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The Value of Quantitative Electroencephalography in Clinical Psychiatry: A Report by the Committee on Research of the American Neuropsychiatric Association

Received and accepted March 3, 2006. Dr. Coburn is affiliated with the Department of Psychiatry and Behavioral Sciences, Mercer University School of Medicine, Macon, Georgia. Dr. Lauterbach is affiliated with the Division of Adult and Geriatric Psychiatry, Mercer University School of Medicine, Macon, Georgia. Dr. Boutros is affiliated with the Department of Psychiatry and Neurology, Wayne State University, School of Medicine, Detroit, Michigan. Dr. Black is affiliated with the Department of Psychiatry, Neurology, and Radiology, Washington University School of Medicine, St. Louis, Missouri. Dr. Arciniegas is affiliated with the Department of Psychiatry and Neurology, University of Colorado School of Medicine, Denver, Colorado. Dr. Coffey is affiliated with the Henry Ford Health System, Detroit, Michigan. Address correspondence to Dr. Coburn, 655 First St., Macon, GA 31201; Coburn_kl{at}Mercer.edu (E-mail).

ABSTRACT

TOP ABSTRACT INTRODUCTION METHOD Methodological Problems Recommendations for Future... CONCLUSIONS REFERENCES

 
The authors evaluate quantitative electroencephalography (qEEG) as a laboratory test in clinical psychiatry and describe specific techniques, including visual analysis, spectral analysis, univariate comparisons to normative healthy databases, multivariate comparisons to normative healthy and clinical databases, and advanced techniques that hold clinical promise. Controversial aspects of each technique are discussed, as are broader areas of criticism, such as commercial interests and standards of evidence. The published literature is selectively reviewed, and qEEG’s applicability is assessed for disorders of childhood (learning and attentional disorders), dementia, mood disorders, anxiety, panic, obsessive-compulsive disorder, and schizophrenia. Emphasis is placed primarily on studies that use qEEG to aid in clinical diagnosis, and secondarily on studies that use qEEG to predict medication response or clinical course. Methodological problems are highlighted, the availability of large databases is discussed, and specific recommendations are made for further research and development. As a clinical laboratory test, qEEG’s cautious use is recommended in attentional and learning disabilities of childhood, and in mood and dementing disorders of adulthood.

INTRODUCTION

TOP ABSTRACT INTRODUCTION METHOD Methodological Problems Recommendations for Future... CONCLUSIONS REFERENCES

 
Quantitative EEG (qEEG) involves computer-assisted imaging and statistical analysis of the EEG for detecting abnormalities, assisting the physician in making a diagnosis, and other purposes relating to patient care. Among the techniques of functional brain imaging, qEEG offers many advantages. It has an ideal temporal resolution in the millisecond time domain characteristic of neuronal information processing, employs no ionizing radiation, noninvasively images both excitatory and inhibitory cortical neuronal activity rather than secondary hemodynamic processes, and is relatively inexpensive and portable. Its formerly poor spatial resolution has increased dramatically as channel capacity has expanded from 20 a decade ago to 256 presently, with a 512-channel system expected for commercial release within the next year. Perhaps most importantly, several large qEEG normative (i.e., statistically representative) databases directly relevant to clinical psychiatry are available, and qEEG technology has advanced to the point where two systems have attained FDA approval.

Previous reviews of this area have lumped together two types of studies: those focusing on the direct clinical applicability of currently available qEEG systems and those involving more speculative areas of qEEG research. Consequently, it remains unclear whether qEEG is ready to be used as a standard laboratory test by practicing psychiatrists. A pivotal question remains unanswered concerning the actual clinical utility of qEEG and related electrophysiological methods: are the techniques sufficiently sensitive and specific to answer practical clinical questions about individual patients suffering from recognized psychiatric disorders? This article reviews briefly the types of assessments that comprise the realm of qEEG, the areas of controversy surrounding the techniques, and the published studies applying them to individual patients. The focus of this report is on whether presently available qEEG systems can tell the practicing psychiatrist anything of practical importance about the individual patient sitting across the desk from him. Its conclusions are less glowing than might be expected on the basis of previous reviews because, although qEEG can provide information of direct clinical relevance, even the most sophisticated qEEG systems now available are still very limited. We make specific recommendations regarding qEEG’s present clinical utility and areas in which additional research and development are needed.

