ERP differences with vs. without concurrent fmri

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1 International Journal of Psychophysiology 62 (2006) ERP differences with vs. without concurrent fmri Nino Bregadze, Aureliu Lavric School of Psychology, University of Exeter, UK MRI Research Centre, University of Exeter, UK Received 16 May 2005; received in revised form 20 December 2005; accepted 5 January 2006 Available online 28 February 2006 Abstract The acquisition of ERPs concurrently with fmri in cognitive paradigms is appealing, but technically challenging. Little is known about the effects of the fmri environment on the time-course and topography of previously documented ERP effects. We examined the replicability of ERP differences in the scanner at the level of individual subjects, using two cognitive paradigms and two statistical procedures. ERP P3 differences found outside the scanner in both paradigms were also robustly detected in the ERPs acquired during fmri scanning. These P3 effects had equivalent time-courses and scalp topographies inside and outside the scanner. This replication at the level of individual data-sets has implications for the clinical applicability of ERP fmri and, more generally, for the quality of scanner recorded ERPs Elsevier B.V. All rights reserved. Keywords: EEG; ERP; Co-registration; Scanner ERP; Integrated EEG fmri 1. Introduction fmri and EEG tend to provide different kinds of information on brain activity. The strength of fmri lies in spatial localisation, whilst EEG and EEG-derived ERPs are better suited for revealing the time-course of neural activity. The rigorous integration of the two techniques requires concurrent data acquisition. Indeed, recently there have been reports of correlations between concurrently acquired EEG and fmri (Goldman et al., 2002; Laufs et al., 2003), as well as ERPs and fmri (Liebenthal et al., 2003). Concurrent acquisition poses non-trivial problems, in particular regarding EEG contamination by artifact from switches of MRI gradients and from motion caused by the cardiac cycle in a large stationary magnetic field. Several methods for the elimination of these artifacts were developed (Allen et al., 1998, 2000; Bonmassar et al., 1999, 2002; Goldman et al., 2000; Kruggel et al., 2000) and studies that used some of these methods compared visual evoked potentials (Bonmassar et al., 1999, 2002), steady-state evoked potentials (Sammer et al., 2005) and lateralised readiness potentials (Sammer et al., 2005) Corresponding author. School of Psychology, Washington Singer Laboratories, University of Exeter, Exeter EX4 4QG, UK. Tel.: ; fax: address: A.Lavric@exeter.ac.uk (A. Lavric). recorded inside and outside the scanner, as well as EEG power and spike measures inside and outside the scanner (Allen et al., 1998, 2000; Sammer et al., 2005; Lemieux et al., 2001). However, it remains largely unknown to what extent differences in ERP components elicited in cognitive paradigms can be measured in the scanner. Although there have been several reports of mid-latency ERPs acquired in the scanner (Liebenthal et al., 2003; Kruggel et al., 2000, 2001; Mulert et al., 2004), detailed statistical comparisons of ERPs recorded with vs. without concurrent fmri (henceforth: ERP MR and ERP-only) are still very scarce. Recently, such comparisons were performed in a study that used an auditory oddball paradigm (Mulert et al., 2004). First, the amplitude and latency of the N1 and P3 ERP components were assessed, irrespective of experimental condition. N1 had a higher amplitude and shorter latency outside the scanner. For P3, both amplitude and latency were similar in the ERP-only and ERP MR data, though the authors noted that P3a (early P3-type component) could not be identified in the ERP MR data. Second, the ERP MR vs. ERP-only data-sets were compared in the size of the P3 difference between the oddball and standard stimuli, by contrasting oddball-minus-standard difference waves in the P3 range: their amplitude was statistically indistinguishable in the ERP-only vs. ERP MR data. We aim to further examine the consistency between ERPonly and ERP MR data by performing statistical comparisons at /$ - see front matter 2006 Elsevier B.V. All rights reserved. doi: /j.ijpsycho

