCs). As a consequence of this, the digital
resolution power of the ADC will decrease, thus leading to
an increase of the quantum noise.
2.1.3. Ballistocardiogram artefact
The ballistocardiogram artefact (BCA, see Fig. 1) relates
to cardiac activity and it represents a relevant source of
noise to the EEG recordings [7–9].
The amplitude may exceed 150 AV at 1.5 T field
strength which is between one and two orders of magnitude
greater than most event-related potential (ERP) signals.
Moreover, the BCA varies across subjects and across time,
Fig. 1. Ballistocardiogram artefact on a
as does cardiac activity. Its frequency range is mainly in the
theta (4 –8 Hz) band [7], but extends to the alpha (8–13 Hz)
and delta (0.5–4 Hz) bands, thus overlapping with the
EEG/ERP signals. For this reason, BCA removing cannot
simply employ band-pass or reject-band filters. Instead the
shape of the BCA signal needs to be estimated and
subtracted from the raw EEG recordings. However, the
efficacy of these methods depends on many factors and it is
still debated. Some authors [10] do not use any BCA
removal procedure as they do not consider the discrimina-
tion power of these methods adequate for ERP signals,
which are one or two orders of magnitude smaller than the
usual EEG signal.
Allen et al. [7] and Benar et al. [11] described two
different methods to remove BCA artefacts. Both methods
resulted adequate for applications aimed at identifying
single epileptic spikes, with peak amplitudes in the order of
10 –150 AV. Allen et al. described a method that subtracts
an averaged BCA waveform calculated for each electrode
during 10 s previous to MRI scanning. On the other hand,
Benar et al. [11] used a filtering method based on principal
and independent components analysis. Another possibility
is to actually measure the BCA artefact in a location that
does not interfere with the full EEG recording. For
instance, Bonmassar et al. [12] proposed the use of a
piezoelectric motion transducer positioned on the tempo-
rary artery enabling simultaneous removal of both motion
artefacts and BCA. Kim et al. [13] described an adaptive
filter that was tested on experimental data of alpha waves
that dominate conventional EEG and epileptic EEG.
Finally, Ellingson et al. [14] proposed a method that was
applied in the simultaneous acquisition of auditory ERPs
and fMRI with results that were comparable to those
obtained outside the scanner, but combined recording
required a great number of trials to be averaged (N250).
2.2. ERP–fMRI synchronization issues
2.2.1. Stimulation timing errors
Devices typically used to present stimuli during EEG
and fMRI experiments have an intrinsic timing uncertainty
due to hardware and software reasons. For example, we can
consider a Microsoft Windows XP-based system that
9-s section of an EEG recording.
G. Garreffa et al. / Magnetic Resonance Imaging 22 (2004) 1445–1455 1447
provides visual or acoustic stimuli for evoked related
potential studies by means of a secondary monitor or an
audio card. Microsoft Windows XP, as well as Linux, OS-
X and many other operating system (OS), is not a real-time
OS. This implies that when the stimulation software sends a
command to the OS, for example, to draw an image to the
screen or to play an audio file, it is generally not possible to
know exactly when the command is executed and when the
stimuli are delivered. This generates two kinds of timing
errors: systematic and random errors, which have different
consequences on acquired and processed data. Usually,
systematic errors affect the overall accuracy of the measure
while random errors tend to produce filtering effect, when
several repetitions of the same stimulus type are averaged.
Timing errors are introduced when the delays introduced by
all the equipment of the experimental setup are not
accurately measured or taken into account. This includes,
for example, transmission and processing delays introduced
by serial ports, video and audio cards, electronic circuits, as
well as context switch in multithreading environments of
modern operating systems.
Systematic timing errors can be easily subtracted if
accurately measured. A typical problem occurs if one
assumes that all the parts of the screen are drawn simulta-
neously: drawing a video frame requires time so that the top
part of the screen is usually drawn before the bottom one
(about 16 ms in the case of a video refresh rate of 60 Hz). If
stimuli are displayed on different part of the screen this
should be taken into account.