METHOD

TOP ABSTRACT INTRODUCTION METHOD Methodological Problems Recommendations for Future... CONCLUSIONS REFERENCES

 
Selection of Literature
The focus of this review on the practical clinical utility of qEEG as a laboratory test in psychiatry requires the exclusion of a vast amount of tangentially related literature. EEG biofeedback ("neurotherapy") is not included because it is a treatment rather than a laboratory test. qEEG drug development studies are excluded because they tend strongly to use group research designs that tell nothing about the individual clinical patient being treated. Psychiatric conditions thought to arise secondary to brain damage (e.g., stroke, traumatic brain injury) or infection (e.g., systemic lupus erythematosus) are excluded, due to the difficulty of determining whether any subsequent psychiatric condition is primary or secondary. Also excluded are psychiatric disorders for which the qEEG literature is sparse, such as Axis II disorders and substance abuse/dependence.

The latter is a particularly messy issue. Substance abuse categories are poorly defined and the criteria are inconsistently applied. Since an alcohol discriminant is included with one qEEG system and has been tested and validated in the literature, it has been included in the Depression section. As for the other recreational drugs, most published studies either involve no predictive classification^1,2 or use psychiatric patients as subjects³ or, most commonly, fail to control for other drug use or important lifestyle variables. When more and better studies have been completed and when appropriate discriminants are included in qEEG systems, it will be important to review them. But for now a review is premature, particularly since discriminants are not available for use by practicing psychiatrists who may wish to use qEEG. There is an additional aspect of this area that extends it beyond the usual realm of clinical psychiatry. Since most recreational drug use is illegal, and since Thatcher has convinced the courts that (at least for brain injury) qEEG meets admissibility standards, it is especially important to tread carefully through this minefield. Discriminants intended to pick out drug users from a population, where false positives involve legal as well as medical hazards, need to be held to the highest standards of validity and reliability before they are made available for general use.

It was also necessary to omit source localization. LORETA and VARETA (low resolution/variable resolution electromagnetic tomography) are extraordinarily important advances in qEEG. They can provide unique information regarding the neurophysiological underpinnings of psychiatric disorders, and they bring qEEG squarely into the realm of functional neuroimaging. Unfortunately, these techniques fail the practical utility test. To the practicing clinical psychiatrist it makes no difference whether the major depression in the patient sitting across from him is linked to disturbances in the right prefrontal area or the left prefrontal area. The treatment will be the same. As the field develops, particularly as the current DSM categories are parsed into more meaningful subcategories (by cluster analysis, etc.), it may well be the case that psychiatric disorders linked to abnormalities in specific brain areas will be found to respond to different treatments. Indeed, functional neuroimaging should be at the forefront of neuropsychiatry/behavioral neurology development. But for the time being, LORETA and VARETA are simply irrelevant to the day to day professional life of the average working psychiatrist. For more general reviews of the EEG in psychiatry the reader is directed to the work of Chabot et al.,⁴ Boutros,⁵ Hughes,⁶ Hughes and John,⁷ and Small.⁸

The following is not intended to provide a comprehensive review of the qEEG literature. Rather it identifies and discusses selected between-subjects studies that are designed to find either differences between an individual patient and a defined healthy group (for simple EEG abnormality detection) or similarities between an individual patient and a defined clinical group (for diagnostic or other classification). With few exceptions, studies of between-group differences rather than between-subjects differences, and work published in non-peer-reviewed sources, are excluded. Individual articles in the published literature were located via a literature search of the National Library of Medicine databases using medical subject headings that included {EEG, qEEG, Evoked Potentials, Event-related Potentials} and {Mental Disorders, Psychiatric Disorders, Depression, Schizophrenia, Anxiety Disorder, Mood Disorder, Bipolar Illness}. Additional studies were found in the bibliographies of the located articles. Major qEEG equipment manufacturers and companies offering qEEG services or products were contacted for information.