2 N. Bregadze, A. Lavric / International Journal of Psychophysiology 62 (2006) the level of individual subjects. In addition to being a rigorous test of the ERP MR data quality, validation in individual datasets is essential for the implementation of ERP MR in the clinical field. We employed two cognitive paradigms, both associated with robust effects in the P3 range: go nogo (Pfefferbaum et al., 1984) and n-back (Gevins et al., 1996). The study will attempt to answer two questions: (1) are known ERP component differences detectable in individual subjects in ERP MR data? (2) if so, is their time-course and scalp distribution markedly different from those obtained outside the scanner? Most ERP MR studies to date, in order to avoid gradient artifact, acquired ERPs either interleaved with fmri (Bonmassar et al., 2002)orinthe silent (no gradient) gaps (Liebenthal et al., 2003; Kruggel et al., 2001; Mulert et al., 2004). In the former procedure, ERP and fmri data are not correlates of the same stimuli. In the latter procedure, the placement of the critical stimuli exclusively in between fmri volumes reduces the amount of collected data. A recent visual evoked potential study found that P1 and N1 peaks recorded during gradient switching were consistent (after appropriate correction) with those recorded in the scanner without concurrent imaging (Comi et al., 2005). Therefore, to assess the possibility of collecting ERPs during fmri volume acquisition, we acquired ERPs throughout the fmri sequence. 2. Method 2.1. Participants Two right-handed female participants (ages 21 and 28) provided informed written consent before undergoing the testing procedure approved by the departmental ethics committee. One completed three n-back sessions (behavioural practice, ERPonly and ERP MR, in this order) and the second completed two go nogo sessions (ERP MR and ERP-only, in this order) Tasks N-back Each trial consisted of a 500ms presentation of a square box with several instances of the same letter inside against a background of randomly arrayed letters. The background was then displayed alone for 2420ms. The box contained one of six possible letters (uppercase or lowercase) and appears in one of six possible non-symmetric locations. The verbal task required one to attend to the letter and the spatial task to the location of the box. The instruction was to indicate whether the stimulus was the same or different from that displayed n trials ago (the verbal and spatial tasks each have two levels: n=1 and n=3). The same and different responses (probability 0.5/0.5) were mapped onto right and left index finger responses, respectively. In the ERP-only and ERP MR sessions, the four tasks (verbal/ spatial, 1-back 3-back) were alternated continuously: 12 blocks per task, presented in random order, 18 trials per block Go nogo The study employed a standard go nogo paradigm that required a right-hand keypress to the presentation of go stimuli (probability 0.75) and no response to nogo stimuli (probability 0.25). Each condition used four equiprobable upper case letters (go: D,F,G,J; nogo: P,S,V,T), displayed for 1000ms, preceded by a fixation cross (750ms) and followed by a blank screen (1300ms). In the ERP-only session, 336 go and 112 nogo stimuli were presented; in the ERP MR session the number of stimuli was doubled. Each session was preceded by a short practice block EEG acquisition Outside the scanner: the EEG was acquired (bandpass: Hz; sampling rate: 500Hz; reference: Cz; ground: AFz) using a 64-channel conventional Ag/AgCl cap (ECI Inc., Eaton, Ohio, USA). 58 electrodes were placed on the scalp in an extended configuration, two were placed on the earlobes, two were used for recording the vertical EOG (supra- and infraorbitally on the right) and two for the horizontal EOG (at the outer canthi of the eyes). In the scanner: the EEG was acquired (bandpass: Hz; sampling rate: 5000Hz; reference: FCz; ground: AFz) with a 32-channel Ag/AgCl MR-compatible cap (EasyCap, FMS, Herrsching-Breitbrunn, Germany). 29 electrodes were placed on the scalp, one was used for the EOG (infraorbitally on the right) and two were used for the ECG (placed medially at the collar bones). All EEG data were acquired using BrainAmp-MR amplifiers (BrainProducts Ltd, Munich, Germany) and analysed using BrainAnalyser 1.04 (BrainProducts Ltd, Munich, Germany) and LORETA/TANOVA software (The KEY Institute for Brain and Mind Research, Zurich, Switzerland). Although the current study is confined to ERP analysis, we outline the fmri acquisition parameters because they impact on the MR-related artifact in the EEG: EPI sequence (run on a 1.5 T Philips Gyroscan scanner); TR 3000ms; TE 40ms; TA (volume acquisition time) 1980ms (1020ms gradient free per TR), flip angle 90, FOV: mm; matrix, 22 contiguous transverse slices; 4mm thick (2mm gap) Gradient and pulse artifact correction We employed a procedure that averages epochs containing gradient artifact and subtracts the average from individual EEG stretches (Allen et al., 2000). The onsets of gradient artifact were either marked on-line based on triggers from the scanner (go nogo data) or detected off-line using a gradient criterion: gradient 350 μv/ms in electrode FP2 (n-back data). Prior to averaging, EEG stretches time-locked to the gradient onset markers (length: n-back 50 to 2950ms; go nogo 100 to 2000ms) were baseline corrected (baseline: n-back 50 to 20ms; go nogo 100 to 5ms). After correction the EEG was low-pass filtered (40Hz) and downsampled to 500Hz. To detect pulse artifact episodes, a typical pulse artifact was selected visually and marked as a template in one of the ECG channels. An automated procedure then searched the ECG timepoint-by-time-point for stretches of data of the same length as the template that matched the template and marked their onset. There were two constraints: (1) minimum interval between matches template length; (2) maximum interval = 1.5 s, corresponding to a minimum heart-rate of 40bpm. There were two criteria for a

