Quantitative electrophysiological characteristics and subtyping of schizophrenia

Quantitative Electrophysiological Characteristics and Subtyping of Schizophrenia E. Roy John, Leslie S. Prichep, Kenneth R. Alper, Francis G. Mas, Robert Cancro, Paul Easton, and Lev Sverdlov

Quantitative descriptors of resting electroencephalogram (EEG) (QEEG) and event-related potentials (QERP) to visual and auditory stimuli were obtained from normal subjects and 94 chronic schizophrenic patients on medication, 25 chronic schizophrenics off medication, and 15 schizophrenics with no history of medication. These schizophrenic groups showed a high incidence of neurometric features that were significantly deviant from normative values. Multivariate discriminant analysis using these features successfully separated the schizophrenic patients from normals with high accuracy in independent replication. The data from the medicated group were subjected to cluster analysis. Newly developed algorithms were usedfor objective selection of the most effective set of variables for clustering and the optimum number of clusters to be sought. Five clusters were obtained, containing roughly equivalent proportions of the sample with markedly different QEEG profiles. The whole sample was then classified into these clusters. Each cluster contained patients both on and off medication, but patients who had never been medicated were classified into only three of these clusters. No significant clinical or demographic differences were found between members of the five clusters; however, clear differences in QERP profiles were seen. These results are described in detail and possible physiological and pharmacological implications are discussed.

Key Worttq: QEEG, ERP factors, neurometrics, schizophrenia, subtyping

Introduction Since the initial observations of Berger (1929), there have been numerous studies of the electroencephalogram (EEG) in schizophrenic patients. Small (1983) has reviewed much of the voluminous literature on qualitative evaluations of the EEG by conventional visual inspection, which encompasses hundreds of studies including monumental efforts From the Department of Psychiatry. New York University Medical Center. New York, NY (EIR, LSP. KRA, FGM, RC, PE, LS); and the Nathan S. Kline Research Institute. Orangeburg, NY (FIR, LSP, PC). Address reprint requests to E.R. John. PhD. Brain Research Labs, New York University Medical Center. Department of Psychiatry, 550 First Avenue.New York, NY 10016. Received June I. 1993; revised April 15,1994. © 1994 Society of Biological psye.hiaL,'y

such as that of Colony and Willis (1956) who studied over 1000 schizophrenic patients. Quantitative studies of the EEG (QEEG) began more than a quarter century ago, pioneered by such workers as Goldstein et al (1963) and Marjerrison et al (1968) using analog circuitry, followed by workers using digital computer analysis methods such as those introduced by ltil and his colleagues (1972a, b). Both qualitative and quantitative studies of the EEG in schizophrenia have recently been thoroughly reviewed by Shagass (1991). A short time after the studies of the EEG in man began, researchers such as Adrian (1944) expressed concern about the limitation of EEG measurements largely to the study of 0006-3223/94/$07.00

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spontaneous cortical activity. This limitation was overcome first by the efforts of Dawson (1947, 1951) who provided the theoretical basis of averaging techniques. With the advent of practical average response computers in the early lt)60s (Clynes and Kohn 1961), research on spontaneous EEG rhythms was complemented by evoked potential studies of sensory information processing and shortly thereafter, with the discovery of P300 by Sutton and colleagues (1965), by studies of aspects of cognitive processes reflected in event-related potentials, or ERPs. Since then, many hundreds of papers have described evoked potential observations in schizophrenic patients. The vast literature on exogenous components of ERPs in schizophrenics has recently been thoroughly reviewed by Freedman and Mirsky (1991), whereas the even larger body of studies of endogenous components of ERPs in schizophrenics has been surveyed by Friedman (199 l). Few studies in this vast literature combine EEG and ERP observations, however. In summary, the existing literature concurs that a high proportion of schizophrenic patients display abnormal aspects of brain electrical activity. Nonspecific abnormalities are reported to have a high incidence ranging from 10% to 80% in different studies. Epileptiform activity consisting of sharp waves and paroxysmal episodes has also been found in up to 25% of patients in some studies. Disturbances of the EEG power spectrum have been reported in very many papers, but the patterns of disturbances were inconsistent. Many papers reported an increase in the amount of activity in the theta band or a lower mean frequency of alpha, but some studies found no change in alpha activity or, rarely, an increase. Increased beta has also been frequently reported, with a smaller number of studies finding no beta increase. Although studies of interhemispheric EEG coherence in schizophienia using monopolar reference montages have not produced consistent results, those utilizing bipolar montages show increased interhemispheric coherence (Fo~ et al 1986; John et al 1988; Memn et al 1989). ='Iuere appears to be no consensus regarding power asymmetries, despite the considerable attention directed at this issue in the context of hypotheses of asymmetric involvement of the cerebral hemispheres in schizophrenia. The major findings reported in evoked potential studies include normal brainstem auditory evoked responses with delayed or diminished responses in a subset of patients, relative nonsuppression of the P50 amplitude to the second stimulus in an auditory conditioning-testing (P50 gating) paradigm, reduced amplitudes and latencies of the auditory P50 and N110, and increased early somatosensory components (P30, N60). Such studies have been interpreted to indicate that schizophrenics have altered or increased sensory responsiveness at very early levels of information processing. Schizophrenics have been reported to have greatly delayed latencies and lower amplitudes of P100 to pattern

E,R. John et al

reversal visual stimuli (Romani et a l 1986). In addition, schizophrenics have been found to have decreased amplitude of NI or P2 regardless of the stimulus modality. This finding of decreased ampfitude of later responses with poor inhibition and hypersensitivity of earlier potentials led Shagass (1976) to suggest that the inability to process information adequately at early stages leads to an overload of processing mechanisms at later stages. Perhaps the greatest consensus exists for the observation that the late positive "cognitive" components of the ERP, such as P300, are markedly attenuated or absent. Patients with a variety of disorders characterized by cognitive dysfunction show reduced P300s (John and Prichep 1992; Friedman 1991), therefore this finding is not specific to schizophrenia. Thus, the ERP studies suggest the possibifity of a failure of inhibitory processes regulating afferent input at very peripheral levels, which might reflect inadequate activation of the descending reticular formation or excessive activity of the ascending reticular activating system, combined with a defect of information processing at more central !~vels that might reflect lack of attention, lack of motivation, effects of medication, or an actual cognitive deficit. This formulation supports, more than 25 years later, the suggestion by Venables (1964) that part of the schizophrenic"s difficulty could be ascribed to poor filtering of incoming sensory input and difficulties in maintaining a focus of attention. Successful separation of schizophrenics from normals has been reported using multivariate discriminant functions based on QEEG variables (Shagass et al 1984) or principal components descriptors of ERPs (Roemer et al 1990). Using a small subset of neurometric QEEG features, we have achieved independently replicable discriminant classification of nonmedicated chronic schizophrenics versus norreals, with a mean accuracy of 91.6% (Prichep et al 1994). It is noteworthy that most of the variables found useful in this discriminant function were multivariate composite features k~l --nk~ ~1~o ~y t~rl (Manman,,m~ ,~atance~l compu,,,,, acro¢. ,.e . ©. a.l.' e. n { n ' ~ m easureoe of the coherence between various brain regions. These multivariate measures, which assess covariance matrices and thereby quantify patterns of interaction within the brain, are unique to neurometrics (John et al 1987, 1988). In other work (John et al 1994), equally high (88.9%) independently replicable accuracy in separating nonmedicated chronic schizophrenics from normals was achieved with a multiple discriminant function based on principal component varimax analysis (PCVA) factor Z-score descriptors of visual and auditory ERPs. Such findings, corroborated across laboratories, indicate that the population of schizophrenics as a whole may share some abnormal electrophysiological features that are sufficiently consistent to permit reasonable accurate recognition of patients with schizophrenia. These reports, although consistent with respect to the high incidence of abnormal findings and discriminability

QEEG Subtypingof Schizophrenia

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Figure 1. Groupgrand average topographic Z maps fortotal EEG power and absolute power (top left panel), relative p°wer (t°pright anel) coherence (bottom left panel), and asymmetry (bottom right panel) in the delta, theta, alpha, and beta frequency bands. In each Panel shown for: First-episode schizophrenics (n = 15, V~ = 3.9), SzFB, top row; Nonmedicated schizophrenics (n = 25, ~ = 5.0), Sz(NoMed)*, middle row; and Medicated schizophrenics (n = 94, V~n = 9.7), SzMed, bottom row.) Note that the color scale encodes 0.28 (r/step. In order to calculate the Z-value of any finding for a particular group, this should be multiplied by V~n,where V~nis the size of the group. Thus, a probability ofp < 0.001 is indicated by hues corresponding to two steps for the SzMed and three steps for the SzFB and Sz(NoMed) groups. *[In this and all other figures Sz(NoMed) is equivalent to SzN in the text.]

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are often inconsistent with respect to specifics of the abnormalities. One source o f inconsistency may come from the large number o f uncontrolled equipment and population

variables. A major source of inconsistency and variability across studies could be contributed by the random selection o f study samples from the schizophrenic population at large, however, if that population is markedly heterogenous. The majority o f studies on electrophysiologic subtypes in schizophrenia have employed the strategy of neurophysiologic comparisons between subgroups defined beforehand by their clinical features. Such an approach frequently involved a two dimensional model, however, such as positive versus negative or paranoid versus nonparanoid. Increasingly, it is being recognized that schizophrenic phenomenology may encompass many dimensions, resulting in considerable distortion if a conswicted model is applied. (Arndt et al 1991; Liddle 1987). Groups of patients classified dichotomously as paranoid versus nonparanoid or negative versus positive have not revealed consistent features across studies. The opposite approach, classification based on electrophysiological features, has only been employed to a limited extent. Kadobayashi (1981; Kadobayashi et al 1980) measured recovery curves of P 1 - N I amplitude o f visual ERPs after performance of an addition test. Subgroups of patients showed distinctive temporal recovery patterns that appeared to be associated with clinical subtype, such as paranoid, hebephrenic, or simple schizophrenia. Etevenon et al (1981) clustered schizophrenics on QEEG features and found clinical differences between two electrophysiologically defined clusters. Prichep et al (1990) carried out cluster analysis using ncuromewic QEEG features and found five distinct electrophysiologic clusters, with different patterns of Brief Psychiatric Rating Scale (BPRS) factors associated with each. In this paper, QEEG and m,fltimodal quantitative eventIUIi:ILUU IJUtUIItldl~J

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tatively in a large group of normal subjects and schizophrenic patients, including a substantial sample of patients kept medication free for an extended period as well as a small set o f first-episode patients studiect before receiving any medication. Neurometric QEEG features (John et ~1 1987) and QERP factor Z-scores (John et al 1993) were extracted from artifact-free recordings and compared to the distribution of these descriptors in our normative databases. A high proportion of the patients but few normals showed statistically significant deviations from the normative values of numerous quantitative descriptors. The major purpose of this study was to identify subtypes within a schizophrenic population. The presence of subtypes within this sample of schizophrenics, in spite of homogeneous features that enabled the sample as a whole to be successfully discriminated from normals, has been examined explicitly by cluster analysis. Five different subtypes were found and their QEEG, QERP, and clinical characteristics are described and discussed.