Diagnostic Terminology
Diagnostic nomenclature has evolved rapidly and can lead to confusion when articles published at different times are compared. A particular hazard involves "rediagnosing" patients in earlier studies by attempting to fit them into current diagnostic categories. For that reason, the authors’ original terminology for patient groups has been retained in the discussions below. In contrast, the authors’ original terminology for healthy individuals used for control purposes ("normal,""control," "nonpatient," etc.) has been changed to "healthy" or "healthy subjects" for the sake of uniformity.

Types of Assessment
A useful nosology of qEEG and related techniques has been provided by Duffy et al.⁹ and Nuwer.¹⁰ Of the two major types of data, the debate has centered on qEEG. Evoked potentials (EPs), event-related potentials (ERPs) and their quantitative counterparts (qEPs and qERPs) have received very little attention. The issues are essentially the same, although clinical qEP/qERP development lags far behind qEEG. The following analysis sequence described by Duffy et al.⁹ proceeds from least to most controversial aspects of qEEG.

Visual Analysis
Visual analysis of the ink-written EEG by a qualified electroencephalographer remains the gold standard and is the first step in any qEEG analysis. Several authors (e.g., Hughes and John⁷) recommend routine visual EEG screening of newly presenting psychiatric patients, particularly if a complete neurological examination is not routinely performed.⁵ The use of "paperless" digital EEG (dEEG) allowing modification of display parameters and electrode montages for visual analysis on a computer screen and easy storage of the EEG record in digital form is noncontroversial once certain minimal technical criteria are met. This lack of controversy is somewhat surprising since there appear to be no studies comparing ink-written to paperless EEG to determine optimal standards of screen resolution, presentation rate, or other display variables. These variables may well exert a strong influence on the detection rate of subtle EEG abnormalities. Even worse, the artifactual production of slow activity through the process of "aliasing"^11,12 is possible if high frequency activity in the EEG is sampled at too low a rate. Nevertheless, there is universal agreement that a traditional visual reading of the EEG constitutes an indispensable first step in qEEG analysis.

Spectral Analysis
Conversion of the time domain EEG record (voltage plotted against time) to the frequency domain (amplitude or power plotted against frequency) using the fast Fourier transformation (FFT) has been widely used by researchers since the 1960s but is only now beginning to be employed by clinical EEG laboratories. The use of stand-alone frequency (spectral) analysis without reference to a normative database, as an adjunct to visual analysis, is relatively noncontroversial. However, here, too, there appear to be no studies comparing the clinical utility of the various analytic algorithms in use. Hanning versus sine versus a multitude of other techniques for handling window edge effects, minimum and maximum epoch lengths, and a host of other questions remain largely unaddressed. FFT spectra showing absolute measures will look very different from those showing relative measures, and spectra showing amplitude in microvolts will appear quite different from those showing power in microvolts squared. But no consideration seems to have been given to the possibility that these differing techniques could mislead the physician. The apparent presumption is that spectral analysis using any variant of the technique can call the physician’s attention to frequency domain characteristics of the ink-written or dEEG, which may aid in forming a clinical impression of the overall record.

