Psychiatry Research, 39:33-44 Elsevier

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Measurement of Maximum Variability Within Event Related Potentials in Schizophrenia

John Anderson, Christopher Rennie, Evian Gordon, Alan Howson, and Russell Meares

Received December 27, 1990; revised version received June 18, 1991; accepted August 25, 1991.

Abstract. One limitation of averaging individual late component event related potential (ERP) responses is that a single average ERP cannot reflect the variability of responses from epoch to epoch. In this article, we describe a method to quantify this variability and determine if any part of the overall ERP reflects a maximum variance through the use of response variance curves. We then apply this method to one disorder, schizophrenia, in which variability of information processing is hypothesized to underlie aspects of the symptomatology. Response variance curves in a group of unmedicated schizophrenic patients reveal systematic differences, maximal between 190 and 250 ms, compared with those in a group of medicated schizophrenic patients and normal control subjects.

Key Words. Electrophysiology, evoked potentials, information processing.

One of the features inherent to schizophrenia since its original description as a nosological entity is variability in expression of symptoms. Not surprising, therefore, has been the observation that schizophrenic patients are more variable than normal subjects on almost any physiological or psychological measure (Callaway, 1975). One measure, the event related potential (ERP), reflects averaged electrophysio- logical processing of discrete information with millisecond resolution. Previous studies have found increased ERP variability in schizophrenic patients; none of them have determined which if any part of the overall processing response is most variable (Callaway et al., 1970; Donchin et al., 1970; Saletu, 1977; Buchsbaum and Coppola, 1979; Roth et al., 1979; Shagass et al., 1979). These studies applied correlational coefficients and mathematical procedures to averaged evoked potentials to deter- mine that schizophrenic patients have a more variable response to target stimuli than normal subjects do. Previous investigations in schizophrenia have used curve- fitting techniques to determine either amplitude or latency variability of individual ERP components (particularly the P300 component). Investigators (e.g., Pfeffer-

John Anderson, B.Pharm., M.Litt., is a Reasearch Officer in the Neuroscience Unit of the University of Sydney and Westmead Hospital. Christopher Rennie, BSC (Hans), M.Biomed.Eng., is a medical physicist in the Department of Medical Physics of Westmead Hospital. Evian Gordon, Ph.D., M.B.B.CH.,is Senior Lecturer in the Department of Psychiatry of the University of Sydney and Westmead Hospital. Alan Howson, A.M.(Harv), is Head of the Department of Statistics at Macquarie University. Russell Meares, M.D., FRANZCP, is Professor of Psychiatry and Head of the Department of Psychiatry, Westmead Hospital. (Reprint requests to J. Anderson, Cognitive Neuroscience Unit, Dept. of Psychiatry, Westmead Hospital, Westmead, N.S.W., 2145, Australia.)

OI65-1781/91/$03.50 @ I991 Elsevier Scientific Publishers Ireland Ltd.

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baum et al., 1984; Michalewski et al., 1986; Thomas et al., 1989) have investigated ERP component variability using single trial analysis, assigning different latency windows for each component in which the measure of amplitude or latency is deter- mined, and then applying curve-fitting procedures or principal component analysis to examine variability of individual ERPs.

The averaging process is a technique to reveal time-locked responses in the presence of noise and is a simple and soundly based method of determining the underlying ERP. However, the averaging process is designed to remove variability. Variability of the single trial epochs that make up the average may contain complementary information to the average measure itself, particularly in disorders such as schizophrenia in which variability in processing information may underlie or be associated with primary aspects of the disorder. In this regard Carr and Wale (1986), for example, have suggested:

What at first glance appears to be a bewildering degree of variability in the manifestations of schizophrenia is neither random nor an incomprehensible agglomeration of symptoms . . variability is itself viewed as the key to identifying the disordered biopsychological events that interact in the pathogenesis of schizophrenia. [p. 1371

In this article, a method is proposed to determine if any part of the overall ERP reflects a maximal trial-to-trial variance-without any attempt to determine a priori the variability of any single ERP component. Rather, the variability is examined of the overall single trial epochs that constitute the average ERP response. This measure is referred to as the response variance curve (Fig. 1). The response variance curve provides information that is complementary to that provided by the traditional average ERP.