Random timing errors have an impact on averaging
techniques, which are employed to increase the signal-to-
noise ratio (SNR) in both fMRI and ERP. These techniques
can be applied provided that the timing of the stimuli and
the acquired data are known. Usually, fMRI and ERP use
Fig. 2. Stimulation designs employed. (A) In Design 1, a sparse sampling scheme w
(B) Stimuli were presented both during MR acquisition and during delay betw
respectively). Time was kept invariant between stimulation types (15 min).
different devices to acquire physiological data and to present
the stimuli to the volunteer, requiring some coordination
among the devices. However, if this is not properly taken
into account, the so-called trigger jitter (TJ) may occur.
Possible sources of this effect are examined below and some
solutions are proposed.
2.2.2. ERP–fMRI design optimization
Two design options are available whenever acquiring
both ERP and fMRI data on a subject inside an MR scanner:
simultaneous and interleaved.
Whenever a perfect filtering of the MPSA is unavail-
able, interleaved acquisition is necessary to measure event-
related EEG responses in the presence of the static
magnetic fields and time-varying magnetic field gradients.
In this context the different time resolution of fMRI and
EEG offers an important advantage: the cerebral-evoked
response to stimulation is observable at different and
usually nonoverlapping times, with longer delays for the
hemodynamic response, thus enabling interleaved acquisi-
tion of the two signal types. In order to avoid effects due to
gradient and RF switching during EEG recordings, time
delays are introduced in the MRI acquisition and hence the
interstimulus interval (ISI) will increase. For the measure-
ment of BOLD activation patterns, such an approach will
inevitably lead to reductions in detection power, and hence
less sensitivity toward the BOLD effect.
3. Data acquisition and analysis
Having discussed the most important methodological
issues concerning simultaneous EEG–fMRI recording, we
now turn to the description of these effects in the
experimental context and we consider possible solutions.
as used, with a resulting number of rare (frequent) stimuli equal to 25 (100).
een volumes (total number of rare/frequent stimuli equal to 60 and 240,
Fig. 3. Initial MRI pulse sequence (EPI) frame of 25 ms where it is possible
to see that the PS events timing correspond to the first part of PSA (this is
also true for the remaining part of PS).
G. Garreffa et al. / Magnetic Resonance Imaging 22 (2004) 1445–14551448
3.1. Paradigm
Four healthy subjects (mean age, 28 years) gave their
written informed consent to participate in the study, which
was approved by the local ethics committee. Three subjects
underwent fMRI-EEG while performing a visual odd-ball
task (see below). The aim of the study was to describe any
interaction between the EEG and the fMRI systems (see
artefacts described in the previous section) and to optimize
the acquisition of simultaneous EEG–fMRI, comparing two
different acquisition protocols (Fig. 2).
In the first protocol we used a long fMRI repetition time
(TR=9.5 s) and presented visual stimuli only in the gaps
between MR volumes (i.e., mean intertrial interval=9.5 s).
In the second protocol we used a shorter TR of 5.5 s and we
presented two visual stimuli for each TR (i.e., mean
intertrial interval=2.75 s). The fourth subject underwent
EEG at rest (eyes closed, no task) inside MR scanner,
providing further data on BCA and MIA.
Fig. 4. Pulse sequence artefact relative to the Pz electrode acquired over 209 m
two slices.
3.2. EEG acquisitions
A standard portable 40-channel digital EEG amplifier
(Mizar, EBNeuro, Florence, Italy) was adapted to operate
inside the MR room. Conic Ag-AgCl electrodes prear-
ranged on a cap of a magnetic material were filled with
conductive gel and placed on the scalp of the participants
prior to positioning in the bore of the MR scanner. The
positioning of the electrodes was performed according to
the 10/20 scheme. The reference electrode was positioned
on the AFz position (according to the 10/10 scheme)
whereas the ground was positioned halfway between Fz
and Cz.
The EEG recording unit was placed inside a shielded
box to eliminate any RF disturbance on the MR images.
The unit amplified the signal and performed A/D conver-
sion and multiplexing. The digital signal was transferred via
optical-fiber connection to a host computer (outside the
magnet room) for de-multiplexing, data acquisition, pro-
cessing and storage.