Methods

Subjects scmzoPHRENIC POPULATION. All patients met DSMII1-R criteria for schizophrenia. The schizophrenic medicated population (SzMed) consisted of a group of 94 patients, 85 men and 9 women, with a mean age of 37.6 years and an age range of 18.4--64.0 years. The schizophrenic unmedicated population (SzN) consisted of 25 patients, all men, with a mean age of 45.0 years and an age range of 19.1 to 61.9 years. These unmedicated (SzN) patients were evaluated when off neuroleptic medication for 7 to 14 days and again while stabifized on medication. The "first-episode" (never medicated) schizophrenic population (SzFB) consisted of 15 patients, 13 men and 2 women, with a mean age of 30.9 years and an age range of 18.3 to 48.7 years. Eighty61. . . . . tia,nt ..... f r a m t h o Man.hatLan Veteran's Adminis-

Figure 2. (A) Group grand average visual ERP [VIS(T)] waveshapes of 44 normal subjects, 20 nonmedicated chronic schizophrenics Sz(NoMed), and 49 medicated schizophrenic (SzMed) patients. The waveshapes of the schizophrenics are shown as solid curves superimposed on the corresponding averaged ERP waveshapes of the normal subjects (dotted curves). Because there were no marked differences between the left and fight sides, only left hemisphere and midline data are shown. Time base = 50 msec/division; amplitudes are in relative units of whole head normalizations. (B) Group grand average auditory ERP waveshapes (AUD) of 90 Normal, 2 i SzN, and 50 SzMed patients. The waveshapes of the schizophrenics are shown as solid curves superimposed on the corresponding ERP waveshapes averaged across the normal subjects (dotted curves), as in (A) above. Time and amplitude as in (A) above. Figure 3. The upper left panel shows the group average topographic maps for the total schizophrenic group (n = 94, X/-nn,= 9.7) for Z absolute power, Z mean frequency, Z relative power, Z coherence, and_Z asymmetry. The group average topographic maps of the schizophrenic patients (SzMed) in clusters I (n = 22, V'n = 4.7), II (n = 20, x/n, = 4.5), III (n = 23, Vn,-- 4.8), IV (n = 15, Vn, = 3.9)2and V (n = 14, ~-nn,= 3.8), are shown for Z absolute power (upper row, middle panel): Z relative power (upper row, fight panel); mean treqaency (bottom row, left panel); Zcoherence (bouom row, middle oanei): and Zasymmetry (bottom row, right pa~lel).Color scale is in 0.28 a/step. To calculate Z-value of any finding, hue should be multiplied by V'~-_-.~(~ee Legend, Figure ! L For all five clusters, findings represented by three or more color steps are significant at p < 0.001 (two steps for the Total group).

806

BIOL PSYCHIATRY 1~4;:~:801-826

E.R. J o h n et al

tration HospitaP (MVAH) and 49 from Bellevue Hospital Center (BHC). The following instruments were used for their clinical evaluation: Brief Psychiatric Rating Scale, Hamilton Psychiatric Rating Scale for Depression (HAM-D), and the Abnormal Involuntary Movement Scale (AIMS). Reliable and complete treatmem histories were available for MVAH patients, who had been in the same healthcare system since the clinical onset of their schizophrenia. Cumulative years of neuroleptic exposure, (in chlorpromazine equivalent years, LN~), years since the first psychiatric hospitalization (YtLL), and duration of the present episode (DPE) were known for these MVAH patients. No such data were available on BHC subjects, who frequently had admissions to municipal and state hospitals, including but not restricted to BHC. All patients were evaluated for a history of a DSM-III-R Substance Dependence Disorder or a current Substance Abuse Disorder. Urine toxicology was used in those cases in which substance use was suspected despite a negative history, and subjects were excluded if the admission urine toxicology was positive for cocaine, opiates, phencyclidine, tricyclic antidepressant (amitriptyline is a commonly abused drug in the community from which the patients were selected) or amphetamine/methamphetamine. Thus, these results are not generalizable to such dual diagnosed patients. psychopharmacologic treatment was limited to neuroleptics, anticholinetgms, antiparkinsonians, and low-dose propranolol in the 30 days preceding electrophysiological examination. The sole exception to this were benzodiazepines, which were discontinued at least 72 hr prior to electrophysiological testing. NORMAL POPULATION. The normal population consisted of 162 normally functioning adults, 2 78 men and 84 women, with a mean age 49.4 years and an age range 18 to 87 years. All "normal" subjects were free of neurological or rru~clL.~ctl aaav~tv~a

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were less than 5000 fl's. Twenty minutes of eyes closed resting EEG, artifact-flee visual ERPs to 100 low contrast full field flashes (VIS ITI) and artifact-free auditory ERPs to 100 binaural randomized clicks delivered via loud speakers (AUD [RAN]) were recorded from the subjects while seated comfortably in a sound and light attenuated chamber (John et al 1993). Amplifier bandwidth was from 0.5-70 Hz (3dB points), with a 60-Hz notch filter; sampling rate was 100 Hz.

Data Analysis EEG FEATURE EXTRACTION. The neurometric EEG feature extraction methods used have been described in detail previously (John et al 1987). One to 2 min of artifactfree data were extracted from the EEG record for quantitative analysis, with the aid of a computerized artifact detection algorithm. All epochs selected for analysis were visually reviewed by one of the authors to exclude any artifacts which eluded this algorithm. Univariate features were computed for absolute and relative power, coherence and asymmetry in four frequency bands (delta, 1.5-3.5 Hz; theta, 3.5-7.5 Hz; alpha, 7.5-12.5 Hz; beta, 12.5-25 Hz) for the 19 monopolar derivations, and for eight bipolar derivations. Each feature was compared with normative age-regression equations to obtain Z-scores, after transformations to ensure Gaussianity. Because Z-scores express the deviations of the disparate univariate features from the predicted normative values in the common metric of relative probability, multivariate or composite features can be computed across sets of univarlate features. Correction for intercorrelations among the features combined in each such composite was accomplished by computing the Mahalanobis distance across the set of features. By procedures analogous to those used for univariate features, normative data were used to permit Z-transformation of these composite features.

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alcohol abuse, were of normal IQ, showed evidence of functioning at job/home/school for the past 2 years, and had not taken any prescription medication (with the exception of antihypertensives) for at least 90 days prior to evaluation. Details of the inclusion and exclusion criteria for "normal" subjects used in studies from our laborato,ries have been given elsewhere (John et ai 1988).

Data Acquisition EEG and ERP data were collected using 19 electrodes placed in accordance with the International 10/20 system, referenced to linked earlobes. All electrode impedances 'In collaboration with Des.J Rotrosen. B. Angrist. -'Supported in part by NSF Grant DAR 78-18772 and Grant #MH32577 and Grant #AG03051 from NIA.

FACTOR ANALYSIS OF ERPS. Group grand average ERPs were computed for the visual and auditory stimuli separately for the normal and schizophrenic samples. ERPs elicited by each type of stimulus in the normals were subjected to principal component analysis followed by varimax rotation (PCVA). Factors were extracted that accounted for the greatest proportion of the variance. The individual normal and schizophrenic ERPs were then each reconstructed as weighted linear combinations of these factor wave shapes, where the weighing coefficients were the factor scores. For each lead, the mean and SD were computed for the distribution of each of the factor scores required to reconstruct optimally the normal ERPs. Using these statistical parameters, the factor scores for the schizophrenic patients were age-regressed and Z-transformed relative to the distribution of factor scores for the normal subjects (Pfef-

QEEG Subtyping of Schizophrenia

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ferbaum et al 1984; John et al 1989, 1993, 1994). Thus, as in the QEEG all QERP factor scores were expressed in probabilistic terms; significant deviations from expected normal values (p <-- 0.05) were considered to be those with a Z value ~> 1.96, that is: i

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topographic maps for total EEG power, and for absolute power in the delta, them, alpha, and beta frequency for the first-episode schizophrenics (SzFB, n - 15) the nonmedicated schizophrenics (SzN, n -- 25), and the medicated schizophrenics (SzMed, n = 94). The nonmedicated and medicated groups show closely similar group grand a,~erage QEEG profiles of absolute ( 1) power, with a slight posterior deficiency of total power, a posterior deficiency of delta activity, a frontal ttmta excess, a posterior alpha deficit, and a very significant posterior beta deficit. A frontal delta excess was seen in the nonmedicated group only. It is highly unlikely that this reflects eye movement artifact, which was meticulously edited out before quantitative analysis. In contrast the SzFBs have an excess of total power, no significant delta abnormality, a right hemispheric theta excess that is statistically most significant over the right frontal region, a diffuse significant alpha excess most marked in frontal and central regions and a beta excess that is spatially rather congruent in distribution with the theta excess, also most significant over right frontal regions.

CLUSTER ANALYSIS. Subtypes within a schizophrenic population were sougta using the K-Means clustering (BMDP KM) algorithm. This method measures the Euclidean distance from each case to the center of each cluster. Cases are iteratively reallocated into the cluster whose center is closest. AnMysis of variance criteria are applied to the variance within clusters and between clusters to seek the clearest structure (b~,t separation) as the number ofchisters is varied. In order to select the optimal subset of variables for cluster analysis, the full feature set was analyzed for heterogeneity of variance within the schizophrenic sample. A t i ~L,ll.U t ~~t ta~~i,l*i i~g

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ordering that set of neuromemc variables for which there were significant t-test differences from normal and tes~ng for heterogeneity of variance across the whole population of patients. The intercormlations among these variables were then computed and those with correlations >i 0.7 were eliminated. Using this clustering set, the number of clusters to be constructed was defined mathematically by computing the mean F ratio across the set of N clusters as N increased, and defining the optimum N as that number of clusters above which the mean F ratio showed no significant decrement.

Results QEEG ABSOLUTE lOWER. The top left panel of Figure 1 shows the group grand average absolute power Z-score

RELATIVE POWER, The top right panel of Figure 1 shows the group grand average relative power Z-score topographic maps for the same three groups. The picture that emerges is somewhat different from that seen in absolute power. The SzN group shows a slight delta excess in frontal and posterior regions, a diffuse and marked theta excess in all regions but most significant in anterior regions, a generalized alpha deficit most marked in frontal regions, and a beta deficit most marked in parietal regions. The SzMed group deviates somewhat from the SzN profile, with a delta deficit frontally instead of an excess, a similar widespread theta excess but even more marked in the frontal regions than the SzN, more normal alpha, and a beta deficit much like that seen in absolute power but extending more into frontal regions. In the SzFB group, a pronounced delta deficit is seen in all but the occipital regions, most significant in the frontal leads. Theta is within normal limits or moderately decreased on the left hemisphere. A diffuse alpha excess is seen, which is extremely significant anteriorly. A beta deficit is seen in posterior regions. COHERENCE. The middle left panel of Figure 1 shows the coherence between homologous brain regions in these three groups. It can be seen that in the delta and theta bands all three groups show similar patterns of departures from normal interhemispheric synchronization, in the theta band, where most of the absolum power is seen, anterior regions are moderately hypercoherent, whereas posterior regions are moderately hypocoherent. The pattern of anterior hypercoherence is most extreme in the SzFB group, somewhat less in the c_hronicSzN group a ~ most nnrmai in the SzMed group.