Previous approaches to the examination of variability range from template matching to peak-picking and perform satisfactorily when ERPs are prominent. When ERPs are not prominent, however, they tend to generate spurious component identifications and biased estimates of variability (Michalewski et al., 1986; Gratton et al., 1989). The response variance curve method makes minimal assumptions about the nature of the scalp potentials. It entails calculation of not only the mean but also the corresponding variance for each of the 256 points within the sampling window. The variance measures of each individual’s single trials compared to his own average will reflect the variability of the ERP response. Fig. 1 is an analytical model that shows how amplitude and latency variability might be reflected in the response variance curve.

In this study the average ERP and response variance curves for task-relevant (target) auditory stimuli are examined in unmedicated schizophrenic patients, medicated schizophrenic patients, and age-matched normal control subjects. One aim of the study is to examine whether schizophrenic patients show greater target ERP variability than normal control subjects and whether there is a window of maximum variance within the response variance curve. Ratings for clusters of positive and negative symptoms (Andreasen and Olsen, 1982) were also used to assess the relationship between symptom profile and variability of information processing as measured by the response variance curve.

Fig. 1. Simulation of latency variability and amplitude variability using a realistic analytical model

ANALYTICAL MODEL

!YC

1

The 4 Gaussian components are shown at the top, and examples of the effect of introducing amplitude (left) and latency (right) variability to the N200 component are shown below. The average event related potential (ERP) and the response variance curve (WC) are also shown.

Methods

Subjects. Ten unmedicated schizophrenic patients, with a mean age of 23.8 years (SD = 5.3) were sex and age matched (within 4 years) to 10 medicated patients with a mean age of 24.7 years (SD = 4.9) and 10 normal control subjects with a mean age of 23.3 years (SD = 4.7). All experimental subjects were men. Seven of the 10 unmedicated schizophrenic patients had

36

never received psychotropic medication and were first-presentation cases with diagnosis subsequently confirmed by an independent psychiatrist. The remaining three unmedicated schizophrenic patients were in the residual phase of illness and had been free of oral medication for at least 8 weeks and of depot medication for at least 6 months. These three subjects had a duration of illness from 4 to 12 years. The medicated patients had a duration of illness from 2 to 15 years and had been receiving medication for at least 2 weeks. Three of these subjects were in an acute phase of illness and currently hospitalised. The remaining seven patients were in the residual phase and were living in the community in parental care on sickness benefits. Patients were interviewed with the Composite International Diagnostic Interview (Robins et al., 1987) from which Research Diagnostic Criteria (Endicott and Spitzer, 1979) and DSM-III-R (American Psychiatric Association, 1987) diagnoses of schizophrenic disorder were derived. Patients were subsequently rated on both the Scale for the Assessment of Positive Symptoms (SANS) and the Scale for the Assessment of Negative Symptoms (SANS) (Andreasen and Olsen, 1982). Normal control subjects with any history of neurological or psychiatric disorder were excluded.

Acquisition of Electrophysiological Measures. The auditory “oddball” paradigm was used. Tone bursts (SO-rns duration, IO-ms rise and fall time) at 60 dB SPL above threshold were presented binaurally through headphones at a rate of l/ 1.3 sec. Eighty-five percent of the tones were of 1000 Hz (background tones) and the remaining 15% were of 1500 Hz (target tones). The background and target tones were randomly intermixed with the constraint that no two target tones were presented in succession.

The stimulus responses were recorded from 15 sites of the International IO-20 system. Linked earlobes served as reference. In addition, two electrodes (one placed 1 cm above the outer canthus of the right eye and the other placed 1 cm below the outer canthus of the left eye) were used to record potentials arising from eye movements.

Auditory thresholds were measured and the two types of tones were demonstrated at 60 dB above threshold. All subjects participating in the study were able to discriminate clearly between the tones. Participants were instructed to press a response button “as fast as possible” to the target tones. Electrical potentials were amplified 5,000 times with a bandpass of 0.5-70 Hz before sampling at 256 Hz for 300 ms before the stimulus and 700 ms after the stimulus. Acquisition continued until 30 target responses were obtained. All single trial ERP epochs to both target and background stimuli were stored.

To control for fatigue and habituation effects, only the first 30 target responses of each subject were analyzed. In a further effort to make the data of the three groups comparable as possible, each unmedicated schizophrenic patient was age-matched within 4 years to a medicated subject and a control subject, and for each trio of subjects, discrete ERP target responses were included for further analysis only if both the experimental and the control subjects correctly identified the target (as indicated by a corresponding button response) and if the responses were considered artifact-free (as determined by applying a threshold of 100 ~1 V to the electro-oculographic channel). With this procedure, 234 target responses (out of a maximum possible of 300) were extracted to represent each of the three groups.