3.3. MRI acquisitions
A Siemens Vision Magnetom MR system (Siemens
Medical Systems, Erlangen, Germany) operating at 1.5 T
and equipped for echo-planar imaging (EPI) was employed
for acquiring functional MR images. A circular polarized
volume head coil was used for RF transmission and
reception. Depending on the experimental paradigm, either
97 (the long TR protocol) or 164 (short TR protocol)
BOLD image volumes were acquired, subdivided in 20
planes, starting from the vertex and stretching caudally.
Each plane was excited with a 908 slice selective RF pulse
followed by echo-planar blipped trapezoidal readout
gradients (bandwidth, 1953 Hz/pixel). The sequence
parameters were duration of acquisition of 22 slices 2 s,
TR 9.5 or 5.5 s, echo time 60 ms, matrix 64�64, field of
view 256 mm, slice thickness 4 mm and gap between
slices 0.8 mm. At the end of each volume, a trigger pulse
was sent from the MR scanner to the PC for synchroni-
zation with EEG and stimulus presentation. Two initial
s at a sampling rate of 8192 Hz and corresponding to the acquisition of
G. Garreffa et al. / Magnetic Resonance Imaging 22 (2004) 1445–1455 1449
BOLD images were discarded from further analysis to
remove any possible T1 saturation effects.
3.4. Analysis of EEG data
EEG data were analyzed with the Galileo Back Averag-
ing ERP Package (EBNeuro) for the PSA mean artefact
computation and with the Galileo Spectral Cartooning And
Coherence 2003 (by Brainware for EBNeuro) for spectral
analysis. No pre-processing was performed on the data (e.g.,
filtering) unless specified.
Fig. 5. Power spectrum of the PSA on the Pz electrode. (A) Full band. (B) The
amplitude modulation: power peaks are regularly repeated with different amplit
carrier corresponds to the 656 Hz peak. (C) The horizontal axis is zoomed in th
beta bands.
3.5. Analysis of fMRI data
Image analysis was performed by SPM2 (Wellcome
Department of ImagingNeuroscience, Institute of Neurology,
London) and included correction for motion during scanning,
brain normalization to Montreal Neurological Institute
coordinates, sinc interpolation, spatial smoothing by a
Gaussian isotropic kernel (full-width at half maximum=6
mm) and temporal filter (high-pass cut-off: 1/128 s plus a
whitening filter based on autocorrelation estimation
of the residuals). Statistical parametric mapping was then
horizontal axis is zoomed in the range 550–750 Hz to show the artefact
udes every 9.5 Hz, which corresponds to the modulation frequency. The
e EEG-ERP band. In this case the periodical peaks overlap the alpha and
Fig. 6. Effect of a low-pass filter on the PSA peak. The cut-off frequency is on the horizontal axis while the PSA peak amplitude is on the vertical one. More
selective filters reduce the PSA peak.
G. Garreffa et al. / Magnetic Resonance Imaging 22 (2004) 1445–14551450
performed by a fixed-effects analysis including three
subjects, for each design separately, using a statistical
threshold of Pb.001.
Table 1
Effects of the averaging on the BCA
Trials Mean abs.
(AV)Peak abs.
(AV)S.D.
(AV)Power
(AV2/s)
1 24.7F4.9 113F17 31.9F5.0 (104F39)�10
2 17.7F2.8 71F12 22.43F3.3 (51F16)�10
4 12.8F2.1 48.2F8.9 16.0F2.5 261F88
8 9.0F1.4 32.7F6.3 11.2F1.7 129F41
16 6.4F1.0 22.6F4.4 8.0F1.2 65F21
32 4.53F0.73 15.8F3.2 5.62F0.86 32.F10
64 3.21F0.51 11.0F2.3 3.98F0.60 16.2F5.1
128 2.27F0.36 7.9F1.6 2.81F0.42 8.1F2.5
256 1.61F0.26 5.6F1.1 2.00F0.30 4.1F1.3
512 1.15F0.19 4.09F0.83 1.42F0.22 2.08F0.68
1024 0.83F0.14 2.95F0.58 1.02F0.16 1.07F0.35
2048 0.60F0.10 2.16F0.41 0.74F0.12 0.56F0.19
4096 0.45F0.08 1.62F0.29 0.55F0.09 0.31F0.10
8192 0.34F0.06 1.25F0.21 0.42F0.07 0.19F0.06
Effects of the averaging on the BCA. Each data (average and S.D.) are
computed from 100,000 simulations obtained by averaging 1 s of signal
randomly selected from an EEG recording. The first column represents the
number of trials that have been averaged, the second column the mean
absolute value of the averaged signal, the third column its peak absolute
value, the forth its S.D. and the fifth the power.
