1 Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA, 2 Cognitive Neuroscience and Schizophrenia Program, Nathan Kline Institute, Orangeburg, NY 10962, USA, 3 Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL 33431, USA, 4 Computational Sciences Division, Code IC, NASA Ames Research Center, Moffett Field, CA 94035-1000, USA, 5 Department of Neurological Surgery, Weill Medical College of Cornell University, New York, NY 10021, USA, 6 Institute for Psychology, Hungarian Academy of Sciences, Budapest, H-1394, Hungary
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Abstract |
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Key Words: current source density (CSD), evoked response, phase resetting, oscillatory activity, single-trial analyses, synchronization
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Introduction |
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Earlier attempts at defining the neural mechanisms of the ERP were limited by two factors: (i) scalp ERPs are indirect measures, recorded at a distance from their neural sources, and (ii) the critical predictions of each model were not clearly delineated. Here we address both limitations. First, we detail several critical predictions of each model (Table 1) that are amenable to empirical testing. Secondly, we report the direct evaluation of each requirement by analysis of single-trial, intracortical activity in awake, behaving monkeys.
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Materials and Methods |
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Data for these analyses were collected from two male macaques performing a visual oddball discrimination paradigm. The Institutional Animal Care and Use Committee at the Nathan Kline Institute approved all experimental procedures. Subjects were presented with random streams of standard and oddball stimuli with standards presented during 86% of the trials and oddballs presented during 14% of the trials. The monkey pressed a switch to initiate stimulus presentation and held it until he detected an oddball. Appropriate release of the switch earned a drop of juice. The standard visual stimulus was a 10 µs, red-light flash presented on a diffusing screen subtending 12 retinal degrees and centered on a fixation point. The deviant stimulus differed slightly in intensity or color. Data analyses concerned only the neural responses to the standard, non-target stimulus. An infrared eye tracker monitored eye position, and stimuli were presented only when fixation was within a 1° window around the fixation point. Each experimental session consisted of between 474 and 3007 (mean = 1430.5 and median = 1443) presentations of the standard visual stimulus. Both of these monkeys also served in selective attention studies not covered by the present report. Further details are available in Mehta et al. (2000a).
Data Collection
An experimental session began with positioning a linear-array multielectrode in either V1 or posterior IT such that the contact array was perpendicular to the layering of the area (Fig. 1, left). A detailed description of these methods can be found in Schroeder et al. (1998) (Schroeder et al., 1998
). In one session, two multielectrodes arrays were inserted intracranially to record simultaneously from sites in V1 and IT. In all cases, electrode contacts had an impedance between 0.l and 0.3 M
and were spaced at equal intervals. Intercontact spacing was either 150 or 200 µm, depending on the particular multielectrode used during that session. Neuroelectric signals were amplified with a pass band of 33000 Hz, digitized at 2000 Hz using a PC-based data acquisition system (Neuroscan, El Paso, TX), and analyzed using custom-made code in MATLAB (The Mathworks Inc., Natick, MA).
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Data from each session were prepared for analyses by first epoching the EEG from 300 to 300 ms, with zero denoting the onset of stimulus presentation. Secondly, the average CSD profile was computed, and the laminar positioning of each CSD channel was defined as either supragranular, granular, or infragranular according to the typical (averaged) CSD profile of that cortical area (Schroeder et al., 1998). Thirdly, the full-wave rectified, average CSD signal for each supragranular and granular channel was integrated, and the two channels (one in each layer) with the largest integral areas were chosen for analyses. Fourth, the post-stimulus average CSD (defined as the average CSD waveform from 0 to 300 ms) in these two channels was multiplied by a Hamming window, and the discrete Fourier transform (DFT) of the resultant was calculated. The dominant frequency of the ERP (see Table 2) in each layer was defined as the frequency bin with the greatest power in this spectrum. Fifth, single-trial CSD signals in both chosen channels were split into pre- and post-stimulus signals, multiplied by a Hamming window, and converted to the frequency domain by a 600-point DFT yielding pre- and post-stimulus, single-trial frequency spectra. With a sampling frequency of 2000 Hz and a 600-sample DFT, the frequency bins were 3.33 Hz in width and encompassed 0 to 1000 Hz.
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Single-trial total power distributions for the pre- and post-stimulus periods were calculated as the integral of the single-trial, power spectra from 0 to 1000 Hz. For each experimental session, these distributions were normalized so that the median post-stimulus total power equalled 1.0 (mV/mm2)2. The distributions for the supragranular tissue are displayed as box plots (see Fig. 6b,c). The lower and upper lines of the box bound the 25th and 75th percentiles of the distribution, and the middle line represents the median of that sample. The position of the median line with respect to the upper and lower lines indicates the skew of the distribution. Notches in the box around the median indicate the confidence interval about that median. Vertical lines above and below the box show the extent of the data, minus outliers.