Data Analysis. Average ERP waveforms were determined. One-way analyses of variance (ANOVAs) that compared the three groups were performed separately on the averaged waveform ERP data obtained from each site (Fz, Cz, Pz) and for each component (NlOO, P200, N200, P300).

Variance curves were subsequently determined from the single trial epochs by calculating the variance at each of the 256 points in the sampling window. This procedure was carried out for each recording site of each subject. Group and individual ERP and response variance curve data were analyzed with respect to the prestimulus baseline.

Results

Group Differences. Fig. 2 shows the conventional average target ERPs for the

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Fin. 2. Group results for conventional averaged event related potential [ERP) target re! mses at Cz

l= Unmedicated Schizophrenics

2= Medicated Schizophrenics

3= Normals

Amplitudes of P300 of unmedicated schizophrenic patients (1) and medicated schizophrenic patients (2) are compared to those of normal controls (3). Each curve is the result of 234 single trial ERPs. The N200 in the experimental groups is reduced (particularly at Pz) compared with that in normal control subjects.

three groups. The differences among groups in P300 amplitude are in the expected direction-that is, the smallest amplitudes in the unmedicated acute group, followed by the chronic medicated group, and with the largest amplitudes in the normal control subjects. However, this difference was not significant at any site according to ANOVA. Fig. 3 shows the corresponding group average response variance curves. The large between-group mean differences, particularly at about the N200 latency, when examined using one-way ANOVA at the maximum 234 ms, were not significantly different, probably because of large SDS (see Table 1).

Individual Differences. Each individual response variance curve was autoanalyzed to obtain the variance score at the group average maximum variance latency of 234 ms (see Table 2). Fig. 4 shows the response variance curves at Cz for each of the subjects. There was a systematic increase in variance evident within the latency range of 190-250 ms in 8 of the 10 schizophrenic patients. Only two unmedicated schizophrenic subjects (S9 and SlO) were considered as not exhibiting a maximum peak within this latency range. In contrast to the unmedicated schizophrenic

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Fig. 3. Group results for response variance curve (WC) at Fz, Cz, and Pz electrode sites

FZ

CZ

PZ

I- I I

100 ido 3bo 400

100 “V2 -i + 8

Unmedicated Schizophrenics In=lOI

Medicated Schizophrenics kl03

Normals Cn=lOI

Unmedicated schizophrenic group ; medicated schizophrenic group .-. - l -e ; normals AA& .Only the unmedicated group showed increased RVC within the N200 wmdow.

Table 1. Mean scores of the variance of the unmedicated schizophrenic, medicated schizophrenic, and normal control groups at 234 ms of the response variance curve

Grout, Site Mean SD Minimum Maximum

Unmedicated

schizophrenics Fz 146/# 147jIv-J lO/lvz 445 I.tvz CZ 141 132 5 358 Pz 116 117 1 357

Medicated

schizophrenics Fz 58 72

cz 78 64

Pz 47 42

220

234

143

Normals Fz 81 101

cz 60 67

Pz 95 62

10

4

2

13

5

7

336

175

180

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Table 2. Maximum variance of each subject within the 190-250 ms latency window

Subject Unmedicated Medicated schizophrenic Subject schizophrenic Subject Normal

Sl 358

s2 313

s3 289

s4 139

s5 137

S6 66

s7 62

S8 22

s9 15

SlO 5

Ml 5

M2 8

M3 13

M4 15

M5 18

M6 35

M7 49 M8 110

M9 168

Ml0 175

Nl 4

N2 27

N3 43

N4 46

N5 70

N6 70

N7 72

N8 92

N9 125

NlO 234

Fig. 4. Individual response variance curve (RCV) results in event related potentials (ERPs) of unmedicated schizophrenics (a), medicated schizophrenics (b), and normals (c)

S7

S8

s9

a Irk103

M6

M7

M8

M9

b b-t-101

Nl

N2

N3

N4

N5

N7

N9

NlC

333 I- uv*

+

Each curve IS the result of between 17 and 30 single trial waveforms, and has been arranged to demonstrate more clearly the range of responses. The site IS Cz, and the solid line marks stimulus onset.

