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Human P50 suppression is not affected by variations in wakeful alertness
Human P50 Suppression Is Not Affected by Variations in Wakeful Alertness Valerie A. Cardenas, Patricia Gill, and George Fein
The amplitude and suppression of the auditory P50 event-related potential may be useful for studying schizophrenia and drug abuse; however, the low reliability of the P50 suppression measure limits its value for correlation with clinical measures. Reliability can be increased either by improving measurement methods or by reducing or eliminating sources of variance in the recordings. In this paper, the effect on P50 amplitude and suppression of variation in wakeful alertness within an experimental session was examined in 20 normal subjects. The percentage of beta power in the interval immediately prior to the P50 stimuli was used as an index of alertness. P50 amplitudes or C-T ratios were estimated using peak-picking and using the singular value decomposition (SVD) method. No effects of variation of wakeful alertness were observed on any P50 amplitude or suppression measure. Comparing the peak-picking vs SVD estimates replicated our prior results showing markedly higher reliabilities with SVD. We conclude: 1) that variation within an experimental session in wakeful alertness level as indexed by the percentage of beta power does not affect P50 amplitude or suppression, and 2) the SVD method brings the reliability of the C-T ratio up to levels where its usefulness in clinical studies can be examined. © 1997 Society of Biological Psychiatry Key Words: Singular value decomposition, C-T ratio, event-related potentials, beta power, auditory P50 B I O L PSYCHIATRY
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Introduction The auditory P50 component is a middle-latency eventrelated potential (ERP). Its suppression in repetitive stimulation experiments indexes a preattentive, or neuronal sensory gating process that may be useful for studying the genetic predisposition for schizophrenia (Adler et al 1992; Waldo et al 1988, 1991). Its amplitude and suppression From the Department of Psychiatry, University of California, San Francisco and Psychiatry Service, San Francisco Veterans Administration Medical Center, San Francisco, California. Address reprint requests to George Fein, PhD, 4•50 Clement Street (II6R), San Francisco, CA 94121. Received March 16, 1995; revised April 8, 1996.
© 1997 Society of Biological Psychiatry
may also be useful for measuring the acute and chronic central nervous system effects of disease and/or drugs of abuse which affect the dopaminergic, cholinergic, or noradrenergic neurotransmitter systems (Adler et al 1988, 1990, 1992; Baker et al 1990, 1991; Boutros et al 1993; B uchwald et al 1991; Dickerson and Buchwald 1991; Fein et al 1995; Harrison et al 1990). Conditioning-testing paradigms afford independent measurement of P50 response amplitude and P50 suppression. In such paradigms, responses are recorded to pairs of clicks, where the interval between pairs of clicks is long (i.e., 8-10 sec) compared to the interval between clicks in a pair (i.e., usually 0.5 sec). The response amplitude to the second 0006-3223/97/$17.00 PII S0006-3223(96)00186-2
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(testing) click as a proportion of the response to the first (conditioning) click is termed the conditioning-testing (C-T) ratio, the P50 suppression measure. A P50 analogue can be recorded in the rat and cat (Adler et al 1986, 1988; Chen and Buchwald 1986), allowing animal models to be used to determine the mechanisms underlying P50 amplitude and suppression changes in humans. Enthusiasm for, and the potential utility of, the P50 response as a research tool is tempered by the low test-retest reliability of P50 suppression as estimated by the standard measurement technique, peak-picking on a single-channel (usually Cz). Previous researchers have measured the reliability of C-T ratios measured using peak-picking and obtained values ranging from r = 0.11-0.37, with r = 1.00 being perfect (Boutros et al 1991; Cardenas et al 1993; Kathmann and Engel 1990). This low reliability severely limits the value of the P50 suppression measure for correlation with clinical measures or for longitudinal analysis. The reliability of a measure such as the C-T ratio is a function of: a) the consistency within individuals over time of the "true" underlying phenomena that the measure is supposed to capture (in this case P50 amplitude or suppression), and b) the contributions of measurement error, which result in the measure being different from the "true" value of the phenomena. In previous work in our laboratory, we have addressed (b) above, via suggested changes in measurement and estimation of P50 amplitude and suppression (Cardenas et al 1993, 1995; Cardenas 1994). In this report, we attempt to address aspects of (a) above, by examining whether variations in alertness levels within an experimental session affect P50 amplitude and suppression. We have previously shown that P50 amplitude and P50 suppression are unaffected by attentional manipulations (Jerger et al 1992), allowing measurement of P50 mechanisms in experiments where sustained attention by subjects is neither monitored nor assured. Our attention findings appear at first glance to disagree with those of Guterman and colleagues (Guterman et al 1992); however, it is likely that the differences in results are consequent to confounding of P50 and N100 phenomena in their study. In our study, the data were digitally bandpass filtered between 10 and 50 Hz, removing the contributions of the predominantly low frequency N100 (known to be affected by attention) while leaving the P50 intact. In fact, we reported very large N100 effects in our paradigm. If N100 is not filtered out, the P50 waves ride on N100 waves and the N100 effects of attention are present in the confounded P50 measures. In Guterman's study, a digital lowpass filter with a passband cutoff of 50 Hz was used. Since no high pass filter was used, the low frequencies that contribute to the N100 were not removed from the P50 measures.
Therefore, in their study, the P50 findings may simply have reflected attention effects on N100 amplitude. Adler and colleagues (Adler et al 1988) showed that the analog of P50 in the rat increased in amplitude and C-T ratio during "hyperarousal." When Adler's rats were first introduced into the testing chamber, exhibiting behavioral signs of high arousal, testing commenced and only nonmoving, alert trials were accepted for averaging. N50 (the rat analog of the human P50) amplitude and C-Tratio were significantly increased during this period of hyperarousal as compared to a second experiment conducted about 1 hour later, when the rats were accustomed to the environment. Consistent with these results, prior research in the cat and human showed that wave A (cat) and P50 (human) amplitude decreased during periods of very low alertness. Chen and Buchwald (Chen and Buchwald 1986) monitored the effects of sleep on wave A in the cat. They presented repetitive, 1-sec clicks to the cats during wakefulness, slow wave sleep (SWS), and rapid eye movement (REM) sleep. The amplitude of wave A decreased then disappeared during SWS and reappeared during REM sleep. Erwin and Buchwald (Erwin and Buchwald 1986) repeated this experiment in humans, and observed P50 amplitude decrements or P50 disappearance during SWS, with a reappearance of P50 during REM sleep, with amplitude approximating that observed during waking. Griffith and colleagues (Griffith et al 1993) examined P50 C-T ratios before and after a brief period of sleep. The C-T ratios of the normals increased immediately following a brief sleep period, when the subjects were presumably still somewhat drowsy. Within a few minutes of waking, the C-T ratios fell to the presleep level. Unfortunately, Griffith et al did not report the effect of sleep on the conditioning amplitudes. If consistent with the other studies, however, the conditioning amplitudes were probably smaller due to low alertness, resulting in larger C-T ratios. Because of these results demonstrating that large changes in alertness affect P50 amplitude and suppression, some researchers attempt to control for alertness when measuring P50 during waking in human studies. For example, Adler and colleagues (Adler et al 1992) routinely monitor the spontaneous EEG during P50 experiments, rejecting single trials which show evidence of decreased alertness. This procedure prolongs the experiment and reduces the number of single trials available for analysis. We believe that this change in P50 recording methodology may be premature since it is based on sleep study results that may reflect more extreme variations in conditions than occur during a waking experimental session. In this paper, we investigated the effect of alertness on human P50 amplitude and suppression, using prestimulus beta as an index of alertness. Beta refers to the frequency range of EEG greater than 13 Hz. Beta waves are large
Wakeful Alertness Does Not Affect P50
during periods of alert wakefulness, with lower frequency activity predominating during periods of quiet restfulness and sleep (Regan 1989). We: 1) recorded 2.56 sec of EEG before the conditioning click of each pair of stimuli, 2) analyzed the frequency spectrum of this prestimulus EEG, 3) computed a measure of alertness based on the power in the beta frequency range (13-30 Hz) as a percentage of power in the 1-30 Hz range, 4) classified each single trial as occurring during low alertness (low beta) or high alertness (high beta), 5) constructed averages of low and high beta single trials, 6) measured conditioning and testing amplitudes and C-T ratios on these low and high beta averages using peak-picking and the singular value decomposition (SVD) method, and 7) evaluated the effect of alertness on these measures. We acknowledge that autonomic measures such as galvanic skin response and heart rate are better indices of alertness, but our goal was to evaluate the effect of "normal" variations in wakeful alertness in order to determine whether it was necessary to monitor alertness during an experimental session. Using beta as an index of alertness mimics the single-trial rejection method used by Adler and colleagues (Adler et al 1992) during their P50 experiments. The auditory P50 conditioning-testing paradigm is particularly arduous and boring. We collect P50 in response to paired clicks, with a 0.5 sec interclick interval and a 7 - 8 sec interval between click pairs. We require our subjects to keep their eyes open and still while we collect a minimum of 100 eye-movement artifact-free trials, with a break halfway through data collection. It takes approximately 15 minutes to complete the experiment, and our subjects often report becoming sleepy during data collection. Because of our subjects' self-report, we were reasonably certain that changes in alertness were occurring during an experimental session.
Methods
Subjects We studied 20 subjects: 8 women and 12 men. These subjects had no history of drug or alcohol abuse, cardiovascular disease, diabetes, serious head injury, seizures, severe psychiatric disorders, or family history of neurologic disorder. Subjects were between 23 and 40 years of age (mean _+ sd, 28.7 __+ 5.1 years), were free of medication at the time of the study, gave informed consent, and were paid for their participation.