Univariate Comparison to Normative Healthy Databases
Serious controversy begins when qEEG data recorded from a patient are compared statistically with normative databases, on the assumption that clinically significant psychiatric disturbances may be accompanied by statistically significant abnormalities in brain activity. Comparisons using single (univariate) spectral measures of the EEG (or single qEP/qERP amplitude measures) to compute z-scores reflecting the degree of statistical abnormality of the patient’s brain activity (e.g., Biologic’s Brain Atlas, Nicolet’s Brain Electrical Activity Mapping [BEAM] system) tend to be better accepted than the multivariate measures used for patient classification discussed below. But even univariate comparisons raise statistical issues. In order to achieve Gaussianity and avoid statistical bias, some qEEG systems include a log transformation of the FFT data. Also, artifact elimination from the raw data and concerns about the length of artifact-free data required for stable spectral estimates become important considerations at this level of analysis. Since the spectral composition of brain electrical activity changes systematically as a function of normal aging, the more capable qEEG systems use either age-stratified normative databases (e.g., Biologic’s Brain Atlas) or age regression (e.g., Neurometric Analysis System) to enhance sensitivity and specificity while avoiding age-related bias. Aside from aging effects, qEEG test-retest stability is remarkably high, even over several years.¹³ For practical clinical applications, most head-to-head comparisons of visually analyzed to computer analyzed EEGs¹⁴ find the computer to have the edge for detecting subtle frequency domain abnormalities. Such detection can then alert the clinician that a reevaluation of the EEG is advisable with attention to certain specific features.

The important epistemological difference between this level of qEEG analysis and conventional EEG, or for that matter techniques, such as positron emission tomography (PET) or single photon emission computed tomography (SPECT), is that conventional EEG does not involve quantitative comparisons with normative healthy or patient databases. It is the difference between having a professional opinion informed by a visual impression alone (EEG, PET, SPECT), and a professional opinion informed by a visual impression supplemented by quantitative information (qEEG). Without a reference database, the physician must rely on an impression. As psychiatry moves toward evidence-based medicine, greater reliance may be placed on quantitative analysis, but at the moment the normative databases simply do not exist for other imaging modalities. Univariate abnormality measures have the advantage of being easy to understand. When displayed as statistical probability maps (SPM; sometimes referred to as statistical parametric maps), they are a valuable aid in patient education since brain areas can be made to "light up" in proportion to the abnormality of their activity. They form vivid illustrations of the clinical point that a brain problem underlies a patient’s symptoms. This serves to destigmatize psychiatric disorders (the brain is malfunctioning just as any other organ can), bringing them into the realm of "real" medicine, and to motivate compliance with treatment. The patient may not understand theta band slowing over the left posterior parietal lobe, but he can see clearly the bright red area on his brain map.

Error checking is relatively easy since statistically abnormal univariate measures generally will correspond to visible features in the original EEG recording. However, normative databases differ in their composition and quality; a qEEG measure deemed abnormal by comparison with one may be normal when compared with another. Since most normative databases are proprietary products, they are difficult to compare systematically and generally have not had their details published in the open literature. For all such comparisons of a patient with a healthy control group, it is assumed that patients and controls differ only in the presence of abnormal brain activity underlying the patient’s disorder. Unfortunately, many patients do not match the often-stringent selection criteria for the normative healthy group (e.g., no history of neurological or psychiatric disorder, no first degree relatives with such disorders, no hypertension or diabetes, no psychoactive medications, etc.). Due to these selection criteria, controls tend to carry much less overall medical burden than do patients. It must be realized that statistically, such "hyper-healthy" controls are abnormal. Comparing a patient with a hyper-healthy control group involves two confounded components—the difference between the patient and the normative healthy population (i.e., the "street normal" population of average health, but excluding the specific disorder being investigated) and the difference between the normative healthy population and the hyper-healthy subjects. The use of hyper-healthy subjects as opposed to more carefully matched "street normal" controls inflates the type I (false positive) error rate. In many clinical applications maximizing sensitivity at the expense of specificity is defensible on the grounds that it is of overriding importance to avoid missing an abnormality (i.e., making a Type II error) and that false alarms can be weeded out by subsequent evaluations.¹⁵ But there are costs to oversensitive screening. In addition to engendering fear and anxiety over a false positive result, subjecting patients to further diagnostic evaluation entails financial costs and a reasonable chance of additional harm in terms of discomfort, missed work, needle sticks, radiation, IV contrast, etc. Prichep and John¹⁶ make the sensible suggestion that the threshold for clinical concern should be set with regard to the consequences of false negative and false positive results.