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patients, perhaps 2 of the 10 medicated schizophrenic patients (M9 and MlO) and 2 of the 10 normal control subjects (N 1 and N4) had a maximum variance peak at the N200 latency: if there was a peak in both of these groups, it tended to occur later, at about the P300 latency. Noise levels were not uniform as the level of electro- encephalographic (EEG) activity varied among subjects, and the number of responses contributing to the curves ranged from 17 to 30.

A multi-sample binomial test with an arc-sine transformation of sample proportions (Cohen, 1967) was used to test the hypothesis of equal proportions for subjects in the three groups, with a maximum variance within the N200 window. The obtained test statistic, 11.05, exceeds 9.21, the tabulated x2 value with 2 degrees of freedom at the 0.01 level. The resulting 99% confidence interval level in the unmedicated and each of the other groups was 0.6 * 1.357 = (0.757, 1.957). This interval does not cross zero, indicating that the interval is statistically significant at the 0.01 level. Therefore, for the visual classification of the maximal response variance curve measure (within the N200 window), the number of subjects identified as having this measure was significantly different in the unmedicated schizophrenic group. The visual classification process enables spatial and temporal patterns in the data to be taken into account and is thereby partly able to compensate for noise.

When the individual response variance curves (Fig. 5a) and the individual ERPs (Fig. 5b) are examined, the response variance curves tend to have a simpler unipolar morphology. The simpler structure of response variance curves probably aids in the task of individual discrimination.

The symptom profiles of the schizophrenic patients were examined using the SANS and the SAPS. A two-sample t test suggested that there were no significant differences in SAPS (t = 1.61, p < 0.13) and SANS (t = 0.32, p < 0.75) scores between the unmedicated (SAPS = 10.5, SD = 5.19; SANS = 14.0, SD = 3.56) and medicated (SAPS = 7.3, SD = 3.56; SANS = 14.7, SD = 4.64) schizophrenic groups.

Discussion

The traditional averaging ERP technique showed no significant P300 amplitude differences between either unmedicated or medicated schizophrenic patients and control subjects. Failure to reach significance may be due to the small sample size and the large SDS. Methodological differences and characteristics of the particular group of patients studied may also have contributed to our nonsignificant findings. In the experimental design, subjects were required to perform a button press response “as quickly as possible” until 30 artifact-free target responses were recorded. The task demands in this study were simpler than those of the silent count or dichotic listening designs in which P300 amplitude differences have been reported. The psychopathological profile of the schizophrenic patients may also have contributed to our nonsignificant findings since 14 of the 20 patients had no current experience of institutionalization. Seven of the 10 unmedicated subjects were first- presentation cases and were tested immediately upon admission. Seven of the 10 medicated patients were in the residual phase of schizophrenia and had been living outside hospital or institutionalized situations for a considerable period of time.

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Fig. 5. Comparison of response variance curves (RCVs) of unmedicated schizophrenics (5a) with corresponding conventional average event related potentials (ERPs) (5b)

Sl -I/

s2 _

s3 -

s4 _._/

S5

S6

T

/I

L’

-l-

/--

2-

L /

I‘

.

I

100 200 300

s7 ~

sa -

s9 -

SlO _

5(a)

s3

s4

S5

S6

s7

S8

s9

sio -- 333

“V 2

I+ S(b)

The site is Cz, and the solid line marks stimulus onset.

The response variance curve measure is a complementary but dependent measure of the ERP waveform since the same raw data simply have different mathematical procedures applied to them (variance vs. average). In this study, the most consistent finding with the response variance curve method was a peak of maximum variance within the N200 latency window in 8 of the 10 unmedicated schizophrenic patients. This maximum variance peak in the N200 latency range was not evident in either the medicated schizophrenic patients or the normal age-matched control subjects (Fig. 4). Such a distinction among the individuals was not apparent from the ERP data (Fig. 5b).

The finding of increased ERP variability within a defined latency window (in this

42

paradigm corresponding to the N200 component in unmedicated schizophrenic patients) may provide a more focused time frame in which to examine further dysfunctional stages of information processing (as reflected by late component ERP) than would a finding of changes in variability, per se, over the entire epoch.

