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Influence of ongoing alpha rhythm on the visual evoked potential
www.elsevier.com/locate/ynimg NeuroImage 39 (2008) 707 – 716
Influence of ongoing alpha rhythm on the visual evoked potential Robert Becker,⁎ Petra Ritter,1 and Arno Villringer1 Berlin NeuroImaging Center, Department of Neurology, Charité-Platz 1, 10117 Berlin, Germany Received 23 May 2007; revised 21 August 2007; accepted 3 September 2007 Available online 19 September 2007
The relationship between ongoing occipital alpha rhythm (8–12 Hz) and the generation of visual evoked potentials (VEPs) has been discussed controversially. While the “evoked theory” sees no interaction between VEP generation and the alpha rhythm, the “oscillatory theory” (also known as “phase-reset theory”) postulates VEP generation to be based on alpha rhythm phase resetting. Previous experimental results are contradictory, rendering a straightforward interpretation difficult. Our approach was to theoretically model the implications of the evoked and oscillatory theory also incorporating stimulus-induced alpha-rhythm desynchronization. As a result, the model based on the oscillatory theory predicts alpha-band dependent VEP amplitudes but constant phase locking. The model based on the evoked theory predicts unaffected VEP amplitudes but alpha-band dependent phase locking. Subsequently, we analyzed experimental data in which VEPs were assessed in an “eyes open” and “eyes closed” condition in 17 subjects. For early components of the VEP, findings are in agreement with the evoked theory, i.e. VEP amplitudes remain unaffected and phase locking decreases during periods of high alpha activity. Late VEP component amplitudes (N175 ms), however, are dependent on pre-stimulus alpha amplitudes. This interaction is contradictory to the oscillatory theory since this VEP amplitude difference is not paralleled by a corresponding difference in alphaband amplitude in the affected time window. In summary, by using a model-based approach we identified early VEPs to be compatible with the evoked theory, while results of late VEPs support a modulatory but not causative role – the latter implied by the oscillatory theory – of alpha activity for EP generation. © 2007 Elsevier Inc. All rights reserved. Keywords: EEG; Alpha rhythm; Models; Additive; Phase reset; Interaction
Introduction The human electroencephalogram (EEG) is dominated by spontaneous rhythms, one of the most prominent being the alpha rhythm (8–12 Hz). Evoked potentials (EPs) being of small ⁎ Corresponding author. E-mail address: [email protected] (R. Becker). 1 These authors contributed equally. Available online on ScienceDirect (www.sciencedirect.com). 1053-8119/$ - see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2007.09.016
amplitudes are often obscured by such rhythms and can be revealed by averaging several epochs. This effect is reconcilable with linear summation of ongoing and evoked activity (Arieli et al., 1996), i.e. by an “evoked theory” or “additive theory” of EP generation. In this theory, ongoing activity, e.g. the alpha rhythm has no functional significance for EP generation. Another theory of EP generation is the “oscillatory theory” also known as “phasereset theory”, assuming the EP to be generated by alpha rhythm phase resetting (Sayers et al., 1974; Makeig et al., 2002). Although both theories apparently differ in their assumed relationship between ongoing and evoked activity, there is controversy on how to validate the theories (Makeig et al., 2002; Makinen et al., 2005; Klimesch et al., 2006; Fuentemilla et al., 2006; Mazaheri and Jensen, 2006; Hanslmayr et al., 2006). Shah et al. (2004) suggested two criteria for differentiation: Sufficient ongoing alpha amplitude is required for EP generation in the oscillatory theory, while an event-related increase in signal power supports the evoked model. However, the conclusion, that in case of no increase or of a decrease in average post-stimulus alpha-band power the oscillatory model – i.e. an alpha rhythm phase reset – holds, may be premature. Such a situation can be caused either by veridical alpha phase resetting but also by a “masking” of an evoked process by a dominant but desynchronizing alpha rhythm (Hanslmayr et al., 2006). Without further positive evidence of a functional significance of the alpha rhythm for EP generation, the oscillatory theory can neither be discarded nor be proven. While several studies on the impact of the ongoing alpha amplitude on the evoked response have been reported (Makeig et al., 2002; Basar, 1980; Jasiukaitis and Hakerem, 1988; Rahn and Basar, 1993; Makinen et al., 2005), findings were contradictory or not related to model predictions and so far have not led to a general consensus on the mechanisms of EP generation. In this study, we combine theoretical modeling and experimental data analysis to examine the functional role of the ongoing alpha rhythm for VEP generation. In theoretical modeling, we evaluate the relationship between ongoing activity and evoked activity for both theories. Modelspecific predictions are derived for varying pre-stimulus alpha amplitudes. Initially, both models contain a common subset of data, where an EP is accompanied by a dominating and desynchronizing alpha rhythm. We aim to demonstrate that by variation of pre-
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stimulus alpha amplitude both models are clearly differentiated with respect to their predictions. Subsequently, in experimental data the influence of pre-stimulus alpha amplitude on EP generation is investigated. Based on the comparison of these empirical findings and the simulated EP parameters, the influence of alpha rhythm on EP generation is defined in the light of the two opposing models. Materials and methods Models In order to model the evoked and the oscillatory theory of EP generation, we simulated EEG single trial activity composed of two types of subunit signals as detailed below and shown in Fig. 1. The evoked model assumes “dual generators” for alpha rhythm and EP generation whereas the oscillatory model assumes “shared generators” producing both the alpha rhythm and the evoked response (Mazaheri and Jensen, 2006). In a first step, a subset of data is generated for each model which assumes an EP being generated during spontaneous alpha activity of slightly larger amplitude than the EP itself. This demarcates the starting point of our actual approach of alpha-variation to differentiate the two theories of EP generation.
Evoked model The EP was created by a 10-Hz sine wave lasting for 100 ms with constant amplitude of 7.5 μV (Fig. 1B). The EP is independent from the simultaneously occurring 10 Hz simulated alpha rhythm which amounted to 10 μV (further referred to as “medium” alpha group) and exhibited a random phase in each trial (superimposed single trials are shown in Fig. 1A). A simple eventrelated desynchronization of the alpha rhythm (alpha-ERD; Pfurtscheller and Aranibar, 1977) was integrated into the model. Alpha ERD strongly depends on the type of visual stimulation and can vary from study to study (typical alpha ERD can vary from 20% to 90%, e.g. see Klimesch et al., 2004, but see also Pfurtscheller, 1989). Here, alpha ERD was modeled by decreasing the simulated alpha rhythm amplitude after EP onset at 0 ms to a level of 25% (i.e. an alpha ERD of 75%) of its initial amplitude (Fig. 1C), lasting for the entire EP time window (100 ms). Fig. 1D shows the sum of the simulated EP and concurrent alpha activity. Oscillatory model In contrast to the evoked model, the EP was simulated by resetting and synchronizing the random phase of the ongoing alpha rhythm from 0 to 100 ms post-stimulus time across trials (Figs. 1E–H). The amplitude of the pre-stimulus alpha rhythm amounted to 10 μV (further referred to as “medium” alpha). Since in expe-
Fig. 1. Schematic view of the two competing models of EP generation. Thin colored lines indicate single-trial signals being superimposed on each other; bold black colored lines indicate the resulting average. (A–D) Constituents of the evoked model. Independent from simultaneously occurring spontaneous alpha rhythm (A), the evoked response is generated (B). An event-related alpha rhythm desynchronization (alpha-ERD; Pfurtscheller and Aranibar, 1977; Klimesch et al., 2006) was included into the models (C), resulting in the compound single trials as depicted in panel D. (E–H) Scheme of the oscillatory model considering the alpha rhythm (E) as a generator of the EP via phase resetting (F). In the oscillatory model, the phenomenon of alpha-ERD is accounted for by modeling a partial phase reset. The partial phase reset results in a minor fraction of non-phase-locked alpha activity after stimulation (G) and in a major fraction of phase-locked alpha, i.e. the EP. The resulting EP amplitude is slightly smaller than the amplitude of previous spontaneous alpha activity (F). In the situation depicted here, a differentiation between the evoked and oscillatory (i.e. alpha-rhythm phase resetting) theory is problematic (see Hanslymayr et al., 2006, Klimesch et al., 2006). Also Yeung et al. (2004) raised concerns about the general suitability of previously proposed methods for identification of the mechanism of EP generation, showing that surrogate data modeled by an evoked mechanism yielded same results for conventional analyses as data believed to be generated by phase resetting of the alpha rhythm.
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rimental data complete alpha-band phase reset has not been observed, a major fraction of the simulated alpha rhythm was phase reset (Fig. 1F) while a minor fraction preserved its phase (Fig. 1G). The ratio between the reset and non-phase-reset simulated alpha rhythm was determined such that the oscillatory model and the evoked model generated the same data. The such determined ratio of the ‘partial phase reset’ is used in all subsequent simulations. The resulting sum of phase-reset and non-phase-reset-simulated alpha rhythm is shown in Fig. 1H.
Evoked model Because of the definition of the EP as being independent from the simultaneous rhythm in the evoked theory, the same additive EP was used for the “low” and the “high” alpha group as in the “medium” alpha group (Figs. 2A–C). Correspondingly in both the “high” and “low” alpha group, alpha rhythm desynchronization was modeled as an alpha rhythm decrease to 25% of the initial prestimulus alpha rhythm amplitude lasting for the entire time window of the EP (Figs. 2A–C).