Recording Methods Subjects were relaxed, awake, and seated upright in a quiet but not acoustically isolated room during the recording
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electrodewith large effect on amplitude estimation; used for SVD analysis electrodewith large effect on amplitude estimation; not used for SVD electrodewith small effect on amplitude estimation; not used for SVD electrodeexcluded from average reference and SVD analysis
Figure 1. The planar projection of the electrodes used to collect P50 is shown. The open squares mark electrodes that contributed little to the SVD analysis and the open circles mark electrodes that contributed greatly to the SVD analysis. The electrodes marked by filled circles were used for the final SVD analysis. The open triangles mark electrodes excluded from the average reference and SVD analysis. sessions. Recordings were made using 32 tin EEG electrodes in an electrode cap (Electro-Cap International, Eaton, Ohio) and referenced to a tin electrode clipped to the left earlobe. The electrode locations are illustrated in Figure 1. Vertical eye movements were monitored using gold cup electrodes placed above and below the right eye, and horizontal eye movements were monitored using electrodes placed at the lateral canthi. Impedances for cap electrodes were lowered using electrode gel (Electro-Cap International), and Grass EC2 Electrode Cream was used for the reference and electrooculogram (EOG) leads. Two kinds of electrode pastes were used because the ElectroCap gel was too thin for use in the cup electrodes or ear clip, and the Grass cream was too thick for use in the electrocap. Although the differences in ion concentration in the two pastes can cause a very slowly varying potential difference, this electrical artifact should be filtered out by our analog filtering. All impedances were below 5000 ohms and signals were amplified 50,000 times by a Grass Model 12 Neurodata Acquisition System with analog filters at 0.1 and 300 Hz. ERPSYSTEM Software (Neurobehavioral Laboratory Software, San Rafael, CA) was used to control stimulus presentation and data acquisition through an Analog Devices RTI 800-815/F laboratory interface card on a 20 MHz Intel 80386-based personal computer. P50 data were sampled at 2000 Hz beginning 20
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msecs before each click and continuing for 200 msecs after each click for a total of 440 msecs of data for each pair of clicks. Resting EEG data were sampled at 100 Hz for 2.56 sec beginning 2.58 sec prior to each click pair. We acknowledge that the sampling rate for the collection of the resting EEG was inadequate given our analog bandpass filtering, and that frequencies greater than 50 Hz would have been aliased; however, there is very little power in the EEG at frequencies greater than 50 Hz, and aliasing should not have greatly affected our beta power estimates. As a check on this, we estimated the EEG spectrum using the first 32 msec of the adequately sampled P50 data (20 msecs of prestimulus EEG and 12 msec of response to the click). We found that the frequencies below 31.25 Hz constituted an average (n = 20 subjects) of 92% of the total power contained below 1000 Hz. Since this estimate was obtained using data that spanned the time period of the high frequency auditory brainstem response, we expect that the percentage of total power below 31.25 Hz would be even higher in the resting EEG, and thus the contamination of any aliased high frequencies in our sampled data would be minimal. Trials, consisting of pairs of clicks, were rejected if activity exceeded _+60 IxV on either eye-movement channel.
Auditory Stimulation Clicks were produced by square electrical pulses of 0.05 msec duration, which were generated by the Analog Devices D/A converter, then passed through a HewlettPackard 350D Attenuator, amplified by a Pioneer SX2300 stereo receiver/amplifier, and delivered to the subject over Realistic NOVA '20 headphones (Tandy Corporation, Houston, TX). The clicks were 55 dB above subject threshold and were separated by 500 msec, with the intertrial interval varying between 7 and 8 sec (Zouridakis and Boutros 1992). A minimum of 100 artifact-free trials were recorded during each run.
Eye Movement Correction In previous work, we rejected single trials if electrooculogram (EOG) activity exceeded _+60 IxV. Even when this rejection procedure is used in normals, there is some danger that smaller (less than _+60 ~zV) amplitude EOG time-locked to the stimulus might still contaminate the average ERP. In this paper, we apply a frequency-domain eye-movement correction algorithm (Gasser et al 1985) to our data after first rejecting EOG trials that exceeded _+60 IzV. Gasser's method estimates the transfer functions between each EOG-EEG channel pair, multiplies these transfer functions by the EOG to estimate the amount of EOG transferred into each EEG channel, then subtracts
this quantity from the EEG channel. The multiplication and subtraction steps of the algorithm are repeated for each single trial, with the conditioning response corrected for the conditioning EOG, and the testing response corrected for the testing EOG. This decreases the effect of eye-movement contamination and increases the signal-tonoise ratio (SNR) of our data before estimation of amplitudes and C-T ratios.
Beta Wave Analysis We estimated the percentage of beta power for each click-pair to index the state of alertness. We estimated the percentage of beta power at 7 electrodes--Fz, FCz, Cz, F3, F4, FC3, and FC4--referenced to the average of 28 electrodes (we excluded electrodes Fpl and Fp2 from the average reference due to artifacts). We chose these electrodes to mimic the situation where alertness during the P50 experiment is assessed by examination of EEG at the P50 recording electrodes. We used all seven of these electrodes because P50 is distributed frontocentrally (Cacace et al 1990), and because we believed that use of all seven electrodes would give us a more stable alertness estimate. We first computed the Fast Fourier Transform (FFT) on the 2.56 sec of prestimulus EEG (256 data points) collected before the conditioning click. We summed the power in the beta (>13 and <-30 Hz) frequency range, divided that by the sum of the power in the 1-30 Hz range, and multiplied by 100 to get a percentage estimate. We then averaged the percentage of beta power over the seven channels used. We computed this beta power estimate for each single trial. If the percentage of beta power was above the median percentage of beta power for that subject, it was classified as a high beta response, which indicates a state of alert wakefulness or high alertness. If the percentage of beta power was below the median, it was classified as a low beta response, which indicates a state of quiet restfulness. In order to verify that the low beta responses truly indexed a more relaxed state, we compared the low and high beta single trials in each subject with respect to the average log power in the delta (-<3.5 Hz), theta (>3.5 and < 8 Hz), and alpha (->8 and -<13 Hz) ranges using paired t tests. We found significantly higher average estimates of delta, theta, and alpha log power in the low beta single trials (t19 = 3.88, p = 0.0010; t19 = 3.87, p = 0.0010; and t19 5.40, p < 0.0001, respectively). The means, standard errors, and p-values of log delta, theta, and alpha are presented in Table 1. Since a shift in the resting EEG to lower frequencies occurred during the low beta single trials, we conclude that these single trials occurred during a state of low alertness. We then averaged the low alertness single trials to create a low alertness (low =
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Table 1. Means, Standard Errors, and p-values for Log Power
Delta Theta Alpha
Low Beta Average
High Beta Average
p-value
5.39 _+0.19 5.41 _+0.19 5.56 _+0.22
5.19 _+0.20 5.28 + 0.20 5.41 + 0.22
0.0010 0.0010 <0.0001
prestimulus beta) P50, and averaged the high alertness single trials to create a high alertness (high prestimulus beta) P50. Our "sampling" was successful for constructing averages of low and high beta single trials; although a statistical test for differences in beta power is not appropriate, the paired t test w a s t19 = 17.90, p < 0.0001. We analyzed the low and high beta averages to determine the effect of alertness on P50 amplitude and suppression.
Peak Picking The average EPs were digitally bandpass filtered between 10 and 50 Hz. Because the dominant frequency constituents of the middle latency response range from 30 to 50 Hz (Suzuki et al 1983) and the dominant constituents of the auditory N100/P200 are below 10 Hz, this filtered out the majority of the auditory N 100 and P200 responses, filtered out 60 Hz noise, and allowed us to analyze the remaining P50 response (see Jerger et al 1992 for a description of the filter). The peak-picking algorithm of ERPSYSTEM was used to choose the peaks at channel Cz versus an average reference using 28 channels, and the peaks chosen were verified by an experienced rater (VC). The peaks were chosen as the most positive (or negative) voltage value within a specific time range after the stimulus. The time window for the negative peak preceding P50 was 30-50 msec, and the window for P50 was 4 0 - 8 0 msec. The algorithm never chose a P50 that preceded the N40. In cases where the auditory P30 overlapped the P50 such that no clear N40 could be identified, the P50 amplitude was measured to the prestimulus baseline. The P50 amplitude used for analysis was the difference between the P50 peak and N40 trough. The C-T ratio was computed by dividing the amplitude of the testing response by the amplitude of the conditioning response.
Singular Value Decomposition Method We also analyzed the data using the SVD method described by Cardenas (Cardenas 1994; Cardenas et al 1995) to estimate the conditioning and testing amplitudes, and the C-T ratio. We used the SVD method because we recently showed that the reliabilities of SVD P50 measures were comparable to dipole components model (DCM) P50 measures and significantly greater than peak-picking P50 measures, while the SVD method was much simpler and
easier to use than dipole source modeling. We also showed that when only a single generator or multiple synchronous generators are active (probably the case for P50 evoked by bilateral auditory stimulation), C-T ratios derived using the SVD method or DCM accurately measure the "true" C-T ratio. This occurs because the effect of model misspecification (or, in the case of SVD, the effect of no model) on the amplitude estimates "cancels" when a ratio is computed. The SVD method uses multiple channels of data, multiple timepoints, and multiple data sets to estimate a single amplitude estimate for each data set. Amplitude ratio estimates between data sets can then be derived. The SVD method estimates amplitudes by applying the singular value decomposition, a standard matrix decomposition (Strang 1988), to a data matrix of dimension n X mk, where n is the number of timepoints, m is the number of electrodes, and k is the number of data sets. For P50 conditioning-testing data, k = 2 since the conditioning and testing responses are estimated together. SVD decomposes the original n X mk matrix into an n X n matrix of left singular vectors, a diagonal matrix of singular values arranged in descending order, and an mk x mk matrix of right singular vectors. We refer to the application of SVD to the original n X mk data matrix as the decomposition into waveshapes and electrode weightings. The first left singular vector is the common waveshape among all channels and data sets, and the first right singular vector contains weighting factors for the channels in each data set; they are referred to as the "first" vectors because they correspond to the first and largest singular value. By arranging the first right singular vector into an m x k matrix multiplied by the first singular value, SVD can be applied again. We refer to the application of SVD to the m x k matrix of electrode weights as the decomposition into topography and data set weightings. Now the first left singular vector is the topography (or set of electrode weights) common among all data sets, and the first fight singular vector contains the amplitude weightings for each data set. The amplitude of each data set is the corresponding amplitude weight multiplied by the first singular value, and amplitude ratios can be derived from these amplitudes. The SVD method is closely related to principal components analysis (PCA). When PCA is applied twice to a data set in a manner similar to that described for SVD, exactly the same amplitude results can be obtained. The algorithm for applying PCA to estimate amplitudes comparable to SVD is completely described by Cardenas (Cardenas 1994). PCA, however, involves additional, unnecessary computation and is thus slower to compute than SVD. We applied the SVD method to average referenced data (excluding Fpl and Fp2 from the average reference) within a time window chosen by one of us (VC) to contain both the ascending and descending aspects of the P50 peak
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at the vertex. The starting point for the window over subjects ranged from 35-55 msec, and the window width ranged between 10 and 35 msec. In general, the vertex P50 within the window resembled one half period of a sinusoid. We used the same window width for all responses within a subject. The conditioning and testing responses for each of the high and low beta averages for each subject were then used to create data matrices of dimension 2n x 2m, where n was the window width in msec for that subject (the width is multiplied by 2 because we used a sampling rate of 2000 Hz), and m is the number of electrodes (m is multiplied by 2 because we analyzed the conditioning and testing responses simultaneously). We applied the SVD method to each subject's high and low beta data matrices, and computed conditioning and testing amplitudes for the high and low beta averages, and then derived C-T ratios from the amplitude estimates.
