R

MGVa

b

c

d

e

f

g

a

ARRA

KCRF(SPO

1

ipnct(fmerwep

0d

Journal of Neuroscience Methods 184 (2009) 213–223

Contents lists available at ScienceDirect

Journal of Neuroscience Methods

journa l homepage: www.e lsev ier .com/ locate / jneumeth

eal-time artifact filtering in continuous VEPs/fMRI recording

uhammad Nabeel Anwara,b,∗, Laura Bonzanoc,d, Davide Rossi Sebastianoe, Luca Roccatagliatac,d,iovanni Gualniera f, Paolo Vitali e, Carla Ogliastroc, Luciano Spadavecchiag, Guido Rodrigueze,ittorio Sanguinetib, Pietro Morassob, Fabio Bandini c

Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-50, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, JapanDepartment of Informatics, Systems and Telecommunications, University of Genoa, Genoa, ItalyDepartment of Neurosciences, Ophthalmology and Genetics, University of Genoa, Genoa, ItalyMagnetic Resonance Research Centre on Nervous System Diseases, University of Genoa, Genoa, ItalyClinical Neurophysiology, University of Genoa, Genoa, ItalyEBNeuro S.p.A., Florence, ItalyInstitute of Cybernetics and Biophysics, National Research Council, Genoa, Italy

r t i c l e i n f o

rticle history:eceived 23 March 2009eceived in revised form 4 August 2009ccepted 5 August 2009

eywords:ontinuous recording

a b s t r a c t

Continuous recording of Visual Evoked Potentials (VEPs) and functional Magnetic Resonance Imaging(fMRI) exploits the VEPs high temporal resolution and the fMRI high spatial resolution. In this work,we present a new method of continuous VEPs/fMRI recording to study visual function in seven normalsubjects. Our real-time artifact filtering is characterized by a procedure based on an analytical study ofecho-planar imaging (EPI) sequence parameters related electro-encephalogram (EEG)-artifact shapes.The magnetic field artifacts were minimized by using a dedicated amagnetic device and by a subtraction

eal-time filteringunctional Magnetic Resonance ImagingfMRI)teady-state and transient Visual Evokedotentials (VEPs)ptic neuritis

algorithm that takes into account the EPI sequence parameters. No significant decrease in signal-to-noiseratio was observed in case of EEG recording simultaneously with MR acquisition; similarly, transient andsteady-state VEPs parameters were comparable during fMRI acquisition and in the off-phase of fMRIrecording. We also applied this method to one patient with optic neuritis, and, compared with controls,found different results. We suggest that our technique can be reliably used to investigate the function ofhuman visual cortex and properly correlate the electrophysiological and functional neuroimaging relatedchanges.

. Introduction

Visual Evoked Potentials (VEPs) allow to observe brain activityn response to visual stimuli at a millisecond time scale but withoor spatial resolution (Regan, 1989). Conversely, functional Mag-etic Resonance Imaging (fMRI) exploits changes in the BOLD signaloupled with neuronal activity in order to localize brain activa-ion, with a high spatial resolution but low temporal resolutionOgawa et al., 1992). VEPs and fMRI have thus complementaryeatures and their integration may provide more detailed infor-

ation than either method alone (Di Russo et al., 2005; Henningt al., 2005). Given the inherent variability of evoked cortical

esponses and BOLD contrast, the integration of both methodsould be more reliable using simultaneous recordings, which

xplore the subject’s brain activity in the same state and providerecise information derived from different biophysical origins on

∗ Corresponding author. Tel.: +81 45 924 5654; fax: +81 45 924 5684.E-mail address: nabeel@ito.dis.titech.ac.jp (M.N. Anwar).

165-0270/$ – see front matter © 2009 Elsevier B.V. All rights reserved.oi:10.1016/j.jneumeth.2009.08.003

© 2009 Elsevier B.V. All rights reserved.

the processing of visual stimuli at the same time (Bonmassar et al.,2001).

The feasibility of recording EEG signals inside an MR scanner wasinitially reported by Ives et al. (1993) and later by other researchers(Huang-Hellinger et al., 1995; Lemieux et al., 1997). These studiesalso investigated the possibility of recording a good quality EEGsignal inside an MR scanner, as well as to obtain high quality MRimages in the presence of EEG equipment, assuring patient safety.These findings opened the way to the acquisition of EEG and fMRIin the study of epilepsy (Bast et al., 2007; Hamandi et al., 2006),the neural mechanisms of sleep (Czisch et al., 2004; Horovitz etal., 2008; Schabus et al., 2007) and the VEPs (Becker et al., 2005;Bonmassar et al., 1999; Comi et al., 2005; Henning et al., 2005; Imet al., 2006; Lemieux et al., 1997; Sammer et al., 2005).

Three methods are available in order to combine VEPs and fMRI

data: (1) the recordings are made separately and data are fusedsubsequently (“conjoint VEPs–fMRI”); (2) the VEPs are recordedinside the MR scanner but the EEG data analysis is performed onlyduring periods without MR acquisition (“interleaved VEPs–fMRI”)(Bonmassar et al., 1999, 2001); in this case EEG–fMRI recordings

2 roscien

ateoVnsescosfaHeiirIasrsaHcas

eet(rd

tpdtatta

rci1ndt

2

2

(yhwal

with ON was demonstrated using coronal acquisitions (short inver-sion time inversion recovery TR/TE = 2000/40 ms; TI = 148 ms; slicethickness = 3 mm; FOV = 220 mm; Matrix 128 × 256) and FSE T2through the orbits (TR/TE = 6025/102 ms; slice thickness = 3 mm;FOV = 240 mm; fat saturation; Matrix 256 × 320).

