qeeg profiles of psychiatric disorders

Brain Topography, Volume 4, Number 4, 1992 249

QEEG Profiles of Psychiatric Disorders

L.S. Prichep* and E.R. John*

Summary: While reports of EEG correlates of psychiatric disorders date back five decades, clinical sensitivity of the EEG to psychiatric disorders has been greatly enhanced with the advent of quantitative methods of analysis (QEEG). Using a QEEG methodology known as neurometrics we have identified distinctive electrophysiological profiles associated with different psychiatric disorders. With this method quantitative features are extracted from 2 minutes of artifact- free eyes closed resting EEG data, log transformed to obtain Gaussianity, age-regressed, and Z-transformed relative to population norms. Using small subsets of neurometric features, multiple stepwise discriminant analyses were used to construct mathematical classifier functions, the values of which are different for members of different a priori defined diagnostic groups. Using this approach, we have demonstrated high discriminant accuracy in independent replications separating many populations of psychiatric patients from normal as well as from each other, including major affective disorder, schizophrenia, dementia, alcoholism, and learning disabilities, as well as high accuracy of discrimination between known subtypes of depression (unipolar vs bipolar). The use of classification accuracy curves (CACs) which allow one to assess the sensitivity and specificity achieved by the discriminant functions is discussed. In addition, using cluster analysis, neurometric subtypes can be identified in several clinically homogenous populations. Preliminary results suggest that baseline membership in some neurometric subtypes may be highly correlated with response to treatment.

Key words: Quantitative electroencephalography; QEEG; Neurometrics; QEEG profiles; Psychiatric discriminants; QEEG subtyping.

Introduction

Since Hans Berger's discovery of the EEG in the 1930's (Berger 1937), many have investigated the clinical correlates of the EEG in psychiatric disorders. Currently, near- ly 6 decades later, the clinical utility of EEG as an adjunct to psychiatr ic diagnosis and t reatment evaluation remains the focus of much research and controversy.

In our l abora to ry , we have s tud i ed the electrophysiological profiles of psychiatric disorders for almost two decades, using a computerized EEG and evoked potential data acquisition and analysis system known as neurometrics. Using this method it has been demonstrated, in multiple independent populations and laboratories, that normally functioning individuals show few values outside expected normal limits, independent of cultural or ethnic background, (Ahn et al. 1980; AI- varez et al. 1987; Gasser et al. 1982; Harmony 1984; Har- mony 1988; Jonkman et al. 1985; Matousek and Peters6n

*New York University Medical Center, New York, N.Y., USA and Nathan S. Kline Research Institute, Orangeburg, N.Y., USA.

Accepted for publication: March 27, 1992 Correspondence and reprint requests should be addressed to Leslie

S. Prichep, Ph.D., Brain Research Laboratories, Department of Psychiatry, NYU Medical Center at Old Bellevue, 27th Street and 1st Avenue, 8th Floor, New York, NY, 10016, USA.

Copyright © 1992 Human Sciences Press, Inc.

1973; Yingling et al. 1986). However, when these features are evaluated in large groups of patients with a variety of cognitive, psychiatric, and neurological dysfunctions, a high proportion of abnormal values are found. These populations were evaluated in studies of head injury (Thatcher et at. 1989); stroke and TIA (Jonkman et al. 1985; Veering et al. 1986; de Weerd et al. 1989); schizophrenia (Czobor and Volavka 1991); depression (Prichep, 1987; Roemer et al. 1991); obsessive compulsive disorder (Prichep et al. 1990a; Maset al. 1991); learning disability (Harmony et al. 1987; Harmony, 1988); psychiatric clinical practice (Maset al. 1991); marijuana abuse (Struve et al. 1989; Struve et al. 1990); rehabilitation medicine (Senf 1988), and carbon monoxide exposure (Fitz-Gerald and Patrick, 1991). Further, significant replicability for these features has been demonstrated, both within a test session and for intervals of days, months and, in cases where no clinical changes have occurred, years (John et al. 1983). Of additional importance in interpreting neurometric abnormalities is the demonstration that the degree of neurometric abnormality (Z score values) in- creases with increasing levels of psychiatric/cognitive dysfunction (Prichep et al. 1990b; John and Prichep 1990).