808

BIOL. PSYCHIATRY 1994;36:801425

A striking contrast is seen between the SzFB group and the two groups of chronic schizophrenics, with respect to coherence in alpha and beta, however. The SzFB patients, who have marked excess of alpha activity, have extreme hypercoherence especially between the anterior halves of the left and right hemispheres in that band. Extreme hypercoherence was also found in the beta band, between the same regions. The overall picture is that in the bands where most power is found, the anterior portions of the two hemispheres, especially frontal and frontopolar regions, are excessively coupled together; posterior regions are relatively uncoupled form each other. Abnormal coupling was also found between cortical regions within each hemisphere. Especially in the delta and theta bands, regions of the frontal cortex were coupled together more in all three Sz groups than in the normals, but were markedly uncoupled from posterior cortical regions, more so in the chronic patients than the first episodes. ASYMMETRY. The lower right panel of Figure 1 shows the asymmetry between homologous bt-ain regions in these three .groups. In the delta, theta, and alpha bands, a tendency toward asymmetry is seen in the SzN and SzMed groups, with more power on the right side in anterior regions. This is especially marked in the beta band in the SzN patients. Findings in posterior regions are less consistent across the groups with the SzN patients showing more power on the left. Asymmetries in the SzFB patients are more extreme, with greater power consistently on the right hemisphere in anterior regions, most markedly in FP2 and Fs. Chronicity and medication leave the asymmetries seen in the SzFB muted but with the same basic profile: consistently more power on the right hemisphere, especially m anterior regions. MEAN FREQUENCY. The bottom left panel of Figure 1 presents the mean frequency findings in these three groups. Little differences were found between the striking deviations from normal shown by beth the SzN and SzMed groups: The total mean frequency is slowed diffusely in all regions except the anterior temporals. Although the mean frequency of the delta band is quite normal, there is a diffuse and highly significant increase in the mean frequency of the them band, especially in posterior temporal and parietal regions, and an equally significant decrease in the mean frequency of the alpha band. The mean frequency of the beta band is diffusely increased in the SzN patients only. The SzFB patients show a mean frequency profile which deviates from that seen in the chronic patients in some essential aspects: the overall mean _frequency of the EEG spectrum is increased, and there is little deviation from normal in mean alpha frequency. The mean beta frequency

E.R. John et al

is relatively normal, unlike the SzN chronic patients, and the mean frequency of delta is increased frontally and decreased in left temporal regions. The percentage of the SzN and SzMed groups for which the group incidence of abnormalities for any QEEG variable was more than twice that expected by chance (p - .05) is shown in Tables 1 A and B. In these tables columns are regions and rows are variables in the five measure sets of absolute power (A), relative power (B), asymmetry (C), coherence (D), and mean frequency (E). The same rule is used in Tables 2 and 3, which follows. These tables illustrate the diffuse anatomical distribution of abnormalities in the chronic groups. The most frequently found abnormalities in the SzN chronic patients (Table 1A) were in alpha mean frequency. observed in 20%--40% of patients and not localized to any particular brain region. In the SzMed patients, incidence of these abnormal features was generally lower, ranging from 20%-33%. The most commonly found abnormalities in absolute power in the SzN patients were seen in the composite (C) features, across all frequency bands, observed in 24%-32% of the patients and not localized to any specific brain region. The incidence of these abnormal features in the SzMed group was lower and in fewer brain regions, relatively restricted to frontal regions. In relative power, theta excesses and beta deficits (as well as excesses) ranged as high as 24% in the SzN patients with a somewhat higher incidence in the SzMed group, reaching as much as 30% in frontal regions. In both absolute and relative power, alpha deficits were sparse in the two groups of patients. Asymmetries were found primarily in frontal and central regions, in about 20% of the SzN patients, with more power on the right side. Incoherence was also seen in these regions and the posterior temporals, in the delta, theta, and beta bands. Asymmetries and coherence abnormalities were decreased or absent in incidence in the SzMed group. Visual E R P s 3

GROUPGRANDAVERAGEVISUALERPS. The upper half (A) of Figure 2 shows the group grand average visual ERP 2 waveshapes of 44 normal subjects (top row), 21 SzN, patients (middle row), and 49 SzMed patients (bottom row). The visual 17~ndasof the schizophrenics are shown as superimposed (solid curves) on the visual ERPs of the normal control group (dotted curves). Because no significant asymmetries were observed, only the waveshapes from left hemisphere and midline leads are shown, to conserve space. 'Note that no ERP data from the first-episode group are included in this paper because visual and auditory stimulus generators with different physical characteristics were used with all of those patients. This precluded comparisons with the ERP data presented here.

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12

20

24

(12+) 24

(12+) 24

12 24

~ 12

16 32

24

20

12

16

20 2@

20

12

~

20

I6

16

20

16

20

12

12

16

24

. 12

.

.

F, .

. 20

.

20

~

(12+)

12

(16+)

(20+)

(12+)

20

12

~

16 12

12

12 ~

~

12

.

.

16

--

~ ~

~

12

.

~

12

.

20

2O

~

~

20

~

2,1

16

~

16

~

16

~

12

~

~

~

!2

~ ~

20 20

16 16

~ ~

~ ~

~ ~

24 !6

12

(12+)

~

20

~

12 .

Mean

T-

(12+)

16

20

20

12

16

Frequency

A-

(12+)

~

-

~

12

.

.

.

~

13C+

.

.

.

~

.

O+

12

12

12

~

12

16

a[3+ C+

32 20 ~

32 12 12

40 20 ~

40 16 ~

40 16 16

28 16 ~

12 .

.

28 32 12 12

.

. 16 .

28 32 ~ ~

Key.: - = Direction o f deviations tabulated. + = Direetion o f deviations tabulated. ( ) = Significance in opposite direction. T = Total power 1 . 5 - 25 Hz. C = Mahalanobis distance across all four bands. H = Head, that is, Mahalonobis distance across all 19 leads. = Below 2 x c h a n c e level. ° = Greater than 2x chance level forp <--0.05.

In the grand average visual ERPs of the two schizop,hrenic samples, a clear P 100 (Pl) can readily be seen, especially in the frontal and central regions. A smaller P1 is elicited in the normals by this low contrast stimulus. NI50 (NI) in the SzN group is superimposable with that of the normals in the frontal regions but smaller and progressively delayed as one moves posteriorally, most markedly in the parietal regions. These N 1 differences are accentuated in the SzMed group where it includes the frontal regions as well. In the SzN group P200 (P2) is smaller than normal and progressively delayed in all regions, most discrepant from normal amplitude in the central leads and from normal latency in the parietal leads. Similar discrepancies are seen in the SzMed group in the frontal, temporal and central regions, but P2 appears to "normalize" somewhat in this group in the posterior regions. FACTOR ANALYSIS RESULTS. Five factors accounted for 77% of the total variance of the visual ERPs in the SzN

. .

12

20

. ~

28 32 ~ 16

t2

~

12

20

~

~

(12+)

~

(20+)

~

20

20 32 ~ ~

12

12

12

--

24

20

12 ~

I6 20

32 20 28

~ ~ ~

16

12

16

~

12

25

36 36 12 20

20 32 ~ 16

12

12

24

t6

40 16

28 12

32 ~

32 20 28

Boldface = > 4x chance level. A = 1.5-3.5Hz. O = 3 . 5 - 7 . 5 Hz. = 7 . 5 - 1 2 . 5 Hz. = 1 2 . 5 - 2 5 Hz.

group. The percentages of the SzN group who displayed visual factor Z-score values significant at p <- 0.05 are shown in Table 2A for each factor and region. The abnormalities indicated were most striking for Factor 2, ranghag from 24% to 43% in central, parietal, and occipital regions. These abnormal factor Z-scores reflect the excessively positive and markedly delayed P1, shifted into the latency domain where NI is usually found and where variance is accounted for by Factor 2. A high incidence of abnormal factor Z-scores was also found for Factor 3, reflecting the deficit of positivity in P2. "Deviation," which represents the overall abnormality of ERP morphology as the square root of the sum of the squared factor Z-scores, ranged as high as 38% in central and par;,etal leads. Note that significant "Residual," which represents the variance in the ERP that could n o t be well accounted for by normal factors, was seldom found except for 24% in lead T,. Visual ERP excessive total power was found in frontal regions in 24%-29% of the SzN cases.

810

clot. PSYCHIATRY

E.R. John et al

1994;36:801--826

Table lB, PercentageofMedicatedPatients Showing Significant" QEEG Abnommlities SzM(n=94) A

Absolute

C

D

F2

F,

C~

Relative power

Asymmetry

Col-,erence

P3

~

15

19

C A.

~

17

20

19

O+

30

. 30

24

24

15

24

13 17

11

~

.

13

[3-

22

28

20

20

22

22

~

~

I1

.

.

.

T -

I1

.

A-

I1

.

[3-

~

~

(II+)

C-

~

~

(11+)

.

. ~

.

[3 .

.

.

C+

.

T-

~

.

. 13

13

A

F,

.

.

.

.

.

17

13

-

~

~

26

24

24

II

15

15

17

~

13

28

~ 19

~

13

22

28

20

13

26

(11+)

--

:!

17

17

15

15

24

11

--

28

!I 30

~

~

30

22

.

.

.

.

~

(15+)

~

~

~

(11+)

--

--

. .

II It

.

.

.

15 19

~

13 15

~ .

!1

19

.

.

~ 13

(15+)

20 26

O+

19

19

~

13

13

~

15

17

13

13

20

19

ct-

31

28

22

24

28

28

24

26

31

30

30

24

.

.

~

~

[3+ C+

I1

.

~

.

. 11

Key: - = Direction of deviationstabulated, + = Directionofdevia~ons tabulated. ( I = Significancein opposite direction. T = Totalpower 1.5 - 25 Hz. C = Mahalanobisdistanceacross all fourbands. H = Head,thatis. Mahalonobisdistanceacross all 19 leads.

15

II

. .

13 15

. ~

.

H

13

--

.

P~

.

(11+)

~

C=

-

.

.

--

F:

13

.

15

'I"6

13

.

15

T,

~

.

.

1"4

~

.

.

.

T,

. (13+)

.

. !1

Fs

15

19 .

.

. 13

20

.

.

.

{3.,

.

C+

~

15

.

.

O, . 19

15

.

1'4

~ ~ 13

T . A-

Mean frequency

C,

O+

o.

E

F,

TA

power

B

F,

15

~ ~

20 22

17

13

15

13

17

33

22

28

22

24

~

~

II

13

~

13

19

Boldface = > 4x chance level. A : 1.5-3,5 Hz, O = 3.5-7.5Hz. ot : 7,5-12,5 l-lz,

p

12.5-25i~

=

= Below 2x chance level. " = Greater than 2x chance level forp ~ 0,05.

I IlK; [ i V U

lnbtUl~

d~.,LUUIILUU

IUI

iltJIJIUAIIIidLUI

~

tllU

~tdl.llU

percentage of the variance for the visual ERPs in the SzlVled group, 79%. Computation of the percentage of the total visual ERP variance accounted for by each factor, in SzMed versus SzN patients, showed no difference greater than 2%, with the exception of Factor 4, which accounted for 5% more of the SzMed then SzN variance. The percentages of the SzMed group who showed visual factor Z-scores significant at p -< 0.05 are shown in Table 2B for each factor and region. In the visual ERPs from the SzMed patients, the incidence of significant factor Z-scores for Factor 2 diminished in most leads, reflecting the diminution of positivity of P 1. A similar decrease in abnormality is seen in Factor 3, reflecting an increase in the positivity of 1>2. The incidence of significant overall morphology deviation also tends to be lower in some regions, but reaches as high as 43%-45% in Pz and total Head. Overall morphology

UI

l~l~I-b

Ill

ULIU O L I V l l g ; I L I I J ~ t i l ~ l i L

L I ~ L L I g U I I ~ t I l l I,I l f

I L U L L I I t ~ I ~t[Jgilk*~

(only one significant residual), and power levels tend to be normalized. Note the basic similarity between visual ERP abnormalities with or without medication.

Auditory ERPs GROUP

GRAND

AVERAGE

AUDITORY

ERPS.

The group

grand average auditory(AUD) ERPs for 20 of the SzN group are shown in the top row of the lower portion (B) of Figure 2 as solid curves superimposed on the normal AUD EILPs (dotted curves). The frontal and central regions show equivalent PS0s (Pl) to the normals, whereas the temporal and more posterior regions show a smaller PS0. N100 (N1) is smaller than normal in the frontal, central, and anterior temporal regions and substantially smaller and delayed posteriorly. P200 (P2) is normal in the frontal regions in the

QEEG Subtyping of Schizophrenia

amL PSYORATltV

8! !