14 M.N. Anwar et al. / Journal of Neu

re not completely simultaneous as the measurements are made inwo separate time-windows; (3) the acquisition of VEPs and fMRI isntirely simultaneous and the whole EEG trace, including periodsbtained during MR-acquisition, is used for analysis (“continuousEPs–fMRI”). Regardless of whether long-lasting or rapidly alter-ating MR and EEG epochs were acquired, the first two methodshare the disadvantages of being time-consuming (particularly forvoked potential studies) and hampering a real investigation of theubject’s brain activity in the same environmental and cognitiveondition; the second method has also restrictive protocols, sincenly periods without MR acquisition are used in the EEG data analy-is. Continuous VEPs–fMRI acquisition is very challenging since theast varying gradient fields and the radio-frequency pulses inducevery intense electric noise that obscures the EEG signal (Huang-ellinger et al., 1995; Ives et al., 1993), thus requiring a carefulxperimental setup and a sophisticated artifact removal techniquen order to filter out the noise and retains the evoked responsesn the signal. Such a challenging technique has been applied quitearely (Becker et al., 2005; Comi et al., 2005; Henning et al., 2005;m et al., 2006; Sammer et al., 2005), by means of the same filteringlgorithm (Allen et al., 2000, 1998). One study investigated steady-tate VEPs (SS-VEPs) (Sammer et al., 2005), while other authorsecorded transient VEPs (T-VEPs) using a stimulation based on aequence of letters (Comi et al., 2005), pattern reversal (Becker etl., 2005; Im et al., 2006), and motion patterns (Henning et al., 2005).ere we propose a new method of real-time artifact filtering inontinuous VEPs (both T-VEPs and SS-VEPs) and fMRI acquisition,t low (1 cycle per degree) and medium-high (4 cycles per degree)patial frequencies.

In the present study, we analyze the electromagnetic interfer-nce in the shape and in space-temporal features. This estimation ofcho-planar imaging (EPI) sequence parameters in EEG data giveshe possibility to build a model that can be used as a referencetemplate) for filtering. Each raw sample is associated with the cor-esponding (in time) sample in the model; cleaned signal is theifference between raw sample and model.

Recent commercially available devices are based on procedureso remove post-processing artifacts, thus hampering the appro-riate control of the electrophysiological activity of the subjecturing fMRI scan. In this work we propose to use an online filteringechnique that allows to monitor the EEG signal during the fMRIcquisition, which is particularly important for clinical investiga-ions (e.g., optic neuritis, epilepsy) and for behavioral studies onhe relationship between electrophysiological changes and brainctivity during learning.

Our aim was to evaluate whether the EEG signal could beestored from fMRI artifacts in order to obtain reliable VEPs. Wehose to record both T-VEPs and SS-VEPs because they may tapndependent pathways in the visual system (Bodis-Wollner et al.,990). In addition, we applied our method in one patient with opticeuritis (ON), a clinical condition characterized by a conductionelay in the optic nerve, in order to investigate whether it is possibleo detect different responses with respect to control subjects.

. Materials and methods

.1. Subjects

We acquired fMRI data and VEPs in seven healthy volunteersmean age 30 ± 2 years) with normal vision. We also studied one 28-

ear-old patient with acute left ON. He presented with a one-weekistory of painful blurred vision in the left eye, accompanied byorsening of pain on eye movement. Examination revealed visual

cuity of 1/10 in the left eye, impaired perception of color, and aeft relative afferent pupillary defect (RAPD) (poor response of the

ce Methods 184 (2009) 213–223

left pupil to light stimulation as compared with the right pupil, ona swinging flashlight test). There was no swelling of the optic disk,and the remainder neurological examination was normal.

The study protocol was approved by the Institutional ReviewBoard, and written informed consent was obtained from all subjectsin accordance with the declaration of Helsinki.

2.2. Experimental protocol

The visual stimulation protocol consisted of full-field checker-board patterns with a rectangular luminance profile, presented ina reversal mode with repetition frequencies of 1 Hz (T-VEPs) and4 Hz (SS-VEPs). Two different fundamental spatial frequencies (SF)of 1 and 4 cycles per degree (cpd) were used. Visual stimulationwas monocular: each eye was tested separately, while the con-tralateral eye was covered by a translucent patch to maintain lightadaptation. A custom-made software, running under the MS-DOSoperating system, was used to deliver the visual stimuli. A digitaltrigger signal was generated at each pattern reversal and sent to theEEG data acquisition hardware for synchronization. The projectorwas located outside the MR room. The black and white checker-board pattern was projected on an acrylic screen inside the MRIroom. The screen mean luminance was 60 cd/m2, the contrast ofthe patterns was 70%. A mirror was placed on the head coil at 45◦

to the screen and the subject’s line of sight. The stimulation protocolconsisted of alternating blocks of 20 s of rest (i.e., display of grey),20 s of checkerboard pattern reversal at 1 cpd and 20 s of checker-board pattern reversal at 4 cpd (Fig. 1). In the rest condition, thescreen had the same luminance as the mean luminance of the pat-tern. Subjects were asked to gaze a fixation point at the centre of thescreen with the uncovered eye. The sequence was repeated threetimes for each temporal frequency for each eye.

We also implemented the procedure on an ON patient, whounderwent VEPs recording outside the MRI room using the samestimulation paradigm. In addition, one healthy subject participatedin two types of recording: one during fMRI acquisition (EPI con-dition) and one in the absence of fMRI (NO-EPI condition). Bothrecordings were made in a single continuous session.