In this article, our efforts to use this tool in identifying neurometric profiles of psychiatric disorders will be reviewed and the sensitivity and specificity of the method addressed, in order to evaluate its clinical utility.

250 Prichep and John

Method

Subjects

A large database of normal, psychiatric and neurological patients (more than 8000 evaluations) has been constructed at the Brain Research Laboratories at NYU Medical Center. This database contains the EEG records submitted for quantitative analyses as well as the features extracted from those records, and the averaged evoked potential data from a number of EP challenges in each case. Only the EEG data in a subset of these individuals will be discussed in this paper. The inclusion and ex- clusion criteria for "normals" have been given in detail elsewhere (John et al. 1988a). All patients were multiple rater DSMIII or DSMIII-R diagnosed, had no evidence of neurological disease, no history of prior head injury, no history of drug or alcohol abuse (except the alcoholics) and were medication free for a minimum of 7 days at the time of the neurometric evaluation. These include patients in the following groups:

(1) Normally functioning individuals 1, (n = 278, age range 17 to 82 years).

(2) Dementia patients (SDAT) 2, (n=125, age range 50 to 87 years).

(3) Primary affective disorders 3 (unipolar and bipolar subtypes), (n=111, age range 18 to 89 years).

(4) Alcoholics 4, (n=30, age range 21 to 59 years).

(5) Schizophrenics 5, (n=57, age range 18 to 62 years).

(6) Obsessive compulsive disorders 6 (OCD), (n=15, age range 25 to 68 years).

(7) Learning disabilities 7 (LD), (n=175, age range 6 to 18 years).

EEG Acquisition

Patients were seated comfortably in a dimly lit sound and light attenuated chamber. Twenty minutes of eyes closed resting EEG data were collected from the 19 monopolar electrodes of the International 10/20 System, referred to linked earlobes. Electrode impedances were below 5000 fls. Transorbital electrodes were used for the detection of eye movement. The EEG amplifiers had a bandpass from 0.5 to 70 Hz (3 dB points ), wi th a 60 Hz notch filter. The data were digitized at 200 Hz with twelve bit resolution.

QEEG Feature Extraction and Neurometric Analysis

With the aid of a computer algorithm, one to two minutes of artifact free data are selected from the full recording for quantitative analysis. Quantitative features are extracted from this artifact-free data, transformed to obtain Gaussianity and age-regressed. All measures are then expressed as Z scores, indicating the probability that the patient's obtained values are within the range expected for a normal individual his/her age. Further details of the neurometric method are described elsewhere, (John et al. 1980; John et al. 1987; John et al. 1988a).

The artifact-free EEG data were subjected to power spectral analysis using the Fast Fourier Transform (FFT). For each of the 19 monopolar derivations, and 8 computer derived bipolar derivations, the absolute and relative (%) power were computed for the delta (1.5 - 3.5Hz), theta (3.5 - 7.5 Hz), alpha (7.5 - 12.5 Hz) and beta (12.5 - 25 Hz) frequency bands. Mean frequency and intra- and inter-hemispheric measures of coherence and asymmetry between homologous leads were also computed.

Since Z-scores express the deviation of disparate neurometric features from the predicted normative values in the common metric of relative probability, multivariate or composite features can be computed, for example "right hemisphere" abnormality. Correction for inter-correlations among the features combined in each composite was accomplished by computing the Mahalanobis distance across the set of features. By pro- cedures analogous to those used for univariate features, normative data were used to permit Z-transformation of these new composite features.

Multivariate Discriminant Functions

Discriminant functions were computed using the multiple stepwise discriminant analysis (BMDP 7M) proce- dure, which defines mathematical classifier functions, the values of which should be different for members of different a priori defined groups. These functions are weighted combinations of some selected subset of variables, each of which makes some independent contribu- tion to the overall discrimination. Methods for data reduction and variable selection are discussed elsewhere, (see Oken and Chiappa 1986, Prichep 1987). In all instan- ces the subject to variable ratios entering the discriminant analysis were greater than 10:1.