1994~,M~80| .4126

Table 2. Percentageof Nonmedicated(A) and MedicatedfB) SchizophrenicPatientswithSignificant'VisualERP FactorZ-Scores A A

SzN(n=21)

F,

Visual E R P Factor Z-scores Factor I Factor 2 Factor 3 Factor4 Factor 5 Deviation Residual Power

10

24

F,

Fs

F,

C3

C,

P3

~ ~ 10 ~ ~ ~ ~ 29

10 14 19 14 19 19 10 14

l0 19 33 10 14 29 ~ 24

. . . 24 14 19 29 . . . 19 14 29 38 10 10 19 10

P,

. 43 19

.

Oz.

F7

Fs

24 10 14 ~ 19 ~ ~

29 10 ~ ~ 10 14 10

19 ~ ~ 14 14 19 ~ 2,4

. 14 10 10 ~ 10 ~ 10

Oi

02

F~

F,

. 18 14 14 . 14

. 22 12 ~

. 38 19

. 14 29 14 10

O,

~ 33 ~ 10

1"3

T,

. I0 ~ 19 t4 10 10 10

. i0 14 !0 ~ 10 24 ~

T3

12 14 ~

.

Ts

I"6

F:

C:

P:

H

19 19 ~ l0 ~ ~ 14

29 19 ~ 19 10 10

14 19 19 14 24 29 19 14

I0 19 10 ~ 19 33 19 I9

10 43 33 14 19 38 14 ~

24 10 10 I4 29 19 19

T~

Ts

T6

F:

C:

P..

H

~ ~ 12 . 14

14 12 ~

12 14 ~ 2.2

10

20

10 18 ~/ 10 10 35

~ 24 18 14 ~ 33

~ 16 20 24 ~ 4S

43

12

.

4

~

~

10

.

B B

Sz M (n = 49)

Fi

F2

F3

F4

Visual E R P Factor Z-scores Factor ! Factor 2 Factor 3 Factor 4 Factor 5 Deviation Residual Power

10 16 18 12 10 12

10 12 16 10 ~ 14

10 22 20 ~ 10 24

. 20 27 ~ -31

.

.

14

. 12

. 16

.

C,

. 20 20 10 ~ 24

. I0

C3

. i0

. 14 22 12 ~ 24 . ~

P3

P4

. 20 18 14 10 18

. 16 !6 20 . 20

.

. --

.

. 10 10 22

.

. 14

. 12

. ~

. 12 10 18 . 12 . ~

.

.

. 14

.

14

.

. 4

.

14

--

--

!0

.

. 10

18 20 10

.

Key: 2

l

Deviation = X 5 = i ,Z/jI-~ where Z i i = Z-trans form of factor score a O. Residual = Original ERP,- X ~ =1 ai'FJ where a,, = factor score for contribution of factorj to ERP at electrode i FI= jth factor. H = head, that is. Mahalonobis distance across all 19 leads. = Below 2x chance level. i" = greater than 2x chance level forp< 0.05. Boldface = > 4x chance level.

SzN group and becomes progressively smaller and slightly delayed toward the back of the head, deviating maximally from the normals in the parietal leads. The same was seen for P200 in the SzMed group, but also included frontal leads. The grand average AUD ERPs for 50 of the chronic SzMed group are shown in the bottom row of the lower half (B) of Figure 2. FACTORANALYSISRESULTS. Six factors accounted for 88% of the total variance of the AUD ERPs in the SzN group. The pereentages of the SzN group who showed AUD factor Z-scores significant at p --< 0.05 are shown in Table 3A. Significant delays and abnormal amplitudes of PI were found in 20%-30% of the auditory ERPs in the SzN group, reflected in the incidence of significant factor Z-scores for Factor 2. This ERP abnormality was found most often in

frontal regions, as were the deficient negativity of N 1 and positivity of P2 reflected in Z-scores for Factor 3 and 4. Overall waveshape morphology of auditory ERPs was most abnormal in frontal regions, where deviations ranged from 25%--45%.Variance was well accounted for by the normal factor descriptors, evidenced by the low incidence of signifleant residuals. Some deficient power was found in 20%25% of ERPs in temporal leads. Six factors accounted for 84% of the AUD ERP variance in the SzMed group. Computation of the percentage of the total variance accounted for by each factor in the SzMed versus the SzN group showed that most factors were 0%2% different, with the exception of Factor 2, which was 5%. The percentages of schizophrenic patients who showed AUD factor Z-scores significant at p -< 0.05 are shown in Table 3 B. Medication accelerated P1 and normalized am-

g 12

E.ILJohnet al

ntOL~CCt~a'gY I994,36:BOI-g26

Table 3. Percentageof Nonmedicated(A) and Medicated(B) SchizophrenicPatientswith Significant'AuditoryERP Factor Z-Scor¢.~(P < 0 . 0 5 ) A

A

SzN(n=20) Auditory ERP Factor Z-scores Factor I Factor 2 Factor 3 Factor4 Factor 5 Factor 6 Deviation

Residual Power

F,

F:

F~

F,

C~

C,

15 25 20 20 20 20 25 -

15 30 20 15 20 15 35 20

10 I0 15 35 25 15 45 ~

10 15 2S 15 10 15 35 10

15 15 15 15 20 15 25

. . . 10 10 10 15 15 gO 10 10 . . . 15 15

10

15

~

~

. . 15 .

P,

.

O,

10 15 15 ~

~ ~ 20 ~

.

. 10

. .

.

P,

~

. .

. .

O_.

F,

I0 ~ 10 10 ~ go ~

. . . gO 28 15 25 15 3S go 15 ~ 45 15

. .

F,

1",

%

I",

F.

C:

!':

H

. . 15 ~ ~ 15 10 20 ~ ~ ~ gO 5

~ ~ 15 t5 10 10

20 ~ 30 tO ~ ~ gO

15 20 go 25 25 10 3$

10 15 15 15 15 ~ gO

i0 10 I0 20 ~ I0

20 15 15 35

20

!0 gO

I0 gO

. . . . 25 ~

. i0

. ~

!0

Fs

T3

1",

T,

To

F:

C:

Pa

H

~

10 14 ~ 14

16 I0 26 ~ I0

10 ~

18 10 ~

!0 ~ 14 22 i0 10 30 22 ~

~ ~ 16 16 14 ~ 28 12 gO

~ ~ 22 16 I0 ~ 18 12 18

.

T~

B A

Sz M (n = 50)

Ft

F:

F3

F~

C~

C,

Auditory ERP F a c t o r Z-scores Factor I Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Deviation Residual Power

14 14 18 22 16 24 12 -

20 12 20 24 18 18 28 18 14

10 ~ 10 20 14 32 14 ~

10 10 20 18 14 16 30 ~ ~

12 10 . . . 14 14 18 8 12 10 . . . 28 14 14 18 14 12

P3

P~

OI

. . . . . !0 24 30 26 12 ~ ~ 14 ~ 12 . . . 16 18 12 12 12 16 10 ~ 12

O,

F7

~0 . 20 IO 16 14 14

.

. 16 16 14 10 26 6

~ . 12 18 14 12 24 ~ 10

~ .

. ~ ~ 10 12 12 ~ . . . 14 10 10 18 18

.

12 20 16

38 16 16

lCey:

D via on = X L , where Kj = Z.transform of factor score a,j. Residt,~al = OtigiI~l ERP • ' ~d6m t j =

!

aijF ~

where a,~= factor score for contribution of factorj to ERP at electrode i F, ==flhfactor. H - head. that is. Mahalonobis distance across all 19 leads. = Below 2x chance level. * ffigreater than 2x chance level for p < 0.05. ~/~¢

= > 4x chance |e~.l.

plitude of positivity, reflected in the lower incidence of significant factor Z-scores for Factor 2. The abnormalities of N1 and 1>2 remain relatively unchanged, as seen by the continued high incidence of abnormal Z-scores for Factor 3 and 4. The overall abnormality of auditory ERP waveshape remains high, ranging from 24%-32% in frontal regions. Residual variance increases somewhat and the incidence of power abnormalities decreases slightly.

ClusrerAnalysis Studies cited above have demonstrated that schizophrenic patients, both on and off medication, share a set of abnormal features sufficiently distinctive so that multiple discriminant functions can recognize schizophrenic patients by their

QEEG or QERP profile and reliably separate them from normal controls. Thus, pathophysiological correlates exist in the clinical syndrome described by the DSM-III-R criteria for the diagnosis of schizophrenia. The QEEG variables, which permit discrimination of schizophrenics from normals, are a subset of features characteristic of a high proportion of schizophrenics (relatively homogeneous). An additional large set of QEEG variables do not differ from normative values uniformly across the population of schizophrenics but are significantly heterogeneous, with significant differences between subgroups of patients with respect to certain subsets of these variables. These patterns of difference may define subtypes of schizophrenics with different distinctive profiles of pathophysiology.

QEEG Subtypingof Schizophrenia

nloLI~YCHIATRY 1994~a01-g2fi

Table 4. Distributionof SchizophrenicPopulationin the 5 Clusters Cluster

SzMed

1

2

3

4

5

Total

22

20

23

15

14

94

(%) (23.4) (21.3) (24.5) (16.0) (14.9) SzN 7 8 3 2 5 25 (%) (28.0) (32.0) (12.0) (8.0) (20.0) SzFB 0 10 2 3 0 15 (%) (0) (66.7) (13.3) (20.0) (0) Totals(M/F) 29(29/0) 38(32/6) 28(26/2) 20(18/2) 19(18/!) 134(12311I) (%)

(21.6)

(28.4)

(20.9)

(14.8)

(14.2)

A set of 16 QEEG variables was selected for use in uninformed cluster analysis using the quantitative criteria described in "Methods," and our population of 94 SzMed patients was classified into 5 clusters. For this population five clusters were found to be optimal using the quantitative criteria described in the "Methods." The top left panel of Figure 3 shows the maps of Z-scores for mean absolute power, relative power, coherence, symmetry, and mean frequency averaged separately across the five clusters for the 94 SzMed. Using the cluster centroids defined by the SzMed population, the SzFb and SzN patients were then classified. Table 4 shows the distribution of SzMed, SzN, and SzFB patients thus obtained in these five clusters. Both medicated and noumedieated chronic patients were classified into each cluster, but SzFB were only classified into clusters 2, 3, and 4. Figure 4 shows the group average visual and auditory ERPs for the patients in each of the five clusters as solid curves superimposed on the group average normal ERP (dotted curves). ERP data were not available for all patients in each cluster, and thus can only be shown for a subset of each cluster (ns specified in figure). Unfortunately, ERPs were available from so few patients in either category that within cluster averaging across SzMed and SzN patients was desirable. Such mergers of both kinds of data were deemed acceptable, as there were relatively few SzN patients in each cluster and only minor ERP differences had been found between the SzN and SzMed groups as a whole, as shown above in Figure 2. Table 5 shows the mean values of the 16 neurometric QEEG variables used for the cluster analysis for each of the five clusters, separately for the SzMed and SzN members of each group, and demonstrates the effect of neuroleptics on each variable for each cluster. Table 6 shows cluster mean values for clinical features. The number of patients with each clinical measure is given in parentheses. Although not included in the table, there were no significant differences between clusters with respect to the conventional clinical