2.3. Anatomical and functional MRI data acquisition

MR images were acquired on a 1.5 T scanner (GeneralElectric, Milwaukee, USA) with a phase-array eight-channelcoil. Anatomical images were acquired using a 3D SPGR(Spoiled Gradient Recalled) sequence (TR/TE = 30/3 ms; slice thick-ness = 3 mm; FOV = 240 mm; Matrix: 256 × 256; Flip Angle = 35◦).Functional images were acquired using echo-planar imaging(EPI) sequences (TR/TE = 3000/50 ms; delay = 1000 ms; slice thick-ness = 6 mm; FOV = 260 mm; Matrix = 64 × 64). Functional MRI wasacquired during the above-described stimulation according to ablock design (Fig. 1). The left optic nerve involvement in the patient

Fig. 1. Block diagram of the stimulation protocol for continuous recording of VEPsand fMRI. The protocol started with a 20-s grey display, followed by 20-s patternreversal black and white checkerboard with spatial frequency of 1 and 4 cpd. Thissequence was repeated three times for each eye (stimulation was monocular) foreach temporal frequency (1 and 4 Hz).

roscien

2

(pva

Fa

M.N. Anwar et al. / Journal of Neu

.4. EEG data acquisition

For EEG recording we used an MR-compatible amplifierEBNeuro Spa, Florence, Italy). The EEG device was placed inside aurpose-built shielded box. The signal amplification and A/D con-ersion were performed and the digital signal was transferred vian optical fiber connection to a standard IBM laptop running EEG

ig. 2. (a) EEG signal with fMRI artifacts during EPI acquisition (TR = 3 s) and (b) EEG siglgorithm.

ce Methods 184 (2009) 213–223 215

data acquisition (Galileo NT) and data processing software (Garreffaet al., 2003). An MR-compatible standard 10–20 system EEG cap

with Ag/AgCl electrodes filled with conductive gel was used (Bio-nen Sas, Florence, Italy); the interelectrode-skin impedance waslower than 5 k�. The cap was connected to the amplifier with anamagnetic cable rested on foam pads attached to the ground andfollowing a straight path in order to minimize the variability in

nal filtered from artifacts by gradient artifact reduction with time-based adaptive

2 roscien

tcwlhra

iCrabwue

Ffs

16 M.N. Anwar et al. / Journal of Neu

he EEG signal due to the MR sequences and to avoid any possibleable movement caused by mechanical vibrations. The amplifieras set at 4 kHz sampling rate, which allows a suitable time reso-

ution to pick up the switching effect of the readout gradient in theigh slew rate condition, 20 bit resolution, and the EEG dynamicange of ±65.5 mV to prevent saturation of the EEG/ECG by the MRIrtifact waveforms.

EEG data were recorded from 19 electrodes positioned accord-ng to the International 10–20 systems (Fp1, Fp2, F3, F4, F7, F8, Fz,3, C4, Cz, P3, P4, Pz, T3, T4, T5, T6, O1, O2), with right earlobe aseference (off-line EEG trace was recomputed to a common aver-ge). The ground electrode was placed on FCz (halfway position

etween Fz and Cz). The ECG signal was synchronously recorded,ith the same amplitude resolution as EEG channels. The stim-lation trigger signal was acquired at the same sampling rate aslectrophysiological data.

ig. 3. Shape of the artifacts in both temporal and frequency domain. (a) Single chanrequencies during gradient switching events. A 1-min amplitude spectrum of one channignals. EPI sequence artifacts (gradients fields, static magnetic field and RF pulse) at low

ce Methods 184 (2009) 213–223

2.5. EPI sequences evaluation and fMRI analysis

First, in order to evaluate possible disturbances induced on MRimages by the EEG acquisition system, the signal to noise ratio (SNR)was estimated in the presence and in the absence of EEG record-ing in 4 and 3 subjects, respectively. For each MR acquisition, theSNR was calculated as the ratio between the mean intensity valueof 3 regions of interest (ROIs) placed in the occipital white matterand the standard deviation of 3 ROIs placed outside the brain in thebackground, avoiding ghosting artifacts. We calculated the SNR fordifferent volumes during the course of each acquisition and con-sidered the corresponding volumes (i.e., same acquisition phase)

in the different acquisitions.

fMRI volumes were then analyzed with SPM2 (WellcomeDepartment of Cognitive Neurology, London, UK) (Friston et al.,1995). All the images were realigned to the first image acquired for

nel EEG recorded during EPI acquisition (TR = 3 s, delay = 1 s). (b) Main dominantel (Fz) recorded inside the MR scanner was computed by averaging 1-s epoch FFTfrequency range are magnified.

M.N. Anwar et al. / Journal of Neuroscien

Fid

eatt8yCarto

2

fificd

tfEiioGEaambr

x

For each healthy subject we used all the epochs of stimulus data.

TM

M

ig. 4. Comparison between the signal to noise ratio (SNR) calculated on EPI imagesn the presence of EEG recording and in the absence of EEG electrodes: no significantecrease in SNR was evident in case of EEG recording during MR acquisition.

ach stimulation condition, and a mean functional image was cre-ted. The mean functional images were then normalized to a brainemplate and the resulting transformation matrix was applied tohe individual functional volumes. The images were smoothed withmm full-width at half maximum Gaussian kernel. Statistical anal-ses were performed using the general linear model (Chatfield andollins, 1980). t-Test contrasts were conducted separately for datacquired at 1 and 4 Hz for both SF (1 and 4 cpd), with respect to theest condition. Clusters of activated voxels surviving an uncorrectedhreshold of P < 0.001 were considered significant in the calculationf the corresponding activation maps.

.6. Artifact removal in EEG recordings

fMRI artifacts were removed from the EEG data by the real-timeltering software. The filtering algorithm calibrates itself from therst EPI acquisitions; when the EPI sequences run, the filter entersalibration mode and only few scans are needed to build a robustynamic model.