4. Results and discussion
In this section we first consider different types of artefacts
induced during simultaneous EEG and fMRI measure-
ments. Then we discuss timing issues related to combined
EEG–fMRI acquisitions, including the optimization of TR
and ISI for a visual odd-ball study.
4.1. Artefacts in EEG recordings
4.1.1. MRI PSA
Fig. 3 shows a diagram of the first 25 ms of the MR pulse
sequence (see Data Acquisition and Analysis section). Fig. 4
shows the PSA induced by two slices acquired using this
MRI pulse sequence on the EEG traces. The sampling rate
of the EEG was 8 kHz.
The effects shown in Fig. 4 can be predicted by
comparing the detailed pulse sequence events scheme. This
approach can be used to fully identify and describe the
EEG artefacts due to gradients switching effects in MRI
sequence [5,6].
Fig. 4 also shows an amplitude peak modulation well
visible on frequency-encoding gradient effect. This is due to
the absence of synchronization with EEG sampling [6].
Furthermore, the frequency-encoding block (in multislice
single-shot EPI) can be described as a periodical signal
modulated in amplitude by a square wave. In the frequency
domain this will result in spectral components that are
distributed according to some related pulse sequence
parameters. Part of these spectral components may overlap
with electrophysiologic frequencies of interest, and this
must be taken into account when selecting experimental
designs and acquisition protocols (e.g., in steady-state EP/
fMRI study). Fig. 5 shows spectral analysis of 1 s of the
EEG data, with different zooming windows.
Spectral components are determined by a square wave
modulation (corresponding to encoding frequency block) on
a periodic signal (readout switching gradient). Fig. 5C
displays the EEG band in the low-frequency region of the
spectrum. In our data, the frequency of the blocks was
approximately 9.5 Hz and the carrier frequency can be
estimated with the expression:
Mx
2NspDro
where Mx is equal to the effective image matrix size, Nsp is
the number of sampling points on each readout gradient
inversion and Dro the readout gradient encoding duration.
Fig. 6 shows that low-pass progressive filtering reduces
the amplitude of the MRI artifact on the EEG traces and this
means that almost all the energy of the artefact is distributed
in the high frequencies (see also Fig. 5).
Fig. 7. Effect of the averaging on the mean absolute value of the BCA: increasing the number of trials to be averaged reduces its mean amplitude. Simulations
(100,000) were performed for each of the 14 different averaged trials by randomly extracting a section of 1 s from an EEG signal affected by the
ballistocardiogram.
G. Garreffa et al. / Magnetic Resonance Imaging 22 (2004) 1445–1455 1451
As expected, a more selective filter (lower cut-off
frequency) reduces the peak amplitude of the PSA. In
particular, a 4-kHz LPF causes the peak PSA amplitude to
be in the order of 50 mV, while filtering the same signal at
200 Hz reduces the peak PSA amplitude to 4.6 mV. In the
most common cases (EEG, ERP), a 200-Hz LPF can be
applied without affecting frequencies of physiological
interest. In some cases, which depend on the EEG
acquisition device, this allows a reduction of the analog to
digital input dynamic range and, as a direct consequence, of
the quantization noise (see below).
4.1.2. Quantum noise of the ADC
The typical digital resolution of ERP acquisition outside
the MR scanner allows for a dynamic range of F4 mV
(quantum size equal to 0.125 AV) at 16 bits. Instead, ERP
acquisition during fMRI requires higher dynamic ranges,
thus, reducing the digital resolution (here the dynamic range
Fig. 8. Five seconds of an EEG recording in which the cold head of magnet refrigerator system artefact is visible: a fast oscillation (48 Hz) is modulated in
amplitude (2 Hz). Ballistocardiogram is also visible.
was F65 mV; quantum size equal to 2 mV). This
corresponds to a loss of 4 bits (i.e., a 24-dB attenuation).
Averaging techniques (e.g., ERP) may reduce such effect
depending on the number of averaged data since the
quantum noise is divided by the number of averaged trials.