In constructing the discriminant function each group is split-halved. The initial discriminant is constructed on the first half and then applied to the independent second/replication half of each population, allowing the determination of the predictive validity of the equation.

QEEG Profiles of Psychiatric Disorders 251

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mm mm . . . . . .

S(:HIZ

:'\)( I

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mm m

mm %

mmmm nnuum

Figure 1, Group average topographic head maps for Z relative power in the delta, theta, alpha and beta frequency bands, computed separately for a test group of normal subjects (first row) and samples of patients from several different psychiatric disorders (rows 2 through 7). These maps represent the mean relative power difference between each group and the reference group (normal), expressed in standard deviations of the reference group (not shown in the figure). Color coding is proportional to the mean Z score for each group, in steps corresponding to those shown on the Z scale, In estimating the significance of Z scale values for group data, the squar~ root of sample size should be multiplied by the Z value of any point on the map (John et al. ]988a. Copyright ]988 by the AAAS.)

Classification A c c u r a c y Curves (CACs)

For the repl icat ion group in each discr iminant analysis, CACs were plotted. These curves show the percentage of correct classifications as a function of the discriminant probability score and allow one to assess

sensitivity and specificity by studying the relationship between false positives, true positives, false negatives,

true negatives and the discriminant probability score. In this way, discriminant probability cutoffs were selected to achieve desired levels of false positives and false negatives, (for further details see John et al. 1988b).

This method is derived from receiver operating char- acteristic curves, ROC curves, used originally in signal detection work. The use of such curves in diagnostic systems is discussed by Swets (1988). The relationship of sensitivity, specificity and diagnostic confidence is discussed by Shagass et al. (1984). In our application, we have extended the model such that cutoff levels can be individually determined for each of the groups classified, since the relative cost/benefit ratio of incorrect versus correct classification may be different for each group. Further, since it can be assumed that the distribution of QEEG features in any of these populations will have some degree of overlap, a "guardband" is defined in which a confident classification cannot be made.

Cluster Analysis

Using a small subset of neurometric variables, a cluster analysis was performed (BMDP, K-Means), with no "a priori" information about diagnosis, to partition cases into subgroups. In this algorithm, the Euclidean distance is used to measure the distance between the value of each individual case and the center of each cluster (mean value of the cases in the cluster). Cases are placed in that cluster for which the distance between their value and the cluster mean is least. Since in a cluster analysis every individual will be placed in a cluster, it is important to note that the "meaningfulness" of the cluster must be assessed. In the current application, this is achieved by studying the relationship between outcome measures (e.g., clinical course or treatment response) and cluster membership.

Results and Discussion

In Figure 1, group average topographic maps for Z relative power in the delta, theta, alpha and beta bands are shown for normal adults and 6 populations of psychiatric patients, (John et al. 1988a).

The top row of this figure shows that an independent group of normals (n=60) displays very little deviation from expected normal values. On the other hand, all the other groups (rows 2 through 7) show many significant deviations from the predicted normal values. Further, considering the pattern of abnormalities in each group suggests that differential profiles of neurometric abnormality may exist for different disorders.

The possible existence of differential profiles of abnormality suggested by these maps must be considered with


Z MATRIX

REGIONS

A B C D E F

1 F E 2 .... ~'K--

A T 3 X U R 4 X

E S 5 X

6

--E ..... ~ UNIVARIATE MAP

X = Significant hit

PROFILE DISORDER I = B2 + 85 + C3 + D4 + E2

Figure2. S c h e m a t i c of t he matr ix of a b n o r m a l neurometric values (marked with an X) for a theoretical patient with Disorder 1. Rows of the matrix are extracted Z transformed features, and columns are brain regions.

caution since topographic maps of univariate features have many limitations. This is demonstrated in the schematic shown in Figure 2.

Figure 2 shows a matrix of abnormal features for an individual with "Disorder 1". Each entry in this matrix represents a significant deviation from normal values for a particular feature or set of features, in a particular region. The individual depicted here can be described as having a B2 + B5 + C3 + D4 + E2 disorder. A topographic map can be used to represent only one row of such a matrix, while the disorder can only be multivariately characterised. The multivariate method we used to describe these profiles quantitatively is stepwise discriminant analysis (see Methods, above).