The filtering method is based on estimating the effect of elec-romagnetic interference on the shape and the space-temporaleatures of EEG data recorded inside the MRI scanner during thePI sequence. It estimates the average waveform of the whole EPInterference (RF pulses and gradient fields) in all EEG channels andncludes the events related to each volume acquisition (i.e., numberf slices/volume, TR, TE, RF pulses, Gradient selection and Readoutradients). The estimation of EPI sequence parameters in the rawEG data gives the possibility of identifying artifacts that representreference (template) for filtering. These artifacts were evaluated

nd then computed with a standard evoked potential averagingethod. Consider x(t) as a set of uncorrelated components, the EEG

asal activity eeg(t), the instrumental noise n(t), and the EPI-evokedesponse epi(t), then:

(t) = eeg(t) + n(t) + epi(t) (1)

able 1edian of percentage absolute power difference between scan and inter-scan periods.

1–4 Hz 4–7 Hz 8–12

%Difference O1 10.1010 9.8648 7.734%Difference O2 11.6824 10.1215 7.731

edian of percentage absolute power difference between scan and inter-scan periods for

ce Methods 184 (2009) 213–223 217

Now consider that eeg(t) and n(t) have zero mean, then for Mdynamics the averaged x(t) would be:

xavg(t) = epiavg(t) (2)

In this way, the EPI synchronously averaged EEG represents an esti-mate of the average waveform of the whole EPI interference (RFpulses and gradient fields) in the EEG channels.

This artifact model was then synchronized with the EEG data byzero-phase band pass, i.e., each raw sample (uncleaned data) wasassociated with the corresponding (in time) sample in the model.The synchronization of the artifact model with the raw data wasdone by finding peaks for identifying the slices and the dynamicsin the raw data. In the calibration mode, the peaks were identifiedby a threshold method: a sample is virtually considered a poten-tial peak if its amplitude exceeds a given threshold (estimated byusing the mean signal amplitude); the peaks were calculated indi-vidually for each channel. The time and the position of the identifiedpeaks were also calculated and used to match their right positionin the raw data. This made it possible to set apart spurious peaksbecause they “do not occur at the right moment”. Finally, this arti-fact model was applied on the continuous EEG recording and thecleaned signal was given by the difference between the raw sam-ple and the model. A zero-phase 10th order Butterworth low-passfilter of 70 Hz was applied to the raw signal before subtraction, toincrease filter quality. Fig. 2 displays artifact-free data; Fig. 3 showsthe shape of the artifacts and the dominant frequencies during thegradient switching events and the slice timing vs. TR.

Differently from other studies with continuous VEPs–fMRIrecording (Becker et al., 2005; Comi et al., 2005; Henning et al.,2005; Im et al., 2006; Sammer et al., 2005), the cardiac pulse arti-fact (originated by the slight micromovements of the head due toheartbeat and normal to the static magnetic field) was limited by:(i) the head-locking of the subject, (ii) the spatial properties of bipo-lar montage and (iii) the proper twisted lead settings (Goldman etal., 2000). In this way, we were able to prevent data corruption andto rebuild reliable VEPs from the EEG signal. We assumed that thenature of the magnetic interference during the EPI sequence is com-pletely known and does not depend on the type of subjects studied;therefore, it is possible to obtain similar results for studying VEPssignal in healthy and pathological subjects. To ensure preservationof information in VEPs after filtering MR artifacts in a simultaneousrecording, we trained the model of the filter for the optimal param-eters with EPIs without visual stimulation (i.e., the first 20 s). In thisway any response elicited by the checkerboard was not filtered out.

2.7. Preprocessing for EEG filtered data

After gradient artifact reduction with time-based adaptive algo-rithm, the data were down-sampled to 512 Hz, filtered with a0.3–35 Hz band-pass filter and stored for further analysis.

2.8. General evaluation of artifact removal algorithm

The artifact-free signal was divided into “inter-scan” and “scan”data (inter-scan = 1000 ms; scan = 3000 ms). We estimated the per-centage differences in the spectral density of five bands (1–4, 4–8,8–12, 12–16, and 16–24 Hz) by applying a previously reported for-

Hz 12–16 Hz 16–25 Hz Total

6 10.5556 10.6642 9.78405 11.2623 11.7858 10.5167

O1 and O2.

2 roscien

m

%

wtfwn1n

2

rcatlFsahmTF

F(

18 M.N. Anwar et al. / Journal of Neu

ula (Allen et al., 2000):

Difference = 100 ×∣∣∣

PIS − PS

PIS

∣∣∣

here PIS is the power density of inter-scan periods and PS ishe power spectral density of scan periods. The analysis was per-ormed for O1 and O2 for each frequency, and the median valuesere calculated across all healthy subjects. We also analyzed theoise cancellation effect inherent in averaging, for frequency range–35 Hz, by calculating the spectral densities as a function of theumber of averaged trials.

.9. VEPs analysis

EEG epochs with eye blinking and eye movement artifacts wereemoved by visual inspection (about 15% of data was rejected). Toharacterize the response to 1 Hz stimulation for both inter-scannd scan data, we computed T-VEPs by averaging the epochs. Wehen estimated the inter-scan and scan period difference in the peakatency of the P100 component. For SS-VEP analysis, a 1024 pointsourier transform was applied on inter-scan and scan epochs of allubjects; it yielded nearly independent Fourier estimates (Victor

nd Mast, 1991) at the stimulus fundamental frequency and its 2ndarmonic. These Fourier estimates were vector averaged to deter-ine the steady-state response. Signal reliability was assessed by

2circ statistics (Victor and Mast, 1991), which analyze whether theourier vector at any particular frequency is significantly differ-

ig. 5. Brain activation maps of a representative control subject (a and b) and an MS (ONa and c) and 4 cpd (b and d). A reduction in the patient visual cortex activation can be no

ce Methods 184 (2009) 213–223

ent than the null vector. The amplitude and phase of the responsevector represent the distance from the origin and the directionrespectively. In plots of amplitude and phase, the 95% error barsshow the largest and smallest distance from this confidence circleto the origin, determined separately for each eye.