Multivariate Comparison to Normative Healthy and Clinical Databases
Although its use in clinical psychiatry is controversial, combining several individual (univariate) qEEG measures into a single multivariate measure may allow individual patients to be classified into categories of clinical interest. These often correspond to specific diagnostic categories (for which the classifiers are relatively well developed), but sometimes relate to more tenuously developed categories of medication responsiveness, clinical course, or other dimensions of psychiatric interest. Patient classifications are based on multivariate analysis of linear combinations of qEEG measures (discriminant functions, or "discriminants"), an approach often termed "neurometric" analysis.¹⁶ (For legal purposes the generic term "neurometric" and its variants should be distinguished from the "Neurometric" and Neurometric Analysis System [NAS] trademarks pertaining to a widely used commercial system. For the didactic purposes of this article the distinction is trivial.) This approach extracts a large number of qEEG features and compares them with a reference database. It is assumed that the more statistically unusual the observation, the more likely it is that the underlying brain system is clinically abnormal. Although statistically significant findings are not pathognomonic, they are intended to draw the physician’s attention to features of the underlying EEG that may have been overlooked. At its most basic level this multivariate approach offers a broad post-hoc filter for determining whether the patient’s EEG is statistically normal or abnormal, much like the univariate approach described above.

Even greater controversy occurs when multivariate methods are extended beyond simple EEG abnormality detection to classify individual patients on a "best fit" basis into specific clinically defined categories. A composite quantitative profile of the individual’s EEG can be statistically defined by the particular pattern of z-score values. Patients within a diagnostic category often have distinctive multivariate profiles that are different from those of patients in other diagnostic categories, suggesting that the descriptive symptomatic taxonomy of DSM-IV and ICD-10 may reflect a biological taxonomy of brain abnormalities, which in turn produces a statistical taxonomy of qEEG results. In principle, once the multivariate statistical profiles of different diagnostic categories have been established and validated, they can be used to help diagnose an individual patient on the basis of the similarity of the patient’s multivariate qEEG profile to the previously defined profiles of the diagnostic categories. Clinical qEEG proponents are quick to point out that matching a patient’s statistical profile to a normative profile most characteristic of a specific disorder is different from using the technique to automate the diagnostic process itself. FDA approval of the Neurometric Analysis System and the NeuroGuide Analysis System (presently the only two approved systems) is for the post-hoc analysis of the EEG, and its developers repeatedly stress the need for a conservative and cautious approach to the interpretation of results.

The unfamiliar nature of multivariate statistical procedures has led some to consider them "mysterious" and consequently to be distrustful of neurometrics and related approaches. However, the mathematics are standard techniques¹⁷ and are clearly described in the open literature.^16,18–23 Multivariate procedures certainly are easier to understand than the mathematics underlying three-dimensional MRI image construction or, for that matter, the quantum mechanics underlying a simple transistor, though few would consider transistor radios to be mysterious and worthy of distrust. But the multivariate approach has its limitations. qEEG findings are not pathognomonic and are appropriately used only in conjunction with other clinical information rather than as stand-alone diagnostic classifiers. Additionally, due to its foundation in Bayesian statistics, for this type of multivariate comparison to be valid it is necessary to ensure that the patient belongs exclusively to one of a limited number of categories, usually healthy versus a specific disorder, but sometimes one specific disorder versus another specific disorder. Due to their non-zero false positive rates and the limited number of defined clinical categories, it is inappropriate to use these procedures as a general diagnostic screening test. Difficulties have arisen when naïive users have employed the procedures as a diagnostic filter, running a patient’s data against all possible diagnostic classifiers. Additionally, multivariate measures of pathology are more difficult for both doctors and patients to understand than their univariate components. They do not map well and therefore are of less use in patient education. They also are more difficult to check for errors since each univariate component of an abnormal multivariate measure need not in itself be abnormal.