The histograms shown in Figure 3 show the discriminant accuracy for the initial discriminant and the independent replication in the separation of normals from a sample of 278 abnormal patients, combined from different diagnostic categories in the database.

Using 8 neurometric composite features, including overall abnormality in the delta, theta, alpha and beta bands, overall frequency abnormality in the anterior and posterior regions, mean coherence abnormality, and overall asymmetry abnormality, the mean discriminant accuracy in the independent replication group was approximately 75%. Due to the heterogeneity of this group, this is the hardest discrimination to make, cleaving a dichotomous separation between normal and "organic" disorders of several types which may differ from normal in different ways.

Vastly better discriminant accuracy is obtained when specific diagnostic groups are discriminated from nor-

I NORMAL II ABNORMAL (N = 60/60) (N = 142/136)

z _0 80 F- < _0 60 1.1_

O9 40 < . - I 0

.1 CLASSIFIED AS: I II I II

I INITIAL INDEPENDENT • DI SCKIMINANT [ ] REPLICATION

Figure 3. Histogram of % classification accuracy for the discriminant function separating Normal adult subjects (n=120, 60 used in the initial discriminant and 60 used in the independent replication) from a mixed sample of patients with different DSMIII-R disorders, selected from the neurometric database (n=278, 142 used in the initial discriminant and 136 in the independent replication). Solid bars are results from the initial discriminant and open bars from the independent replication. Overall discriminant accuracy was approximately 78%.

mal or from each other. Table I shows the classification accuracy obtained by applying a large number of classifier functions constructed taking two groups at a time (top portion of table) and for multiple group separations (bottom of the table). The discriminant accuracy for the separation of normal (n=95) and primary depression (n=111), shown in the first line of this table, was 90% for the independent replication. Once again, only a small number of neurometric variables were used in this classifier function, including bipolar anterior coherence and asymmetry and the composite for absolute power in the left fronto-temporal regions. A high accuracy was achieved by this discriminant in spite of the fact that "Primary Depression" is comprised of two subtypes: unipolar and bipolar depressed patients. Further, the unipolar and bipolar subtypes were well discriminated from each other using variables which are different from those which distinguished them so well as a group (primary depression) f rom normal, (Prichep 1987; Prichep et al. 1990a). That is, a l though they are neurophysiologically more like each other than they are like normals, they are still clearly dist inguishable neurophysiologically from each other.

This table demonstrates that, using small subsets of


Table 1. Summary of discriminant results for two groups (top panel) and multiple groups (bottom panel) Neurometric QEEG discriminant functions, The initial discriminant accuracy is indicated first (X) followed by the accuracy in the independent replication (Y), shown as (X/Y).

Groups

I vs. II

Neurometric QEEG Two Group Discriminants

n Mean Discriminant Accuracy (%)

(Initial Discrim~Independent Replication)

I II

95 111

65 32

150 52

149 57

103 46

120 30

32-97 30

158 175

93 13

16 12

I II

88/86 83/93

84/87 88/94

91/84 89/92

96/99 90/82

84/88 84/85**

95/95 75/90*

91/88 96/93*

89/79 72/71

94/82 92/85*

81/81" 83/83*

N vs. Dep

Uni vs. Bip

N vs. MHI

N vs. Sz

Dep vs. Sz

N vs. Alc

Abn vs. Alc

N vs. LD

Vas Dem vs. Dem

RitResp vs. NonResp

Groups

I vs. II vs. III vs. IV

N vs. Dep vs. Dem

N vs. Dep vs. Alc vs.

*Jack-knifed replication

**Med. group used for replication

Dem

Neurometric QEEG Multiple

n

125

~roup Discriminants

Mean Discriminant Accuracy (%)

(Initial Discrim./Independent Replication)

I II III IV I II III IV

85 87 125

120 103 30

84/85 84/80 84/71

77/75 72/85 80/80 79/77

Group codes are as follows: N = Normal; Dep = Major Affective Disorder, Depression; Uni = Unipolar Depression; Bip = Bipolar Depression; MHI = Mild Head Injury; Sz = Chronic Schizophrenia; Abn = Abnormal groups combined; Alc = Alcoholic; LD = Learning Disabled; RitResp = Responders to Ritalin; NonResp = Nonresponders to Ritalin; Dem = Dementia (SDAT); Vas Dem = Dementia of vascular etiology.

neurometr ic QEEG variables, classifier functions can be constructed which achieve h igh accuracies of classification in i ndependen t test popula t ions (shown by the split- half results). Classification accuracies in the independen t replications, i.e., predic t ive validity, range f rom 76% in the separa t ion of no rma l f rom a he te rogeneous g roup of " learning disabled children", to as high as 92% in the separa t ion of normals f rom alcoholic patients.