Impact of pre-stimulus alpha amplitude variation on the model data
Oscillatory model According to the oscillatory theory, the EP (or at least part of the EP) is directly generated by the alpha rhythm via phase resetting. Based on the previously determined ratio of partial phase reset, the here modeled amplitude of the resulting EP is 75% of the pre-stimulus alpha amplitude. In all groups (high, medium, low pre-stimulus alpha rhythm), 25% of the pre-stimulus alpha rhythm is not phase reset, representing the non-phase-locked fraction of the alpha rhythm (Figs. 2I–K). Subsequently, the effect of pre-stimulus alpha-variation on the following parameters was analyzed: (1) average EP amplitude, (2) relative and absolute alpha-band event-related spectral perturbation (ERSP) and (3) alpha-band phase-locking index, PLI (Tallon-
Starting from the initial data subset as described above, we now systematically varied the amplitudes of the simulated alpha rhythm in both models which additionally resulted in a “low” and “high” alpha-amplitude group (Fig. 2). The aim was to test the hypothesis that variation of the amplitude of the ongoing rhythm leads to divergent model predictions for both theories. The “medium” group (see Figs. 2B, J) comprises the initial data subsets, as depicted in Fig. 1, with alpha amplitude of 10 μV, while the “low” and “high” alpha amplitudes amount to 5 μV and 20 μV, respectively.
Fig. 2. Predictions of the two models on the impact of pre-stimulus alpha amplitude variation on EP parameters. Single trials of the evoked model are depicted for low (A), medium (B) and high (C) alpha amplitude, superimposed on each other with the average EP in bold lines (blue, black, red for varying alpha amplitudes). (D) No effect of alpha variation on the EP amplitude. (E) Relative alpha-band event-related spectral perturbation (ERSP). (F) Absolute alpha-band ERSP. (G) Alpha-band phase-locking index (PLI). (I–O) Corresponding analysis for the oscillatory model.
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Baudry et al., 1996; Makeig et al., 2002); for detailed description of these indices please refer to the following, see Experimental data section. Due to the intrinsic differences of the two models in the relation between ongoing and evoked activity, differential predictions were expected. In a next step, these differential predictions serve as reference for the analysis of experimental data and for the determination of underlying EP mechanisms. Experimental data In order to compare the model predictions with real data, we chose a visual stimulation protocol with two conditions: eyes open and eyes closed, allowing for statements on the validity of the two theories of EP generation under different conditions. Subjects Seventeen healthy subjects (8 female/9 male; mean age 28.1 ± 2.7 years) participated in the study. Subjects had given written informed consent prior to the investigation. All experiments were performed in compliance with the relevant laws and institutional guidelines and were approved by the ethics committee of the Charité, University Medicine Berlin. Data from subjects reaching a vigilance level below sleep state 1 (Rechtschaffen and Kales, 1968) during the experiment were not used in the analysis which resulted in rejection of 2 subjects. Stimulation protocol The experiment consisted of a passive viewing task with two conditions, eyes open and eyes closed. As stimulus during eyes open condition, a circular checker-board with central fixation cross was shown, for the eyes closed condition a white uniform flash was presented. Duration of stimulation was 1.1 s, with an inter-stimulus interval (ISI) ranging from 9.25 to 11.5 s with uniform distribution showing a flat gray screen. Subjects were instructed to attentively perceive stimuli keeping their eyes fixated on the center of the screen throughout the eyes open condition of the experiment. They were informed of the end of each block acoustically. The experiment was programmed using the Cogent toolbox (developed by the Cogent 2000 team at Functional Imaging Laboratory (FIL) and Institute of Cognitive Neuroscience (ICN) and Cogent Graphics by John Romaya at the Laboratory of Neurobiology (LON) at the Wellcome Department of Imaging Neuroscience). Each condition consisted of four blocks containing 30 stimuli each summing to 120 trials per condition. Eight blocks of resting periods of a duration of 75 s, half of the blocks with eyes closed and half with eyes open were randomly interspersed with stimulation periods. In order to achieve comparable conditions when transferring this experiment into an MR environment, subjects lay on an examination couch and received the stimuli via a mirror reflecting the picture located on a plexi-glass screen. Stimuli were presented by a modified LCD projector (NEC Multisync MT 800, Japan). EEG preprocessing A 32-electrode cap (Easy-Cap; FMS, Herrsching-Breitbrunn, Germany) was used for EEG recordings following the international 10/20 system. All electrodes were referenced against FCz position. Electrooculogram of vertical (EOGv) and horizontal (EOGh) eye movements was recorded by one electrode placed below the right eye and two electrodes at the outer canthi. Impedances were maintained below 5 kΩ by applying an abrasive electrode paste
(ABRALYT 2000; FMS, Herrsching-Breitbrunn, Germany). A high dynamic range EEG amplifier with a sampling rate of 5 kHz (BrainAmp; BrainProducts GmbH, Munich, Germany) and Vision Recorder Software v1.02 was used to record EEG data. Data were band pass-filtered between 0.5 and 70 Hz. A notch filter of 50 Hz was applied. For further processing, data were down-sampled to 200 Hz. Offline analysis was performed with EEGLab 4.515 (Delorme and Makeig, 2004) and Matlab v7.0 (The Mathworks Inc., Natick, USA). Artifact rejection was performed in two steps. First, the following EEGLAB artifact rejection methods were used: an exclusion threshold of 100 μV for EEG/EOG channel data and improbability of data as estimated by joint-probability and kurtosis-of-activity analysis using EEGLAB preset defaults. Second, visual inspection was performed for double-checking of proposed artifact removal and removal of then-remaining artifacts. For the eyes closed condition, in average 108 trials remained after artifact rejection. Due to occasional eye blinking, on average 96 trials remained for eyes open condition. For analysis of EPs, trials were segmented in − 1 s to 9 s epochs time locked to the stimulus. Subsequently a baseline correction of data using the time window from − 1 s to 0 s was applied. ICA decomposition To further increase signal-to-noise ratio, an independent component analysis (ICA) was performed using Infomax ICA. This was done separately for each subject and each condition using the artifact corrected, segmented data of all EEG/EOG channels, excluding all ECG and EMG channels. The resulting 10 out of 24 components explaining most of the variance were classified into main clusters. Components of artifactual or ocular origin were identified using their topographical, spectral and time–domain properties and only components of non-artifactual clusters were back-projected to EEG channels for further analysis as proposed by Jung et al. (2000a). The resulting corrected signal at the occipital electrode O2 was used for subsequent analysis to allow for comparison with conventional EP data. EEG postprocessing The grand average EP and the time–frequency plot were calculated for both eyes open and eyes closed condition separately. The grand average time–frequency plot includes the frequency range from 0.5 Hz to 40 Hz. Impact of pre-stimulus alpha amplitude variation on experimental data Analogous to the simulated data, the impact of ongoing alpha amplitude on the experimental data was examined for comparison with model predictions. Analysis of EP parameters was done separately for “eyes open” and “eyes closed” condition. By sorting trials according to their pre-stimulus alpha amplitude, each trial was assigned to either a “high-alpha” or “low-alpha” group. Trials were sorted individually for each subject according to absolute alpha amplitude in a pre-stimulus temporal window of − 800 ms to −100 ms using a wavelet-based analysis (3-cycle Hanningwindowed sinusoidal wavelets, frequency stepping of 1 Hz). The approach of a pre-stimulus window sorting was employed to avoid the assumed confounding issue of sorting ongoing activity in a post-stimulus time window (Yeung et al., 2004; Makinen et al., 2005). The weighted sorting window of the 3-cycle wavelet centered at − 100 ms ranged up to 50 ms into the post-stimulus epoch, however the earliest pronounced evoked components in the
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present study were found to arise at a latency of N 70 ms after stimulus onset. Analogous to the analysis of the modeled data, results of both experimental conditions were compared for the following parameters in an analysis window from 0 to 400 ms after stimulation: (1) The grand average of the broadband EP (0.5–70 Hz) (2) Alpha-band event-related spectral perturbation (alpha-band ERSP) based on the 3-cycle wavelet analysis (as mentioned above). The amplitudes in the frequency range of 8–12 Hz (in steps of 1 Hz) were extracted and the dominant frequency within this range was determined for each subject separately to be used for further analysis. Please note that the eventrelated phase-locked component, i.e. the EP waveform, was not removed beforehand, thus yielding an estimate of total alpha-band amplitude. The alpha-band ERSP is shown both as “relative” ERSP, i.e. as a relative change with respect to its baseline (the pre-stimulus time window) as well as “absolute” ERSP providing absolute alpha-band amplitude changes. (3) The alpha-band phase locking index (alpha-band PLI) according to Makeig et al. (2002) in order to estimate the degree of phase locking of (total) post-stimulus alpha activity. Phase information (φ, see Eq. (1)) was extracted from the wavelet analysis in the frequency range of 8–12 Hz for each time point (t). N 1 X iðuk ðtÞÞ PLIðt Þ ¼ e N k¼1
ð1Þ
To account for individual differences in alpha frequency, the pre-stimulus window was examined for the dominant alpha frequency, which was then analyzed further. Increasing values signify increasing phase coherence across trials (with N = number of trials), ranging from zero (completely random distribution of phases) to one (perfect alignment of phase across all trials). For statistical testing of differences of the “high” and “low” alpha-amplitude groups, the pair-wise signed-ranking test was used as a non-parametric Student’s t-test analogue to estimate significances of intra-individual alpha-amplitude dependent differences of the above mentioned parameters (EP amplitude, relative/ absolute alpha-band ERSP and alpha-band PLI). A time window of 0 to 400 ms after stimulus onset was analyzed. Data were Bonferroni corrected for multiple comparisons. Results In the following, we first show the results of our theoretical modeling approach, thereby demonstrating the usefulness of the proposed pre-stimulus alpha-variation for the differentiation of prevailing theories of EP generation. Secondly, we compare the outcome of the models with experimental data in order to identify the model that is more compatible with our experimental data.