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We knew from previous work that the SVD method did not work well when multiple components were active, and that SVD would work best if only the channels at which P50 dominated were used; however, we had no objective method for choosing these channels, only our intuition based on reports that P50 is frontocentrally distributed. Therefore, we initially applied the SVD method to the 28 electrode channels used in the average reference, then examined the quantity sv~ / ~ sv~, which is the ratio of the first (largest) singular value squared to the sum of all the squared singular values. In principal components analysis, this quantity gives the proportion of variance explained by the first principal component (Jackson 1991), and although the SVD method has no such statistical framework, this quantity is useful for evaluating the validity of the SVD method assumption of a single active component. The closer this quantity is to one, the more likely only a single component is active. When using 28 channels, this quantity was 0.71 (computed over 19 subjects) for the decomposition into waveshapes and electrode weights, and 0.88 for the decomposition into topography and data set weights. Because these quantities were low, we suspected the presence of time-locked activity other than P50 at some channels. We examined the first left singular vector of the decomposition into topography and data set weights (the entries of this vector are electrode "weightings" common across all data sets), and we reasoned that the largest magnitude entries corresponded to the channels contributing most to the estimation of P50 amplitude and suppression. Of the 28 electrodes examined, the half that contributed most to the fit are designated in Figure 1 by circles. Of the 14 that contributed most to the fit, we chose to reanalyze the P50 C-T data using only the seven
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Figure 2. A normal (top) P50 conditioning-testing (C-T) response and a C-T response illustrating overlap (bottom) of the P30 and P50 responses are shown.
frontocentral electrodes--Fz, FCz, Cz, F3, F4, FC3, F C 4 - - b e c a u s e previous research has shown P50 to be distributed frontocentrally (Cacace et al 1990). These electrodes are designated in Figure 1 by filled circles (0). After we applied the SVD method to the 7 frontocentral electrode channels described above, we recomputed the quantity sv~ / "Z svai, and obtained values of 0.83 for the decomposition into waveshapes and electrode weights and 0.94 for the decomposition into topography and data set weights. We suspected that the comparatively low value for the decomposition into waveshapes and electrode weights was due to either P30/P50 overlap, since four of our subjects exhibited P30 overlapping with P50 in the conditioning response for one or more of their averages, or the very low SNR of our averages of approximately 50 single trials, and decided that the seven-channel SVD analysis was adequate. Figure 2 shows the P50 conditioning-testing response for representative subjects that did and did not exhibit P30/P50 overlap.
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For both SVD and peak-picking, if no P50 was present in response to the first click, that subject's measures were classified as missing. This was the case for one subject's high beta average using peak-picking, and for a different subject's high beta average using SVD. Looking at these subjects' multiple channel data, it was clear that P50 was more prominent at Fz than at Cz for the subject whose data were missing using peak-picking, and that a positivity was present at around 50 ms at Cz but no other channels for the subject whose data were missing using SVD. Similar to Nagamoto and colleagues (Nagamoto et al 1991), any C-T ratio greater than two was pegged to two to eliminate the effect of outliers on the statistics. This was the case for two subjects using peak-picking, and one subject (one of the peak-picking subjects, but the high beta average instead of the low beta average) using SVD. We observed no systematic effect of alertness on P50 amplitude or suppression, measured using peak picking (n = 19) or the SVD method (n = 19). We used paired t tests to compare the amplitudes and C-T ratios between the low and high beta averages. The peak-picking mean _+ standard error (se) of the conditioning amplitude for the low and high beta averages were 0.87 ___0.13 and 0.90 _+ 0.16 p,V, with no significant difference in the means (t~8 = - 0 . 3 5 , p = 0.73). The SVD mean + se were 12.54 + 1.16 and 12.46 + 1.16, with no difference in the m e a n s (t19 = 0 . 1 0 , p = 0.92). Note that the SVD amplitudes are not measured in txV, because the SVD amplitudes have no physical meaning and simply index the relative magnitude of each response to other responses. This also means that the magnitudes of the peak-picking and SVD amplitudes will not be comparable, although the C-T ratios estimated using either method should have similar magnitudes (the ratio is dimensionless). The testing amplitude responses were similarly unaffected by alertness. The peak-picking amplitudes were 0.38 -+ 0.07 ~V and 0.53 _.+ 0.15 ~V and the SVD amplitudes were 6.58 + 1.02 and 5.54 __+ 1.24. There were no differences in the means for either peak-picking or SVD (t18 = - 0 . 9 6 , p = 0.35 and t19 = 0 . 8 8 , p = 0.39, respectively). There was also no effect of alertness on the C-T ratio. The peak-picking C-T ratios were 0.54 _+ 0.12 and 0.52 _+ 0.12 (tt8 = 0.14, p = 0.89), and the SVD results were 0.60 --- 0.12 and 0.44 + 0.09 (t19 = 1.36,p = 0.19). The peak-picking amplitudes reported here are small compared to published normative data (Erwin et al 1991; Nagamoto et al 1991). This is entirely due to the fact that we average reference our data before peak-picking using a montage that contains a cluster of frontocentral electrodes. Although average referencing affects the amplitude estimates, the ratio estimates should be unaffected. When we peak-picked the vertex electrode referenced to the ear (n = 18, because no clear P50 was present to the
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Median Pementage of Beta in Resting EEG
Figure 3. The difference between the C-T ratio estimated on the low vs high pre-stimulus beta averages is plotted against the median percentage of beta power for each subject. There is no relationship between the amount of variation in the C-T ratio and the amount of beta in the subjects' EEG.
first click for two subjects) we obtained amplitudes comparable to those reported in the literature (low and high beta average conditioning amplitudes of 1.56 + 0.27 and 1.71 _+ 0.26), but still observed no differences in amplitudes or C-T ratios between the low and high beta averages. The paired t tests for the conditioning and testing amplitudes w e r e tit ~ - 0 . 6 8 , p = 0.50 and t17 = - 0 . 9 9 , p = 0.34 and for the C-Tratios w e r e t17 = 0.02,p = 0.98. The amount of beta present in the resting EEG varied greatly among subjects; some subjects simply had more beta activity than others. Although the mean + se beta computed over 1998 single trials (all single trials for 20 subjects) was 38.80 + 0.37%, with a median of 38.08%, the median percent beta across subjects varied between 10.74 and 71.63%. We questioned whether the effects of alertness were different for subjects with relatively low vs high beta percentages. Figure 3 charts the median percentage of beta for each subject vs the difference in the C-T ratio between the low and high beta averages. Figure 3 shows that prestimulus beta, and presumably wakeful alertness, does not systematically affect the C-T ratio. Since we found no effect of prestimulus beta on P50 amplitude or suppression, we treated the high and low beta averages as replications and computed the reliabilities of the amplitudes and C-T ratios estimated by peak-picking and the SVD method. We used the intraclass correlation coefficient (ICC) described by Shrout and Fleiss (Shrout
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and Fleiss 1979) to compute the reliabilities. We did this to attempt to replicate in an independent sample our prior demonstration that the C-T ratio estimated by SVD is more reliable than the C-T ratio estimated via peak-picking. The reliabilities (denoted by r) of the SVD estimates were r = 0.72 for the conditioning amplitude, r = 0.47 for the testing amplitude, and r = 0.37 for the C-T ratio. The reliabilities of the peak-picking estimates were r = 0.89 for the conditioning amplitude, r = 0.10 for the testing amplitude, and r = - 0 . 2 4 for the C-T ratio (the ICC is negative if the true reliability is small and the estimates are noisy). As expected, the SVD C-T ratio estimates were more reliable than the peak-picking ratio estimates, and the peak-picking and SVD amplitudes were at least comparably reliable. The C-T ratio reliabilities reported above are not as large as those Cardenas, et al. (Cardenas et al 1995) reported for C-T ratios estimated from averages of 120 single trials. Our low and high alpha responses were noisier averages of approximately 50 single trials, so some decrease in reliability is expected. Using formulas found in Dunlap and Silver (Dunlap and Silver 1986) and Shrout and Fleiss (Shrout and Fleiss 1979), we obtained a ballpark estimate of the reliabilities, had the ratios been estimated from averages of 120 single trials. We found the adjusted reliability of the C-T ratio estimated using SVD to be r = 0.68, and the adjusted reliability of the C-T ratio estimated using peak-picking to be r = 0.20. Cardenas, et al. computed r = 0.66 SVD C-T ratios, and r = 0.37 for peak-picking C-T ratios.