It is impor tan t to note that the discr iminat ing variables used in each discr iminant are a subset of the full feature set available for each subject, and represent only o n e such feature set capable of achieving discrimination. There is h igh r e d u n d a n c y in the measures extracted f rom the full

10/20 system, as demons t r a t ed by factor analysis of the m e a s u r e m e n t space. Thus, for each variable used in a discr iminant there are a set of h ighly correlated variables, which could replace the one which was selected wi thout major change in discr iminant accuracy. Therefore, when a t t empt ing to in terpret the d iscr iminant var iable set, it is appropr ia te to think in te rms of a "class" of variables ra ther than a specific m e m b e r of that class (for example, frontal incoherence ra ther than incoherence be tween F7 and F8).

Figure 4 shows the classif icat ion accuracy curves (CACs) for the r ep l i ca t ion g r o u p in the n o r m a l vs p r imary depressed discr iminant function. Two sets of


Normal vs Primary Depression

Classification as Normal

it 9O

O O

t o 7O O

60 O

50

40

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i [ o • • •

50 6O 70 80

PHOBABILITY LEVEL

O

O

Classification as Primary

+ - - - O

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Q

O

O O O O o O

90 1O0 ~0 60 70 80 90 100

PROBABILITY LEVEL

Classification % Classification o Normal as Normal (TN) 81 - Primary as Normal (FN) 10

23028

Primary as Primary (TP) 81 Normal as Primary (FP) 10

Figure 4: Classification curves for normal subjects (left panel) and patients with a primary depressive disorder (right panel). The % correct classification is plotted as a function of the discriminant probability score obtained with this discriminant function, constructed from QEEG features, Values plotted are based on the independent replication of the discriminant function, Percentage of true negatives (TN) and true positives (TP) are shown in the bottom panel for the 10% false positive and false negative cutoffs, as indicated on the curves above. Note that the 10% error rate corresponds to two different discriminant scores.

curves are constructed, one for true negatives (TN) and false negatives (FN), (left panel labelled "Classification as Normal", and a second showing the curves for true positives (TP) and false positives (FP), (right panel, labelled "Classification as Primary"). Using these curves, we can establish the discriminant probability score necessary to achieve specified levels of FPs and FNs. For example, in the figure, the probability scores necessary to achieve FPs and FNs of 10% are indicated. Note that the discriminant probability scores to achieve a specified accuracy of classifying normality and abnormality need not be the same. The implications of the use of these curves should be apparent when considering the different criteria appropriate for use in a third order referral situa- tion, where the pat ients repor t active funct ional symptomatology, as compared with that in a general screening tool, where the patient may not report the existence of any symptoms.

Only in conjunction with the corresponding CACs can discriminant functions be used to achieve judicious classification of a patient. Classification of a patient cannot be meaningfully accomplished solely on the basis of the discriminant probability score. The score required to attain a given confidence of classification can only be in- ferred from a knowledge of the CAC. Thus, in our clinical use of discriminant functions, we always report the confidence of classification if the score exceeds the threshold

necessary for classification. In addition, we use a hierachical method of classification which depends for its start point upon the clinical report of the presence of particular relevant symptoms.