813

subtypes of schizophrenia as defined by DSM-III-R. The number of SzN patients for whom clinical measures were obtained was too small for meaningful statistical comparison with the SzMed patients within each cluster, with the exception of the decrease in Tsptts in Cluster I (p -< 0.05). Thinking Disorder, Withdrawal-Retardation, HostilitySuspicion, Anxiety-Depression, and Paranoid Quotiem represent subscales computed from the BPRS (Overall et al 1967; Karson and Bigelow 1986). Analysis of variance (ANOVAs) were computed across the five clusters for each clinical variable. The only sig#.ficant values (p - 0.05) were obtained for item 18 (disorientation) of the BPRS (highest in members of cluster five) and for the tGtal score on the Abnormal Involuntary Movement Scale (highest for cluster 4). EFFECTS OF MEDICATION. If we exan~:~,; the mean values of Z-scores for the It, clustering variables of the SzMed and SzN patients in the five clusters, as shown in Table 5, we can evaluate the degree to which treatment with neuroleptic drugs has the same effect on all schizophrenic patients. Note that bipolar mean frequency of alpha (variables 2,3) decreases in all five clusters, whereas monopolar alpha mean frequency (variables 12,13,14) increases in clusters I, 2, and 4 but decreases in clusters 3 and 5. Theta relative power increased in clusters 2 and 3 for all them variables (variables 1,4,5,6,7,16) but decreases in clusters 4 and 5 for some (variables 4,5 or 6) and in cluster 1 for another (variable 16). Beta relative power responds differentially in different brain regions in clusters 1,2, and 4, and consistently decreases in clusters 3 and 5 (variables 8,9,10). Note that although variables 8, 9, and 10 are all monopolar relative power in the beta band, they seem to vary independently in different regions of frontal cortex. Clusters I, 2, 4, and 5 share an increase in coherence between regions "1"5/'I"6 in the delta band (variable 11), whereas duster 3 shows a marked decrease. Finally, clusters i, 3, and 4 show a marked decrease in delta power in lead T3, whereas clusters 2 and 5 show an increase. PROFILESOF SUBTYPEPATHOPHYSIOLOGY. The most salient QEEG and ERP features of the five clusters are summarized below. Cluswr 1. QEEG--In absolute power, the QEEG was characterized in anterior regions by a mild (p <- 0.05) excess of them and deficits in all other frequency bands and in total power. In posterior regions, there was an extreme (p -< 0.001) deficit of delta and beta. In relative power, there was a diffuse extreme excess of theta, normal delta, a diffuse tendency toward a deficit of alpha and of beta, moderate (p -- 0.01 ) in anterior regions. Mean frequency of theta was mildly increased and of alpha and total power moderately decreased, suggesting a slowing of alpha into the them

814

alOLPSYCHIATRY

E.R. John et al

1994;36:801-826

Group Average Visual (T) Evoked Potentials for lqormais and Schizophrenic Subtypes (bold)

Group Average Auditory (nAN) Evoked Potentiab Nor,-*t. and Schisophrenic Subtypes (bold) IPl !:

l~J "" :

$'/ ~;

T$ . )~:

T$ ~

C~J ~.,' ~

P$ li

Ol . ~,

4,

(a=IO)

CLUS ! !

(n=4)

~ut HI (n=lt)

CLus IV

(a=*)

cta~v

(a---~4)

~.:T| m

Figure 4. In the left panel, group average Visual (T) ERP waveshapes (solid curves) for all of the five schizophrenic clusters are shown as solid curves superimposed on the group average Visual (T) waveshapes of the normal group (dotted curves). Time base = 50 msecldivision; Amplitudes in relative units because of whole head normalization. In the right panel, group average Auditory (RAN) ERP waveshapesfor all five clusters of patients, as shown as solid curves superimposed on the waveshapesof the normal group (dotted curves), as for Visual (13 waveshapesabove. Time and amplitude as for Visual (13 above.

range. Coherence of theta was mildly increased and for alpha decreased between anterior regions, extremely low for beta in mesial and lateral frontal regions, and high for beta in anterior temporal regions (T3/T4). Power asymmetries were most significant in beta, with the left hemisphere showing more power than the right, especially in occipital areas.

ERPs--ERPs from the four SzN and six SzMed patients in this cluster are averaged together, separately for visual and auditory conditions (see Figure 4). In visual ERPs, primary positive peaks (PI) were of normal amplitude (except O,, where it was excessive) and progressively delayed from frontal to occipital regions. Primary negative components (N l) were slightly smaller than normal in all regions and delayed in posterior regions. Secondary positive components (P2) were mild to moderately smaller than normal in all regions, with delays in central and parietal regions. In auditory ERPs, P1 components were of normal latency and amplitude in all regions. N1 and P2 components were moderately smaller than normal (maximally in C3 and Pa) but with normal latency in all regions. Cluster 2. QEEG--In absolute power, the QEEG was abnormally low in all regions in all bands, most marked for posterior regions in delta and theta. Relative power was diffusely moderately excessive in beta, most significantly in

anterior temporalregions (T3, "1"4)and normal in other bands. Mean frequency of alpha and beta were moderately increased especially in anterior regions, suggesting a shift of alpha into the beta range. Coherence was normal in all bands except beta, which was moderately low between anterior regions but high between anterior temporal ( T 3 / " [ ' 4 ) . Power asymmetries were found in all bands between anterior regions, with more power on the right hemisphere. ERPs--ERPs for the one SzN patient and three SzMed patients in this cluster are averaged together, separately for visual and auditory conditions. The visual ERPs had a moderately to extremely enhanced Pl in most regions, delayed in posterior regions. N1 was diminished in posterior regions, where it was also delayed. P2 was considered to be of normal latency and amplitude in all regions. The auditory ERPs displayed a different pattern; in frontal, central and parietal regions, PI was moderately excessive and delayed. N1 was mildly to moderately greater than normal in all regions and also delayed in most anterior regions. P2 was mildly to moderately diminished and early in anterior regions, and moderately diminished but of normal latency in posterior regions. Cluster 3. QEEG--In absolute power, the QEEG had diffuse excesses in both theta and alpha, most marked in all frontal regions, and a beta deficit, especially in posterior

U ~ L PSYOI~I'RY 1994:36:801-826

QEEG Subtyping of Schizophrenia

815

T a b l e 5. M e a n Z - S c o r e s o f C l u s t e r i n g V a r i a b l e s f o r 5 S c h i z o p h r e n i c O u s t e r s 1 Off

On

7

22

Clusters 3

2 Change

Off

On

8

20

Change

Off

On

3

23

Variable BRE 0,F,T.

1.0

t.3

0.?

-o.8

~ 11

-4).? o.l

0,7

0.4

-L5 0.4

T 1'

SMFa, C,C,

I

-I,?

-2.0

B M F a , P402 lVIRE 0, F,

-0.0 1.9

-0.6 1.8

l~, --

0.7 -0.2

0.2 -0.2

l ~

-!,2 0.1

-2.1 1.2

MP,E0,F~ MRE 0, Fs

1.6 1.4

1.7 1.7

MRE 0, F~

1.6

MREI3,F, MREI~,F, MREiB, F~ MCOS,TsT 6

MMFa, P, M M F a , O,

MMFa, T6 MABS, T3 MABO, F4

T

Change

-

~,~, '~1~

-o.7

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TT

0.6

IA

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-0.3

-0.3

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-0.7 -0.4 -0.5

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0.4 1.2 -1.1

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1.0 -2.0 -I.0 -2.2

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Key M = monopolar. B = bipolar. RE = relative. AB = absolute. MF = mean fw.quency. CO = coherence. V= 1,5-3.5 Hz

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On

2

15

-1.8

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off

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"b= 3.5- 7.5 Hz Q =7.5-12.5 Hz I~= 12.5-25 Hz t • ] =changebetween0.2and0_SSD. ~[ , ~ J, = change between 0.S and I.OSD. t~ • ~ =change> 1.0SD. ~ = no change.

T a b l e 6. Cluster M e a n V a l u e s for Clinical V a r i a b l e s , O f f and O n M e d i c a t i o n Cluster I

Age Y I11 Lne Dpe Tbprs Tnk Dis Wth Rtd

Hos Dep Anx Dep Par Quo Taims Thamd

Cluster 2

Off(n)

On (n)

48.3(7) 22.4(6) 23635(6) 70.2(6) 49.0(7) 1 i .7(7) 9.4(7)

39.0(22) 21.2(6) 16320(7) 24.9(8) 39.8(19) 11.2(19) 9.3(19)

6.7(7) 5.3(I9) 7.1(7) 4.8(19) 0.36(7) 0.39(19) 4.5(10) 7.4(6)

Cluster 3

Cluster 4

Cluster 5

Off(n)

On (n)

off(n)

On (n)

Off(n)

On(u)

41.6(8) 16.9(7)

33.3(20) 10.0(5)

48.9(3) 15.3(3)

36.3(23} 15.3(12)

3859(5) 13.3(8) 43.8(19) 11.3(17) 9.7(17) 5.4(17) 6.4(17) 0.40(17) 5.1(8) I 1.2(9)

5569(3) 29.7(3) 60.3(3) 15.7(3) 12.0(3) 9.7(3) 8.3(3) 0.49(3)

27.0(2) I 1.5(I) 38708(I) 72.0(I) 44.0(I) 12.0(I) 9.0(I) 8.0(I) 6.00) 0.50(I)

37.4(15) 12.3(4) 14695(5) 17.6(6) 42.0(13) 11.8(12) 9.1(12) 5.8(12) 6.2(12) 0.42(12) 12.7(7) 13.0(6)

10563(6) 71.9(8) 46.3(7) 10.9(7) 8.7(7) 6.9(7) 8.0(7) 0.49(7)

18340(12) 52.0(13) 43.9(21) 11.4(20) 8.8(20) 6.7(20) 6.7(20) 0.51(20) 3.4(7) 9.7(7)

Off(n)

On(n)

50.8(5) 44.3(14) 27.0(2) 18.0(4) 15026(3) 9577(3) 45.3(3) 32.4(5) 49.0(3) 44.9(14) 10.0(3) 11.7(14) 11.3(3) 10.4(14) 7.0(3) 6.1(14) 5.7(3) 5.9(14) 0.40(3) 0.39(14) 4.1(7) 10.0(6)

Note: T-tests were calculated only among medicated members of each cluster, showing: Key: ( 1) Cluster 5 significandy older than Cluster 2 (P<0.01) and Cluster 3 (P<0.03); Y 111- - Years ill. (21 Cluster x significantly less Anxiety-Depression than Cluster 3 (P<0.01) and Cluster 2 (P<0.02); Lne-- Lifetime neuroleptic exposure. (3) Cluster 3 significanOy higher Paranoid Quotient than Cluster 1 (P<0.03) and Clus~'r 5 (P<0.04): Dpe - - Days present episode. (41Cluster 4 h~-.Asignificantly more (ANOVA. P<0.05) abnormal involunlary movements than the Tbprs --Totalscore,Brief Pyschialric Rating Scale. Tnk Dis - - Thinking disorder. other four clusters. Wth Rtd --Withdrawal - - Retardation. Hos Sus - - Hostility-- Suspicion. Anx Dep - - Anxiety - - Depression. Par Quo - - Paranoid Quotient. Taims - - Total score, Abnormal Involuntary Movement Scale. Thamd-- Total score, Psychiatric Rating Scale for Depression.