Advanced Techniques Holding Clinical Promise
qEEG has been reported to do more than simply assist the physician in detecting EEG abnormalities and forming a diagnosis. In a number of instances, qEEG cluster analysis, which groups individuals on the basis of qEEG features without a priori outcome information, has defined subtypes within a single diagnostic class, suggesting that markedly different pathophysiological processes may produce essentially the same clinical symptoms.⁴ Sometimes it is found that individuals within different qEEG clusters respond differently to treatment. Two subtypes of attention deficit/learning disabled children have been found, only one of which responds well to methylphenidate.²⁴ Similarly, Prichep et al.²⁵ and Hansen et al.²⁶ have identified two subtypes of obsessive-compulsive disorder (OCD) patients showing differing responses to selective serotonin reuptake inhibitor (SSRI) medications. Although this aspect of qEEG has not been developed sufficiently for clinical application, a physiological method for predicting a patient’s response to a medication could have profound value for clinical care, helping to select the medication most likely to benefit the individual patient and thereby shorten unsuccessful medication trials. This is a developing area of qEEG research. Another technique holding clinical promise is LORETA, which back-projects surface recorded qEEG onto a realistic three-dimensional brain model, optionally the patient’s own MRI. A LORETA normative database has been described and validated recently²⁷ and its potential clinical utility has been demonstrated.²⁸ It remains to be seen whether LORETA can be developed into a useful clinical laboratory test in psychiatry.

Review of the Present Controversy
The great fear seems to be that unsophisticated practitioners will attempt to use the classification ability of multivariate analysis to substitute for, rather than aid in, clinical diagnosis and treatment selection.¹⁰ This fear is not without substance. During the 1980s one commercial vendor aggressively marketed a qEEG instrument incorporating an early version of the Neurometric system as virtually a stand-alone automatic diagnostic test. Marketing targeted psychiatrists, family practitioners, and other medical specialists unsophisticated in the use of clinical EEG. The system’s limitations—particularly those related to recording artifacts and the Bayesian structure of allowable comparisons—were ignored, and there was a very real possibility of harming patients by misinterpretation of the results. Experienced electroencephalographers of the neurological community were quick to voice their concerns²⁹ and have had a continuing chilling effect on qEEG.

In a 1994 paper on behalf of the American Medical EEG Association, Duffy et al.⁹ assessed qEEG’s clinical efficacy and set minimum standards for its use. Central to these standards is the requirement that only specifically trained individuals should use this technology. Nuwer,¹⁰ writing for the American Academy of Neurology and the American Clinical Neurophysiology Society, dismissed Duffy’s paper out of hand and damned the technique by faint praise. Replies by Hoffman et al.³⁰ representing the Association for Applied Psychophysiology and Biofeedback and the Society for the Study of Neuronal Regulation, and by Thatcher et al.³¹ representing the EEG and Clinical Neuroscience Society, pointed to bias and sloppy scholarship in the Nuwer report. In 1999, a Texas court held that Nuwer’s criticisms of qEEG failed to meet acceptable scientific standards.³² Chabot et al.⁴ and Hughes and John⁷ have provided more complete reviews of the qEEG literature while other authors^33,34 have addressed conceptual issues.

Group v. Individual Differences
Two basic approaches may be discerned to the study of psychiatric illnesses. One approach compares groups of patients to groups of healthy subjects employing research designs intended to find between-group differences attributable to the illness. An enormous research literature documents significant statistical differences between psychiatric patient groups and healthy control groups on a wide variety of qEEG measures. Such between-group designs yield a great deal of information about the workings of the normal brain and the functional alterations characteristic of psychiatric disorders. Examples include studies of the qEEG in schizophrenia,³⁵ dementia,³⁶ and depression.³⁷ The practical clinical problem is that even very significant between-group statistical differences on a measure do not necessarily mean that the measure is capable of classifying individuals into their respective groups with any useful degree of accuracy.^38,39 Unfortunately, much of the literature cited in support of clinical qEEG^4,7 is made up of papers, such as these—good science with unclear clinical application.