As was shown above in the case of unipolar and bipolar depression, subtypes known to exist within a DSMIII-R category can be separated accurately by discriminant analysis. A cluster analytic approach to the identification of subtypes is preferable when the existence of subtypes is not known. Using this method, thorough study of the distribution of neurometric features within samples of psychiatric patients who share a clinical profile and DSMIII-R diagnosis, has revealed the existence of neurometric subtypes within a number of diagnostic categories. The relationship between membership in a neurometric subtype and response to treatment has been studied in some populations where treatment response was known. In obsessive compulsive disorder patients, we have reported a clear relationship between neurometric subtype and response to treatment (Prichep et al. 1991; Mase t al. 1991). Based on cluster analysis using a small number of neurometric relative power features, we found two clusters within a sample of 15 obsessive compulsive disorder patients. 71% of the members of CAuster I were found to be non-responders to OCD specific medication regimes, while 88% of the members of Cluster II were found to be medication

QEEG Profiles of Psychiatric Disorders

Delta

Cl~s 1 ~ [n=7t

Clus 2 In=S]

Theta Alpha Beta

0 • 0

255

Figure 5: Group average topographic maps for Z relative power in the delta, theta, alpha and beta frequency bands, for the two neurometric clusters of OCD patients. Color coding is proportional to the mean Z score for the each cluster, in steps corresponding to those shown in the Z scale. Note that in estimating the significance of the Z scale value for group data, the mean deviation (Z score) must be multiplied by the square root of the sample size (Mas et al. 1991).

r e sponde r s . F igure 5 s h o w s the g roup average topographic maps of Z-relative power for the members of Cluster I and II. Thus, it appears that there exist, within this disorder, pathophysiological subtypes sharing a common clinical expression.

Prognosis of illness is another area in which it appears that QEEG might be of clinical utility. Preliminary data in a group of 39 elderly patients, seen initially when they displayed only subjective reports of cognitive decline with no confirmation from objective testing, suggest that QEEG data may have predictive utility relative to subsequent cognitive deterioration. After baseline cogni- t ive/psychiatr ic and neurometric evaluations, these patients were followed up for 3 to five years, and then re-evaluated clinically. Using the neurometric profile at baseline, we were able to predict with over 78 % accuracy those who remain unchanged and with over 70% accuracy those who go on to deteriorate. The later group showed clear cognitive decline with global deterioration scale scores (GDS) in the mild to moderate dementia range, upon retest three to five years after baseline (John and Prichep 1990). These preliminary findings are now being explored in a much larger group and will be reported elsewhere.

With the proliferation of a generation of topographic mapping devices there is increasing concern expressed related to the interpretation and clinical utility of such data. Such concerns are well founded, which is why in our work we emphasize the importance of careful analysis of false-positives and false-negatives, use of statistical methods for data reduction and, especially, prospective validation. As stated by Duffy et al. (1986)

it is the intent of topographic mapping to "...locate and obtain an approximation of the magnitude and extent of a d e v i a t i o n of "no rma l i t y " , and fu r the r that "neurophysiological studies may provide clinical information important to the making of a diagnosis, but it is the physician who always makes the diagnosis." When applied judiciously to the appropriate populations and used conservatively as an adjunct to conventional clinical evaluations, QEEG may provide important information previously unavailable to the clinician.

Footnotes

1Supported in part by NSF Grant DAR 78-18772 and Grant# MH32577 and Grant# AG03051 from NIA of NIMH

2This population was studied in collaboration with S. Ferris and B. Reisberg, Millhauser Laboratories, Dept. of Psychiatry, New York University Medical Center. Supported in part by NIA Grant #AG03051 and #MH32577.

3This population was studied in collaboration with Dr. A. Lieber, Dept. Neuroscience, St. Francis Hosp. FLA.; Dr. F. Mas and Dr. A. Georgotas, Dept. Psychiatry, NYU Medical Center

4This population was studied in collaboration with Dr. C. Rohrs, Manhattan Veterans Admin. Hospital, N.Y., N.Y.

5This population was studied in collaboration with K. Alper, M.D., Dept. of Psychiatry, New York University Medical Center and Bellevue Hospital Center, N.Y., N.Y.; this work was partially supported by Cadwell Laboratories, Washington.

6This population was studied in collaboration with Drs. F. Mas and Levine, New York University Medical Center, N.Y.

7This population was studied in collaboration with Board of Cooperative Educational Services (BOCES), District III, Dix Hills, N.Y. Supported in part by Grant# G007604516 from the Office of Education, Bureau of Education for the Handicapped


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