Likewise, digital filtering (e.g., low-pass FIR filters) easily
remove part of this noise.
4.1.3. Ballistocardiogram artefact
Generally, in ERP studies, classical averaging methods
can reduce any uncorrelated source of noise by a factor offfiffiffin
p, where n is the number of averaged trials. Here,
simulations were performed to verify if this was the case
for the BCA. We considered a data segment of 60 s
(corresponding to 491,520 samples) from the Pz electrode
during rest (eyes closed, no task) inside the bore of the MR
scanner, but without any fMRI acquisition (see Fig. 1). The
EEG data were analyzed after removing the mean value. The
Fig. 10. Trigger jitter transfer function effect for a jitter uniformly
distributed in the range of 0–16.6 ms.
G. Garreffa et al. / Magnetic Resonance Imaging 22 (2004) 1445–14551452
S.D. of the signal was 31.8 AV, the mean power was 1040
AV2/s and its mean rectified value was 24 AV. For 100,000times, randomly chosen portions of 1 s of signal were
averaged over a number of trials of 2^N (N=0, 1,. . ., 13). For
each of the 1,400,000 computed averages the mean of the
rectified signal, the S.D. (an estimator of the signal power),
the power and the peak of the rectified signal were computed.
Their mean and S.D. values are illustrated in Table 1.
As expected, all these parameters decreased as the
number of averaged trials increased. Fig. 7 illustrates how
the mean of the rectified signal decreases with increasing
number of averaged trials. As can be noted, a large number
of trials have to be combined in order to minimize the BCA
at levels close to the expected ERP amplitude.
4.1.4. Mechanically induced artefacts
Examination of the EEG traces revealed undue signal
components. Fig. 8 shows these periodic oscillations in the
EEG signal that may be mechanically induced by the cold
head of magnet refrigerator system.
This effect can be described in terms of amplitude-
modulated mechanical oscillations (48 Hz) superimposed
onto the EEG signal. An FFT analysis of this signal (Fig. 9)
highlights the typical spectrum of an amplitude-modulated
signal which results from the convolution of the carrier (50
Hz) and the modulator (2 Hz) frequencies.
The 2 and 50 Hz are, respectively, related to the
operating rate of the control piston and the rotary valve
activated by means of an electric synchronous motor that
are part of the magnet cold head. The lower side band
(46 – 48 Hz) is enhanced probably because of the acoustic
resonance determined by drum-like effect of the control
piston on magnet room that has, in our case, one
dimension corresponding to the 48-Hz acoustic wave-
length. These mechanical effects induce vibrations of the
scanner structures that are in contact with the EEG wiring
determining further noise on EEG traces (microphone
effect). Further and similar effects may also be determined
by gradients switching-related vibrations induced inside
the scanner (patient bed, external/internal scanner surfa-
ces, etc.).
Fig. 9. Power spectrum of the Pz electrode signal of Fig. 7 in which the
carrier frequency (48 Hz) and the modulation (2 Hz) effect are visible.
4.2. ERP–fMRI synchronization issues
4.2.1. Stimulation timing errors
The TJ effect is a temporal shift that propagates
differently across successive acquisitions. If the acquisition
and the stimulation are not synchronized this problem
occurs and averaging methods are not able to correlate the
evoked response and the stimulation accurately.
The main consequence of TJ is a filtering effect on the
averaged data. The characteristics of the filtering will
depend on the TJ distribution (TJD). The averaged data
will be a convolution of the TJ bfreeQ data with the TJD.
There are many possible sources of TJ in fMRI/ERP
paradigms that can arise at different stages of the record-
ing/stimulation apparatus.
4.2.1.1. Stimulators. Two technical limitations may induce
TJ effects: the first regards the frame rate of the video card
employed. When a command is sent to the video card to
draw something on the screen, the latter can be updated only
when a video frame is redrawn. If the operating frame rate
of a video card is 60 Hz this occurs every 16.6 ms and the
TJD will be uniform in the interval (0–16.6 ms). The
frequency response and the power spectrum of the TJD for
this case are illustrated in Fig. 10.