A second approach focuses on the individual patient, using qEEG measures to detect abnormalities broadly, and more narrowly to help classify the patient into a specific diagnostic, prognostic, or treatment group. Several univariate measures, such as absolute and relative power, spectral ratios, phase, coherence, and symmetry may be linearly combined to form multivariate measures.⁴⁰ In doing so the measures are found to be complementary; they are additive for the detection of abnormality but yield different topographic distributions.⁴¹ Multivariate techniques have the decided advantage of assessing the relative contributions of multiple univariate qEEG measures, thereby reducing the likelihood that important information will be overlooked.¹⁹ This approach not only yields information about the disorder itself, but also in principle can be useful for guiding the clinical care of individual patients. For example, Prichep et al.⁴² used multivariate qEEG to classify a mixed group of 54 unipolar and 23 bipolar depression patients into their correct diagnostic groups, achieving classification accuracies of 91% and 83%, respectively. To the extent that mania may ensue in a bipolar patient being treated as a unipolar patient, this might be important information for a clinician to have.

Technology Demonstrations V. Clinical Tools
The use of quantitative statistical procedures for EEG abnormality detection, particularly when employed on a post hoc basis to call attention to features that might have been overlooked by the electroencephalographer during an initial visual reading, is supported by a convincing literature (discussed below). The application of such procedures to assist in clinical diagnosis by classifying a patient into the "best fit" multivariate category is less well supported by the peer reviewed literature, but a reasonable case can be made for its cautious use. Unfortunately, qEEG proponents, such as Hughes and John,⁷ go beyond these modest boundaries and cite studies using techniques, such as cluster analysis that have no direct clinical application. Though cluster analysis is an important research tool and may lead to the development of clinically useful discriminants, the uncritical mixing of such studies with the more conservative citations undermines the credibility of their argument.

Another aspect of this general problem is that many qEEG studies in the literature bearing upon psychiatric problems use idiosyncratic methods of data recording and analysis involving ad hoc healthy and clinical normative groups. Such idiosyncratic research methods are of little help to the clinical psychiatrist who needs a standardized laboratory test. This problem is discussed in more depth later.

Commercial Interests
Nuwer¹⁰ questions the veracity of reports published by authors having commercial interests in qEEG systems. However, there appears to be a wide range of professionalism among authors with commercial interests, paralleling the professionalism among academic authors. Both groups profit from their endeavors, whether through promotion/tenure/salary/consulting fees or through patent royalties/corporate profits. The academic who hires himself out as an expert witness testifying against qEEG has little to distinguish himself from the commercially involved researcher promoting the technique. And although it is unfortunate that qEEG requires very large databases that are available only as commercial products, it would be difficult to name a medical test that does not involve a commercial vendor. Scientific quality is where one finds it and the gold standard must remain articles, particularly independent replications, published in peer-reviewed journals.

A more troubling aspect of the commercial interest problem is the advertising by individual physicians. For example, in the advertising material for a recent "antiaging" seminar for clinicians in Las Vegas, a well known physician claimed that by using his "Brain Code, based on four electrical signals" derived from brain electrical activity mapping performed on a laptop computer in any doctor’s office, the attendees could treat "any brain disease" including dopaminergic brain dysfunctions (Parkinson’s, depression, dysthymic [sic], narcolepsy, chronic fatigue syndrome), acetyl-cholinergic [sic] brain dysfunction (Alzheimer’s, memory loss, dyslexia, attention deficit disorder[ADD], cognitive disorder, mild learning disability), gamma aminobutyric acid (GABA)-dominant brain disorder (anxiety, manic depression, headaches, migraine headaches, chronic pain), and serotonin brain disorder (social phobias, insomnia, dysthymia, mixed anxiety states, somatization, irritable bowel syndrome, fibromyalgia). But as disturbing as such claims may be in this era of evidence-based medicine, the behavior of individual physicians cannot reasonably be used as a criterion for the acceptance or rejection of a laboratory procedure. The facts must speak for themselves.