Discussion No effect of variations in wakeful alertness on P50 amplitude or suppression: We observed no effect of prestimulus beta on P50 amplitudes or C-T ratios estimated using either peak-picking or the SVD method. Because the percentage of beta present in prestimulus EEG is an indicator of alertness, we conclude that P50 amplitude and suppression are not affected by variations in alertness as indexed by prestimulus beta during waking recordings. Our results conflict with Adler et al.'s (Adler et al 1988), who reported increased N50 amplitudes and C-T ratios between rats that exhibited behavioral signs of high alertness and rats that did not. Even though our high beta averages represent periods where our subjects were relatively less relaxed and more aroused, it is likely that our subjects were not as "hyperaroused" as Adler's rats. Our subjects were normal, paid volunteers who spent approximately 40 minutes in the recording chamber while the electrodes were applied and their hearing threshold to the paired click stimulus was assessed, were acclimated to the
environment, and subsequently exhibited no change in P50 amplitude or suppression due to "hyperarousal." The lack of alertness effects could also reflect differences between rat N50 and human P50. Our low beta averages represent periods of quiet restfulness, during which our subjects often reported becoming drowsy; however, although our subjects were drowsy, they remained awake. Erwin and Buchwald (Erwin and Buchwald 1986) showed that the amplitude of P50 was diminished then disappeared as subjects progressed from sleep stage 2 through sleep stages 3 and 4, but did not systematically examine drowsiness. Although we expected decreased P50 amplitude during our low beta averages, it may be that sleep must occur before P50 amplitude is diminished. Griffith's subjects (Griffith et al 1993) showed increased C-T ratios after sleeping for 10 minutes. We found no change in C-T ratios due to changes in alertness, and suspect that C-T ratios only increase following sleep. Erwin and Buchwald also recorded P50 in a repeated click paradigm, whereas we recorded P50 in a paired click paradigm, and it is possible that the paired click paradigm is more resistant to changes in wakeful alertness. The results reported here, together with our prior results (Jerger et al 1992) suggest that one can validly record P50 in a conditioning-testing paradigm in humans without monitoring attention or level of wakeful alertness. Extremes of alertness, such as a subject falling asleep or experiencing anxiety due to the experiment, may still affect P50. This facilitates study of difficult subject groups such as schizophrenics or cocaine abusers, where attention and alertness are often difficult to control. It is just such subject groups where P50 amplitude and suppression abnormalities may give us insight into how these disorders affect the brain. Replication of increased reliability of P50 suppression estimated via SVD: This paper is another example of how analysis of P50 using multiple data sets (i.e., the conditioning and testing responses), multiple channels, and multiple timepoints improves the reliability of the C-T ratio compared to peak picking. The reliability of the C-T ratio estimated from averages of 50 single trials using the SVD method was r = 0.37, with an estimated reliability (for 120 single trials) of r = 0.68, comparable to those previously reported by Cardenas et al (Cardenas et al 1995). As in the prior paper, we note that the SVD method is simpler and faster than DCM; however, the SVD method is only valid when a single component is present during the time window of interest. This limits the application of the SVD method to early potentials such as the auditory brainstem response, the early somatosensory evoked potential, and the auditory middle latency response (i.e., auditory P50). We conclude that the SVD method,
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applied to multiple channel recordings, brings the reliability of the C-T ratio up to levels where its usefulness in clinical studies can be examined. Smith and colleagues (Smith et al 1994) suggested that the difference between the testing and conditioning amplitude measurements might be a more reliable measurement of P50 suppression, and should therefore be used instead of the C-T ratio. While Smith showed that the C-T difference (computed from C and T amplitudes estimated using single channel peak-picking) was more reliable than the C-T ratio, he reports the reliability of the average of k scores (k ranged between 2 and 6). Using this measure of reliability, we found that the C-T ratio ICC over two sessions of approximately 50-trial P50 data was r = 0.54, which is comparably reliable to their C-T difference (they report r = 0.47 over two sessions of 60-trial P50 data). We also computed the reliability of the C-T difference using the SVD estimated C and T amplitudes. We found the SVD C-Tdifference ICC over two sessions to be r = 0.47, no improvement over the SVD C-T ratio reliability. By using multiple channels of data, multiple timepoints, and multiple data sets the C-T ratio can be estimated at least as reliably as the C-T difference. In addition, the SVD C-T ratio is estimated accurately when only a single generator or multiple synchronous generators are active (Cardenas 1994; Cardenas et al 1995), which cannot be said for the C-T difference. In all our P50 studies to date, we recorded P50 from subjects seated upright, even though some researchers speculate that P50 cannot be reliably recorded from a seated position due to neck muscle artifact (Freedman 1990), and that recording P50 from a seated subject leads to higher C-T ratios. In order to determine if our relatively large, unreliable peak-picking C-T ratios were due to subject position, we recorded conditioning-testing P50s (500 msec interclick interval, 10 sec interpair interval) using a 32-channel montage while the subjects were seated. After a break during which we placed a mattress in the recording chamber, we repeated the recordings with the subjects supine. After eye-movement correction, we used SVD to estimate C-T ratios. We found no difference between C-T ratios recorded in the seated vs supine position (C-T ratio ± se = 0.37 4- 0.07 and 0.42 ± 0.07, respectively, t~2 = -0.74, p = 0.48). Because neck muscle artifact can appear as a large positive deflection at approximately 30 msec (Bickford et al 1964; Cody et al 1964; Robinson and Rudge 1982), we also noted the presence, and if present, the amplitude of P30 using peak-picking at the channel of maximal P50 amplitude. We found no difference in the presence or amplitude of P30 in seated and supine subjects, and conclude that subject position does not contribute to P50 unreliability.
We have prepared a separate short report detailing this study (McCallin et al 1996).
Appendix In this appendix we describe how we estimated the reliability that would have occurred had we had averages of 120 single trials instead of 50 single trials. We used the approximation for the confidence interval for ratios of normal variables described by Dunlap and Silver (Dunlap and Silver 1986), then used this approximation in the computation of the intraclass correlation coefficient described by Shrout and Fleiss (Shrout and Fleiss 1979). According to Dunlap and Silver, when the numerator and denominator of a ratio are independent, and the numerator (C) is much greater than its sample standard deviation (so), the standard error (SE) of the C-T ratio is:
SE-
C
'
where T is the denominator of the ratio and s t is the sample standard deviation of T. From the Central Limit Theorem (Hogg and Tanis 1988) we know that as more samples are added to an average, the expectation of the averaged sample is the true mean (Ix), and the variance of the averaged sample is the true variance divided by Y (~a/y), where Y is the number of samples in the average. Using this formula, we can easily show that the SE of the C-T ratio estimated using 50 and 120 single trials is:
SE, o
=
2s )c,
,[l(S~ + T2
\/1(2 SE12° =
T2 2' C
'
and that
1 \,;120
SE12o -
1
SEso = 0.645.SE50.
From Shrout and Fleiss we know that the statistical model for each C-T ratio measurement is: xij = tx + bj + wij, where x o is the C-T ratio for data set i, subject j, bj N(0, or2) is the true amount by which subject j ' s C-T ratio differs from the population mean Ix, and wij ~ N(0, ~2) is the error term. As the number of single trials in the average
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increases from 50 to 120, the distribution of wij will change to wij -- N(0, 0.6452 ~ ) , and the distribution of bj will remain unchanged. The adjusted reliability is then: B M S - 0.6452 W M S r = B M S + (k - 1)0.6452 W M S '
(1)
where B M S and W M S are quantities from a one-way analysis of variance. B M S is the between subjects mean square error, with (l-l) degrees of freedom, W M S is the within subjects mean square error with k(1-1) degrees of freedom, 1 indexes the number of subjects, and k indexes the number of repeated measurements on each subject.
Using equation (1), we can compute the adjusted reliabilities for the amplitude and C-T ratio estimates. These adjusted reliabilities are only ballpark estimates, because the C-T ratio standard error estimate is only valid when C > > s c and in our case C is only about twice s c.
This work was supported by General Medical Research funds from the Department of Veterans Affairs, the DVA career scientist program (Dr. Fein), and NIDA grant R01DA08365.
References Adler LE, Gerhardt GA, Franks R, et al (1990): Sensory physiology and catecholamines in schizophrenia and mania. Psychiatry Res 31:297-309. Adler LE, Hoffer LJ, Griffith J, Waldo MC, Freedman R (1992): Normalization by nicotine of deficient auditory sensory gating in the relatives of schizophrenics. Biol Psychiatry 32:607616. Adler LE, Pang K, Gerhardt G, Rose GM (1988): Modulation of the gating of auditory evoked potentials by norepinephrine: Pharmacological evidence obtained using a selective neurotoxin. Biol Psychiatry 24:179-190. Adler LE, Rose G, Freedman R (1986): Neurophysiological studies of sensory gating in rats: Effects of amphetamine, phencyclidine, and haloperidol. Biol Psychiatry 17:639. Baker NJ, Kirch DG, Waldo M, et al (1991): Plasma homovanillic acid and prognosis in schizophrenia. Biol Psychiatry 29:192-196. Baker NJ, Staunton M, Adler LE, et al (1990): Sensory gating deficits in psychiatric inpatients: Relation to catecholamine metabolites in different diagnostic groups. Biol Psychiatry 27:519-528. Bickford RG, Cody DT, Jacobson JL, Lambert EH (1964): Fast motor systems in man. Physiopathology of the sonomotor response. Trans Am Neurol Ass 89:56-58. Boutros N, Rustin T, Zouridakis G (1993): Cocaine use and the P50 auditory evoked response. Proceedings of the Society of Biological Psychiatry. San Francisco, CA, pp 346-347. Boutros NN, Overall J, Zouridakis G (1991): Test-retest reliability of the P50 mid-latency auditory evoked response. Psychiatry Res 29:181-192. Buchwald JS, Rubinstein EH, Schwafel J, Strandburg RJ (1991): Midlatency auditory evoked responses: Differential effects of a cholinergic agonist and antagonist. Electroencephalogr Clin Neurophysiol 80:303-309. Cacace AT, Satya-Murti S, Wolpaw JR (1990): Human middlelatency auditory evoked potentials: Vertex and temporal components. Electroencephalogr Clin Neurophysio177 :6-18. Cardenas V, Gerson J, Fein G (1993): The Reliability of P50 suppression as measured by the conditioning/testing ratio is vastly improved by dipole modeling. Biol Psychiatry 33:335344. Cardenas VA (1994): The Effect of Dipole Model Misspecifica-
tion on the Bias and Variance of Evoked Potential Amplitude and Amplitude Ratio Estimates: University of California, San Francisco and Berkeley. Cardenas VA, Yingling CD, Jewett D, Fein G (1995): A Multichannel model-free method for estimation of eventrelated potential amplitudes and its comparison to dipole source localization. J Med Eng Technol 19:88-98. Chen BM, Buchwald JS (1986): Midlatency auditory evoked responses: differential effects of sleep in the cat. Electroencephalogr Clin Neurophysiol 65:373-382. Cody DTR, Jacobson JL, Walker JC, Bickford RG (1964): Averaged evoked myogenic and cortical potentials to sound in man. Ann Otol (St. Louis) 73:763-778. Dickerson LW, Buchwald JS (1991): Midlatency auditoryevoked responses: Effect of scopolamine in the cat and implications for brain stem cholinergic mechanisms. Exp Neurol 112:229-239. Dunlap WP, Silver NC (1986): Confidence intervals and standard errors for ratios of normal variables. Behav Res Meth Instruments Comput 18:469- 471. Erwin R, Buchwald JS (1986): Midlatency auditory evoked responses: Differential effects of sleep in the human. Electroencephalog Clin Neurophysiol 65:383-392. Erwin RJ, Mawhinney-Hee M, Gur RC, Gut RE (1991): Midlatency auditory evoked responses in schizophrenia. Biol Psychiatry 30:430-442. Fein G, MacKay S, Biggins C (in press): Cocaine addicts have reduced auditory P50 amplitude compared to both normal control and chronic alcoholics. Biol Psychiatry, in press. Freedman R (1990): Evoked Response to Repeated Auditory Stimuli. Biol Psychiatry 28:1065-1067. Gasser T, Sroka L, M6cks J (1985): The transfer of EOG activity into the EEG for eyes open and closed. Electroencephalog Clin Neurophysiol 61:181-193. Griffith JM, Waldo M, Adler LE, Freedman R (1993): Normalization of auditory sensory gating in schizophrenic patients after a brief period for sleep. Psychiatry Res 49:29-39. Guterman Y, Josiassen RC, T.R. Bashore J (1992): Attentional influence on the P50 component of the auditory event-related brain potential. Int J Psychophysiol 12:197-209. Harrison JB, Woolf NJ, Buchwald JS (1990): Cholinergic neu-
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rons of the feline pontomesencephalon. I. Essential role in "Wave A" generation. Brain Res 520:43-54. Hogg RV, Talfis EA (1988): Probability and Statistical Inference, 3rd ed. New York, NY: Macmillan Publishing Company. Jackson JE (1991): A User's Guide to Principal Components. New York: John Wiley & Sons, Inc. Jerger K, Biggins C, Fein G (1992): P50 suppression is not affected by attentional manipulation. Biol Psychiatry 31:365377. Kathmann N, Engel RR (1990): Sensory gating in normals and schizophrenics: a failure to find strong P50 suppression in norrnals. Biol Psychiatry 27:1216-1226. McCallin K, Cardenas V, Fein G: P50 ERP Suppression Measurements are Equivalent with Subjects Seated or Supine. Biol Psychiatry, submitted. Nagamoto HT, Adler LE, Waldo MC, Griffith J, Freedman R (1991): Gating of auditory response in schizophrenics and normal controls: Effects of recording site and stimulation interval on the P50 wave. Schizophr Res 4:31-40. Regan D (1989): Human Brain Electrophysiology: Evoked Potentials and Evoked Magnetic Fields in Science and Medicine. New York: Elsevier.