VEPs were calculated for the ON patient with the same proce-dure as stated above. The latency of T-VEP P100 was measured. Forthe SS-VEPs, we calculated the coherence level at the 2nd harmonic(8 Hz) of the stimulation frequency; coherence was calculated as anindicator of evoked response on occipital brain areas, involved indetection and analysis of visual stimuli. The phase angle for SS-VEPswas calculated by Fourier analysis. The procedure was carried outfor both eyes and the results of the ON patient were compared withthe healthy subjects.

For the subject tested in both EPI and NO-EPI conditions,dynamic field and static field artifact filtering models were appliedon EEG data with EPI sequence whilst only the static field artifactfiltering model was applied in NO-EPI condition. Results were com-pared in terms of P100 shape, amplitude and latency (T-VEPs) andin terms of coherence for the 2nd harmonic (SS-VEPs).

3. Results

3.1. EPI sequences evaluation and fMRI analysis

The SNR in EPI images acquired during EEG recording was com-pared with the SNR of EPI images in absence of EEG electrodes

) patient (c and d) at a temporal stimulation of 4 Hz and spatial frequency of 1 cpdted at both 1 and 4 cpd.

M.N. Anwar et al. / Journal of Neuroscience Methods 184 (2009) 213–223 219

F nd ind

(rTs

3

saFoeio

F

ig. 6. Power spectrum density (1–35 Hz) on the number of averaged trials for scan aependency on number of averaged trials for O2.

Fig. 4). No significant decrease in SNR was observed in case of EEGecording simultaneously with MR acquisition (Mann–Whitney U-est, p = 0.5). The activation maps of a representative subject arehown in Fig. 5.

.2. Effect of filtering on EEG signal

The inter-scan data show a frequency spectrum similar to thepectrum of the scan period. Table 1 presents the median percent-ge difference of spectral power density in 5 bands at O1 and O2.

ig. 6 shows the power spectral dependency as a function of numberf trials and spectral power density. In small number of trials, theffect of noise cancellation for non-phase-lock components is sim-lar in inter-scan and scan periods. This indicates that no residualf MRI artifacts are present in low frequency range.

ig. 7. Averaged VEP response from all healthy subjects. The bold lines represent group a

ter-scan data. Top: averaged power spectra for O1 and O2. Bottom: power spectrum

3.3. T-VEPs

The shape of the P100 component was identifiable after applyingthe filtering technique on EEG data acquired during fMRI acquisi-tion. The data were first averaged within each subject, then alignedto the onset of P100 component and averaged over all subjects. Themean and the standard deviation of the latency of P100 componentin inter-scan and scan period was measured as 101.3 ± 2.16 ms for1c/d and 101.3 ± 2.8 ms for 4c/d (Fig. 7).

3.4. SS-VEP

The T2circ analysis provided us with significant results. The

null hypothesis that the signal does not contain the fundamen-tal and the 2nd harmonic was rejected for both inter-scan and

veraged data and the dashed lines indicate ± SD (only data from O2 is shown).

220 M.N. Anwar et al. / Journal of Neuroscience Methods 184 (2009) 213–223

Table 2shows the T2

circ, Amplitude in microvolt and phase values in radians of the SS-VEP for the fundamental frequency and 2nd harmonic obtained from healthy subjects.

O1 O2

1st Harmonic 2nd Harmonic 1st Harmonic 2nd Harmonic

Scan Inter-scan Scan Inter-scan Scan Inter-scan Scan Inter-scan

1 ± 09 ± 0.4 ± 0

s1tmOfhqTi

nacm

FOec

T2circ

12.53 ± 0.4 11.65 ± 0.4 12.36 ± 0.4 11.6Amplitude 6.22 ± 1.0 5.39 ± 0.8 5.34 ± 0.8 4.8Phase −0.12 ± 0.6 −0.3 ± 0.6 −0.4 ± 0.5 −0

can data (p < 0.05). For scan data the mean ± SEM was T2circ =

2.5 ± 0.41 for the fundamental frequency; T2circ = 12.3 ± 0.49 for

he 2nd harmonic at O1 and T2circ = 12.15 ± 0.47 for the funda-

ental frequency; T2circ = 10.87 ± 0.48 for the 2nd harmonic at

2. For inter-scan data the mean ± SEM was T2circ = 11.65 ± 0.43

or the fundamental frequency, T2circ = 11.61 ± 0.51 for the 2nd

armonic at O1 and T2circ = 11.22 ± 0.53 for the fundamental fre-

uency, T2circ = 12.19 ± 0.43 for the 2nd harmonic at O2. The mean

2circ, amplitudes and phases for the control group are summarizedn Table 2 and plotted in Fig. 8.

For the subject tested in both EPI and NO-EPI conditions, at 1 Hzo significant changes were observed between the filtered signalcquired during fMRI scanning and the signal acquired in the sameonditions but in absence of fMRI scanning (Fig. 9a). The 2nd har-onic response at O1 is shown in Fig. 9b: a defined peak at 8 Hz can

ig. 8. SS-VEP responses of subjects for the left eye stimulation: (a) the T2circ

values at fun1 and O2 for scan and inter-scan period, the error bars represent the SEM. (b) The Fourach subject at fundamental stimulus frequency and its 2nd harmonic. Amplitude and phonfidence level.