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EPs equally well (see Fig. 1). While in the evoked model the EP is generated independently from and additive to the simultaneous alpha rhythm, in the oscillatory model the EP is generated by partially phase resetting the ongoing alpha rhythm. Thus, the phase-locked part of the signal, i.e. the EP can be explained by both theories. The non-phase-locked part of the post-stimulus signal is desynchronized alpha rhythm in the evoked model and non-phase-reset alpha rhythm in the oscillatory model. Thus, by theoretical modeling, we demonstrate a substantial intersection of the two EP theories despite their opposing assumptions on the relationship between ongoing and evoked activity. Impact of variation of pre-stimulus alpha amplitude on model data For the evoked model, increasing pre-stimulus alpha amplitude (Figs. 2A–C) was paralleled by a constant EP amplitude (Fig. 2D), a transition from a relative increase of total alpha-band amplitude to a relative decrease (see relative alpha-band ERSP in Fig. 2E) and a decrease in alpha-band phase locking (Fig. 2G). In contrast, in the oscillatory model, increasing pre-stimulus alpha amplitude (Figs. 2I–K) was associated with enhanced EP amplitudes (Fig. 2L). The relative alpha-band amplitude and the degree of phase locking remained constant (see relative alpha-band ERSP and alpha-band PLI in Figs. 2M, O). Despite the subset of data both models share, they deliver distinct predictions on the effect of prestimulus alpha-variation on simulated EPs and simultaneously occurring alpha background activity. Experimental data ICA decomposition Back-projection of non-ocular and non-artifactual sources comprising the “visual” clusters among the first 10 independent components yielded data with a clear occipital topography as in Jung et al. (2000b). Excluded sources were either event related but of frontal origin comprising eye movements or their topography or raw time courses identified them as being of ocular origin. In all subjects, each of the first 10 components could be assigned by visual inspection to one of the spatial patterns shown in Fig. 3. Grand averages Grand average EPs and grand average time–frequency plots (range from 0.5 to 40 Hz) are provided in Fig. 4 for both eyes open and eyes closed condition. Relative alpha-band amplitudes (Figs. 4A, B) change similarly in both conditions (for absolute alpha-band amplitudes, please refer to Figs. 5C, H), exhibiting an event-related decrease in amplitude (alpha rhythm desynchronization, alphaERD). Notably, these experimental data reflect the “ambiguous” situation as simulated in the “medium” alpha group (depicted in Fig. 1), i.e. no clear post-stimulus alpha-band amplitude increase is observed which implies the possible validity of both theories of EP generation.
Models In the situation of an ongoing rhythm exhibiting, an amplitude equal or larger (in the case of the simulated “medium” group 10 μV) than the amplitude of the arising EP (7.5 μV for the “medium” alpha group) both models can explain the generation of
Fig. 3. Visualization of typical ICA components explaining most of the variance. (A) Visual component clusters. (B) Non-visual, ocular and artifactual sources.
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significant differences in the absolute alpha-band amplitude (or alpha ERSP) between the “low” and “high” alpha groups. For eyes open condition, the resulting time window lasts from 0 to 158 ms after stimulation (Fig. 5C). For eyes closed condition, a significant difference between absolute alpha-band amplitude remains throughout the entire analysis time window from 0 to 400 ms, indicating a slower sorting “decay” of ongoing alpha amplitudes (Fig. 5H). In general, absolute alpha amplitudes during eyes closed condition are higher than during eyes open condition.
Fig. 4. Time–frequency plots and grand average EPs of experimental data. (A, B) Time–frequency plots (0.5–40 Hz) of both eyes open and eyes closed visual stimulation. Vertical horizontal lines indicate begin and end of visual stimulation. Event-related amplitude increases in frequencies b8 Hz suggest an evoked process. The alpha-band amplitude in both conditions does not show a signal increase, but a dominating desynchronization, i.e. an ambiguous situation similar to the simulated situation in Fig. 1. Possible interpretations are as follows: Either an evoked process is “masked” by concurrent alpha desynchronization or the alpha-band component of the EP is created by phase reset of the ongoing alpha rhythm. (C, D) Corresponding grand average EPs of both conditions.
Impact of variation of pre-stimulus alpha amplitude on experimental data and comparison to models Duration of alpha sorting as indicated by absolute alpha-band ERSP. In contrast to modeled data, the sorting effect of prestimulus alpha amplitudes in experimental data decays in the poststimulus time window. Thus, it is necessary to identify the relevant time window in the experimental data for comparison with our model predictions. The duration of efficient sorting is indicated by
Results of EP analysis. For the eyes open condition, early EP components were constant, i.e. unaffected by alpha sorting, despite the enduring effect of alpha sorting within the EP time window (Fig. 5A, see also comments above). A late EP component (cf. Fig. 5A), in turn, showed a significant (Bonferroni corrected, p b 0.05) enhancement with a maximum of 2.5 μV difference at a time window of 220 to 310 ms. Notably, it is not paralleled by a concurrent increase in absolute alpha amplitude (see absolute alpha-band ERSP analysis, Fig. 5C). This indicates an already decayed effect of pre-stimulus alpha-amplitude sorting. Shape and time course of the enhancement indicate a frequency component slower than alpha frequency. One additional significantly different time point was found at 175 ms for a negative evoked component whose negativity decreased for high alpha. For the eyes closed condition, alpha sorting did not result in any significant differences in the EP over the entire analysis window. A positive trend for the high alpha group was observable from 200 ms on (Fig. 5F). In order to exclude alpha-band contribution to this positive trend, spectral alpha-band components of the EP were removed by filtering, not eliminating the trend (results not shown). Thus, the observed positive trend does not appear to be caused directly by phase-reset alpha activity. Results of relative alpha-band ERSP analysis. In the eyes open condition, a switch from an increase in the relative alpha-band
Fig. 5. Effects of pre-stimulus alpha amplitude variation on eyes open (A–D) and eyes closed (F–I) visual stimulation data. The time range used for alphaamplitude sorting is indicated by the light blue cones on the left side of panels A and F. In dark gray, Bonferroni corrected significant effects (p b 0.05) are shown. Filled rectangles mean testing groups against each other, dark gray stripes (B, G) mean testing against the respective group pre-stimulus baseline. The dotted line indicates the end of the analysis window; the solid line indicates begin and end of visual stimulation. (A, F) EP Amplitudes. (B, G) Relative alpha-band eventrelated spectral perturbations (relative alpha-band ERSP). (C, H) Absolute alpha-band ERSP (sorting effect). (D, I) Alpha-band phase-locking index (PLI).
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amplitude to a relative decrease was found when comparing “low” and “high” alpha groups (Fig. 5B). The increase for the “low” alpha group paralleled the time course of the increase in alphaband phase locking (see results of alpha-band phase locking below). For the eyes closed condition, the relative alpha-band ERSP also showed a significant increase after stimulation for the “low” alpha group; however, this was smaller in amplitude and temporal extent (Fig. 5G). Alpha-band PLI analysis. There were intervals of significantly reduced phase locking of the high-alpha group in both eyes open and eyes closed conditions (for eyes open ranging from 60 to 158 ms, Fig. 5E; for eyes closed ranging from 110 to 158 ms, Fig. 5J) being paralleled by constant EP amplitudes. Comparison of experimental results with model predictions. For the eyes open condition, the observed effect of alpha-variation during the relevant time window is well predicted by the evoked model. Unaffected early EP components together with a decreased phase locking for increasing alpha and also the relative alpha-band amplitude, showing a transition from an increase to a decrease (from the “low” to the “high” alpha group), are predictions of the evoked model. The eyes closed early EP components exhibit a corresponding pattern and are equally well predicted by the evoked model. The observed significant enhancement of the eyes open late positive EP component in the “high” alpha group is not predicted by any model. Both models basically assume a difference in absolute post-stimulus alpha-band amplitude which is not present in the late EP component of the experimental data. Neither model states the possibility of an indirect effect of pre-stimulus alphasorting without a simultaneous alpha-amplitude difference in the relevant time window of the EP component of interest. Discussion We have demonstrated that variation of the ongoing alpha amplitude allows differentiation of the two theories of EP generation, even when a subset of the data is ambiguous with respect to the two theories. Our experimental data show significant differences in EP parameters for varying pre-stimulus alpha amplitudes with early EP components being most consistent with the evoked theory, while later components co-vary with prestimulus but are not generated by post-stimulus alpha rhythm. Models Both oscillatory and evoked theory are consistent with phaselocked and non-phase-locked components. The oscillatory model only requires the assumption of a partial phase reset in order to explain the same data as the evoked theory (Figs. 1 and 2). Thus, given sufficient alpha activity, the absence of a single-trial post-stimulus alpha-band amplitude increase (model data in Figs. 3E, M, experimental data in Figs. 4A, B) does not exclude an evoked mechanism as previously assumed by some authors (Sayers et al., 1974; Fuentemilla et al., 2006) but presents an ambiguous situation. Accordingly, the origin of EPs can only be unveiled by assessing the influence of amplitude variations of ongoing alpha activity on the EP. Especially EPs recorded during visual stimulation are often paralleled by a spontaneous alpha rhythm having larger amplitudes than the EP itself (also reflected in our data, see Figs. 5C, H).