816

BIOLPSYCHIATRY 1994;36:801-826

regions. Relative power was diffusely moderately to extremely excessive in theta, excessive in alpha, and extremely deficient in beta. Mean frequency of theta was extremely high and of alpha extremely low in all regions, suggesting that slow alpha ~as appearing in the theta band. Coherence was moderately to extremely low between posterior regions in the delta and theta bands, mildly increased between frontal regions for theta and alpha, and between posterior regions for alpha and beta; T3/T4 coherence was high in all except the delta band. Power asymmetries were found in all bands, with the right hemisphere showing more power in frontal regions in all bands; and in posterior regions for slow waves; and the left hemisphere showing more power in fast waves. ERPsmERPs for the 1 SzN and 10 SzMed patients in this cluster are averaged together, separately for visual and auditory conditions. In the visual ERP, P1 was moderately excessive and delayed in all regions, maximally posteriorally. N 1 was moderately to extremely diminished and delayed in all leads. P2 was essentially absent in anterior, diminished and delayed in central, but excessive with a slight delay in occipital regions. In the auditory ERPs, P1 was essentially of normal size and latency in all regions. N 1 was moderately diminished and of normal latency in all regions. P2 was moderately excessive and early in anterior regions and moderately reduced in amplitude but of normal latency in central and parietal regions, and essentially absent in posterior temporal and occipital regions. Ouster 4. QEEGBIn absolute power, the QEEG had a moderate excess in the alpha band in all anterior regions and a diffuse mild to extreme deficit in all other bands. Relative power was moderately to extremely excessive for alpha in all regions. Moderate to extreme diffuse deficits in relative power were found in delta and theta, especially in frontal regions, and for beta except in the anterior temporal regions ~-3,¢'r'i".o~ where there was an exc~s. Mean frequency was significantly increased for theta especially anteriorally, and decreased for alpha in all regions, suggesting that the alpha excess may reflect an increase of frequency in the generatots responsible for theta. Coherence was very significantly increased between all homologous regions in the alpha band (especially for those frontal regions where alpha power was most excessive), between frontal regions for theta and between frontopolar regions for beta. Power asymmetries between anterior regions were extreme for all bands, with more power on the right side, and between occipital regions for fast activity with more power on the left side. ERPs--ERPs from the four SzMed patients in Cluster 4 were averaged together, separately for visual and auditory conditions. In the visual ERP, P1 in anterior and central regions was very much greater than normal but moderately delayed, whereas in posterior regions it was essentially nor-

E.R. John et al

mal. N 1 was of normal or sfightly enhanced amplitude but delayed in anterior and central regions, whereas in the occipital regions it was greatly enhanced in negativity and was early, t'2 was greatly reduced in anterior regions but greatly enhanced in posterior regions, with normal latency. Auditory ERPs had a P! that was slightly decreased in anterior and posterior regions with a slight posterior delay. N 1 was markedly diminished in negativity in most leads, with normal latency in anterior but a 20-50 msec delay in temporal and posterior regions (especially in the parietais). P2 was markedly increased in the frontal pole but markedly diminished elsewhere, with normal latencies. Cluster 5. QEEG--In absolute power, the QEEG had a moderate to extreme excess in the delta and theta bands, normal alpha, and a marked deficit of beta in all regions. Relative power was diffusely extremely excessive in theta, mildly excessive in delta in posterior regions of the right hemisphere, mildly to moderately deficient in alpha, and extremely deficient in beta in all regions. Meanfrequency of delta and theta were significantly increased and of alpha and beta significantly decreased in all regions, suggesting acceleration of generators of low frequencies and slowing of those for high frequencies. Coherence was extremely low for delta and theta between central regions and between more posterior regions, indicating that the activity in delta and theta that was so excessive in those bands was poorly synchronized between the two hemispheres. Frontal and anterior temporal regions (T3/T4) were extremely hypercoherent in theta, but hypocoberent in alpha and beta. Anterior temporal and more posterior regions were moderately hypercoherent in alpha and beta, indicating that the activity in those bands was excessively synchronized although decreased in abundance. Power asymmetries were extreme in delta and theta across all regions, and in alpha and beta between frontal regions, with the right hemisphere displaying more power in all bands. ERPs--Visual ERPs from one SzN and three SzMed patients were averaged together in this cluster, but auditory ERPs were only obtained from the three SzMed patients. In the visual ERPs, P 1 was moderately to extremely enhanced in all regions with a delay in posterior regions. NI was moderately diminished in negativity and delayed maximarly in posterior regions. P2 was smaller in anterior but enhanced in posterior temporal and occipital regions except parietal, with a delay anteriorly but normal latency in central and posterior regions. In the auditory ERPs, P1 was of normal latency and amplitude in anterior and central regions, but enhanced and slightly delayed in the posterior regions. NI was very diminished with no delay in anterior regions and less diminished but slightly delayed (maximally in the posterior temporals). P2 was greatly enhanced in all leads, especially in the frontal pole, with a delay in posterior regions.

a~m. tSYCItIATRy

QEEG Subtyping of Schizophrenia

817

Table 7. Mean Deviations of Cluster Members from Normative V alees of QEEG and ERP Variables

C, Ant

~

C. Post

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0 L L LL

RR R R R

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T A

o

Coherence (High Ts/T, = +)

A O 13

Asymmetry

A O 15

Vis ERPs

Post

TT TT TTT

T T TTT

0

0

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TTT

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~ 1" 1'+

T

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~+

TT+ t

R R L L

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R 0 L L

RR R RR RRR

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0

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0

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20

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0

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40

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0

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T

TTT

L

TT

LAT (A msec)

0

20

0

0

0

20

0

0

20

0

PIAMP LAT (A msec)

0 0

0 0

T~ 25

0 0

0 0

0 0

1 0

~, 20

0 0

~ 20

mAMp

P~A~' LAT (A msec)

mAMP LAT (A reset)

~ Aud ERPs

~

TT

Am

~

~

~

TT

t

1~

1~

~

~

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LAT (A msec)

0

0

25

0

0

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0

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0

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LAT (A msec)

0

0

- I0

0

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0

0

0

0

20

Key: J,, ~ =Mflddeviadon;(~p _<0.05). [ ~. T~ = Moderatedeviation;(~p ~ 0.01). J,J,J-, TT] = Extreme deviation; (~p <-0,001).

CLUSTER SUMMARY. Table 7 summarizes, separately for electrodes for anterior (FP. FP2,F3, F4, Fz, FT, Fs, T3,1"4) and for posterior (C3, C4, Cz, P3, P4, Pz, Ts, T6, Or, 02) head regions, the salient deviations from normal QEEG vahes of absolute power, relative power, mean frequency, coherence and asymmetry, and from normal morphology of P1, N1, and P2 in the visual and auditory ERPs, only for the medicated members of the five clusters.

Discussion

Overview of the Findings The data presented in this study indicate that certain global features of pathophysiology are shared by a high proportion of schizophrenics, but other featth-es permit this population to be parsed into electrophysiologically distinctive sub~oups. The findings demonstrate a number of important points.

818

BIOUPSYCHIATRY |994136:801-826

FINDINt~. When features extracted from carefully edited EEGs and ERPs from large samples of chronic schizophrenics, off and on medication, are compared quantitatively to those extracted from a normative database, numerous highly significant differences appear. Tables I to 3 show that significant proportions of the patients displayed abnonnalities in different leads and variables. Subgroups of patients abnormal on different elements of this measure set are substantially nonoverlapping. By analytical methods, a subset of these elements can be combined that constitutes a common denominator of electrophysiological correlates of the DSM-HI-R clinical definition of "schizophrenia," sufficiently comprehensive across a large sample to permit replicated discrimination with a mean accuracy of 91.6% using EEG features (Prichep et al 1994) and 88.9% using ERP descriptors (John et al 1994). This high accuracy d o e s not imply that every element in the discriminant set will be abnormal in every patient. It is noteworthy that uncontrolled resting EEG and passive ERP features are so pervasively abnormal in schizophrenics. Task-related EEG and ERP data might yield quite a different picture. Salient among the observed differences are an excess of theta activity in both absolute and relative power in many regions, hypercoherence between anterior regions of the two hemispheres in the theta, alpha, and beta frequency bands, but hypocoherence between posterior regions, and more power on the right than the left hemisphere especially in anterior regions. The highest rate of QEEG abnormality was found for decrease of mean alpha frequency ~n every region except 1"4, with an incidence up to 40% in the SzN and 33% in the SzMed group. Taken in conjunction with the excess of theta and deficit of alpha seen in both SzN and SzMed, the observed increase in mean frequency in the theta band and decrease in mean frequency of the alpha band suggests a true slowing of alpha rhythms into the theta frequency range, rather than augmentation of some independent them generator as is seen in cognitive impairment of elderly patients (John and Prichep 1990). The discrepancy between the theta excess in the SzN and SzMed and the alpha excess in the SzF'3 suggests that inhibitory influences on the thalamus, whether or not they originate in chronic schizophrenics from homeostatic shifts as discussed below, are not salient features of the early disease, but are progressive, either due to physiological changes with chronicity, cumulative effects of medication, or both. The coherence findings indicate that in all three groups of Sz patients, the anterior regions were abnormally preempted by each other, but at the same time uncoupled from the posterior regions, suggesting poor access of sensory informarion to regions concerned with evaluation of sensations. These findings are highly reminiscent of the pioneering observations of Gavrilova (1970). The SzFB displayed extreme anterior hypercoherence. ~l~ae chronic patients, both medicated and nonmedicated,

E.R. John et al

tend to show reduction in extremes of interhemispheric hypocoherence and hypercoherence. The nonmedicated continue to display marked anterior temporal hypercoherence as well as central hypocoherence, however. The extremely tight coupling between homologous anterior temporal regions (T3/T4) in all but the delta band is of particular interest, as those electrodes overlie Heschl's gyros, which one might expect to be activated during auditory hallucinations. At the same time, anterior and posterior temporal cortices within the same hemisphere are uncoupled from one another. Intrahemispheric uncoupling increases with chronicity and is not improved with medication. Chronicity and medication leave the asymmetries seen in the SzFB muted but with the same basic profile: consistently more power on the right hemisphere, especially in anterior regions. These findings were basically highly similar in the SzN and SzMed patients, as shown in Figure I and in Tables I to 3. This might mean that there are long-lasting effects of neuroleptic medication upon the QEEG and ERP features of chronic schizophrenics with a long medication history, or that chronic patients with a long history of illness undergo structural changes or changes in the levels of synthesis of certain neurotransmitters that alter these electrophysiological features. Observations that we were fortunate to obtain from a single patient with a 20-year history of illness under conditions in which neuroleptic medication was never administered displayed the diffuse theta excess described above, suggesting that some physiological change with chronicity rather than medication may be the primary cause slowing alpha to theta activity. ERP FINDINGS. Visual The reduced contrast of the visual ERP condition, in which dim flashes are presented while the subject views a defocussed video screen, markedly reduces Pl00 (PI) in the normal group. The large anterior PI00 of the visual ERPs displayed by both the S~N and SzMed, shown in Figure 2A, suggests that the schizophrenic is either manifesting frontal hyperexcitability or is abnormally preempted by the flash stimulus in spite of the low contrast viewing condition. Comparison of the waveshapes in Figure 2A from SzN patients (middle row) with the waveshapes from SzMed patients (bottom row--which include the evaluations of most of the SzN group after resumption of medication) indicates that the visual ERPs of these schizophrenic groups, off and on medication, are essentially identical. The most marked difference, and it is not very marked, is the diminution after medication of the delayed PI in the posterior temporal, parietal, and occipital regions. Inspection of the visual ERP topographic factor Z-score maps of the SzMed group (John et al 1994) revealed them to be remarkably similar to those of the SzN group. Auditory. As with the visual ERPs, comparison of the

QEEG Subtyping of Schizophrenia

AUD ERPs from the SzMed with those from the SzN patients seen in Figure 2B reveals them to be almost identical. Primary responses (P50) of the auditory ERPs are greater than normal in anterior regions, as was the case with Pi 00 of the visual ERPs. As with visual ERPs, the distribution of auditory ERP abnormalities is much the same in both the SzMed and SzN groups, and topographic factor Z-score maps were very similar (John et al 1994). ERP Implications. These data indicate not only that the schizophrenics display ERP waveshapes markedly different from those found in normal subjects, but also strongly suggest that abnormal ERP morphology in the schizophrenic population may serve as a trait rather than a state marker. This proposal is supported by the fact that the extremely abnormal morphology of both visual and auditory ERPs is only minimally different between large samples of SzMed and SzN patients (recall that about 40% of the SzMed group is composed of patients who were also studied when off medication as part of the SzN group). In general, abnormal morp~',~logy is widespread. Abnormality of visual ERPs is seer. most markedly in frontal (1:3, F4), central (C3, (24), parietal (P3, P4), and occipital ( O , 02) regions. Abnormal auditory ERPs are most marked in frontal (F,, F,, F3, F~, FT, Fs) and temporal (1"5,1"6)regions. In visual ERPs, PI was excessively large and delayed, suggesting a possible sensory overload. In ERPs of both modalities, N1 was diminished, suggesting low levels of attention, and P2 was diminished, suggesting a lessened subjective awareness or perception of the sensory input.