.5 12.15 ± 0.4 11.22 ± 0.5 10.87 ± 0.5 12.19 ± 0.4

.9 6.15 ± 0.6 4.95 ± 0.8 4.72 ± 0.7 4.24 ± 0.9

.7 −0.3 ± 0.6 −0.1 ± 0.6 −0.2 ± 0.5 −0.3 ± 0.7

be observed in both cases. The coherence level remains the samein both conditions.

3.5. VEPs analysis: ON patient

The SS-VEP response of the normal eye and the ON eye is shownin Fig. 10a (1 cpd) and in Fig. 10b (4 cpd). The amplitude of SS-VEP decreased sharply in the affected eye at high spatial frequencywhile at low spatial frequency the responses were similar. We usedcoherence as an indicator for the magnitude of the stimulus-evokedresponse on a particular brain area, e.g., O1 and O2 (Fig. 11a). As

expected, the coherence between stimulus and affected eye of thepatient was very low as compared to the same eye of a normal sub-ject, which indicated the absence of response in the affected eye.When we analyzed the phase lag of the 2nd harmonic response,it proved to be significantly longer in the ON eye compared to the

damental frequency (black) and 2nd harmonic (grey). The values were derived atier components at O1 and O2 in terms of amplitude and phase was calculated forase represent the distance and the direction from the origin, respectively, with 95%

M.N. Anwar et al. / Journal of Neuroscience Methods 184 (2009) 213–223 221

F eft pas ould bc

f(

3

gcopucisranb

4

yt

ig. 9. Effect of filtering on VEPs: filtered signals acquired during fMRI scanning (lignificant signal changes could be observed. (b) SS-VEPs: a defined peak at 8 Hz coherence level remained unchanged in both conditions.

ellow eye, while no difference was found in the control subjectsFig. 11b).

.6. Integration of SS-VEPs and fMRI

The continuous recording of VEPs and fMRI allowed us to inte-rate the data obtained from the two techniques. Fig. 11c shows theombined results of SS-VEPs and fMRI for both SF. The responsesf the normal and the affected eye of the patient were plotted ashase lag and coherence of VEPs against the activated brain vol-me in fMRI. The affected eye had a longer latency and a decreasedoherence with respect to the normal eye. The activated volumen visual areas (Brodmann areas 17, 18 and 19) of the patient wasignificantly smaller than the controls. The activation maps of aepresentative control subject and the patient for 4 Hz stimulationre shown in Fig. 5. A reduction of the activated brain areas can beoticed in the visual cortex activation maps of the ON patient atoth 1 and 4 cpd.

. Discussion

Continuous EEG/fMRI recording was first reported for the anal-sis of epileptic events (Lemieux et al., 2001). Notwithstandinghe reported usefulness of combining these two techniques (Di

nel) and signals acquired in absence of fMRI scanning (right panel). (a) T-VEPs: noe observed in the 2nd harmonic response at O1 for the right eye stimulation; the

Russo et al., 2005; Menon et al., 1997), only a few studies havebeen conducted so far in order to record VEPs (Becker et al., 2005;Comi et al., 2005; Henning et al., 2005; Im et al., 2006; Sammeret al., 2005). The interference with the MR system, which resultsin distortion of the EEG signal recordings (i.e., static magnetic fieldinterference, dynamic magnetic field interference and cardiac pulseinterference), could account for the lack of this kind of study. Thedisadvantage of a continuous recording approach could be the pos-sible loss of EEG information due to artifacts (Becker et al., 2005).In addition, a pulse artifact is induced by blood flow inside thehead, normal to the static magnetic field. To address these prob-lems an adaptive subtraction filtering algorithm (Allen et al., 2000,1998) was used in previous studies (Becker et al., 2005; Comi et al.,2005; Henning et al., 2005; Im et al., 2006; Sammer et al., 2005).Such an algorithm is mainly tailored to remove post-processingartifacts. Consequently, it has neither control on the electrophys-iological changes occurring during fMRI scan nor a strong ‘priorknowledge’ of the artifact, which is a mandatory condition to avoidloss of information on the physiological signals recovered after fil-

tering. In a SS-VEPs study, it was reported that gradient and/orballistocardiographic artifacts could cause small distortions of theperiodical behavior of the SS-VEP in the scanner, removing someof the magnitude of the targeted peak and shifting its frequency toa slightly lower value than the stimulation frequency (Sammer et

222 M.N. Anwar et al. / Journal of Neuroscience Methods 184 (2009) 213–223

F ) TheS ial fre

aaer2

aiqpEpeittfaatcatdsltlc

aac

ig. 10. (a) The SS-VEP response of the fellow eye and the ON eye for 4 Hz–1 cpd. (bS-VEP decreased sharply in the ON eye at high spatial frequency while at low spat

l., 2005). The differences in latency of VEPs in inter-scan periodsnd outside the scanner have been already addressed (Bonmassart al., 1999). Other authors investigated the differences in the VEPsecorded during inter-scan period and scan period (Becker et al.,005).