In this study we also examined whether variability of response was associated with symptom profile. Specific symptoms or groups of symptoms may reflect the outward expressions of variability of information processing (Callaway, 1975; Venables, 1984; Carr and Wale, 1986). The results of previous efforts to correlate specific symptoms with particular ERP component measures have been equivocal (Roth et al., 1979, 1980; Josiassen et al., 1981; Brecher and Begleiter, 1983; Kessler and Steinberg, 1989). Differences in variability of response were not found to be associated with differences in SANS and SAPS symptom profiles in the patient groups. The difference in response variance curve findings between the unmedicated and medicated groups therefore seems likely to reflect a medication effect. Nevertheless, such an interpretation needs to be substantiated by serially examining the same patients before and after medication. In this study, intersession reproducibility of the response variance curve measure was demonstrated in a small number of subjects. Future studies need to examine the reproducibility and specificity of the response variance curve findings in schizophrenia in larger groups of patients and other psychiatric control groups.

A useful feature of the response variance curve approach is that it avoids some of the difficulties of single trial variability analysis. Other methods analyze single trial epochs individually, which is conceptually attractive but not easy in practice: the difficulties stem from the unfavorable signal-to-noise ratio generally found in single trials. In contrast to this, the response variance curve method (similar to the averaging procedure) involves calculation of a single curve from all single trial ERPs, and thereby enhances time-locked variability and aids in delineating peaks or windows of maximum variance within an epoch. The presence of any superimposed signal, such as EEG, that is not time-locked to the stimulus affects the response variance curve only by adding a baseline shift, and the effect of this offset can be removed from the response variance curve by the usual technique of measuring peak amplitudes with respect to the prestimulus baseline.

Other properties of response variance curves can be demonstrated from appropriate numerical simulations. For example, taking a model ERP consisting of four superimposed Gaussian curves (representing the four components NlOO, P200, N200, and P300) and varying the amplitude and latency of the N200 component produces the results shown in Fig. 1 and Table 1. It is evident from these examples that the response variance curve method is sensitive both to amplitude and to latency variability. Note also that the shapes of the response variance curves differ in the two cases, and that in the case of pure amplitude variability, the shape and latency of the square root of the variance exactly matches that of the corresponding component.

Strictly speaking, any systematic change in variance from the prestimulus baseline level may be due not only to response variability (nonstationary ERP) but also to changes in background variability (time-locked EEG variation). This is an important distinction, but one that is phenomenologically difficult to make. At present, only the near coincidence of the ERP peaks and the response variance curve peaks can be

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Table 3.Example of the relationship (left) between latency variability and peak variance, and (right) between amplitude variability and peak variance

N2 latency Peak variance N2 amplitude Peak variance

220 f 5 ms 2.6 /.M -10+5/N 28/N

10 6.9 10 87

15 16.2 15 165

20 18.4 20 390

Note. Each variance is the result of 50 simulations similar to those shown in Fig. 5. Variances associated with latency variability are small relative both to those due to amplitude variability and to observed variances. See also the relationship (amplitude SD) w J(peak variance). (The relationship is imprecise only because of the limited number of simulations.)

pointed to as evidence of ERP variability as the immediate cause of the observed effects.

Attempts to account quantitatively for the experimental results shown in Fig. 3, in contrast to modeled response variance curve results, are somewhat hampered by the lack of a priori knowledge of the mean amplitudes and latencies of the underlying pure components. A better understanding of generation mechanisms would help in unraveling the several overlapping components that constitute ERPs. In the meantime, it may be noted, with reference to Fig. 1 and Table 1, that the observed variance peaks are unlikely to be due to latency variability alone: the magnitudes of the observed variances are such that amplitude variability is a more plausible explanation of the observed response variance curve amplitudes and shapes. As already noted, the correspondence between the size of the peak of the response variance curve and the SD of the amplitude is in this case simple.

Future studies need to address the issue of possible mechanisms of how the variance arises within a defined latency range following a stimulus and its implications for understanding dynamic information processing in schizophrenia. The issue of the reproducibility and robustness of the response variance curve measure must also be addressed in future studies of subjects who do show significant P300 amplitude differences versus control subjects.

In conclusion, response variance curves provide complementary information to that provided by the average ERP. The response variance curve method has the potential to help illuminate the complexities of information processing in all disorders in which late component average ERPs have been found to be dysfunctional.

Acknowledgment. The authors are grateful for the financial support of the Rebecca Cooper Foundation of NSW and IBM Australia.

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