Robinson K, Rudge P (1982): Centrally generated evoked potentials. In Halliday AM (ed), Evoked Potentials in Clinical Testing. London: Churchill Livingstone, pp 368-372. Shrout P, Fleiss J (1979): Intraclass correlation: uses in assessing rater reliability. Psychol Bull 86:420-428. Smith DA, Boutros NN, Schwarzkopf SB (1994): Reliability of P50 auditory event-related potential indices of sensory gating. Psychophysiology 31:495-502, Strang G (1988): Linear Algebra and its Applications, 3rd ed. San Diego, CA: Harcourt, Brace, Jovanovich. Suzuki T, Kobayashi K, Hirabayashi M (1983): Frequency composition of auditory middle responses. Br J Audiol 17:1-4. aldo MC, Adler LE, Freedman R (1988): Defects in auditory sensory gating and their apparent compensation in relatives of schizophrenics. Schizophr Res 1:19-24. Waldo MC, Carey G, Myles-Worsley M, et al (1991): Codistribution of a sensory gating deficit and schizophrenia in multi-affected families. Psychiatry Res 39:257-268. Zouridakis G, Boutros NN (1992): Stimulus parameter effects on the P50 evoked response. Biol Psychiatry 32:839-841.
The amplitude and suppression of the auditory P50 event-related potential may be useful for studying schizophrenia and drug abuse; however, the low reliability of the P50 suppression measure limits its value for correlation with clinical measures. Reliability can be increased either by improving measurement methods or by reducing or eliminating sources of variance in the recordings. In this paper, the effect on P50 amplitude and suppression of variation in wakeful alertness within an experimental session was examined in 20 normal subjects. The percentage of beta power in the interval immediately prior to the P50 stimuli was used as an index of alertness. P50 amplitudes or C-T ratios were estimated using peak-picking and using the singular value decomposition (SVD) method. No effects of variation of wakeful alertness were observed on any P50 amplitude or suppression measure. Comparing the peak-picking vs SVD estimates replicated our prior results showing markedly higher reliabilities with SVD. We conclude: 1) that variation within an experimental session in wakeful alertness level as indexed by the percentage of beta power does not affect P50 amplitude or suppression, and 2) the SVD method brings the reliability of the C-T ratio up to levels where its usefulness in clinical studies can be examined. © 1997 Society of Biological Psychiatry Key Words: Singular value decomposition, C-T ratio, event-related potentials, beta power, auditory P50 B I O L PSYCHIATRY
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Introduction The auditory P50 component is a middle-latency eventrelated potential (ERP). Its suppression in repetitive stimulation experiments indexes a preattentive, or neuronal sensory gating process that may be useful for studying the genetic predisposition for schizophrenia (Adler et al 1992; Waldo et al 1988, 1991). Its amplitude and suppression From the Department of Psychiatry, University of California, San Francisco and Psychiatry Service, San Francisco Veterans Administration Medical Center, San Francisco, California. Address reprint requests to George Fein, PhD, 4•50 Clement Street (II6R), San Francisco, CA 94121. Received March 16, 1995; revised April 8, 1996.
© 1997 Society of Biological Psychiatry
may also be useful for measuring the acute and chronic central nervous system effects of disease and/or drugs of abuse which affect the dopaminergic, cholinergic, or noradrenergic neurotransmitter systems (Adler et al 1988, 1990, 1992; Baker et al 1990, 1991; Boutros et al 1993; B uchwald et al 1991; Dickerson and Buchwald 1991; Fein et al 1995; Harrison et al 1990). Conditioning-testing paradigms afford independent measurement of P50 response amplitude and P50 suppression. In such paradigms, responses are recorded to pairs of clicks, where the interval between pairs of clicks is long (i.e., 8-10 sec) compared to the interval between clicks in a pair (i.e., usually 0.5 sec). The response amplitude to the second 0006-3223/97/$17.00 PII S0006-3223(96)00186-2
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(testing) click as a proportion of the response to the first (conditioning) click is termed the conditioning-testing (C-T) ratio, the P50 suppression measure. A P50 analogue can be recorded in the rat and cat (Adler et al 1986, 1988; Chen and Buchwald 1986), allowing animal models to be used to determine the mechanisms underlying P50 amplitude and suppression changes in humans. Enthusiasm for, and the potential utility of, the P50 response as a research tool is tempered by the low test-retest reliability of P50 suppression as estimated by the standard measurement technique, peak-picking on a single-channel (usually Cz). Previous researchers have measured the reliability of C-T ratios measured using peak-picking and obtained values ranging from r = 0.11-0.37, with r = 1.00 being perfect (Boutros et al 1991; Cardenas et al 1993; Kathmann and Engel 1990). This low reliability severely limits the value of the P50 suppression measure for correlation with clinical measures or for longitudinal analysis. The reliability of a measure such as the C-T ratio is a function of: a) the consistency within individuals over time of the "true" underlying phenomena that the measure is supposed to capture (in this case P50 amplitude or suppression), and b) the contributions of measurement error, which result in the measure being different from the "true" value of the phenomena. In previous work in our laboratory, we have addressed (b) above, via suggested changes in measurement and estimation of P50 amplitude and suppression (Cardenas et al 1993, 1995; Cardenas 1994). In this report, we attempt to address aspects of (a) above, by examining whether variations in alertness levels within an experimental session affect P50 amplitude and suppression. We have previously shown that P50 amplitude and P50 suppression are unaffected by attentional manipulations (Jerger et al 1992), allowing measurement of P50 mechanisms in experiments where sustained attention by subjects is neither monitored nor assured. Our attention findings appear at first glance to disagree with those of Guterman and colleagues (Guterman et al 1992); however, it is likely that the differences in results are consequent to confounding of P50 and N100 phenomena in their study. In our study, the data were digitally bandpass filtered between 10 and 50 Hz, removing the contributions of the predominantly low frequency N100 (known to be affected by attention) while leaving the P50 intact. In fact, we reported very large N100 effects in our paradigm. If N100 is not filtered out, the P50 waves ride on N100 waves and the N100 effects of attention are present in the confounded P50 measures. In Guterman's study, a digital lowpass filter with a passband cutoff of 50 Hz was used. Since no high pass filter was used, the low frequencies that contribute to the N100 were not removed from the P50 measures.