BtOt.Ps¥CmATRy

5.

6.

7.

CLUSTERFINDINGS.

1. Despite the existence ofa "common denominator" of electrophysiological abnormalities enabling schizophrenics to be reliably discriminated from normals, the schizophrenic sample was markedly heterogeneous. When subjected to cluster analysis based on a small set of QEEG variables selected for heterogeneity of variance, five clusters or subtypes of schizophrenic patients were identified. The QEEG and ERP profiles of these clusters differed greatly. 2. Clustering of SzFB. SzFB patients were classified into Clusters 2, 3, and 4 but not 1 or 5. This suggests that Clusters 2, 3, and 4 may comprise the initial subtypes of schizophrenia, whereas Cluster 1 and 5 reflect the effects of chronicity and/or medication. This suggestion requires a larger sample of SzFB before it can be made as a conclusion. 3. Visual and auditory ERPs behaved very differently from cluster to cluster. In general, there was a tendency toward increased P1 positivity and latency, decreased N1 and decreased P2. None of these generalizations holds true across all clusters, however. 4. Within a given cluster, visual and auditory EROs ap-

8.

819

pear m arise from generators with different ~ mical dependencies, because corresponding components often deviate from normal in different amounts, different directions and with different delays. Striking differences were sometimes observed between the abnormal QEEG features of anterior and posterior cortical regions, similarly suggesting different neurocbemical dependencies. Within a given modality, ERPs in the anterior and posterior brain regions also seem to arise from different generators. Components in these regions behave quite independently and display different delays. Within a given region, sequential ERP components appear to be relatively independent, since delays or leads in latency of earlier components cannot readily be reconciled with those of later components. Independent information about homeostatic perturbations reflected in ERPs is therefore potentially available from 2 modalities × 2 regions (ant/post) × 3 components (Pl, NI, P2) × 5 clusters = 60 variations. We studied the cluster membership of a~l patients for whom data were classified both on and off medication. Some patients were classified into the same cluster in both conditions. Some changed cluster membership. No systematic effects of medication were seen. Patients in the same cluster when nonmedicated often moved to several different clusters after medication. These movements were "bi-directional," in that medication might move some patients from cluster "A" to "B" but other patients might move from "B'" to "A." Table 5 shows that medication effects on some electrophysiological variables can be opposite in members of different clusters. Since these variables hypothetically reflect neurochemical equilibrium in homeostatic systems, these differences in response indicate that the neurochemical imbalances may vary among patients in these five subtypes. Table 5 also shows that the effects of a drug can differ diametrically in different cortical regions, even within the same lobe. A number of questions arise about the "universality" of structure obtained from a cluster analysis. In a previous study, a remarkably similar five cluster structure was found in a group comprised only of SzN patients (Frichep et al 1990a). This suggests a stable structure that replicates on medication. To further test the stability of these schizophrenia clusters we recomputed the clusters adding the SzN and normals (total n = 217), however. Although this solution required seven clusters, 76% of all SzMed patients remained in the same clusters as before. Thus, we were reassured that these profiles afford a better than tentative description of five different subtypes of schizophrenics.

820

alOL P s Y c l ~ Y

1~:36:801-g26

.

The salient electrophysiological features that characterize these five subtypes subsume the various abnormal profiles encountered in a careful reading of the inconsistent prior electrophysiologlcal literature in schizophrenia. This suggests that such contradictory reports reflect fortuitous differences between relatively small samples studied with one or another incomplete set of measures and/or electrode placements.

Thus, the information about pathophysiology contained in QEEG and in visual and auditory ERPs is not redundant, nor is there redundancy between anterior and posterior regions or early and late positive and negative components. The neurochemical insights to be gained by unraveling this com~qex mass of ERP phenomena, together with the QEECs differentially displayed by these five clusters of chronic schizophrenics, are potentially very great. Conversely, consideration of this great diversity of pathophysiological manifestations among the schizophrenics makes one cognizant of the size and scope of the outcome studies that must be carried out before optimum differential management of these subtypes can be achieved.

Heterogeneity of Schizophrenia One of the earliest attempts to find a characteristic EEG pattern in schizophrenic patients was made by Jasper et al in 1939. Their conclusions, and their acknowledgment to Bleuler's even earlier point of view, are quoted here because they are so concordant with the results of this study performed more than half a century later. The wide varietyof abnormal and normal electroencephalographic records found in patients diagnosed schizophrenic probably means. . . . present diagnosis is based upon "form" of the reaction to a variety of pathological conditionsor to situation~ conditions.... It is hoped that, with the progress of our knowledge of the basic mechanisms of cerebral functions underlying the activity revealed by the electroencephalogram, this new technique may serve as an important guide to the more fundamental pathological mechanisms underlying the heterogeneous group of disorders now classified as schizophrenia.4 (Jasper et a11939) Since then, many have argued that genetic heterogeneity must exist within the group of the schizophrenias (Cancro

"It is of interest to be reminded of Bieuler's original point of v i e w . . , schizophrenia does not appeax to us as a disease in the narrower sense but as a disease group, about analogous with the grvup of the organic dementias, which are divided into paresis, senile forms, etc. One should, therefore, really speak of schizophrenia as the plural.,. (Bleuler 1924, p. 373).

E.R. J o h n et

al

1970, 1979). Pathogenic mechanisms are likely to be fewer than genetic variants as different genes can lead to the same mechanism. Focusing on variation in pathogenic mechanisms may produce a more useful typology, which may contribute significantly to an understanding of appropriate medical interventions. How can the observed diversity of electrophysiological profiles be accounted for in patients receiving the same DSM-III-R diagnosis on the basis of their clinical syndromes? The clinical disorder that constitutes the group of the schizophrenias is characterized by major alterations in all of the fundamental psychological functions, with disturbances in sensation, and the matching of sensory input to memory, which is required for appropriate perception of reality, disorders of mood and levels of anxiety, defects in logical language production and thinking. Mechanisms mediating each of these basic mental functions are broadly distributed in the nervous system and must be potentially vulnerable to dysfunctions at a multiplicity of neuroanatomical levels. This illness can disrupt multiple brain functions to differing relative extents, with manifestations which, although they may vary widely from case to case and within a case across time, nonetheless generate a recognizable clinical syndrome (i.e., chronic psychosis). This "patchwork" hypothesis is probably appropriate not only to schizophrenia but may also pertain to other psychopathological conditions (Prichep et al 1990b). It seems unwise, therefore, to look for specific nuclei or highly localized areas of dysfunction. The large effects of the schizophrenic disorders most likely reflect the action of extensive systems that are not behaving in an appropriate fashion. Imaging techniques that capture the action of large ensembles of neurons can therefore reasonably be expected to yield fruitful results. For this reason, quantitative analyses of EEG and ERPs may provide insights into upAer!ying mechanisms that might lead to a useful typology. It follows from the argument above that if one were to examine a wide spectrum of brain functions using precise measures and a large sample of schizophrenic patients, one might plausibly expect to find a high incidence of deviations from normal values of different features of brain activity. Since the number of basic f~c,.ie:,~ ::~.uroanatomical systems mediating these five or six major facets of behavior is limited, no matter how anatomically distributed each of these systems might be, it seems possible that these deviations from normal values might display a limited number of profiles of covariance. In fact, the findings of this study as reported above conform well to these expectations. It seems appropriate, then, to seek a set of explanatory concepts that might underlie our observations and might be amenable to future tests.

QEEG Subtypingof Schizophrenia

One Model--Failure of Pruning of Cholinergic Neurons One simple model of etiology involves the failure of the intrauterine pruning of the excessive number of chofinergic fibers present prior to the fifth month of gestation (Feinberg 1983). Such failure would result in an excess number of cholinergic fibers in postpartum life. Compatible with this proposal, Karson et al (1991) have demonswated the postmortem presel~ce of more than a twofold excess of cbofinergic neurons in regions of the reticular formation of their schizophrenic brains. Interestingly, this model suggests that there can be either a defect in the genotype that regulates this pruning process or a phenocopy. The genetic defect would be transmissible. There are data that suggest that certain viral infections during the second trimester of pregnancy predispose the fetus toward a schizophrenic disorder (Mednick et al 1988; Ban" et al 1990). This viral alteration of gene expression would represent a phenocopy that is both more common and not transmissible. These observations are consistent with the finding that the vast majority of schizophrenics (90%) fail to demonstrate the disease in their firstdegree relatives. The consequence of the failure to regulate the pruning of the cholinergic fibers would be variable. This might include excessive activation, a cinonic sensory overload. Different downstreain dysregulations would constitute different pathogenic mechanisms. These pathogenic mechanisms could lead to behavioral disturbances of the type seen in the schizophrenic syndrome, reflecting failure to balance excitation and inhibition and to maintain essential homeostatic regulation of various substances in the nervous system.

Another Model--The DopamineHypothesis Perhaps the most broadly accepted theoretical construct under!yi_n.gconteln~no_ra_ry__resea_rc_hand treatment approaches to schizophrenia is the hypothesis that dopaminergic systems are somehow dysfunctional in the brains of schizophrenic patients. As pointed out by Goldstein and Deutch (1992), this hypothesis has largely been based upon pharmacological evidence. The fact that all known clinically effective antipsychotic drugs block DA De recep~rs and that amphetamine, which is a DA agonist, can cause a psychotic state suggests that there is a DA excess in schizophrenia. Yet there does not exist a compelling body of evidence supporting the proposal that there exists a umtary cause for the schizophrenic disorders such as hyperactivity of the dopaminergic systems in the brain. For example, findings of changes in DA concenuation, differences in the density of D2 receptor sites or regional changes inenergy metabolism have not been consistently forthcoming from the diverse strategies devisedto seek confirmation of this hypothesis (Goldstein and Deutch 1992; Sedvali 1992).

slot. PS¥CHIATI~Y t 994"2~6"~i0I - ~ 6

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Such results have broadened the search for ~ v e explanations and extended the spectrum of research techniques focussed on this unitary hypothesis. The cumulative evidence from these increasingly in-dep~.hstudies suggests that the pathophysiology underlying the core symptoms of the schizophrenic syndrome must be heterogeneous, subtle disturbances in homeostasis, which may involve complex derangements of interactions among several transmiuer systems in different brain regions. Cortical regions, particularly the prefrontal cortex, regions of the limbic system, especially the anterior cingulate cortex and the hippocampus, the striatum, the thalamus, and the mesencephalic reticular formation have all yieldea evidence that impficates them as potential contributors to an elaborate homeostatic system, which may be chronically unbalanced in schizophrenia. Much of this evidence has recently been reviewed by workers such as Goldstein and Deutch ( i 992) and Weinberger (1986).