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We compared the predictions of the evoked and the oscillatory model concerning the impact of pre-stimulus alpha variation on EP amplitude with our experimental data. Within eyes open and eyes closed condition, higher pre-stimulus alpha activity was accompanied by constant early EP amplitudes and a concurrent decrease in alphaband phase locking. Also the total alpha-band amplitude changed from a relative decrease to a relative increase for low pre-stimulus alpha activity. All these phenomena are predicted by the evoked model and correspond to the assumption of dual and separate generators of EP and alpha rhythm (Fig. 2). The change in the degree of phase locking is explained by the superposition of alpha activity on the evoked potential, i.e. “masking” of the single-trial EP by a strong alpha rhythm. With increasing alpha activity, the non-phase-locked part of the single-trial signal (i.e. post-stimulus alpha rhythm) increases, which is reflected by less phase locking across trials (see Fig. 5E, J, respectively). For periods of low alpha activity in turn, the additional evoked EP causes an increase in total alpha-band amplitude instead of a decrease of alpha-band amplitude. Thus, for the data presented, the combined approach of alpha-amplitude variation of experimental data and comparison with model predictions succeeded in differentiation of early EP mechanisms. Concerning other modeling approaches, Makinen et al. (2005) suggests a single parameter for differentiation: standard deviation across trials (SDT). Straight-forward predictions of this approach were that EPs generated by an evoked model show no decrease in SDT, while EPs yielded by an oscillatory model exhibit an SDT decrease. Experimental auditory data of this study showed no decrease in SDT, which was regarded as evidence against an oscillatory mechanism of EP generation. In a comment to Makinen et al., Klimesch et al. (2006) in turn argued that integrating eventrelated decreases in alpha amplitude, i.e. alpha desynchronization, questions the validity of these claims. Concerning the experimental and model data of our study, we observed the following: (a) our experimental data do show an SDT decrease (see Supplementary Fig. 3), which is mainly caused by event-related decrease of the alpha rhythm amplitude (for an early report of decrease of visual data SDT see Pfurtscheller et al., 1989), and (b) both our models, in concordance with our experimental data, show an SDT decrease as well. Thus, we discarded the SDT measure as a sufficient parameter for model differentiation and adopted the more complex approach based on combined EP parameters. For the models in our study, we used a noise-free approach, which has the advantage of visualizing the general principles of the two theories even in single trials that otherwise would easily disappear under noisy conditions. We excluded, however, the possibility of different model behavior under noisy conditions by adding noise to our simulated data. As can be seen in Supplementary Fig. 1, both models preserve their differential predictions despite single trials being obscured by intense noise. In order to test whether the chosen value of alpha rhythm desynchronization (or of residual, non-phase-locked alpha amplitude) influences the model outcome, we tested two additional values, i.e. 50% (weak ERD) and 12.5% residual alpha amplitude (strong ERD) for the evoked model, which corresponds to 50% and 12.5% of non-phase-reset alpha rhythm for the oscillatory model. Again, differential model predictions were well preserved (for results see Supplementary Fig. 2). Experimental data The enhancement of a late evoked component due to prestimulus alpha sorting was found to be incompatible with the
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oscillatory model. For an enhancement taking place, the oscillatory model requires a concomitant increase in post-stimulus alpha-band amplitude (see models, Fig. 2F). This was not the case in the data, which excludes a direct causative role of alpha activity. Since the observed enhancement is neither predicted by the pure evoked model, the results can only be reconciled with a model integrating an additional indirect interaction between pre-stimulus alpha activity and the evoked potential. As the focus of this study was to determine whether and how well the two dominant theories of EP generation in their strict interpretation would predict empirical findings, we did not change our models post-hoc-wise. In principle, the observed empirical interaction between alpha and EP amplitude could be integrated, for example by an additional arousal or vigilance term which is related to the amplitude of spontaneous alpha activity. Another way of accounting for the empirical findings in future modelling may be based on the recently proposed hypothesis of Nikulin et al. (2007) on alpha-dependent baseline shifts in the EEG. This hypothesis accounts for the empirical phenomenon that each level of alpha activity appears to be associated with a different EEG baseline level. This implies, that during alpha amplitude fluctuations also the mean baseline level is modulated, which could explain modulations of EP components as a function of the amount of pre-stimulus alpha activity being desynchronized. The view of the evoked response as being modulated – but not generated – by the alpha rhythm, is also supported by Jasiukaitis and Hakerem (1988), who, using pre-stimulus alpha-amplitude sorted auditory oddball data, also observed an exclusive enhancement of a late positive evoked component, the P300. They stated that it is not the case “that the P300 is a phase constrained alpha wave, but that the state of which alpha is a sign, predicts the larger P300”, giving support to a co-variation but not causation hypothesis of alpha activity. Similar positive correlations between ongoing alpha activity and the P300 were reported by Polich (1997) and by Basar et al. (1984). Consistently with the results of Jasiukaitis and Hakerem (1988), Barry et al. (2000) reported a direct relationship between pre-stimulus alpha amplitude and following EP amplitudes for auditory oddball data. Thut et al. (2003) reported an exclusive enhancement of a late positive component using a visual stimulation setup with repetitive TMS (rTMS). The enhancement was observed after application of rTMS. No early component was modulated, despite a concurrent decrease in the amount of event-related alpha desynchronization after rTMS application. The authors concluded that ongoing and evoked activity seem to be rather separate phenomena. In summary, while alpha-dependent modulations on late EP components are frequently observed, no direct involvement of concurrent alpha activity has been reported. With respect to findings in early potentials, Makinen et al. (2005), reported early auditory potentials (N1m, ~ 100 ms) to be unaffected by increasing pre-stimulus alpha activity. Complementary to this negative evidence supporting the evoked model is our result of a significant decrease in phase locking and at the same time unaffected EP amplitudes for significantly higher (pre- and post-stimulus) alpha activity, which provides additional positive evidence for the evoked model and also argues for sufficient signal-to-noise ratio of the data. Also intracranial studies in monkeys support the evoked nature of early EPs (e.g. Schroeder, 1991; Rols et al., 2001). In these studies, early scalp EPs were linked to increased multi-unit activity in low-level sensory areas. Additionally, early EPs are also
reported to be relatively unaffected by attentional fluctuations (Mehta et al., 2000), which fits our observation of early EPs being unaffected by alpha rhythm amplitude. Assuming that alpha rhythm is an indicator of attentional and/or vigilance fluctuation one would expect attention-dependent EP components to be related to these fluctuations. An inverse relation between pre-stimulus alpha amplitude and visual EP amplitude (N1–P2) has been proposed by Rahn and Basar (1993). For stages of low pre-stimulus alpha activity recorded at the vertex an enhancement of the N1–P2 peak-to-peak amplitude (100–250 ms) was demonstrated in fronto-central but not in occipital electrodes. These results are not directly comparable with our data, since the channels exhibiting this effect, the reference used and the topographical definition of the alpha rhythm at the vertex differ from our study. On the other hand, Basar et al. (1998) reported an inverse correlation between a factor termed “EP enhancement” – which was defined as the ratio of the EP amplitude to pre-stimulus alpha amplitude – and pre-stimulus alpha amplitude. This finding was neither accompanied by simulations nor interpreted in the light of the two opposing EP theories. Thus, we analyzed our models analogously for this factor, which yielded a comparably clear inverse relation between “EP enhancement” and pre-stimulus alpha amplitude only for the evoked model, while the EP enhancement index for the oscillatory model remains constant for varying alpha amplitudes (see Supplementary Fig. 4). The results for the evoked model also show the previously reported inverse but more exactly “curvilinear” relationship between VEP enhancement and the pre-stimulus alpha amplitude as found by Brandt and Jansen (1991) when replicating the analysis of Basar et al. (1998). Also Barry et al. (2000) showed convincingly for auditory oddball data that a direct relationship between prestimulus alpha activity and EP component amplitude (as proposed by Brandt and Jansen) is compatible with the existence of an inverse relationship between pre-stimulus alpha and the mentioned “EP enhancement factor” (as proposed by the group of Basar). This implies, that the reported inverse correlation allows manifold interpretations on the relationship between EP amplitude and ongoing alpha amplitude. Actually, any relationship which is weaker than directly proportional, even a clear independence of EP and ongoing alpha amplitude yields such inverse correlation (see Supplementary Fig. 4). In the specific case of the pure evoked model the curve behaves even “curvilinear”, as demonstrated in Supplementary Fig. 4A. The results of Basar’s and Brandt’s study on the “EP enhancement factor” do not contradict but rather support the evoked theory and its implications. However, at least a strict phase reset can be excluded, since such a behavior would prevent the consistently reported inverse relationship between prestimulus and post-stimulus alpha enhancement. In case of a pure phase reset the relation between pre-stimulus alpha activity and the post-stimulus enhancement remains constant, as can be seen for the phase-reset model in Supplementary Fig. 4B. In contrast to our results on early EP components, Brandt and Jansen (1991) reported a positive enhancement of eyes closed early VEP components for higher pre-stimulus alpha activity. Again, for comparison we applied the same kind of data analysis as Brandt and Jansen to our experimental “eyes closed” data yielding similar results (data not shown). In contrast to our actual approach, Brandt and Jansen had analyzed sets of averages of 40 trials each and maximum peak-to-peak amplitudes were individually determined for each such average. This analysis is susceptible to systematic
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confounding effects of residual alpha activity being superimposed on the evoked potential, because estimated peak-to-peak amplitudes with variable latencies are affected especially by strong residual ongoing alpha activity. In the study presented here, the analysis approach was to average a large number of trials (N ~ 750) pooled over subjects and to analyze the grand average EPs with fixed latencies. Thus, residual effects of ongoing alpha activity were canceled out more effectively. For this analysis the mentioned early EP-enhancing effect disappeared. It has been demonstrated by Makeig et al. (2002) that for increased alpha activity measured in a post-stimulus time window there is an increase of EP amplitudes with accentuated alphaactivity and increased phase locking. By using a pre-stimulus time window, we observed the contrary effect, i.e. strong alpha background activity was accompanied by weaker phase locking and constant early EP amplitudes during the critical post-stimulus time window. On the other hand, sorting our data according to the post-stimulus time window yielded an alpha-amplitude dependent increase of EP amplitude (data not shown). Thus, differences in results between the two studies can be explained by differences in the definition of the sorting window. Since a post-stimulus sorting window tends to be biased by stimulus-related activity (Makinen et al., 2005; Yeung et al., 2004), a pre-stimulus time window appeared more appropriate to us. In summary, while some previous studies on the relation between ongoing alpha rhythm and early evoked potentials contradict our study with respect to the interpretations regarding the prevailing mechanism of EP generation, the experimental results of these studies can be reconciled with our data. Our combined simulated and empirical data approach on studying the relationship between background alpha rhythm and the VEP favors the evoked model for early components and at the same time demonstrates an interaction between pre-stimulus alpha amplitude and VEP features occurring after 175 ms, arguing for a modulatory rather than direct causative role of alpha activity. In conclusion, we propose to study empirical data in the context of a modeling-based approach to further shed light on mechanisms of EP generation. Acknowledgments The authors thank Steven Lemm, Peter Brunecker, Robert Schmidt and Matthias Reinacher for helpful comments. This work was supported by the German Federal Ministry for Education and Research BMBF (Berlin NeuroImaging Center and Bernstein Center for Computational Neuroscience Berlin) and the German Research Foundation DFG (Berlin School of Mind and Brain).
Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.neuroimage.2007.09.016.
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Influence of ongoing alpha rhythm on the visual evoked potential Robert Becker,⁎ Petra Ritter,1 and Arno Villringer1 Berlin NeuroImaging Center, Department of Neurology, Charité-Platz 1, 10117 Berlin, Germany Received 23 May 2007; revised 21 August 2007; accepted 3 September 2007 Available online 19 September 2007
The relationship between ongoing occipital alpha rhythm (8–12 Hz) and the generation of visual evoked potentials (VEPs) has been discussed controversially. While the “evoked theory” sees no interaction between VEP generation and the alpha rhythm, the “oscillatory theory” (also known as “phase-reset theory”) postulates VEP generation to be based on alpha rhythm phase resetting. Previous experimental results are contradictory, rendering a straightforward interpretation difficult. Our approach was to theoretically model the implications of the evoked and oscillatory theory also incorporating stimulus-induced alpha-rhythm desynchronization. As a result, the model based on the oscillatory theory predicts alpha-band dependent VEP amplitudes but constant phase locking. The model based on the evoked theory predicts unaffected VEP amplitudes but alpha-band dependent phase locking. Subsequently, we analyzed experimental data in which VEPs were assessed in an “eyes open” and “eyes closed” condition in 17 subjects. For early components of the VEP, findings are in agreement with the evoked theory, i.e. VEP amplitudes remain unaffected and phase locking decreases during periods of high alpha activity. Late VEP component amplitudes (N175 ms), however, are dependent on pre-stimulus alpha amplitudes. This interaction is contradictory to the oscillatory theory since this VEP amplitude difference is not paralleled by a corresponding difference in alphaband amplitude in the affected time window. In summary, by using a model-based approach we identified early VEPs to be compatible with the evoked theory, while results of late VEPs support a modulatory but not causative role – the latter implied by the oscillatory theory – of alpha activity for EP generation. © 2007 Elsevier Inc. All rights reserved. Keywords: EEG; Alpha rhythm; Models; Additive; Phase reset; Interaction
Introduction The human electroencephalogram (EEG) is dominated by spontaneous rhythms, one of the most prominent being the alpha rhythm (8–12 Hz). Evoked potentials (EPs) being of small ⁎ Corresponding author. E-mail address: [email protected] (R. Becker). 1 These authors contributed equally. Available online on ScienceDirect (www.sciencedirect.com). 1053-8119/$ - see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2007.09.016
amplitudes are often obscured by such rhythms and can be revealed by averaging several epochs. This effect is reconcilable with linear summation of ongoing and evoked activity (Arieli et al., 1996), i.e. by an “evoked theory” or “additive theory” of EP generation. In this theory, ongoing activity, e.g. the alpha rhythm has no functional significance for EP generation. Another theory of EP generation is the “oscillatory theory” also known as “phasereset theory”, assuming the EP to be generated by alpha rhythm phase resetting (Sayers et al., 1974; Makeig et al., 2002). Although both theories apparently differ in their assumed relationship between ongoing and evoked activity, there is controversy on how to validate the theories (Makeig et al., 2002; Makinen et al., 2005; Klimesch et al., 2006; Fuentemilla et al., 2006; Mazaheri and Jensen, 2006; Hanslmayr et al., 2006). Shah et al. (2004) suggested two criteria for differentiation: Sufficient ongoing alpha amplitude is required for EP generation in the oscillatory theory, while an event-related increase in signal power supports the evoked model. However, the conclusion, that in case of no increase or of a decrease in average post-stimulus alpha-band power the oscillatory model – i.e. an alpha rhythm phase reset – holds, may be premature. Such a situation can be caused either by veridical alpha phase resetting but also by a “masking” of an evoked process by a dominant but desynchronizing alpha rhythm (Hanslmayr et al., 2006). Without further positive evidence of a functional significance of the alpha rhythm for EP generation, the oscillatory theory can neither be discarded nor be proven. While several studies on the impact of the ongoing alpha amplitude on the evoked response have been reported (Makeig et al., 2002; Basar, 1980; Jasiukaitis and Hakerem, 1988; Rahn and Basar, 1993; Makinen et al., 2005), findings were contradictory or not related to model predictions and so far have not led to a general consensus on the mechanisms of EP generation. In this study, we combine theoretical modeling and experimental data analysis to examine the functional role of the ongoing alpha rhythm for VEP generation. In theoretical modeling, we evaluate the relationship between ongoing activity and evoked activity for both theories. Modelspecific predictions are derived for varying pre-stimulus alpha amplitudes. Initially, both models contain a common subset of data, where an EP is accompanied by a dominating and desynchronizing alpha rhythm. We aim to demonstrate that by variation of pre-
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stimulus alpha amplitude both models are clearly differentiated with respect to their predictions. Subsequently, in experimental data the influence of pre-stimulus alpha amplitude on EP generation is investigated. Based on the comparison of these empirical findings and the simulated EP parameters, the influence of alpha rhythm on EP generation is defined in the light of the two opposing models. Materials and methods Models In order to model the evoked and the oscillatory theory of EP generation, we simulated EEG single trial activity composed of two types of subunit signals as detailed below and shown in Fig. 1. The evoked model assumes “dual generators” for alpha rhythm and EP generation whereas the oscillatory model assumes “shared generators” producing both the alpha rhythm and the evoked response (Mazaheri and Jensen, 2006). In a first step, a subset of data is generated for each model which assumes an EP being generated during spontaneous alpha activity of slightly larger amplitude than the EP itself. This demarcates the starting point of our actual approach of alpha-variation to differentiate the two theories of EP generation.
Evoked model The EP was created by a 10-Hz sine wave lasting for 100 ms with constant amplitude of 7.5 μV (Fig. 1B). The EP is independent from the simultaneously occurring 10 Hz simulated alpha rhythm which amounted to 10 μV (further referred to as “medium” alpha group) and exhibited a random phase in each trial (superimposed single trials are shown in Fig. 1A). A simple eventrelated desynchronization of the alpha rhythm (alpha-ERD; Pfurtscheller and Aranibar, 1977) was integrated into the model. Alpha ERD strongly depends on the type of visual stimulation and can vary from study to study (typical alpha ERD can vary from 20% to 90%, e.g. see Klimesch et al., 2004, but see also Pfurtscheller, 1989). Here, alpha ERD was modeled by decreasing the simulated alpha rhythm amplitude after EP onset at 0 ms to a level of 25% (i.e. an alpha ERD of 75%) of its initial amplitude (Fig. 1C), lasting for the entire EP time window (100 ms). Fig. 1D shows the sum of the simulated EP and concurrent alpha activity. Oscillatory model In contrast to the evoked model, the EP was simulated by resetting and synchronizing the random phase of the ongoing alpha rhythm from 0 to 100 ms post-stimulus time across trials (Figs. 1E–H). The amplitude of the pre-stimulus alpha rhythm amounted to 10 μV (further referred to as “medium” alpha). Since in expe-
Fig. 1. Schematic view of the two competing models of EP generation. Thin colored lines indicate single-trial signals being superimposed on each other; bold black colored lines indicate the resulting average. (A–D) Constituents of the evoked model. Independent from simultaneously occurring spontaneous alpha rhythm (A), the evoked response is generated (B). An event-related alpha rhythm desynchronization (alpha-ERD; Pfurtscheller and Aranibar, 1977; Klimesch et al., 2006) was included into the models (C), resulting in the compound single trials as depicted in panel D. (E–H) Scheme of the oscillatory model considering the alpha rhythm (E) as a generator of the EP via phase resetting (F). In the oscillatory model, the phenomenon of alpha-ERD is accounted for by modeling a partial phase reset. The partial phase reset results in a minor fraction of non-phase-locked alpha activity after stimulation (G) and in a major fraction of phase-locked alpha, i.e. the EP. The resulting EP amplitude is slightly smaller than the amplitude of previous spontaneous alpha activity (F). In the situation depicted here, a differentiation between the evoked and oscillatory (i.e. alpha-rhythm phase resetting) theory is problematic (see Hanslymayr et al., 2006, Klimesch et al., 2006). Also Yeung et al. (2004) raised concerns about the general suitability of previously proposed methods for identification of the mechanism of EP generation, showing that surrogate data modeled by an evoked mechanism yielded same results for conventional analyses as data believed to be generated by phase resetting of the alpha rhythm.
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rimental data complete alpha-band phase reset has not been observed, a major fraction of the simulated alpha rhythm was phase reset (Fig. 1F) while a minor fraction preserved its phase (Fig. 1G). The ratio between the reset and non-phase-reset simulated alpha rhythm was determined such that the oscillatory model and the evoked model generated the same data. The such determined ratio of the ‘partial phase reset’ is used in all subsequent simulations. The resulting sum of phase-reset and non-phase-reset-simulated alpha rhythm is shown in Fig. 1H.
Evoked model Because of the definition of the EP as being independent from the simultaneous rhythm in the evoked theory, the same additive EP was used for the “low” and the “high” alpha group as in the “medium” alpha group (Figs. 2A–C). Correspondingly in both the “high” and “low” alpha group, alpha rhythm desynchronization was modeled as an alpha rhythm decrease to 25% of the initial prestimulus alpha rhythm amplitude lasting for the entire time window of the EP (Figs. 2A–C).
Impact of pre-stimulus alpha amplitude variation on the model data
Oscillatory model According to the oscillatory theory, the EP (or at least part of the EP) is directly generated by the alpha rhythm via phase resetting. Based on the previously determined ratio of partial phase reset, the here modeled amplitude of the resulting EP is 75% of the pre-stimulus alpha amplitude. In all groups (high, medium, low pre-stimulus alpha rhythm), 25% of the pre-stimulus alpha rhythm is not phase reset, representing the non-phase-locked fraction of the alpha rhythm (Figs. 2I–K). Subsequently, the effect of pre-stimulus alpha-variation on the following parameters was analyzed: (1) average EP amplitude, (2) relative and absolute alpha-band event-related spectral perturbation (ERSP) and (3) alpha-band phase-locking index, PLI (Tallon-
Starting from the initial data subset as described above, we now systematically varied the amplitudes of the simulated alpha rhythm in both models which additionally resulted in a “low” and “high” alpha-amplitude group (Fig. 2). The aim was to test the hypothesis that variation of the amplitude of the ongoing rhythm leads to divergent model predictions for both theories. The “medium” group (see Figs. 2B, J) comprises the initial data subsets, as depicted in Fig. 1, with alpha amplitude of 10 μV, while the “low” and “high” alpha amplitudes amount to 5 μV and 20 μV, respectively.