3 56 N. Bregadze, A. Lavric / International Journal of Psychophysiology 62 (2006) match: cross-correlation and amplitude similarity. The latter was computed as the average of template/data ratios of amplitudes at each time-point (prior to ratios being computed, amplitudes were demeaned to minimise the impact of baseline shifts or trends). The points of the best matches of the data to the template were marked if cross-correlation was at least 0.6 and the amplitude similarity between 0.6 and 1.2. The stretches that were best matches but were below these values were also marked and later verified by visual inspection. Epochs of pulse artifact in the EEG were then marked (based on ECG pulse episodes) and corrected as described in Allen et al. (1998) ERP analysis The EEG was filtered (n-back: Hz; go nogo: Hz), and ERPs segmented to the onset of stimuli (ERP length: n-back 800ms; go nogo 550ms) and baseline corrected (baseline: 100 to 0 ms). ERP epochs containing amplitudes over a certain criterion (established for each subject based on ocular artifact) were excluded automatically. Statistical analyses were performed on individual data-sets, hence single ERP epochs were used as observations. Initially, amplitude differences (1-back vs. 3-back; go vs. nogo ) were assessed in the ERPonly data. Subject to statistically reliable differences outside the scanner, the contrasts were also performed on the ERP MR amplitudes. Two statistical procedures were employed. The first, TANOVA (Topographic Analysis of Variance, Pascual-Marqui et al., 1995), treats the scalp as a vector of n electrodes. It computes the dissimilarity between the scalp vectors of experimental conditions at each ERP time point (as in Lavric et al., 2004), while correcting for α-inflation by permutation. Thus, TANOVA's outputs are time-windows in which conditions are reliably different. Only time-windows 20 ms were considered. Prior to TANOVA, ERPs were transformed to a global field power of 1. Secondly, the effect of session (ERP-only vs. ERP MR) on the scalp distribution of ERP differences were assessed with ANOVA, run on the mean amplitude of time-windows identified by TANOVA (the Greenhouse Geisser correction was applied where necessary). Analyses used only electrodes common to the two acquisition sessions. For ANOVA, 24 electrodes were averaged into 4 regions in each hemisphere: anterior (FP1, F3, F7), central (FC5, FC1, C3), temporo-parietal (T7, CP5, P7), parieto-occipital (CP1, P3, O1) and the homologous regions on the right. 27 electrodes were used in TANOVA (the above plus midline electrodes Fz, Cz and Pz). To illustrate ERP components, mastoid-referenced ERPs are presented (see Figs. 1 and 2). However, the caps used outside and inside the scanner did not contain equivalent mastoid electrodes (TP7, TP8 and TP9, TP10, respectively). Since it was essential to use equivalent referencing in analysing the results from both sessions, ANOVA was run on data referenced to the original reference in Fig. 1. (A) Top: Mastoid-referenced ERPs obtained over the right hemisphere show similar 1-back vs. 3-back differences. Bottom: Windows of reliable differences identified by TANOVA. (B) The scalp distribution of the difference.

4 N. Bregadze, A. Lavric / International Journal of Psychophysiology 62 (2006) Fig. 2. (A) Top: Go nogo ERP differences in the two sessions. Bottom: TANOVA windows in the two sessions. (B) Similar scalp distribution of the difference was obtained. (C) Scanner recorded ERPs are larger overall in panel A, because mastoid referencing of ERP-only and ERP MR data was not based on the same electrodes. However, mastoid referencing is only used for illustration. In panel C, the same reference is used (FCz) and ERP amplitudes in the two sessions are similar. An occipital electrode illustrates the detectability of early visual peaks. the scanner (FCz) present in both sessions. TANOVA used the average reference. 3. Results 3.1. Behavioural performance 1-back accuracy was near-perfect both outside and inside the scanner (verbal, 99.5% and 99.5%; spatial, 98.5% and 97.5%, respectively). The corresponding mean RTs were as follows: verbal, 783 and 637ms; spatial, 745 and 670ms, respectively. 3-back accuracy was somewhat lower in both the ERP-only and the ERP MR sessions: verbal, 78.9% and 85%; spatial, 88.3% and 86%, respectively; the mean RTs were: verbal, 1005 and 775ms; spatial, 847 and 694ms, respectively. In the go nogo task, accuracy on go trials was 100% both outside and inside the scanner (mean RTs: 470 and 476ms, respectively). The proportion of false alarms (on nogo trials) was 8% in the ERP-only session and 8.5% in the ERP MR session.