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Results |
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Strict interpretations of the phase resetting and evoked models pose differing predictions for three specific properties of single-trial field potentials, as outlined in Table 1. Phase resetting makes three main predictions. Under property 1, it predicts that activity at the dominant frequency of the ERP must be present in the pre-stimulus period. Under property 2, phase resetting predicts that the transition between the pre- to the post-stimulus periods must involve phase concentration (i.e. synchronization of an EEG rhythm across trials) (Sayers et al., 1974; Makeig et al., 2002
; Jansen et al., 2003
). These two predictions are actually critical requirements for the operation of phase resetting. On the other hand, the evoked model is tolerant of a wide range of pre-stimulus activity, and it predicts phase concentration because an evoked, single-trial response is relatively phase-locked to stimulus onset. Regarding property 3, a strict version of the phase resetting model predicts (requires) that the pre- to post-stimulus transition must occur without increase in power at the dominant frequency of the ERP. The evoked model, in contrast, requires a post-stimulus increase in power at that frequency. It is important to emphasize that the phase resetting model does not deny that evoked activity occurs. Rather it assumes that the evoked responses occur outside the dominant frequency of the ERP and serve as a trigger for resetting the phase of the local EEG oscillations. What the strict phase resetting model does hold is that in averaging over numerous trials, phase-reset EEG rhythms form the substrate for the ERP rather than the local evoked responses. Thus increase in EEG power at the dominant frequency of the ERP can rule out an exclusive account in terms of phase resetting.
Direct Empirical Analysis
Our analysis considered cortical areas at both low and high levels of visual processing, namely primary visual cortex (area V1) and inferotemporal (IT) cortex. This allowed us to test the possibility that the evoked model may account for ERP generation in cortical areas closer to the receptor surface (retina), while the phase resetting model may describe ERP contributions from areas more removed from the retina and increasingly influenced by state variables such as attention (Maunsell and Newsome, 1987; Mehta et al., 2000a
; Fries et al., 2001b
). Data were collected from two male macaque monkeys while they performed a task requiring discrimination between target and non-target visual stimuli. In each experimental session, a linear-array multielectrode with 14 equally spaced contacts was inserted acutely into V1 and/or IT such that the array spanned all layers of that area at an angle perpendicular to the laminae (Fig. 1, left). Local field potentials were sampled, and a second-derivative approximation of the current source density (CSD) was computed (Freeman and Nicholson, 1975
; Schroeder et al., 1995
). Quantitative analyses (presented below) utilized the CSD measure instead of the field potential because the CSD approximation eliminates activity generated outside the cortical area of interest, which may contaminate local field potential recordings. Moreover, the CSD profile directly addresses the electrogenesis of ERPs, because it is an index of the transmembrane current flow patterns responsible for generating the local field potential. The local multiunit action potential (MUA) profile was sampled from the same electrode contacts to provide an independent link between the ERP and neuronal activity. In each animal, two V1 and two IT recordings were analyzed for a total of four sessions in each area.
Qualitative Properties
Qualitative properties of the raw data from V1 support the Evoked Model over Phase Resetting. Figure 1 illustrates field potentials recorded from V1 of one subject during presentation of non-target stimuli. A concurrent EEG recording from an electrode over the occipital brain surface near the recording site is shown (top trace) along with recordings from all fourteen contacts of the multielectrode array. A prominent single-trial, stimulus-evoked response is observed as a negative field potential above layer 4, which inverts to a positive potential below layer 4 (arrowhead and arrow in Fig. 1), indicating that it is locally generated (Schroeder et al., 1995). However, the pre-stimulus activity is relatively small and displays no apparent oscillations at the frequency of the evoked response. Since previous studies show that the generator for this potential lies in the layer 4C (Schroeder et al., 1992
, 1995, 1998), we examined the data from that layer across all trials. Figure 2 depicts the concomitant single-trial ERPs (field potentials), multi-unit activities (MUA) and transmembrane current flow densities (CSD). Relatively few pre-stimulus oscillations are observed across trials, and the post-stimulus period shows a discrete field potential negativity, which, accompanied by a burst of MUA and a current sink in the CSD, indicates net local excitation. When viewed at this scale, the obvious event-related responses appear to support the evoked model, and the lack of comparable pre-stimulus oscillations discount the phase resetting model.
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Quantitative analyses were performed to evaluate properties 13 of Table 1. To evaluate property 1, we computed the median ratio of pre- to post-stimulus power at the dominant frequency of the ERP across all trials, experimental sessions, and subjects. For the supragranular layer recordings, this ratio increased from 0.13 (mV/mm2)2 in V1 to 0.27 (mV/mm2)2 in IT. The corresponding comparison for the granular layer showed an increase from 0.20 (mV/mm2)2 in V1 to 0.37 (mV/mm2)2 in IT. The V1 to IT differences in both layers were significant (Wilcoxon rank-sum test, P < 0.01). These results indicate two important properties. First, both cortical areas have some pre-stimulus activity at the dominant frequency, which is required for the phase resetting model and consistent with the evoked model under property 1. Second, the pre-stimulus power in IT is significantly greater than that in V1, suggesting that IT may be more prone to phase resetting, and hence the mechanisms of ERP generation may differ across levels of the hierarchy.