Fig. 2. Predictions of the two models on the impact of pre-stimulus alpha amplitude variation on EP parameters. Single trials of the evoked model are depicted for low (A), medium (B) and high (C) alpha amplitude, superimposed on each other with the average EP in bold lines (blue, black, red for varying alpha amplitudes). (D) No effect of alpha variation on the EP amplitude. (E) Relative alpha-band event-related spectral perturbation (ERSP). (F) Absolute alpha-band ERSP. (G) Alpha-band phase-locking index (PLI). (I–O) Corresponding analysis for the oscillatory model.
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Baudry et al., 1996; Makeig et al., 2002); for detailed description of these indices please refer to the following, see Experimental data section. Due to the intrinsic differences of the two models in the relation between ongoing and evoked activity, differential predictions were expected. In a next step, these differential predictions serve as reference for the analysis of experimental data and for the determination of underlying EP mechanisms. Experimental data In order to compare the model predictions with real data, we chose a visual stimulation protocol with two conditions: eyes open and eyes closed, allowing for statements on the validity of the two theories of EP generation under different conditions. Subjects Seventeen healthy subjects (8 female/9 male; mean age 28.1 ± 2.7 years) participated in the study. Subjects had given written informed consent prior to the investigation. All experiments were performed in compliance with the relevant laws and institutional guidelines and were approved by the ethics committee of the Charité, University Medicine Berlin. Data from subjects reaching a vigilance level below sleep state 1 (Rechtschaffen and Kales, 1968) during the experiment were not used in the analysis which resulted in rejection of 2 subjects. Stimulation protocol The experiment consisted of a passive viewing task with two conditions, eyes open and eyes closed. As stimulus during eyes open condition, a circular checker-board with central fixation cross was shown, for the eyes closed condition a white uniform flash was presented. Duration of stimulation was 1.1 s, with an inter-stimulus interval (ISI) ranging from 9.25 to 11.5 s with uniform distribution showing a flat gray screen. Subjects were instructed to attentively perceive stimuli keeping their eyes fixated on the center of the screen throughout the eyes open condition of the experiment. They were informed of the end of each block acoustically. The experiment was programmed using the Cogent toolbox (developed by the Cogent 2000 team at Functional Imaging Laboratory (FIL) and Institute of Cognitive Neuroscience (ICN) and Cogent Graphics by John Romaya at the Laboratory of Neurobiology (LON) at the Wellcome Department of Imaging Neuroscience). Each condition consisted of four blocks containing 30 stimuli each summing to 120 trials per condition. Eight blocks of resting periods of a duration of 75 s, half of the blocks with eyes closed and half with eyes open were randomly interspersed with stimulation periods. In order to achieve comparable conditions when transferring this experiment into an MR environment, subjects lay on an examination couch and received the stimuli via a mirror reflecting the picture located on a plexi-glass screen. Stimuli were presented by a modified LCD projector (NEC Multisync MT 800, Japan). EEG preprocessing A 32-electrode cap (Easy-Cap; FMS, Herrsching-Breitbrunn, Germany) was used for EEG recordings following the international 10/20 system. All electrodes were referenced against FCz position. Electrooculogram of vertical (EOGv) and horizontal (EOGh) eye movements was recorded by one electrode placed below the right eye and two electrodes at the outer canthi. Impedances were maintained below 5 kΩ by applying an abrasive electrode paste
(ABRALYT 2000; FMS, Herrsching-Breitbrunn, Germany). A high dynamic range EEG amplifier with a sampling rate of 5 kHz (BrainAmp; BrainProducts GmbH, Munich, Germany) and Vision Recorder Software v1.02 was used to record EEG data. Data were band pass-filtered between 0.5 and 70 Hz. A notch filter of 50 Hz was applied. For further processing, data were down-sampled to 200 Hz. Offline analysis was performed with EEGLab 4.515 (Delorme and Makeig, 2004) and Matlab v7.0 (The Mathworks Inc., Natick, USA). Artifact rejection was performed in two steps. First, the following EEGLAB artifact rejection methods were used: an exclusion threshold of 100 μV for EEG/EOG channel data and improbability of data as estimated by joint-probability and kurtosis-of-activity analysis using EEGLAB preset defaults. Second, visual inspection was performed for double-checking of proposed artifact removal and removal of then-remaining artifacts. For the eyes closed condition, in average 108 trials remained after artifact rejection. Due to occasional eye blinking, on average 96 trials remained for eyes open condition. For analysis of EPs, trials were segmented in − 1 s to 9 s epochs time locked to the stimulus. Subsequently a baseline correction of data using the time window from − 1 s to 0 s was applied. ICA decomposition To further increase signal-to-noise ratio, an independent component analysis (ICA) was performed using Infomax ICA. This was done separately for each subject and each condition using the artifact corrected, segmented data of all EEG/EOG channels, excluding all ECG and EMG channels. The resulting 10 out of 24 components explaining most of the variance were classified into main clusters. Components of artifactual or ocular origin were identified using their topographical, spectral and time–domain properties and only components of non-artifactual clusters were back-projected to EEG channels for further analysis as proposed by Jung et al. (2000a). The resulting corrected signal at the occipital electrode O2 was used for subsequent analysis to allow for comparison with conventional EP data. EEG postprocessing The grand average EP and the time–frequency plot were calculated for both eyes open and eyes closed condition separately. The grand average time–frequency plot includes the frequency range from 0.5 Hz to 40 Hz. Impact of pre-stimulus alpha amplitude variation on experimental data Analogous to the simulated data, the impact of ongoing alpha amplitude on the experimental data was examined for comparison with model predictions. Analysis of EP parameters was done separately for “eyes open” and “eyes closed” condition. By sorting trials according to their pre-stimulus alpha amplitude, each trial was assigned to either a “high-alpha” or “low-alpha” group. Trials were sorted individually for each subject according to absolute alpha amplitude in a pre-stimulus temporal window of − 800 ms to −100 ms using a wavelet-based analysis (3-cycle Hanningwindowed sinusoidal wavelets, frequency stepping of 1 Hz). The approach of a pre-stimulus window sorting was employed to avoid the assumed confounding issue of sorting ongoing activity in a post-stimulus time window (Yeung et al., 2004; Makinen et al., 2005). The weighted sorting window of the 3-cycle wavelet centered at − 100 ms ranged up to 50 ms into the post-stimulus epoch, however the earliest pronounced evoked components in the
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present study were found to arise at a latency of N 70 ms after stimulus onset. Analogous to the analysis of the modeled data, results of both experimental conditions were compared for the following parameters in an analysis window from 0 to 400 ms after stimulation: (1) The grand average of the broadband EP (0.5–70 Hz) (2) Alpha-band event-related spectral perturbation (alpha-band ERSP) based on the 3-cycle wavelet analysis (as mentioned above). The amplitudes in the frequency range of 8–12 Hz (in steps of 1 Hz) were extracted and the dominant frequency within this range was determined for each subject separately to be used for further analysis. Please note that the eventrelated phase-locked component, i.e. the EP waveform, was not removed beforehand, thus yielding an estimate of total alpha-band amplitude. The alpha-band ERSP is shown both as “relative” ERSP, i.e. as a relative change with respect to its baseline (the pre-stimulus time window) as well as “absolute” ERSP providing absolute alpha-band amplitude changes. (3) The alpha-band phase locking index (alpha-band PLI) according to Makeig et al. (2002) in order to estimate the degree of phase locking of (total) post-stimulus alpha activity. Phase information (φ, see Eq. (1)) was extracted from the wavelet analysis in the frequency range of 8–12 Hz for each time point (t). N 1 X iðuk ðtÞÞ PLIðt Þ ¼ e N k¼1
ð1Þ
To account for individual differences in alpha frequency, the pre-stimulus window was examined for the dominant alpha frequency, which was then analyzed further. Increasing values signify increasing phase coherence across trials (with N = number of trials), ranging from zero (completely random distribution of phases) to one (perfect alignment of phase across all trials). For statistical testing of differences of the “high” and “low” alpha-amplitude groups, the pair-wise signed-ranking test was used as a non-parametric Student’s t-test analogue to estimate significances of intra-individual alpha-amplitude dependent differences of the above mentioned parameters (EP amplitude, relative/ absolute alpha-band ERSP and alpha-band PLI). A time window of 0 to 400 ms after stimulus onset was analyzed. Data were Bonferroni corrected for multiple comparisons. Results In the following, we first show the results of our theoretical modeling approach, thereby demonstrating the usefulness of the proposed pre-stimulus alpha-variation for the differentiation of prevailing theories of EP generation. Secondly, we compare the outcome of the models with experimental data in order to identify the model that is more compatible with our experimental data.