5 58 N. Bregadze, A. Lavric / International Journal of Psychophysiology 62 (2006) ERP results N-back TANOVA identified a reliable 1-back vs. 3-back window of difference in the verbal task, with larger amplitude in the 3-back ERPs in the P3 range ( ms; see Fig. 1). A similar broad window ( ms) of difference was identified in the ERP MR data. ANOVA, performed on the verbal n-back data on the ms time-window with factors Task (1-back, 3-back), Session (ERP-only, ERP MR), Scalp region and Hemisphere, found a reliable main effect of Task (F(1,421)=24.43, pb0.001). The interactions Task Region, Task Hemisphere, and Task Region Hemisphere were also significant (F(3, 1263) = 4.55, p b 0.05; F(1,421)=4.23, p b0.05; F(3,1263)=4.84, p b 0.01, respectively). Importantly, there was no suggestion of a difference between ERP sessions (with vs. without MR) with regard to the scalp distribution of the verbal 1-back vs. 3-back difference (see Fig. 1): the interactions between factors Session and Task and factors describing the topography (Region and/or Hemisphere) did not approach significance. The Session Task interaction also did not approach significance, suggesting that the 1-back vs. 3-back difference in the verbal task was of similar magnitude inside and outside the scanner Go nogo TANOVA found in the ERP-only data a time-window of reliable difference ( ms), which corresponds to a larger P3 in nogo with a central, somewhat left-lateralised distribution (see Fig. 2). A window in the P3 range ( ms) was also identified in the ERP MR TANOVA. This window is immediately preceded by a range of time-points, in which the go nogo difference is marginally non-significant (p b 0.1, see Fig. 2). ANOVA on the ms time-window with factors Condition (go, nogo), Session (ERP-only, ERP MR), Scalp region and Hemisphere, found a significant main effect of Condition (F(1,851)=55.56, pb0.0001). The interactions Task Region, Task Hemisphere, and Task Region Hemisphere were also reliable (F(3,2553)=11.05, p b0.001; F(1,851)=17.99, pb0.001; F(3,2553)=13.95, pb0.001, respectively). The Session Condition interaction was reliable (F(1,851)= 7.15, p b0.01), indicating that go nogo differences were overall larger in the ERP-only session. None of the interactions that tested the effect of session (with vs. without MR) on the topography of the go nogo difference was significant. 4. Discussion The present study sought to determine whether ERP effects, which are commonly found in cognitive ERP paradigms, can be replicated in ERP MR recordings at the level of individual subjects. Two paradigms known to elicit robust differences in the P3 range were selected and two analytical approaches applied. TANOVA assessed the dissimilarity between conditions across the entire ERP. ANOVA was used to examine the effect of session (ERP-only vs. ERP MR) on the scalp distribution of EPR differences. The outcomes were unequivocal. In both paradigms, the ERP differences between experimental conditions identified in the ERP-only data were also reliably detected in the ERP MR data (see Figs. 1 and 2). Fig. 2C also illustrates the good detectability of visual peaks (e.g. P1) in these single-subject ERP MR data. In the n-back paradigm, the verbal 3-back task was associated with a larger P3-type component than the verbal 1-back task, in both sessions (ERP-only and ERP MR). The scalp topography of this effect was very similar in the two sessions. Early P3 components with anterior topography were previously shown to be sensitive to working memory (Gevins et al., 1996). In the go nogo data, a previously documented P3 difference (Pfefferbaum et al., 1984), with a central distribution was identified by TANOVA in the ERP-only, as well as the ERP MR data. Although the difference was somewhat larger in the ERP-only data, the scalp distribution of this difference was statistically indistinguishable across the ERP-only and ERP MR sessions. The present go nogo data-set does not show a larger N2 for nogo trials. However, this is the case in both the ERP-only and the ERP MR data. Scanner ERPs appear to be less noisy in the go nogo than the n-back paradigm. In the go nogo data the onsets of gradient artifact were based on triggers sent by the scanner at the start of each fmri volume, whereas in the n-back data gradient onsets were determined from off-line computations (in the absence of triggers from the