Multiple Determined Dysfunctions-Transmitter Interactions An important contribution toward the clarification of these complex interactions was made by Carlsson and Carlsson (1990). These workers proposed that cortical influences can regulate the level of arousal by modulating interactions between the striatum, the thalamus, and the midbrain reticular formation. They envisaged cortical arousal by sensory input via thalamo-cortical projections to be modulated by inhibitory gamma-aminobutyric acid (GABA)ergic influences from striatofugal neurons. Dopaminergic (DA) inputs from the substantia nigra to the striatal complex diminish the inhibitory influences from these striatofugal neurons on the thalamus and reticular formation. Such activation of the striatum is, of course, itself a function of the level of sensory input. The reticular formation, released from this inhibition, could more strongly inhibit nucleus reticularis. The inhibition of thalamic neurons by nucleus reticularis would thereby be released and sensory input to the cortex would increase. This increased sensory input and over-aronsal might lead to disorganized thought process. The basic "trigger" for the hyper-arousal in this process had been envisaged to be the striatai increase of DA. A critical component of the formulation proposed by Carlsson and Carlsson was the hypothesis that glutamatergic (GLU) cortico-striatal projections could regulate striatal dopaminergic tone by inhibiting DA release. This provided a mechanism whereby cortical activation could block the DA inhibition of the inhibitory outflow from the striatum, thereby increasing inhibition of the reticular formation and decreasing the level of arousal.

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BiOLPSYCHIATRY 1994;36:801-826

Research in a number of laboratories is evolving a modified version of these feedback relationships. This model envisages a "direct" pathway that facilitates cortical excitation by sensory throughput from the thalamus and an "indirect" pathway that leads to inhibition of the thalamus. The direct pathway, more dependent on type D1 receptors in the suiatum, involves a cortico--neostfiatal (caudate/putamen)pallido/nigro-4halamo-cortical loop. The indirect pathway, more dependent on type D2 receptors in the striatum, interposes the subthalamic nucleus in the loop between globus pallidus and the substantia nigra. The neurotransmitters in these convergent loops ale such that activity in the indirect pathway opoosed the effect of the direct pathway. Normal thalamo-corticai ~elationships therefore depend on the balance between these pa~ways as well as between the cortical and nigral iafiuc~ces upon the striatum (Albin et al 1989). The emergent details of these complex homeostatic networks emphasize that the net effects of many aspects of these interactions are contextual. The outcome of any perturbation depends as much on the state of the system as on the perturbation. Dynamic physiological monitoring of pharmacological maneuvers may be a necessary adjunct for clarification of the interactions between treatment and state in individual schizophrenic patients.

QEEG and ERP Features and Homeostatic Regulation We have described elsewhere in detail (John et al 1987), more than 1000 age-regression equations for quantitative features of the spectrum of the spontaneous, resting EEG. Together with the normative waveshapes of a wide variety of sensory evoked and event-related potentials (Chabot and John 1986), quantifiable by the normative contributions of varimax factors to the ERP waveshapes recorded from any individual (John et al 1989, 1993, 1994), the QEEG features define a set of s i m u l t ~ n , ~ ,-n.~t,.~;.t . . . . , * o n , , i , ~ k o human central nervous system in health)' individuals. To the extent that homeostatic balance among the various functional neuroanatomical systems mediating the EEG and ERPs is properly maintained, the values of the quantitative descr~tors of spontaneous and evoked brain electrical activity observed in recordings from an individual shouldfall within the range predicted by the corresponding age-corrected normative data above. If departure from homeostatic balance occurs within some functional neuroanatomical system, an avalanche of perturbations and compensatory corrections will ripple through the other systems with which it interacts. Usually, after a brief search for new equilibrium parameters reconcilable with the global system, homeostasis will be restored. In vulnerable individuals a lasting shift to a new steady state may sometimes occur, eventually producing

E.R. John et al

some part of a clinical pathological syndrome. A lasting shift, resulting in a persistent thought and/or affective disorder, will characterize a person diagnosed as a schizophrenic. "Escape" from such a shift will be clinically perceived as remission.

Factors Influencing the EEG Power Spectrum Recent studies of mechanisms undedying rhythmic oscillations in the EEG and the effects on them of various neurotransmitters are relevant to interpreting the observed alterations of the resting EEG spectrum (Steriade et al 1990; Lopes da Silva 1991; Llifias 1988). These studies attribute the alpha peak that dominates the EEG spectrum to cortical excitation from coherent sets of thalamic "pacemaker" oscillator cells. After each afferent discharge, these thalamic oscillators enter a refractory period with cell membranes hyperpolarized. Eventually, the oscillators "escape" from the hyperpolarization, rebounding to discharge another afferent volley followed again by a refractory period. The oscillators are in a homeostatic network, involving portions of the tegmentum, striatum, and cortex. Dopamine (DA), acetylcholine (ACh), serotonin (5-HT), ~-aminobutyric acid (GABA) and glutamate (GLU) mediate synaptic transmission in different parts of this feedback system, and shifts in their availability affect the excitability cycle of the oscillators. The mean frequency of the EEG spectrum ultimately depends on the balance point achieved by this homeostatic network and is not uniquely determined by concentration of any single substance. Decrease in DA, excess of GABA or deficiency of GLU or ACh may all result in a decrease in mean EEG frequency, a shift of beta activity into alpha or of alpha into theta, reflecting a tendency to hyperpolarize the membranes of the pacemaker neurons in the thalamus, whereas any of the opposite changes may lead to an increase in the mean EEG frequency.

Factors Influencing ERP Components Similarly, the amplitude of the major different components of the ERP may reflect the availability of different neurotransmitters. Delays in visual ERP components, especially PI, may arise from a blockade of DA receptor or DA deficiency, with normalization after L-Dopa (Bodis-Wollner and Yahr 1978; Bodis-Wollner et al 1982; Cosiet al 1984). Amplitude increases of P1 may reflect a cholinergic excess or a deficit of ACHE, whereas excessively negative NI components may reflect a GABA excess (Sannita et al 1988; Sannita 1991a, 1991b). Increased P2 amplitude has been correlated with increased occupancy of 5-HT II receptors (R.A. Roemer, personal communication, 1992). Current research in schizophrenia, cited above, provides abundant evidence that suggests an excess or deficit of one,

QEEG Subtyping of Schizophrenia

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another or several of these neurotransmitters in various brain regions. The heterogeneity of QEEG and ERP abnormalities that was found in our schizophrenic sample is much easier to reconcile with this complex picture than with a unitary DA hypothesis.

Subtype Profiles In spite of marked differences in their average profiles of electrophysiological abnormality, the members of the five

clusters were clinically indistinguishable on the measures used. Using variables selected analyticallyrather than heuristically and defining the number of clusters to be sought using a statistical criterion rather than our previous empirical approach, the five clusters obtained in this analysis are very similar to those found in our preliminary endeavor (Prichep et al 1990). The profiles of these five clusters display few abnormalities in common. All show some abnormality in the EEG power spectrum: DeltaDMost show normal or decreased absolute or relative delta power, but cluster 5 shows a marked increase; ThetaDThe finding of them excess, perhaps the most common in the previous literature, is not at all uniform in our sample. Three clusters show a moderate to extreme excess of absolute or relative theta power, but clusters 2 and 4 she-~,a moderate to extreme theta deficit; Alpha---Clusters 3 and 4 show a marked to extreme increase of alpha power, especially in anterior regions, but with extreme alpha slowing. Beta--Four of our clusters show a marked tendency to extreme diffuse deficit of absolute power in beta, but cluster 2 shows a beta excess in relative power. These data support the generalization that with the exception of cluster 2, the mean frequency of the alpha band seems to have decreased, whereas that of the them aand has increased. No shared pattern of changed coherence can be discerned across all clusters, with the exception of hypercoherence between 3"3 and i"4 in one or another frequency band, most often in beta. This may relate to the fact that members of all five clusters have auditory hallucinations, reflected by synchronized activation of anterior regions of the temporal lobe. All five clusters display a power asymmetry in anterior regions, with more power on the fight side in most or all frequency bands. This consistent lateralization of power is not seen in posterior regions. No unitary hypothesis plausibly accounts for this diversity of findings. Some part of these observations, such as the high incidence of decrease in mean alpha frequency but increase in mean theta frequency, can reasonably be attributed to tJyperpolarization of the membranes of pacemaker neurons in the thalamus. This might arise from a variety of causes: inadequate inhibition of nucleus reticularis by the reticular formation, excessive inhibition of the reticular formation by the basal ganglia, possibly due to subcortical

deficiencies of acetylcholine, or excesses of dopamine, serotonin and/or GABA, or cortical excesses of glutamate. Another aspect of our findings that argues against some possibly unitary explanation for these diverse profiles of abnormality are the striking differences between the deviations from normative values seen in anterior and posterior brain regions. Changes in the relationship between the fron* tal cortex and the dorsom~al nucleus of the thalamus (and perhaps between the anterior nucleus and the cingulate cortex) seem to occur quite independently of changes in relationships between posterior cortex and other nuclei of the thalamus, particularly with respect to coherence. All clusters shared a marked enhancement and delay of the first positive component, Pl, of the visual ERP, which might reflect a DA deficiency and a cholinergic excess. This was also seen in the auditory ERP in two clusters. In three of the five clusters, there was a diffuse reduction ef N1 in visual ERPs and a more marked N 1 reduction was found in four clusters in auditory ERPs, which might reflect a deficiency of GABA. Widespread P2 abnormalities were found, variously seen as excesses or deficiencies in different groups. These may reflect imbalances in serotonin availability. The increased amplitude of P1 and the decrease in N1 and P'2 are compatible with the suggestion of Shagass (1976) and Venables (1964) that part of the schizophrenic's problem may be due to poor filtering of input information and inability to maintain a focus of attention. The delays observed in primary ERP components ere in accordance with Romani's (1986) reports of delays in early components of visual responses. Our ability to construct a discriminant function that accurately and repi2cably separates schizophrenics from normals confirms Shagass et al's (1984) earlier demonstration that this could be done, as well as our previous report (Prichep et al 1990). Finally, in contrast with previous efforts to subtype schizophrenics using electrophysiological features (Etevenon et al ! 982; Kadobay~hi et al 1980; Kadobayashi 1981), no clinical difference can be discerned among the five subtypes found in this study.

Implications for Further Work Data were available both en and off medication for 14 chronic patients, only three of whom showed improvement (decrease ~> 25% on total BPRS) with neuroleptics. All three of these "responders" were in Cluster 1 off medication, two remaining there with tleatment, and one moving to Cluster 3. Cluster 1 also contained 3 nonresponders from this group of ! 4 patients, however. Two of these patients also remained in Cluster 1 after treatment, and one patient moved to Cluster 3. Thus, in this small sample there was no clear relationship between initial cluster membership and response to treatment. In order to properly evaluate the

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practical implications of these observations a large prospective study would be required, however. A baseline neurometric EEG/ERP and clinical examination should be obtained on a large group of acute never medicated as well as chronic nonmedicated psychotics, prior to the initiation or resumption of treatment. Treatment should be selected without any information about the results of the neurometric evaluation and a follow-up evaluation should take place after 1-3 months of treatment. The changes in brain activity and clinical status should then be correlated with premedication cluster membership and with the presumed mode of action of the treatment. The efficacy of different treatments on members of each cluster could then be evaluated. Clearly, the number of patients who must be selected and longitudinally followed in such a treatment evaluation pro-

E.R. John et al

gram would have to be quite substantial in order for statistically significant findings to be collected for each clusterdrug combination. This large a sample would appear to be beyond the capability of any single research group. Accordingly, the need is evident for a multi-institutional collaboration and the adoption of the standardized electrophysiological and clinical protocols, in order to construct a shared database.

Thisworkwas supportedin pan byCadwelllaboratories,Kennewick,WA. We wish to acknowledgeHeater Stein for preparation of this manuscript and Henry Merkin, MeeLeeTom, and BryantHowardfor performing the neurometricevaluations.

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