Therefore, in their study, the P50 findings may simply have reflected attention effects on N100 amplitude. Adler and colleagues (Adler et al 1988) showed that the analog of P50 in the rat increased in amplitude and C-T ratio during "hyperarousal." When Adler's rats were first introduced into the testing chamber, exhibiting behavioral signs of high arousal, testing commenced and only nonmoving, alert trials were accepted for averaging. N50 (the rat analog of the human P50) amplitude and C-Tratio were significantly increased during this period of hyperarousal as compared to a second experiment conducted about 1 hour later, when the rats were accustomed to the environment. Consistent with these results, prior research in the cat and human showed that wave A (cat) and P50 (human) amplitude decreased during periods of very low alertness. Chen and Buchwald (Chen and Buchwald 1986) monitored the effects of sleep on wave A in the cat. They presented repetitive, 1-sec clicks to the cats during wakefulness, slow wave sleep (SWS), and rapid eye movement (REM) sleep. The amplitude of wave A decreased then disappeared during SWS and reappeared during REM sleep. Erwin and Buchwald (Erwin and Buchwald 1986) repeated this experiment in humans, and observed P50 amplitude decrements or P50 disappearance during SWS, with a reappearance of P50 during REM sleep, with amplitude approximating that observed during waking. Griffith and colleagues (Griffith et al 1993) examined P50 C-T ratios before and after a brief period of sleep. The C-T ratios of the normals increased immediately following a brief sleep period, when the subjects were presumably still somewhat drowsy. Within a few minutes of waking, the C-T ratios fell to the presleep level. Unfortunately, Griffith et al did not report the effect of sleep on the conditioning amplitudes. If consistent with the other studies, however, the conditioning amplitudes were probably smaller due to low alertness, resulting in larger C-T ratios. Because of these results demonstrating that large changes in alertness affect P50 amplitude and suppression, some researchers attempt to control for alertness when measuring P50 during waking in human studies. For example, Adler and colleagues (Adler et al 1992) routinely monitor the spontaneous EEG during P50 experiments, rejecting single trials which show evidence of decreased alertness. This procedure prolongs the experiment and reduces the number of single trials available for analysis. We believe that this change in P50 recording methodology may be premature since it is based on sleep study results that may reflect more extreme variations in conditions than occur during a waking experimental session. In this paper, we investigated the effect of alertness on human P50 amplitude and suppression, using prestimulus beta as an index of alertness. Beta refers to the frequency range of EEG greater than 13 Hz. Beta waves are large
Wakeful Alertness Does Not Affect P50
during periods of alert wakefulness, with lower frequency activity predominating during periods of quiet restfulness and sleep (Regan 1989). We: 1) recorded 2.56 sec of EEG before the conditioning click of each pair of stimuli, 2) analyzed the frequency spectrum of this prestimulus EEG, 3) computed a measure of alertness based on the power in the beta frequency range (13-30 Hz) as a percentage of power in the 1-30 Hz range, 4) classified each single trial as occurring during low alertness (low beta) or high alertness (high beta), 5) constructed averages of low and high beta single trials, 6) measured conditioning and testing amplitudes and C-T ratios on these low and high beta averages using peak-picking and the singular value decomposition (SVD) method, and 7) evaluated the effect of alertness on these measures. We acknowledge that autonomic measures such as galvanic skin response and heart rate are better indices of alertness, but our goal was to evaluate the effect of "normal" variations in wakeful alertness in order to determine whether it was necessary to monitor alertness during an experimental session. Using beta as an index of alertness mimics the single-trial rejection method used by Adler and colleagues (Adler et al 1992) during their P50 experiments. The auditory P50 conditioning-testing paradigm is particularly arduous and boring. We collect P50 in response to paired clicks, with a 0.5 sec interclick interval and a 7 - 8 sec interval between click pairs. We require our subjects to keep their eyes open and still while we collect a minimum of 100 eye-movement artifact-free trials, with a break halfway through data collection. It takes approximately 15 minutes to complete the experiment, and our subjects often report becoming sleepy during data collection. Because of our subjects' self-report, we were reasonably certain that changes in alertness were occurring during an experimental session.
Methods
Subjects We studied 20 subjects: 8 women and 12 men. These subjects had no history of drug or alcohol abuse, cardiovascular disease, diabetes, serious head injury, seizures, severe psychiatric disorders, or family history of neurologic disorder. Subjects were between 23 and 40 years of age (mean _+ sd, 28.7 __+ 5.1 years), were free of medication at the time of the study, gave informed consent, and were paid for their participation.
Recording Methods Subjects were relaxed, awake, and seated upright in a quiet but not acoustically isolated room during the recording
BIOLPSYCHIATRY 1997;41:891-901
893
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electrodewith large effect on amplitude estimation; used for SVD analysis electrodewith large effect on amplitude estimation; not used for SVD electrodewith small effect on amplitude estimation; not used for SVD electrodeexcluded from average reference and SVD analysis
Figure 1. The planar projection of the electrodes used to collect P50 is shown. The open squares mark electrodes that contributed little to the SVD analysis and the open circles mark electrodes that contributed greatly to the SVD analysis. The electrodes marked by filled circles were used for the final SVD analysis. The open triangles mark electrodes excluded from the average reference and SVD analysis. sessions. Recordings were made using 32 tin EEG electrodes in an electrode cap (Electro-Cap International, Eaton, Ohio) and referenced to a tin electrode clipped to the left earlobe. The electrode locations are illustrated in Figure 1. Vertical eye movements were monitored using gold cup electrodes placed above and below the right eye, and horizontal eye movements were monitored using electrodes placed at the lateral canthi. Impedances for cap electrodes were lowered using electrode gel (Electro-Cap International), and Grass EC2 Electrode Cream was used for the reference and electrooculogram (EOG) leads. Two kinds of electrode pastes were used because the ElectroCap gel was too thin for use in the cup electrodes or ear clip, and the Grass cream was too thick for use in the electrocap. Although the differences in ion concentration in the two pastes can cause a very slowly varying potential difference, this electrical artifact should be filtered out by our analog filtering. All impedances were below 5000 ohms and signals were amplified 50,000 times by a Grass Model 12 Neurodata Acquisition System with analog filters at 0.1 and 300 Hz. ERPSYSTEM Software (Neurobehavioral Laboratory Software, San Rafael, CA) was used to control stimulus presentation and data acquisition through an Analog Devices RTI 800-815/F laboratory interface card on a 20 MHz Intel 80386-based personal computer. P50 data were sampled at 2000 Hz beginning 20
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msecs before each click and continuing for 200 msecs after each click for a total of 440 msecs of data for each pair of clicks. Resting EEG data were sampled at 100 Hz for 2.56 sec beginning 2.58 sec prior to each click pair. We acknowledge that the sampling rate for the collection of the resting EEG was inadequate given our analog bandpass filtering, and that frequencies greater than 50 Hz would have been aliased; however, there is very little power in the EEG at frequencies greater than 50 Hz, and aliasing should not have greatly affected our beta power estimates. As a check on this, we estimated the EEG spectrum using the first 32 msec of the adequately sampled P50 data (20 msecs of prestimulus EEG and 12 msec of response to the click). We found that the frequencies below 31.25 Hz constituted an average (n = 20 subjects) of 92% of the total power contained below 1000 Hz. Since this estimate was obtained using data that spanned the time period of the high frequency auditory brainstem response, we expect that the percentage of total power below 31.25 Hz would be even higher in the resting EEG, and thus the contamination of any aliased high frequencies in our sampled data would be minimal. Trials, consisting of pairs of clicks, were rejected if activity exceeded _+60 IxV on either eye-movement channel.
Auditory Stimulation Clicks were produced by square electrical pulses of 0.05 msec duration, which were generated by the Analog Devices D/A converter, then passed through a HewlettPackard 350D Attenuator, amplified by a Pioneer SX2300 stereo receiver/amplifier, and delivered to the subject over Realistic NOVA '20 headphones (Tandy Corporation, Houston, TX). The clicks were 55 dB above subject threshold and were separated by 500 msec, with the intertrial interval varying between 7 and 8 sec (Zouridakis and Boutros 1992). A minimum of 100 artifact-free trials were recorded during each run.
Eye Movement Correction In previous work, we rejected single trials if electrooculogram (EOG) activity exceeded _+60 IxV. Even when this rejection procedure is used in normals, there is some danger that smaller (less than _+60 ~zV) amplitude EOG time-locked to the stimulus might still contaminate the average ERP. In this paper, we apply a frequency-domain eye-movement correction algorithm (Gasser et al 1985) to our data after first rejecting EOG trials that exceeded _+60 IzV. Gasser's method estimates the transfer functions between each EOG-EEG channel pair, multiplies these transfer functions by the EOG to estimate the amount of EOG transferred into each EEG channel, then subtracts
this quantity from the EEG channel. The multiplication and subtraction steps of the algorithm are repeated for each single trial, with the conditioning response corrected for the conditioning EOG, and the testing response corrected for the testing EOG. This decreases the effect of eye-movement contamination and increases the signal-tonoise ratio (SNR) of our data before estimation of amplitudes and C-T ratios.
Beta Wave Analysis We estimated the percentage of beta power for each click-pair to index the state of alertness. We estimated the percentage of beta power at 7 electrodes--Fz, FCz, Cz, F3, F4, FC3, and FC4--referenced to the average of 28 electrodes (we excluded electrodes Fpl and Fp2 from the average reference due to artifacts). We chose these electrodes to mimic the situation where alertness during the P50 experiment is assessed by examination of EEG at the P50 recording electrodes. We used all seven of these electrodes because P50 is distributed frontocentrally (Cacace et al 1990), and because we believed that use of all seven electrodes would give us a more stable alertness estimate. We first computed the Fast Fourier Transform (FFT) on the 2.56 sec of prestimulus EEG (256 data points) collected before the conditioning click. We summed the power in the beta (>13 and <-30 Hz) frequency range, divided that by the sum of the power in the 1-30 Hz range, and multiplied by 100 to get a percentage estimate. We then averaged the percentage of beta power over the seven channels used. We computed this beta power estimate for each single trial. If the percentage of beta power was above the median percentage of beta power for that subject, it was classified as a high beta response, which indicates a state of alert wakefulness or high alertness. If the percentage of beta power was below the median, it was classified as a low beta response, which indicates a state of quiet restfulness. In order to verify that the low beta responses truly indexed a more relaxed state, we compared the low and high beta single trials in each subject with respect to the average log power in the delta (-<3.5 Hz), theta (>3.5 and < 8 Hz), and alpha (->8 and -<13 Hz) ranges using paired t tests. We found significantly higher average estimates of delta, theta, and alpha log power in the low beta single trials (t19 = 3.88, p = 0.0010; t19 = 3.87, p = 0.0010; and t19 5.40, p < 0.0001, respectively). The means, standard errors, and p-values of log delta, theta, and alpha are presented in Table 1. Since a shift in the resting EEG to lower frequencies occurred during the low beta single trials, we conclude that these single trials occurred during a state of low alertness. We then averaged the low alertness single trials to create a low alertness (low =
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Table 1. Means, Standard Errors, and p-values for Log Power
Delta Theta Alpha
Low Beta Average
High Beta Average
p-value
5.39 _+0.19 5.41 _+0.19 5.56 _+0.22
5.19 _+0.20 5.28 + 0.20 5.41 + 0.22
0.0010 0.0010 <0.0001
prestimulus beta) P50, and averaged the high alertness single trials to create a high alertness (high prestimulus beta) P50. Our "sampling" was successful for constructing averages of low and high beta single trials; although a statistical test for differences in beta power is not appropriate, the paired t test w a s t19 = 17.90, p < 0.0001. We analyzed the low and high beta averages to determine the effect of alertness on P50 amplitude and suppression.