In this study we emphasized the feasibility of our real-time fMRIrtifact filtering and tested its validity for both T-VEP and SS-VEPn time and frequency domain at low and medium-high spatial fre-uencies. The main feature of our real-time artifact filtering is thereliminary analytical study of EPI sequence parameters relatedEG-artifact shapes, which also allows a further post-processingrocedure. The basic idea of the filtering method adopted in thisxperiment is an identification of the electromagnetic interferencen its shape and space-temporal features. The exact estimation ofhe effects of EPI sequence parameters on the raw EEG data giveshe possibility of identifying an analytical expression of “artifactsunction”. This represents a reference (template) for filtering andlso permits to find “endogenous” trigger events. By utilizing ourlgorithm we were able to filter the data with fMRI artifacts ando reconstruct a cleaned EEG signal. Our results show that artifact-orrected scan data had similar spectrum to inter-scan data withn average difference of about 10% in five different bands, similaro a previous study with offline filtering of fMRI artifacts from EEGata (Becker et al., 2005). No difference was found between inter-can and scan VEPs after real-time artifact removal both in P100atency and shape for transient evoked potentials and in the ampli-ude and phase response for steady-state evoked potentials. Theatter are characterized by the amplitude and phase of the Fourier

omponents at one or more frequency of interest.

In addition, EEG acquisition in two different conditions (i.e.,cquisition inside the MR scanner during EPI acquisition andcquisition in the MR scanner without EPI gradients applied) wasompared in one subject in terms of shape, amplitude and latency of

SS-VEP response of the fellow eye and the ON eye for 4 Hz–4 cpd. The amplitude ofquency the response amplitudes were similar.

P100 component in T-VEP and coherence and phase lag of the 2ndharmonic in SS-VEP. The shape and the latency of P100 componentwere preserved after applying filtering technique. The amplitudeof the 2nd harmonic response of the stimulation frequency wasunchanged and a clear peak was observed at 8 Hz.

In the affected eye of the ON patient, T-VEPs recording showedthe typical latency delay of the P100 component (Halliday et al.,1972) and SS-VEPs demonstrated a significantly longer phase lagof the 2nd harmonic, which has been associated with elongation ofneuronal processing time.

Our fMRI results were consistent with previous findings inpatients with acute ON, with a dramatic reduction of volume of acti-vation in the primary visual cortex in response to stimulation of theaffected eye (Gareau et al., 1999; Rombouts et al., 1998; Russ et al.,2002). At a variance with either conjoint or interleaved recording ofVEPs and fMRI, the continuous acquisition of electrophysiologicaland BOLD signals allowed us to reliably and accurately integrateVEPs and fMRI data. We were therefore able to analyze the correla-tion of the phase lag or the coherence of the 2nd harmonic responseat 4 Hz for both 1 and 4 cpd with the activated volumes. We founda negative correlation between the phase lag of the 2nd harmonicresponse of the affected eye and the activated volumes. We alsofound a positive correlation between the coherence at the 2nd har-monic response and the activated volumes. Caution is needed todraw conclusions from data obtained in a single patient; howeverour findings might suggest that the electrophysiological and func-tional imaging changes in the visual brain during ON can be directlycorrelated.

Continuous recording of VEPs and fMRI provides noninvasive,precise and reliable information on visual processing by studyingthe subjects during exactly the same states of visual brain activ-ity. Furthermore, we showed that real-time fMRI artifact filtering,combined with an optimal EPI protocol, does not modify the mor-

M.N. Anwar et al. / Journal of Neuroscien

Fig. 11. (a) Distribution of coherence: in a control subject the coherence of 2ndharmonic response at the occipital sites had a higher value than the patient’s affectedeye. (b) Polar plot of the averaged phase response of all the subjects and for thepatient (affected eye). The response was measured at O1 and O2. A decrease in theamplitude and phase lag of the 2nd harmonic is evident for the affected eye. (c)Integration of phase and coherence with the activated volumes at occipital cortex:the 2nd harmonic response of the affected eye (♦) had a longer latency than thefat

pvrcdttoci

R

A

2005;24:1012–24.Schabus M, Dang-Vu TT, Albouy G, Balteau E, Boly M, Carrier J, et al. Hemodynamic

cerebral correlates of sleep spindles during human non-rapid eye movement

ellow eye (+). The value of coherence decreased in the affected eye at 4 cpd. Thectivated volume in visual areas (Brodmann areas 17, 18 and 19) by stimulation ofhe affected eye was smaller than the fellow eye.

hology and the amplitude of evoked potentials recorded from theisual cortex. It also allows to obtain reliable VEPs, proving to be aeliable tool for studying human visual function. Only a true con-urrent approach enables proper correlation of data derived fromifferent biophysical sources on the processing of visual stimuli athe same time. This is crucial for a better understanding of the rela-ionship between neurophysiological and hemodynamic responsesf the activated visual cortex. Even if recorded in just one patient,ontinuous VEP–fMRI recording seems to be also promising for clin-cal application in the study of visual function in optic neuropathies.

eferences

llen PJ, Josephs O, Turner R. A method for removing imaging artifact from contin-uous EEG recorded during functional MRI. Neuroimage 2000;12:230–9.

ce Methods 184 (2009) 213–223 223

Allen PJ, Polizzi G, Krakow K, Fish DR, Lemieux L. Identification of EEG events inthe MR scanner: the problem of pulse artifact and a method for its subtraction.Neuroimage 1998;8:229–39.

Bast T, Wright T, Boor R, Harting I, Feneberg R, Rupp A, et al. Combined EEG and MEGanalysis of early somatosensory evoked activity in children and adolescents withfocal epilepsies. Clin Neurophysiol 2007;118:1721–35.

Becker R, Ritter P, Moosmann M, Villringer A. Visual evoked potentials recoveredfrom fMRI scan periods. Hum Brain Mapp 2005;26:221–30.

Bodis-Wollner I, Brannan J, Ghilardi M, Mylin L. The importance of physiology tovisual evoked potentials. In: Desmedt J, editor. Visual evoked potentials. Ams-terdam: Elsevier; 1990. p. 1–24.