The plots show that there is a complete loss of data at
frequencies that are integer multiples of the frame rate of the
video card (60, 120, 180 Hz, . . .) and that there is a loss of 3
dB (the components are divided by two) or more at a
frequency of 36.6 Hz or higher. The second limitation is that
if a non-real-time OS is used as stimulation platform it is not
possible to know in a deterministic way when the OS
actually delivered the stimulus to the subject. This is another
source of TJ filtering effect whose consequences are
difficult to be determined a priori.
4.2.1.2. Synchronization among devices. fMRI and EEG
systems acquire and process signals with different timing
characteristics and they are designed with different timing
specifications. For this reason averaging data with a
uniform TJD in the range 0 –100 ms is adequate for most
G. Garreffa et al. / Magnetic Resonance Imaging 22 (2004) 1445–1455 1453
fMRI studies but inadequate for almost all EEG/ERP
measurements. This problem increases when fMRI and
ERP acquisitions must be synchronized. Depending on the
design of the experiment and the characteristics of the
acquisition systems some additional delay may also be
introduced. For example, if the visual stimulation is
triggered by some MR device signals one should measure
the jittering of this trigger to verify its compatibility with
ERP studies.
In many cases, a way to solve all timing error issues is
to acquire a representation of the stimulus. This allows
performing synchronous averaging related to the brealQstimulus presentation time, instead of the bdesiredQ stimulus
time. In a visual task this can be done by means of a
photocell positioned on the surface of the video screen and
directed inside it: below the photocell and out of the field
of view of the subject (e.g., on a corner of the video
screen), a small white rectangle is drawn at the same time
the stimulus is drawn, otherwise that video area is black.
The acquisition of the signal coming by the photocell gives
a reasonable measure about when the stimulus was
effectively drawn.
4.2.2. ERP–fMRI design optimization
In the present work, we first adopted a sparse sampling
scheme with a TR of 9.5 s and hence with a 7.5-s-long
delay before start of next volume in order to obtain
a sufficiently long-lasting, undisturbed EEG trace. This
long delay was also necessary to allow jittering of the onset
of stimulus presentation with respect to the MRI acquisi-
Fig. 11. Hemodynamic response for Designs 1 and 2 (red line) and sampling points
only the maximum and minimum BOLD response is sampled in Design 1, Design
for the rare events, but at the cost of a reduced BOLD response (around 50% of De
number of available sampling points for the frequent events is to introduce the s
tion, thereby a full sampling of the hemodynamic res-
ponse across all brain slices could be achieved (see Fig. 2,
Design 1, and Fig. 11 for the sampling points of hemo-
dynamic response).
The expected parameter estimation efficiency can be
calculated according to:
PE¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
1
Hð ÞþððHÞþÞT
s
where H is the ideal hemodynamic response obtained by
assuming a linear time-invariant (LTI) system, the + symbol
indicates the pseudoinverse and T the transpose operator.
Design 1 yielded a low parameter estimation efficiency,
being 0.6. Therefore, we aimed at optimizing this value.
Because of the LTI characteristics of the hemodynamic
response, an assumption that is valid at reasonably long
ISIs, an alternative paradigm that allowed full sampling of
the hemodynamic response despite a shorter TR, could be
adopted. For this second design, stimuli were presented
both during MR acquisition and during the delay between
volumes. In order to accommodate the EEG acquisition
after decay of the MR imaging gradients, this delay had to
be 3.5 s, thus yielding a TR of 5.5 s (Fig. 2, Design 2).
With Design 2, the parameter estimation efficiency for
detecting rare events increased twofold (1.26) despite a
slightly lower hemodynamic signal amplitude (Fig. 11:
sampling points of hemodynamic response). Both designs
were tested in the experimental setting by combined EEG–
fMRI acquisitions. In agreement with the predicted increase
for the frequent events (black dots) and the rare events (blue circles). While
2 offers a more complete coverage of the full hemodynamic curve, at least
sign 1). A possible way to overcome such signal reductions and increase the
o-called null events (i.e., absence of visual stimulation).