Peak Picking The average EPs were digitally bandpass filtered between 10 and 50 Hz. Because the dominant frequency constituents of the middle latency response range from 30 to 50 Hz (Suzuki et al 1983) and the dominant constituents of the auditory N100/P200 are below 10 Hz, this filtered out the majority of the auditory N 100 and P200 responses, filtered out 60 Hz noise, and allowed us to analyze the remaining P50 response (see Jerger et al 1992 for a description of the filter). The peak-picking algorithm of ERPSYSTEM was used to choose the peaks at channel Cz versus an average reference using 28 channels, and the peaks chosen were verified by an experienced rater (VC). The peaks were chosen as the most positive (or negative) voltage value within a specific time range after the stimulus. The time window for the negative peak preceding P50 was 30-50 msec, and the window for P50 was 4 0 - 8 0 msec. The algorithm never chose a P50 that preceded the N40. In cases where the auditory P30 overlapped the P50 such that no clear N40 could be identified, the P50 amplitude was measured to the prestimulus baseline. The P50 amplitude used for analysis was the difference between the P50 peak and N40 trough. The C-T ratio was computed by dividing the amplitude of the testing response by the amplitude of the conditioning response.
Singular Value Decomposition Method We also analyzed the data using the SVD method described by Cardenas (Cardenas 1994; Cardenas et al 1995) to estimate the conditioning and testing amplitudes, and the C-T ratio. We used the SVD method because we recently showed that the reliabilities of SVD P50 measures were comparable to dipole components model (DCM) P50 measures and significantly greater than peak-picking P50 measures, while the SVD method was much simpler and
easier to use than dipole source modeling. We also showed that when only a single generator or multiple synchronous generators are active (probably the case for P50 evoked by bilateral auditory stimulation), C-T ratios derived using the SVD method or DCM accurately measure the "true" C-T ratio. This occurs because the effect of model misspecification (or, in the case of SVD, the effect of no model) on the amplitude estimates "cancels" when a ratio is computed. The SVD method uses multiple channels of data, multiple timepoints, and multiple data sets to estimate a single amplitude estimate for each data set. Amplitude ratio estimates between data sets can then be derived. The SVD method estimates amplitudes by applying the singular value decomposition, a standard matrix decomposition (Strang 1988), to a data matrix of dimension n X mk, where n is the number of timepoints, m is the number of electrodes, and k is the number of data sets. For P50 conditioning-testing data, k = 2 since the conditioning and testing responses are estimated together. SVD decomposes the original n X mk matrix into an n X n matrix of left singular vectors, a diagonal matrix of singular values arranged in descending order, and an mk x mk matrix of right singular vectors. We refer to the application of SVD to the original n X mk data matrix as the decomposition into waveshapes and electrode weightings. The first left singular vector is the common waveshape among all channels and data sets, and the first right singular vector contains weighting factors for the channels in each data set; they are referred to as the "first" vectors because they correspond to the first and largest singular value. By arranging the first right singular vector into an m x k matrix multiplied by the first singular value, SVD can be applied again. We refer to the application of SVD to the m x k matrix of electrode weights as the decomposition into topography and data set weightings. Now the first left singular vector is the topography (or set of electrode weights) common among all data sets, and the first fight singular vector contains the amplitude weightings for each data set. The amplitude of each data set is the corresponding amplitude weight multiplied by the first singular value, and amplitude ratios can be derived from these amplitudes. The SVD method is closely related to principal components analysis (PCA). When PCA is applied twice to a data set in a manner similar to that described for SVD, exactly the same amplitude results can be obtained. The algorithm for applying PCA to estimate amplitudes comparable to SVD is completely described by Cardenas (Cardenas 1994). PCA, however, involves additional, unnecessary computation and is thus slower to compute than SVD. We applied the SVD method to average referenced data (excluding Fpl and Fp2 from the average reference) within a time window chosen by one of us (VC) to contain both the ascending and descending aspects of the P50 peak
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at the vertex. The starting point for the window over subjects ranged from 35-55 msec, and the window width ranged between 10 and 35 msec. In general, the vertex P50 within the window resembled one half period of a sinusoid. We used the same window width for all responses within a subject. The conditioning and testing responses for each of the high and low beta averages for each subject were then used to create data matrices of dimension 2n x 2m, where n was the window width in msec for that subject (the width is multiplied by 2 because we used a sampling rate of 2000 Hz), and m is the number of electrodes (m is multiplied by 2 because we analyzed the conditioning and testing responses simultaneously). We applied the SVD method to each subject's high and low beta data matrices, and computed conditioning and testing amplitudes for the high and low beta averages, and then derived C-T ratios from the amplitude estimates.
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We knew from previous work that the SVD method did not work well when multiple components were active, and that SVD would work best if only the channels at which P50 dominated were used; however, we had no objective method for choosing these channels, only our intuition based on reports that P50 is frontocentrally distributed. Therefore, we initially applied the SVD method to the 28 electrode channels used in the average reference, then examined the quantity sv~ / ~ sv~, which is the ratio of the first (largest) singular value squared to the sum of all the squared singular values. In principal components analysis, this quantity gives the proportion of variance explained by the first principal component (Jackson 1991), and although the SVD method has no such statistical framework, this quantity is useful for evaluating the validity of the SVD method assumption of a single active component. The closer this quantity is to one, the more likely only a single component is active. When using 28 channels, this quantity was 0.71 (computed over 19 subjects) for the decomposition into waveshapes and electrode weights, and 0.88 for the decomposition into topography and data set weights. Because these quantities were low, we suspected the presence of time-locked activity other than P50 at some channels. We examined the first left singular vector of the decomposition into topography and data set weights (the entries of this vector are electrode "weightings" common across all data sets), and we reasoned that the largest magnitude entries corresponded to the channels contributing most to the estimation of P50 amplitude and suppression. Of the 28 electrodes examined, the half that contributed most to the fit are designated in Figure 1 by circles. Of the 14 that contributed most to the fit, we chose to reanalyze the P50 C-T data using only the seven
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Figure 2. A normal (top) P50 conditioning-testing (C-T) response and a C-T response illustrating overlap (bottom) of the P30 and P50 responses are shown.
frontocentral electrodes--Fz, FCz, Cz, F3, F4, FC3, F C 4 - - b e c a u s e previous research has shown P50 to be distributed frontocentrally (Cacace et al 1990). These electrodes are designated in Figure 1 by filled circles (0). After we applied the SVD method to the 7 frontocentral electrode channels described above, we recomputed the quantity sv~ / "Z svai, and obtained values of 0.83 for the decomposition into waveshapes and electrode weights and 0.94 for the decomposition into topography and data set weights. We suspected that the comparatively low value for the decomposition into waveshapes and electrode weights was due to either P30/P50 overlap, since four of our subjects exhibited P30 overlapping with P50 in the conditioning response for one or more of their averages, or the very low SNR of our averages of approximately 50 single trials, and decided that the seven-channel SVD analysis was adequate. Figure 2 shows the P50 conditioning-testing response for representative subjects that did and did not exhibit P30/P50 overlap.
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For both SVD and peak-picking, if no P50 was present in response to the first click, that subject's measures were classified as missing. This was the case for one subject's high beta average using peak-picking, and for a different subject's high beta average using SVD. Looking at these subjects' multiple channel data, it was clear that P50 was more prominent at Fz than at Cz for the subject whose data were missing using peak-picking, and that a positivity was present at around 50 ms at Cz but no other channels for the subject whose data were missing using SVD. Similar to Nagamoto and colleagues (Nagamoto et al 1991), any C-T ratio greater than two was pegged to two to eliminate the effect of outliers on the statistics. This was the case for two subjects using peak-picking, and one subject (one of the peak-picking subjects, but the high beta average instead of the low beta average) using SVD. We observed no systematic effect of alertness on P50 amplitude or suppression, measured using peak picking (n = 19) or the SVD method (n = 19). We used paired t tests to compare the amplitudes and C-T ratios between the low and high beta averages. The peak-picking mean _+ standard error (se) of the conditioning amplitude for the low and high beta averages were 0.87 ___0.13 and 0.90 _+ 0.16 p,V, with no significant difference in the means (t~8 = - 0 . 3 5 , p = 0.73). The SVD mean + se were 12.54 + 1.16 and 12.46 + 1.16, with no difference in the m e a n s (t19 = 0 . 1 0 , p = 0.92). Note that the SVD amplitudes are not measured in txV, because the SVD amplitudes have no physical meaning and simply index the relative magnitude of each response to other responses. This also means that the magnitudes of the peak-picking and SVD amplitudes will not be comparable, although the C-T ratios estimated using either method should have similar magnitudes (the ratio is dimensionless). The testing amplitude responses were similarly unaffected by alertness. The peak-picking amplitudes were 0.38 -+ 0.07 ~V and 0.53 _.+ 0.15 ~V and the SVD amplitudes were 6.58 + 1.02 and 5.54 __+ 1.24. There were no differences in the means for either peak-picking or SVD (t18 = - 0 . 9 6 , p = 0.35 and t19 = 0 . 8 8 , p = 0.39, respectively). There was also no effect of alertness on the C-T ratio. The peak-picking C-T ratios were 0.54 _+ 0.12 and 0.52 _+ 0.12 (tt8 = 0.14, p = 0.89), and the SVD results were 0.60 --- 0.12 and 0.44 + 0.09 (t19 = 1.36,p = 0.19). The peak-picking amplitudes reported here are small compared to published normative data (Erwin et al 1991; Nagamoto et al 1991). This is entirely due to the fact that we average reference our data before peak-picking using a montage that contains a cluster of frontocentral electrodes. Although average referencing affects the amplitude estimates, the ratio estimates should be unaffected. When we peak-picked the vertex electrode referenced to the ear (n = 18, because no clear P50 was present to the
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Figure 3. The difference between the C-T ratio estimated on the low vs high pre-stimulus beta averages is plotted against the median percentage of beta power for each subject. There is no relationship between the amount of variation in the C-T ratio and the amount of beta in the subjects' EEG.