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EPs equally well (see Fig. 1). While in the evoked model the EP is generated independently from and additive to the simultaneous alpha rhythm, in the oscillatory model the EP is generated by partially phase resetting the ongoing alpha rhythm. Thus, the phase-locked part of the signal, i.e. the EP can be explained by both theories. The non-phase-locked part of the post-stimulus signal is desynchronized alpha rhythm in the evoked model and non-phase-reset alpha rhythm in the oscillatory model. Thus, by theoretical modeling, we demonstrate a substantial intersection of the two EP theories despite their opposing assumptions on the relationship between ongoing and evoked activity. Impact of variation of pre-stimulus alpha amplitude on model data For the evoked model, increasing pre-stimulus alpha amplitude (Figs. 2A–C) was paralleled by a constant EP amplitude (Fig. 2D), a transition from a relative increase of total alpha-band amplitude to a relative decrease (see relative alpha-band ERSP in Fig. 2E) and a decrease in alpha-band phase locking (Fig. 2G). In contrast, in the oscillatory model, increasing pre-stimulus alpha amplitude (Figs. 2I–K) was associated with enhanced EP amplitudes (Fig. 2L). The relative alpha-band amplitude and the degree of phase locking remained constant (see relative alpha-band ERSP and alpha-band PLI in Figs. 2M, O). Despite the subset of data both models share, they deliver distinct predictions on the effect of prestimulus alpha-variation on simulated EPs and simultaneously occurring alpha background activity. Experimental data ICA decomposition Back-projection of non-ocular and non-artifactual sources comprising the “visual” clusters among the first 10 independent components yielded data with a clear occipital topography as in Jung et al. (2000b). Excluded sources were either event related but of frontal origin comprising eye movements or their topography or raw time courses identified them as being of ocular origin. In all subjects, each of the first 10 components could be assigned by visual inspection to one of the spatial patterns shown in Fig. 3. Grand averages Grand average EPs and grand average time–frequency plots (range from 0.5 to 40 Hz) are provided in Fig. 4 for both eyes open and eyes closed condition. Relative alpha-band amplitudes (Figs. 4A, B) change similarly in both conditions (for absolute alpha-band amplitudes, please refer to Figs. 5C, H), exhibiting an event-related decrease in amplitude (alpha rhythm desynchronization, alphaERD). Notably, these experimental data reflect the “ambiguous” situation as simulated in the “medium” alpha group (depicted in Fig. 1), i.e. no clear post-stimulus alpha-band amplitude increase is observed which implies the possible validity of both theories of EP generation.
Models In the situation of an ongoing rhythm exhibiting, an amplitude equal or larger (in the case of the simulated “medium” group 10 μV) than the amplitude of the arising EP (7.5 μV for the “medium” alpha group) both models can explain the generation of
Fig. 3. Visualization of typical ICA components explaining most of the variance. (A) Visual component clusters. (B) Non-visual, ocular and artifactual sources.
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significant differences in the absolute alpha-band amplitude (or alpha ERSP) between the “low” and “high” alpha groups. For eyes open condition, the resulting time window lasts from 0 to 158 ms after stimulation (Fig. 5C). For eyes closed condition, a significant difference between absolute alpha-band amplitude remains throughout the entire analysis time window from 0 to 400 ms, indicating a slower sorting “decay” of ongoing alpha amplitudes (Fig. 5H). In general, absolute alpha amplitudes during eyes closed condition are higher than during eyes open condition.
Fig. 4. Time–frequency plots and grand average EPs of experimental data. (A, B) Time–frequency plots (0.5–40 Hz) of both eyes open and eyes closed visual stimulation. Vertical horizontal lines indicate begin and end of visual stimulation. Event-related amplitude increases in frequencies b8 Hz suggest an evoked process. The alpha-band amplitude in both conditions does not show a signal increase, but a dominating desynchronization, i.e. an ambiguous situation similar to the simulated situation in Fig. 1. Possible interpretations are as follows: Either an evoked process is “masked” by concurrent alpha desynchronization or the alpha-band component of the EP is created by phase reset of the ongoing alpha rhythm. (C, D) Corresponding grand average EPs of both conditions.
Impact of variation of pre-stimulus alpha amplitude on experimental data and comparison to models Duration of alpha sorting as indicated by absolute alpha-band ERSP. In contrast to modeled data, the sorting effect of prestimulus alpha amplitudes in experimental data decays in the poststimulus time window. Thus, it is necessary to identify the relevant time window in the experimental data for comparison with our model predictions. The duration of efficient sorting is indicated by
Results of EP analysis. For the eyes open condition, early EP components were constant, i.e. unaffected by alpha sorting, despite the enduring effect of alpha sorting within the EP time window (Fig. 5A, see also comments above). A late EP component (cf. Fig. 5A), in turn, showed a significant (Bonferroni corrected, p b 0.05) enhancement with a maximum of 2.5 μV difference at a time window of 220 to 310 ms. Notably, it is not paralleled by a concurrent increase in absolute alpha amplitude (see absolute alpha-band ERSP analysis, Fig. 5C). This indicates an already decayed effect of pre-stimulus alpha-amplitude sorting. Shape and time course of the enhancement indicate a frequency component slower than alpha frequency. One additional significantly different time point was found at 175 ms for a negative evoked component whose negativity decreased for high alpha. For the eyes closed condition, alpha sorting did not result in any significant differences in the EP over the entire analysis window. A positive trend for the high alpha group was observable from 200 ms on (Fig. 5F). In order to exclude alpha-band contribution to this positive trend, spectral alpha-band components of the EP were removed by filtering, not eliminating the trend (results not shown). Thus, the observed positive trend does not appear to be caused directly by phase-reset alpha activity. Results of relative alpha-band ERSP analysis. In the eyes open condition, a switch from an increase in the relative alpha-band
Fig. 5. Effects of pre-stimulus alpha amplitude variation on eyes open (A–D) and eyes closed (F–I) visual stimulation data. The time range used for alphaamplitude sorting is indicated by the light blue cones on the left side of panels A and F. In dark gray, Bonferroni corrected significant effects (p b 0.05) are shown. Filled rectangles mean testing groups against each other, dark gray stripes (B, G) mean testing against the respective group pre-stimulus baseline. The dotted line indicates the end of the analysis window; the solid line indicates begin and end of visual stimulation. (A, F) EP Amplitudes. (B, G) Relative alpha-band eventrelated spectral perturbations (relative alpha-band ERSP). (C, H) Absolute alpha-band ERSP (sorting effect). (D, I) Alpha-band phase-locking index (PLI).
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amplitude to a relative decrease was found when comparing “low” and “high” alpha groups (Fig. 5B). The increase for the “low” alpha group paralleled the time course of the increase in alphaband phase locking (see results of alpha-band phase locking below). For the eyes closed condition, the relative alpha-band ERSP also showed a significant increase after stimulation for the “low” alpha group; however, this was smaller in amplitude and temporal extent (Fig. 5G). Alpha-band PLI analysis. There were intervals of significantly reduced phase locking of the high-alpha group in both eyes open and eyes closed conditions (for eyes open ranging from 60 to 158 ms, Fig. 5E; for eyes closed ranging from 110 to 158 ms, Fig. 5J) being paralleled by constant EP amplitudes. Comparison of experimental results with model predictions. For the eyes open condition, the observed effect of alpha-variation during the relevant time window is well predicted by the evoked model. Unaffected early EP components together with a decreased phase locking for increasing alpha and also the relative alpha-band amplitude, showing a transition from an increase to a decrease (from the “low” to the “high” alpha group), are predictions of the evoked model. The eyes closed early EP components exhibit a corresponding pattern and are equally well predicted by the evoked model. The observed significant enhancement of the eyes open late positive EP component in the “high” alpha group is not predicted by any model. Both models basically assume a difference in absolute post-stimulus alpha-band amplitude which is not present in the late EP component of the experimental data. Neither model states the possibility of an indirect effect of pre-stimulus alphasorting without a simultaneous alpha-amplitude difference in the relevant time window of the EP component of interest. Discussion We have demonstrated that variation of the ongoing alpha amplitude allows differentiation of the two theories of EP generation, even when a subset of the data is ambiguous with respect to the two theories. Our experimental data show significant differences in EP parameters for varying pre-stimulus alpha amplitudes with early EP components being most consistent with the evoked theory, while later components co-vary with prestimulus but are not generated by post-stimulus alpha rhythm. Models Both oscillatory and evoked theory are consistent with phaselocked and non-phase-locked components. The oscillatory model only requires the assumption of a partial phase reset in order to explain the same data as the evoked theory (Figs. 1 and 2). Thus, given sufficient alpha activity, the absence of a single-trial post-stimulus alpha-band amplitude increase (model data in Figs. 3E, M, experimental data in Figs. 4A, B) does not exclude an evoked mechanism as previously assumed by some authors (Sayers et al., 1974; Fuentemilla et al., 2006) but presents an ambiguous situation. Accordingly, the origin of EPs can only be unveiled by assessing the influence of amplitude variations of ongoing alpha activity on the EP. Especially EPs recorded during visual stimulation are often paralleled by a spontaneous alpha rhythm having larger amplitudes than the EP itself (also reflected in our data, see Figs. 5C, H).