scanner). We believe this resulted in a more precise gradient onset identification and, hence, superior gradient correction, in the go nogo data-set. Our results have significant implications for the quality of ERP MR data, especially considering that ERPs were acquired throughout gradient switching. The observed replicability of longlatency ERP effects in the scanner suggests that they are not significantly compromised by concurrent fmri. There are also important implications for the applicability of ERP MR in clinical research, because they show that ERP effects can be reliably detected in the scanner in single-subject data-sets and with a relatively small number of ERP trials (as few as 60 in the nogo condition). References Allen, P.J., Polizzi, G., Krakow, K., Fish, D.R., Lemieux, L., Identification of EEG events in the MR scanner: the problem of pulse artifact and a method for its subtraction. NeuroImage 8, Allen, P.J., Josephs, O., Turner, R., A method for removing imaging artifact from continuous EEG recorded during functional MRI. NeuroImage 12, Bonmassar, G., Anami, K., Ives, J., Belliveau, J.W., Visual evoked potential (VEP) measured by simultaneous 64-channel EEG and 3T fmri. NeuroReport 10, Bonmassar, G., Purdon, P.L., Jääskeläinen, I.P., Chiappa, K., Solo, V., Brown, E.N., Belliveau, J.W., Motion and ballistocardiogram artifact removal for interleaved recording of EEG and EPs during MRI. NeuroImage 16, Comi, E., Annovazzi, P., Silva, A.M., Cursi, M., Blasi, V., Cadioli, M., Inuggi, A., Falini, A., Comi, G., Leocani, L., Visual evoked potentials may be recorded simultaneously with fmri scanning: a validation study. Hum. Brain Mapp. 25, Gevins, A., Smith, M.E., Le, J., Leong, H., Bennett, J., Martin, N., McEvoy, L., Du, R., Whitfield, S., High resolution evoked potential imaging of the

6 N. Bregadze, A. Lavric / International Journal of Psychophysiology 62 (2006) cortical dynamics of human working memory. Electroencephalogr. Clin. Neurophysiol. 98, Goldman, R.I., Stern, J.M., Engel Jr., J., Cohen, M.S., Acquiring simultaneous EEG and functional MRI. Clin. Neurophysiol. 111, Goldman, R.I., Stern, J.M., Engel Jr., J., Cohen, M.S., Simultaneous EEG and fmri of the alpha rhythm. NeuroReport 13, Kruggel, F., Wiggins, C.J., Herrmann, C.S., von Cramon, D.Y., Recording of the event-related potentials during functional MRI at 3.0 Tesla field strength. Magn. Reson. Med. 44, Kruggel, F., Herrmann, C.S., Wiggins, C.J., von Cramon, D.Y., Hemodynamic and electroencephalographic responses to illusory figures: recording of the evoked potentials during functional MRI. NeuroImage 14, Laufs, H., Kleinschmidt, A., Beyerle, A., Eger, E., Salek-Haddadi, A., Preibisch, C., Krakow, K., EEG-correlated fmri of human alpha activity. NeuroImage 19, Lavric, A., Pizzagalli, D., Forstmeier, S., When go and nogo are equally frequent: ERP components and cortical tomography. Eur. J. Neurosci. 20, Lemieux, L., Salek-Haddadi, A., Josephs, O., Allen, P., Toms, N., Scott, C., Krakow, K., Turner, R., Fish, D.R., Event-related fmri with simultaneous and continuous EEG: description of the method and initial case report. NeuroImage 14, Liebenthal, E., Ellingson, M.L., Spanaki, M.V., Prieto, T.E., Ropella, K.M., Binder, J.R., Simultaneous ERP and fmri of the auditory cortex in a passive oddball paradigm. NeuroImage 19, Mulert, C., Jäger, L., Schmitt, R., Bussfeld, P., Pogarell, O., Möller, H.J., Juckel, G., Hegerla, U., Integration of fmri and simultaneous EEG: towards a comprehensive understanding of localization and time-course of brain activity in target detection. NeuroImage 22, Pascual-Marqui, R.D., Michel, C.M., Lehmann, D., Segmentation of brain electrical activity into microstates: model estimation and validation. IEEE Trans. Biomed. Eng. 42, Pfefferbaum, A., Wennegat, B.G., Ford, J.M., Roth, W.T., Kopell, B.S., Clinical application of the P3 component of event-related potentials. II. Dementia, depression and schizophrenia. Electroencephalogr. Clin. Neurophysiol. 59, Sammer, G., Blecker, C., Gebhardt, H., Kirsch, P., Stark, R., Vaitl, D., Acquisition of typical waveforms during fmri: SSVEP, LRP and frontal theta. NeuroImage 24,

Clinical Neurophysiology

Clinical Neurophysiology 122 (2011) 267 277 Contents lists available at ScienceDirect Clinical Neurophysiology journal homepage: www.elsevier.com/locate/clinph Detection of experimental ERP effects in