Property 2 specifies model-relevant changes in the phase of oscillatory activity, and so we compared pre- and post-stimulus phase distributions at the dominant frequency of the ERP (in our case, the dominant frequency of the averaged, post-stimulus CSD in each electrode contact considered for each session). Examples from supragranular V1 and IT are shown in Figure 4. Pre-stimulus activity at the dominant frequency should yield a uniform phase distribution because stimulus presentation, which defines the pre-stimulus period, occurred at random interstimulus intervals. Across sessions and layers, the majority (13/16) of pre-stimulus phase distributions did not differ from uniformity, while all post-stimulus distributions were statistically different from uniformity (modified Kuiper V statistic, P < 0.01) (Fisher, 1996). In all cases, the pre- to post-stimulus transition demonstrated a drastic decrease in circular variance suggesting stimulus-induced phase concentration. The phase resetting model requires this result, but the finding is also compatible with the evoked model; therefore, the observation of phase concentration does not differentiate between the models. In fact, further analyses (see property 3 results below) suggest that the majority of the effect is likely caused by addition of relatively phase-locked power at the dominant frequency of the ERP.
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As a second control, we examined key qualities of the pre-stimulus spectrum to see if it resembled the post-stimulus spectrum as might be expected if phase resetting generated the ERP. First, as illustrated in Figure 6a, we found that the peak frequency of the pre-stimulus period most often differed from the dominant frequency of the ERP (three of four recordings in supragranular V1, two of four recordings in granular V1, three of four recordings in supragranular IT, and three of four recordings in granular IT). Additionally, the half-maximal bandwidth about the peak frequency of the pre-stimulus spectrum was wider than that around the dominant frequency of the post-stimulus spectrum. Fourteen of 16 recordings showed this effect, while one showed no change (supragranular V1 in session R69) and one showed an increase (supragranular IT in session V71) in half-maximal bandwidth. The shift in peak frequency and the stimulus-induced sharpening of power about the dominant frequency strongly indicate that the pre-stimulus activity has a fundamentally different organization than the post-stimulus activity, which again argues against the phase resetting model as the mechanism underlying ERPs.
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Discussion |
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Penny et al. (2002) proposed amplitude and phase modulation as possible mechanisms for ERP generation, and they likened these processes to the evoked and phase resetting models, respectively. Our data demonstrate empirically that amplitude and phase modulation both operate in neocortex. However, it is also obvious that phase modulation (concentration) will be detected in single-trial analyses even when a strict evoked mechanism is operating. That is, transmembrane currents triggered by stochastic firing of the inputs to local neurons and by random non-synaptic currents will generate the baseline EEG. Thus, pre-stimulus activity will contain power in the frequency bands of the ERP, and these frequency components will likely have uniform phase distributions because the generating events are random. When a stimulus is presented, the evoked response will be produced, and it will be relatively phase-locked to stimulus presentation. Phase modulation in the form of concentration will be observed simply because there is random pre-stimulus activity in frequency bands of the ERP and a relatively phase-locked post-stimulus response.
It is noteworthy that under the present conceptual framework, the evoked model can incorporate most of the major observations cited as support for phase resetting. For example, although the widely reported interactions between EEG oscillations and the ERP (Brandt et al., 1991; Mast and Victor, 1991
; Fries et al., 2001a
,b; Liang et al., 2002
; Makeig et al., 2002
) are required by phase resetting, these observations also fit with the evoked model as there is no restriction on pre-stimulus activity influencing evoked responses. More specifically, ERP enhancement during trials in which pre-stimulus activity is large (Brandt et al., 1991
; Liang et al., 2002
; Makeig et al., 2002
) may result because ongoing rhythms and evoked responses are generated by overlapping components of the same biophysical machinery. Any modulation of either would tend to affect both processes in a yoked fashion.
While the present results clearly support a predominant role for evoked responses in generating sensory event-related potentials, they leave open the possibility that phase resetting contributes to this process. The fact that pre-stimulus oscillations show a systematic increase in power from V1 to IT suggests the possibility of a shift from a mainly evoked mechanism at low levels of sensory processing to a mechanism more influenced by phase resetting at higher processing levels. Further, phase resetting may play an important role in cortical feedback-mediated ERP components such as the selection negativity, which is observed in the comparison between attended and non-attended, non-target stimuli in selective attention experiments (Harter et al., 1982; Hillyard, 1985
; Mehta et al., 2000a
,b). Finally, phase resetting may operate by default in the generation of ERPs that have no defined sensory evoking stimulus, such as the missing stimulus P3 (Simson et al., 1977
; Michalewski et al., 1982
) and motor potentials associated with self-paced movements (Arezzo et al., 1987
). Although further experiments will be required to address these questions, the conceptual framework outlined in the present study identifies conditions necessary for phase resetting to contribute to ERPs (properties 1 and 2) as well as conditions under which its contributions can be ruled out (property 3).
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Notes |
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Address correspondence to Charles E. Schroeder, PhD, 140 Old Orangeburg Road, Orangeburg, NY 10962, USA. Email: schrod{at}nki.rfmh.org.
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References |
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