Fig. 12. Activation patterns from a fixed-effects analysis performed for
three subjects and two different experimental designs (voxel-level
significance P b.001, uncorrected for multiple comparisons). Clusters
revealed by Design 1, with a TR of 9.5 s, are shown in red while those
detected by Design 2 (TR=5.5 s) are shown in green. Due to a higher
efficiency of Design 2, brain areas related to visual perception of rare events
combined with mental calculation can be detected, for example, calcarine
cortex (upper row), bilateral BA6, supplementary motor area BA8 and
parietal lobe BA40 (lower row).
G. Garreffa et al. / Magnetic Resonance Imaging 22 (2004) 1445–14551454
in parameter estimation efficiency, the higher detection
power of Design 2 also lead to more activated brain regions
for the rare events, for example, including the calcarine
cortex, a brain area that was expected to activate due to the
visual nature of the stimulus (Fig. 12, activation pattern), as
well as other brain areas supposedly involved in mental
calculation of the rare events.
Moreover, brain areas common to both techniques
contained more voxels within each activated cluster for
Design 2. In summary, Design 2, with shorter TR, and more
importantly shorter ISI, is obtained from Design 1 by
introducing additional stimuli during the MRI acquisition
window. This approach was motivated by the LTI character
of the hemodynamic response that permits detection of
cumulative effects of a series of stimuli. Design 2 lead to an
increased parameter efficiency including more trials within
the experimental run and furthermore allowed a tighter
sampling scheme of the BOLD response. In the experimen-
tal situation, a higher number of activated brain regions
could be detected. Design 2 could be further improved by
introducing so-called null events (i.e., absence of visual
stimulation) in the design, however, at the cost of longer
scanning times.
5. Conclusions
From our brief discussion it emerges that there are some
open questions that introduce a given number of variables
when EEG–fMRI (but particularly ERP/fMRI) experiment
is performed. The number and kind of these variables may
critically affect the performance of the experiment if an
accurate analysis of them is not done. A class of
unexpected effects came out from MIAs and a mechanical
decoupling between EEG cap and wires is recommended,
for example, an enclosure of wires from cap to EEG box
in a tubular soft case and locking it in same way.
Sometimes, additional timing constraints impose mutual
(EEG, fMRI) protocols restrictions that may limit the
effectiveness of the whole protocol (e.g., we have
discussed about different situations related to hemodynam-
ic response sampling vs. ISI).
Solutions are possible but up to now are frequently
customized to the particular protocol setup. For this reason,
it is not straightforward, from a technical point of view, to
adapt these solutions to different operative conditions.
Regarding the ballistocardiogram, from simulations and
from theory, it emerges that to reduce by one order of
magnitude its effects there are two nonmutually exclusive
choices: the increase of the number of trials and the
removal of at least a part of the ballistocardiogram. Both
strategies have, however, some drawbacks. Increasing the
number of trials is not always feasible, for example, to
obtain a P300 signal, 100 rare events, and consequently
400 frequent events, are needed to reduce BCA by one
order of magnitude, which comport an experiment more
than 45 min long, with an ISI of 5.5 s. Estimating and
subtracting the BCA is not a noiseless solution. Probably,
using both techniques will improve the SNR. Ballistocar-
diogram artefact is less predictable than PSA, even if the
amplitude is much lower. For steady-state visual evoked
potentials (SSVEP), one should carefully select the
stimulation rate and the sequence parameters in order not
to overlap amplitude modulation effects of the MRI scanner
with the physiological response (same rate as the SSVEP
stimulation). Setting acquisition parameters can affect the
accuracy of the system (higher SR means a better sampling
of the PSA but generally will reduce the SNR as the A/D
quantum noise will increase as its size). However, filtering
and averaging will reduce this problem. Averaging,
nevertheless, requires a perfect timing between stimulation
and acquisition; otherwise, there is the trigger-jitter
multiband filtering effect.
We also observed that a comfortable head lock together
with another mechanical decoupling may reduce the
amplitude of ballisto. This may be done by interposing
a soft material around the posterior electrodes between
scalp and inferior head coil support surface in order to
realize a sort of shock absorber of the heart beat-related
strengths that may induce an impulsive coherent motion.
This hard solution determines a preliminary reduction
G. Garreffa et al. / Magnetic Resonance Imaging 22 (2004) 1445–1455 1455
of BCA that may be sufficient and useful in many cases
like continuous or spike-triggered EEG–fMRI study in
epilepsy [5].
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