first click for two subjects) we obtained amplitudes comparable to those reported in the literature (low and high beta average conditioning amplitudes of 1.56 + 0.27 and 1.71 _+ 0.26), but still observed no differences in amplitudes or C-T ratios between the low and high beta averages. The paired t tests for the conditioning and testing amplitudes w e r e tit ~ - 0 . 6 8 , p = 0.50 and t17 = - 0 . 9 9 , p = 0.34 and for the C-Tratios w e r e t17 = 0.02,p = 0.98. The amount of beta present in the resting EEG varied greatly among subjects; some subjects simply had more beta activity than others. Although the mean + se beta computed over 1998 single trials (all single trials for 20 subjects) was 38.80 + 0.37%, with a median of 38.08%, the median percent beta across subjects varied between 10.74 and 71.63%. We questioned whether the effects of alertness were different for subjects with relatively low vs high beta percentages. Figure 3 charts the median percentage of beta for each subject vs the difference in the C-T ratio between the low and high beta averages. Figure 3 shows that prestimulus beta, and presumably wakeful alertness, does not systematically affect the C-T ratio. Since we found no effect of prestimulus beta on P50 amplitude or suppression, we treated the high and low beta averages as replications and computed the reliabilities of the amplitudes and C-T ratios estimated by peak-picking and the SVD method. We used the intraclass correlation coefficient (ICC) described by Shrout and Fleiss (Shrout
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and Fleiss 1979) to compute the reliabilities. We did this to attempt to replicate in an independent sample our prior demonstration that the C-T ratio estimated by SVD is more reliable than the C-T ratio estimated via peak-picking. The reliabilities (denoted by r) of the SVD estimates were r = 0.72 for the conditioning amplitude, r = 0.47 for the testing amplitude, and r = 0.37 for the C-T ratio. The reliabilities of the peak-picking estimates were r = 0.89 for the conditioning amplitude, r = 0.10 for the testing amplitude, and r = - 0 . 2 4 for the C-T ratio (the ICC is negative if the true reliability is small and the estimates are noisy). As expected, the SVD C-T ratio estimates were more reliable than the peak-picking ratio estimates, and the peak-picking and SVD amplitudes were at least comparably reliable. The C-T ratio reliabilities reported above are not as large as those Cardenas, et al. (Cardenas et al 1995) reported for C-T ratios estimated from averages of 120 single trials. Our low and high alpha responses were noisier averages of approximately 50 single trials, so some decrease in reliability is expected. Using formulas found in Dunlap and Silver (Dunlap and Silver 1986) and Shrout and Fleiss (Shrout and Fleiss 1979), we obtained a ballpark estimate of the reliabilities, had the ratios been estimated from averages of 120 single trials. We found the adjusted reliability of the C-T ratio estimated using SVD to be r = 0.68, and the adjusted reliability of the C-T ratio estimated using peak-picking to be r = 0.20. Cardenas, et al. computed r = 0.66 SVD C-T ratios, and r = 0.37 for peak-picking C-T ratios.
Discussion No effect of variations in wakeful alertness on P50 amplitude or suppression: We observed no effect of prestimulus beta on P50 amplitudes or C-T ratios estimated using either peak-picking or the SVD method. Because the percentage of beta present in prestimulus EEG is an indicator of alertness, we conclude that P50 amplitude and suppression are not affected by variations in alertness as indexed by prestimulus beta during waking recordings. Our results conflict with Adler et al.'s (Adler et al 1988), who reported increased N50 amplitudes and C-T ratios between rats that exhibited behavioral signs of high alertness and rats that did not. Even though our high beta averages represent periods where our subjects were relatively less relaxed and more aroused, it is likely that our subjects were not as "hyperaroused" as Adler's rats. Our subjects were normal, paid volunteers who spent approximately 40 minutes in the recording chamber while the electrodes were applied and their hearing threshold to the paired click stimulus was assessed, were acclimated to the
environment, and subsequently exhibited no change in P50 amplitude or suppression due to "hyperarousal." The lack of alertness effects could also reflect differences between rat N50 and human P50. Our low beta averages represent periods of quiet restfulness, during which our subjects often reported becoming drowsy; however, although our subjects were drowsy, they remained awake. Erwin and Buchwald (Erwin and Buchwald 1986) showed that the amplitude of P50 was diminished then disappeared as subjects progressed from sleep stage 2 through sleep stages 3 and 4, but did not systematically examine drowsiness. Although we expected decreased P50 amplitude during our low beta averages, it may be that sleep must occur before P50 amplitude is diminished. Griffith's subjects (Griffith et al 1993) showed increased C-T ratios after sleeping for 10 minutes. We found no change in C-T ratios due to changes in alertness, and suspect that C-T ratios only increase following sleep. Erwin and Buchwald also recorded P50 in a repeated click paradigm, whereas we recorded P50 in a paired click paradigm, and it is possible that the paired click paradigm is more resistant to changes in wakeful alertness. The results reported here, together with our prior results (Jerger et al 1992) suggest that one can validly record P50 in a conditioning-testing paradigm in humans without monitoring attention or level of wakeful alertness. Extremes of alertness, such as a subject falling asleep or experiencing anxiety due to the experiment, may still affect P50. This facilitates study of difficult subject groups such as schizophrenics or cocaine abusers, where attention and alertness are often difficult to control. It is just such subject groups where P50 amplitude and suppression abnormalities may give us insight into how these disorders affect the brain. Replication of increased reliability of P50 suppression estimated via SVD: This paper is another example of how analysis of P50 using multiple data sets (i.e., the conditioning and testing responses), multiple channels, and multiple timepoints improves the reliability of the C-T ratio compared to peak picking. The reliability of the C-T ratio estimated from averages of 50 single trials using the SVD method was r = 0.37, with an estimated reliability (for 120 single trials) of r = 0.68, comparable to those previously reported by Cardenas et al (Cardenas et al 1995). As in the prior paper, we note that the SVD method is simpler and faster than DCM; however, the SVD method is only valid when a single component is present during the time window of interest. This limits the application of the SVD method to early potentials such as the auditory brainstem response, the early somatosensory evoked potential, and the auditory middle latency response (i.e., auditory P50). We conclude that the SVD method,
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applied to multiple channel recordings, brings the reliability of the C-T ratio up to levels where its usefulness in clinical studies can be examined. Smith and colleagues (Smith et al 1994) suggested that the difference between the testing and conditioning amplitude measurements might be a more reliable measurement of P50 suppression, and should therefore be used instead of the C-T ratio. While Smith showed that the C-T difference (computed from C and T amplitudes estimated using single channel peak-picking) was more reliable than the C-T ratio, he reports the reliability of the average of k scores (k ranged between 2 and 6). Using this measure of reliability, we found that the C-T ratio ICC over two sessions of approximately 50-trial P50 data was r = 0.54, which is comparably reliable to their C-T difference (they report r = 0.47 over two sessions of 60-trial P50 data). We also computed the reliability of the C-T difference using the SVD estimated C and T amplitudes. We found the SVD C-Tdifference ICC over two sessions to be r = 0.47, no improvement over the SVD C-T ratio reliability. By using multiple channels of data, multiple timepoints, and multiple data sets the C-T ratio can be estimated at least as reliably as the C-T difference. In addition, the SVD C-T ratio is estimated accurately when only a single generator or multiple synchronous generators are active (Cardenas 1994; Cardenas et al 1995), which cannot be said for the C-T difference. In all our P50 studies to date, we recorded P50 from subjects seated upright, even though some researchers speculate that P50 cannot be reliably recorded from a seated position due to neck muscle artifact (Freedman 1990), and that recording P50 from a seated subject leads to higher C-T ratios. In order to determine if our relatively large, unreliable peak-picking C-T ratios were due to subject position, we recorded conditioning-testing P50s (500 msec interclick interval, 10 sec interpair interval) using a 32-channel montage while the subjects were seated. After a break during which we placed a mattress in the recording chamber, we repeated the recordings with the subjects supine. After eye-movement correction, we used SVD to estimate C-T ratios. We found no difference between C-T ratios recorded in the seated vs supine position (C-T ratio ± se = 0.37 4- 0.07 and 0.42 ± 0.07, respectively, t~2 = -0.74, p = 0.48). Because neck muscle artifact can appear as a large positive deflection at approximately 30 msec (Bickford et al 1964; Cody et al 1964; Robinson and Rudge 1982), we also noted the presence, and if present, the amplitude of P30 using peak-picking at the channel of maximal P50 amplitude. We found no difference in the presence or amplitude of P30 in seated and supine subjects, and conclude that subject position does not contribute to P50 unreliability.
We have prepared a separate short report detailing this study (McCallin et al 1996).
Appendix In this appendix we describe how we estimated the reliability that would have occurred had we had averages of 120 single trials instead of 50 single trials. We used the approximation for the confidence interval for ratios of normal variables described by Dunlap and Silver (Dunlap and Silver 1986), then used this approximation in the computation of the intraclass correlation coefficient described by Shrout and Fleiss (Shrout and Fleiss 1979). According to Dunlap and Silver, when the numerator and denominator of a ratio are independent, and the numerator (C) is much greater than its sample standard deviation (so), the standard error (SE) of the C-T ratio is:
SE-
C
'
where T is the denominator of the ratio and s t is the sample standard deviation of T. From the Central Limit Theorem (Hogg and Tanis 1988) we know that as more samples are added to an average, the expectation of the averaged sample is the true mean (Ix), and the variance of the averaged sample is the true variance divided by Y (~a/y), where Y is the number of samples in the average. Using this formula, we can easily show that the SE of the C-T ratio estimated using 50 and 120 single trials is:
SE, o
=
2s )c,
,[l(S~ + T2
\/1(2 SE12° =
T2 2' C
'
and that
1 \,;120
SE12o -
1
SEso = 0.645.SE50.
From Shrout and Fleiss we know that the statistical model for each C-T ratio measurement is: xij = tx + bj + wij, where x o is the C-T ratio for data set i, subject j, bj N(0, or2) is the true amount by which subject j ' s C-T ratio differs from the population mean Ix, and wij ~ N(0, ~2) is the error term. As the number of single trials in the average
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increases from 50 to 120, the distribution of wij will change to wij -- N(0, 0.6452 ~ ) , and the distribution of bj will remain unchanged. The adjusted reliability is then: B M S - 0.6452 W M S r = B M S + (k - 1)0.6452 W M S '
(1)
where B M S and W M S are quantities from a one-way analysis of variance. B M S is the between subjects mean square error, with (l-l) degrees of freedom, W M S is the within subjects mean square error with k(1-1) degrees of freedom, 1 indexes the number of subjects, and k indexes the number of repeated measurements on each subject.
Using equation (1), we can compute the adjusted reliabilities for the amplitude and C-T ratio estimates. These adjusted reliabilities are only ballpark estimates, because the C-T ratio standard error estimate is only valid when C > > s c and in our case C is only about twice s c.
This work was supported by General Medical Research funds from the Department of Veterans Affairs, the DVA career scientist program (Dr. Fein), and NIDA grant R01DA08365.
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