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We compared the predictions of the evoked and the oscillatory model concerning the impact of pre-stimulus alpha variation on EP amplitude with our experimental data. Within eyes open and eyes closed condition, higher pre-stimulus alpha activity was accompanied by constant early EP amplitudes and a concurrent decrease in alphaband phase locking. Also the total alpha-band amplitude changed from a relative decrease to a relative increase for low pre-stimulus alpha activity. All these phenomena are predicted by the evoked model and correspond to the assumption of dual and separate generators of EP and alpha rhythm (Fig. 2). The change in the degree of phase locking is explained by the superposition of alpha activity on the evoked potential, i.e. “masking” of the single-trial EP by a strong alpha rhythm. With increasing alpha activity, the non-phase-locked part of the single-trial signal (i.e. post-stimulus alpha rhythm) increases, which is reflected by less phase locking across trials (see Fig. 5E, J, respectively). For periods of low alpha activity in turn, the additional evoked EP causes an increase in total alpha-band amplitude instead of a decrease of alpha-band amplitude. Thus, for the data presented, the combined approach of alpha-amplitude variation of experimental data and comparison with model predictions succeeded in differentiation of early EP mechanisms. Concerning other modeling approaches, Makinen et al. (2005) suggests a single parameter for differentiation: standard deviation across trials (SDT). Straight-forward predictions of this approach were that EPs generated by an evoked model show no decrease in SDT, while EPs yielded by an oscillatory model exhibit an SDT decrease. Experimental auditory data of this study showed no decrease in SDT, which was regarded as evidence against an oscillatory mechanism of EP generation. In a comment to Makinen et al., Klimesch et al. (2006) in turn argued that integrating eventrelated decreases in alpha amplitude, i.e. alpha desynchronization, questions the validity of these claims. Concerning the experimental and model data of our study, we observed the following: (a) our experimental data do show an SDT decrease (see Supplementary Fig. 3), which is mainly caused by event-related decrease of the alpha rhythm amplitude (for an early report of decrease of visual data SDT see Pfurtscheller et al., 1989), and (b) both our models, in concordance with our experimental data, show an SDT decrease as well. Thus, we discarded the SDT measure as a sufficient parameter for model differentiation and adopted the more complex approach based on combined EP parameters. For the models in our study, we used a noise-free approach, which has the advantage of visualizing the general principles of the two theories even in single trials that otherwise would easily disappear under noisy conditions. We excluded, however, the possibility of different model behavior under noisy conditions by adding noise to our simulated data. As can be seen in Supplementary Fig. 1, both models preserve their differential predictions despite single trials being obscured by intense noise. In order to test whether the chosen value of alpha rhythm desynchronization (or of residual, non-phase-locked alpha amplitude) influences the model outcome, we tested two additional values, i.e. 50% (weak ERD) and 12.5% residual alpha amplitude (strong ERD) for the evoked model, which corresponds to 50% and 12.5% of non-phase-reset alpha rhythm for the oscillatory model. Again, differential model predictions were well preserved (for results see Supplementary Fig. 2). Experimental data The enhancement of a late evoked component due to prestimulus alpha sorting was found to be incompatible with the
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oscillatory model. For an enhancement taking place, the oscillatory model requires a concomitant increase in post-stimulus alpha-band amplitude (see models, Fig. 2F). This was not the case in the data, which excludes a direct causative role of alpha activity. Since the observed enhancement is neither predicted by the pure evoked model, the results can only be reconciled with a model integrating an additional indirect interaction between pre-stimulus alpha activity and the evoked potential. As the focus of this study was to determine whether and how well the two dominant theories of EP generation in their strict interpretation would predict empirical findings, we did not change our models post-hoc-wise. In principle, the observed empirical interaction between alpha and EP amplitude could be integrated, for example by an additional arousal or vigilance term which is related to the amplitude of spontaneous alpha activity. Another way of accounting for the empirical findings in future modelling may be based on the recently proposed hypothesis of Nikulin et al. (2007) on alpha-dependent baseline shifts in the EEG. This hypothesis accounts for the empirical phenomenon that each level of alpha activity appears to be associated with a different EEG baseline level. This implies, that during alpha amplitude fluctuations also the mean baseline level is modulated, which could explain modulations of EP components as a function of the amount of pre-stimulus alpha activity being desynchronized. The view of the evoked response as being modulated – but not generated – by the alpha rhythm, is also supported by Jasiukaitis and Hakerem (1988), who, using pre-stimulus alpha-amplitude sorted auditory oddball data, also observed an exclusive enhancement of a late positive evoked component, the P300. They stated that it is not the case “that the P300 is a phase constrained alpha wave, but that the state of which alpha is a sign, predicts the larger P300”, giving support to a co-variation but not causation hypothesis of alpha activity. Similar positive correlations between ongoing alpha activity and the P300 were reported by Polich (1997) and by Basar et al. (1984). Consistently with the results of Jasiukaitis and Hakerem (1988), Barry et al. (2000) reported a direct relationship between pre-stimulus alpha amplitude and following EP amplitudes for auditory oddball data. Thut et al. (2003) reported an exclusive enhancement of a late positive component using a visual stimulation setup with repetitive TMS (rTMS). The enhancement was observed after application of rTMS. No early component was modulated, despite a concurrent decrease in the amount of event-related alpha desynchronization after rTMS application. The authors concluded that ongoing and evoked activity seem to be rather separate phenomena. In summary, while alpha-dependent modulations on late EP components are frequently observed, no direct involvement of concurrent alpha activity has been reported. With respect to findings in early potentials, Makinen et al. (2005), reported early auditory potentials (N1m, ~ 100 ms) to be unaffected by increasing pre-stimulus alpha activity. Complementary to this negative evidence supporting the evoked model is our result of a significant decrease in phase locking and at the same time unaffected EP amplitudes for significantly higher (pre- and post-stimulus) alpha activity, which provides additional positive evidence for the evoked model and also argues for sufficient signal-to-noise ratio of the data. Also intracranial studies in monkeys support the evoked nature of early EPs (e.g. Schroeder, 1991; Rols et al., 2001). In these studies, early scalp EPs were linked to increased multi-unit activity in low-level sensory areas. Additionally, early EPs are also
reported to be relatively unaffected by attentional fluctuations (Mehta et al., 2000), which fits our observation of early EPs being unaffected by alpha rhythm amplitude. Assuming that alpha rhythm is an indicator of attentional and/or vigilance fluctuation one would expect attention-dependent EP components to be related to these fluctuations. An inverse relation between pre-stimulus alpha amplitude and visual EP amplitude (N1–P2) has been proposed by Rahn and Basar (1993). For stages of low pre-stimulus alpha activity recorded at the vertex an enhancement of the N1–P2 peak-to-peak amplitude (100–250 ms) was demonstrated in fronto-central but not in occipital electrodes. These results are not directly comparable with our data, since the channels exhibiting this effect, the reference used and the topographical definition of the alpha rhythm at the vertex differ from our study. On the other hand, Basar et al. (1998) reported an inverse correlation between a factor termed “EP enhancement” – which was defined as the ratio of the EP amplitude to pre-stimulus alpha amplitude – and pre-stimulus alpha amplitude. This finding was neither accompanied by simulations nor interpreted in the light of the two opposing EP theories. Thus, we analyzed our models analogously for this factor, which yielded a comparably clear inverse relation between “EP enhancement” and pre-stimulus alpha amplitude only for the evoked model, while the EP enhancement index for the oscillatory model remains constant for varying alpha amplitudes (see Supplementary Fig. 4). The results for the evoked model also show the previously reported inverse but more exactly “curvilinear” relationship between VEP enhancement and the pre-stimulus alpha amplitude as found by Brandt and Jansen (1991) when replicating the analysis of Basar et al. (1998). Also Barry et al. (2000) showed convincingly for auditory oddball data that a direct relationship between prestimulus alpha activity and EP component amplitude (as proposed by Brandt and Jansen) is compatible with the existence of an inverse relationship between pre-stimulus alpha and the mentioned “EP enhancement factor” (as proposed by the group of Basar). This implies, that the reported inverse correlation allows manifold interpretations on the relationship between EP amplitude and ongoing alpha amplitude. Actually, any relationship which is weaker than directly proportional, even a clear independence of EP and ongoing alpha amplitude yields such inverse correlation (see Supplementary Fig. 4). In the specific case of the pure evoked model the curve behaves even “curvilinear”, as demonstrated in Supplementary Fig. 4A. The results of Basar’s and Brandt’s study on the “EP enhancement factor” do not contradict but rather support the evoked theory and its implications. However, at least a strict phase reset can be excluded, since such a behavior would prevent the consistently reported inverse relationship between prestimulus and post-stimulus alpha enhancement. In case of a pure phase reset the relation between pre-stimulus alpha activity and the post-stimulus enhancement remains constant, as can be seen for the phase-reset model in Supplementary Fig. 4B. In contrast to our results on early EP components, Brandt and Jansen (1991) reported a positive enhancement of eyes closed early VEP components for higher pre-stimulus alpha activity. Again, for comparison we applied the same kind of data analysis as Brandt and Jansen to our experimental “eyes closed” data yielding similar results (data not shown). In contrast to our actual approach, Brandt and Jansen had analyzed sets of averages of 40 trials each and maximum peak-to-peak amplitudes were individually determined for each such average. This analysis is susceptible to systematic
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confounding effects of residual alpha activity being superimposed on the evoked potential, because estimated peak-to-peak amplitudes with variable latencies are affected especially by strong residual ongoing alpha activity. In the study presented here, the analysis approach was to average a large number of trials (N ~ 750) pooled over subjects and to analyze the grand average EPs with fixed latencies. Thus, residual effects of ongoing alpha activity were canceled out more effectively. For this analysis the mentioned early EP-enhancing effect disappeared. It has been demonstrated by Makeig et al. (2002) that for increased alpha activity measured in a post-stimulus time window there is an increase of EP amplitudes with accentuated alphaactivity and increased phase locking. By using a pre-stimulus time window, we observed the contrary effect, i.e. strong alpha background activity was accompanied by weaker phase locking and constant early EP amplitudes during the critical post-stimulus time window. On the other hand, sorting our data according to the post-stimulus time window yielded an alpha-amplitude dependent increase of EP amplitude (data not shown). Thus, differences in results between the two studies can be explained by differences in the definition of the sorting window. Since a post-stimulus sorting window tends to be biased by stimulus-related activity (Makinen et al., 2005; Yeung et al., 2004), a pre-stimulus time window appeared more appropriate to us. In summary, while some previous studies on the relation between ongoing alpha rhythm and early evoked potentials contradict our study with respect to the interpretations regarding the prevailing mechanism of EP generation, the experimental results of these studies can be reconciled with our data. Our combined simulated and empirical data approach on studying the relationship between background alpha rhythm and the VEP favors the evoked model for early components and at the same time demonstrates an interaction between pre-stimulus alpha amplitude and VEP features occurring after 175 ms, arguing for a modulatory rather than direct causative role of alpha activity. In conclusion, we propose to study empirical data in the context of a modeling-based approach to further shed light on mechanisms of EP generation. Acknowledgments The authors thank Steven Lemm, Peter Brunecker, Robert Schmidt and Matthias Reinacher for helpful comments. This work was supported by the German Federal Ministry for Education and Research BMBF (Berlin NeuroImaging Center and Bernstein Center for Computational Neuroscience Berlin) and the German Research Foundation DFG (Berlin School of Mind and Brain).
Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.neuroimage.2007.09.016.
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