Bonmassar G, Anami K, Ives J, Belliveau JW. Visual evoked potential (VEP) measuredby simultaneous 64-channel EEG and 3T fMRI. Neuroreport 1999;10:1893–7.

Bonmassar G, Schwartz DP, Liu AK, Kwong KK, Dale AM, Belliveau JW. Spatiotem-poral brain imaging of visual-evoked activity using interleaved EEG and fMRIrecordings. Neuroimage 2001;13:1035–43.

Chatfield C, Collins AJ. Introduction to multivariate analysis. London: Chapman andHall; 1980.

Comi E, Annovazzi P, Silva AM, Cursi M, Blasi V, Cadioli M, et al. Visual evoked poten-tials may be recorded simultaneously with fMRI scanning: a validation study.Hum Brain Mapp 2005;24:291–8.

Czisch M, Wehrle R, Kaufmann C, Wetter TC, Holsboer F, Pollmacher T, et al. Func-tional MRI during sleep: BOLD signal decreases and their electrophysiologicalcorrelates. Eur J Neurosci 2004;20:566–74.

Di Russo F, Pitzalis S, Spitoni G, Aprile T, Patria F, Spinelli D, et al. Identificationof the neural sources of the pattern-reversal VEP. Neuroimage 2005;24:874–86.

Friston KJ, Holmes AP, Poline JB, Grasby PJ, Williams SC, Frackowiak RS, et al. Analysisof fMRI time-series revisited. Neuroimage 1995;2:45–53.

Gareau PJ, Gati JS, Menon RS, Lee D, Rice G, Mitchell JR, et al. Reduced visual evokedresponses in multiple sclerosis patients with optic neuritis: comparison of func-tional magnetic resonance imaging and visual evoked potentials. Mult Scler1999;5:161–4.

Garreffa G, Carni M, Gualniera G, Ricci GB, Bozzao L, De Carli D, et al. Real-time MRartifacts filtering during continuous EEG/fMRI acquisition. Magn Reson Imaging2003;21:1175–89.

Goldman RI, Stern JM, Engel Jr J, Cohen MS. Acquiring simultaneous EEG and func-tional MRI. Clin Neurophysiol 2000;111:1974–80.

Halliday AM, McDonald WI, Mushin J. Delayed visual evoked response in optic neu-ritis. Lancet 1972;1:982–5.

Hamandi K, Salek-Haddadi A, Laufs H, Liston A, Friston K, Fish DR, et al.EEG–fMRI of idiopathic and secondarily generalized epilepsies. Neuroimage2006;31:1700–10.

Henning S, Merboldt KD, Frahm J. Simultaneous recordings of visual evoked poten-tials and BOLD MRI activations in response to visual motion processing. NMRBiomed 2005;18:543–52.

Horovitz SG, Fukunaga M, de Zwart JA, van Gelderen P, Fulton SC, Balkin TJ, et al.Low frequency BOLD fluctuations during resting wakefulness and light sleep: asimultaneous EEG–fMRI study. Hum Brain Mapp 2008;29:671–82.

Huang-Hellinger FR, Breiter HC, McCormack G, Cohen MS, Kwong KK, Sutton JP, et al.Simultaneous functional magnetic resonance imaging and electrophysiologicalrecording. Hum Brain Mapp 1995;3:13–23.

Im CH, Liu Z, Zhang N, Chen W, He B. Functional cortical source imaging from simul-taneously recorded ERP and fMRI. J Neurosci Methods 2006;157:118–23.

Ives JR, Warach S, Schmitt F, Edelman RR, Schomer DL. Monitoring the patient’s EEGduring echo planar MRI. Electroencephalogr Clin Neurophysiol 1993;87:417–20.

Lemieux L, Allen PJ, Franconi F, Symms MR, Fish DR. Recording of EEG during fMRIexperiments: patient safety. Magn Reson Med 1997;38:943–52.

Lemieux L, Salek-Haddadi A, Josephs O, Allen P, Toms N, Scott C, et al. Event-relatedfMRI with simultaneous and continuous EEG: description of the method andinitial case report. Neuroimage 2001;14:780–7.

Menon V, Ford JM, Lim KO, Glover GH, Pfefferbaum A. Combined event-relatedfMRI and EEG evidence for temporal-parietal cortex activation during targetdetection. Neuroreport 1997;8:3029–37.

Ogawa S, Tank DW, Menon R, Ellermann JM, Kim SG, Merkle H, et al. Intrinsic sig-nal changes accompanying sensory stimulation: functional brain mapping withmagnetic resonance imaging. Proc Natl Acad Sci USA 1992;89:5951–5.

Regan D. Evoked potentials and evoked magnetic fields in science and medicine.Human brain electrophysiology. New York: Elsevier; 1989.

Rombouts SA, Lazeron RH, Scheltens P, Uitdehaag BM, Sprenger M, Valk J, et al.Visual activation patterns in patients with optic neuritis: an fMRI pilot study.Neurology 1998;50:1896–9.

Russ MO, Cleff U, Lanfermann H, Schalnus R, Enzensberger W, Kleinschmidt A.Functional magnetic resonance imaging in acute unilateral optic neuritis. J Neu-roimaging 2002;12:339–50.

Sammer G, Blecker C, Gebhardt H, Kirsch P, Stark R, Vaitl D. Acquisition of typ-ical EEG waveforms during fMRI: SSVEP, LRP, and frontal theta. Neuroimage

sleep. Proc Natl Acad Sci USA 2007;104:13164–9.Victor JD, Mast J. A new statistic for steady-state evoked potentials. Electroen-

cephalogr Clin Neurophysiol 1991;78:378–88.