Resurrecting Moot Points
Many of the once-valid criticisms of qEEG have been addressed but continue to be raised by those opposing acceptance of the technique. Examples of such dead horse flogging^43,44 include standard EEG artifacts, recording errors, patient characteristics, and misapplication of techniques. It is certainly true that any of these can bias qEEG (especially multivariate) results in ways difficult to detect. Closely related are issues of technical competence and lab certification. However, these are essentially training and regulatory issues and have been dealt with through minimum practice standards, such as those proposed by Duffy et al.⁹

qEEG has been criticized for employing too many statistical tests,^43–45 thereby generating spurious statistical significance. This continues to be true of some systems using univariate comparisons and SPM to call the electroencephalographer’s attention to possibly important features as discussed above. Duffy et al.^9,46 recommends replicating each clinical test and accepting as true abnormalities that replicate. The more capable qEEG systems employing multivariate comparisons use Principal Components Analysis (PCA) to reduce the large number of variables to a much smaller set of uncorrelated factors representing the intrinsic dimensionality of the data set. Either of these procedures answers the "too many statistical tests" criticism.

Another criticism¹⁰ is that many qEEG findings are not replicated. The validity of this criticism may be judged by the reader (Tables 1 and 2). Caution should be exercised, however, in determining what constitutes a replication. Studies using discriminant analysis generally form the discriminant function from the first subject sample and test its accuracy on a second sample. In such cases the third sample would constitute the first true replication. However, the classification ability of the discriminant is assessed as early as the first sample, so has become common practice in the qEEG literature to refer to the second sample as the first replication, and this terminology has been incorporated into the present paper.

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TABLE 1. Diagnostic Classification Analysis

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TABLE 2. Drug Response Prediction Analyses

Yet another aspect of the problem is qEEG’s differential sensitivity. It may be sensitive to statistically significant but clinically trivial normal variants, such as an absence of a posterior resting rhythm, while being insensitive to clinically important patterns, such as fast transient epileptiform spike activity and slower triphasic waves.^41,47 Closely related is the criticism that computers cannot diagnose disorders.^43,44 To overcome these limitations the electroencephalographer’s trained eye is necessary. But qEEG does not remove the expert physician from the loop. For at least the past decade there has been universal agreement that the indispensable initial step in qEEG analysis is the "gold standard" of a clinical (visual) reading of the raw EEG by a trained electroencephalographer. qEEG is a post-hoc supplementary and complementary technique of data analysis that is specifically not intended to function as a stand-alone diagnostic instrument.

Standards of Evidence
The argument has been made that levels of specificity found in qEEG studies are often higher than those found in routinely used clinical tests, such as mammograms, cervical screenings, or CT or SPECT brain scans.^7,48,49 The unfortunate marketing history of qEEG in the 1980s has led to a situation today in which extraordinary evidence is required for its acceptance and endorsement by professional societies. Even FDA findings of safety and efficacy do not appear to be sufficient. The opponent camp championed by Nuwer maintains that additional evidence is needed, while the proponent camp championed by John counters that existing evidence is overlooked or misinterpreted. It is possible that clinical turf issues may play a role in this dispute. However, it has yet to be shown that any qEEG system available to the working clinical psychiatrist meets the methodological standards for diagnostic tests (spectrum composition, analysis of pertinent subgroups, avoidance of workup bias, avoidance of review bias, precision of results for test accuracy, presentation of indeterminate test results, test reproducibility) enumerated by Reid et al.⁵⁰

Information Availability
A major problem faced by the psychiatrist wishing to assess the practical clinical usefulness of commercial qEEG systems is that information about most systems’ capabilities is extremely difficult to obtain. The FDA has in the past placed severe restrictions on the information available to potential users, even forbidding a listing of the specific analyses available, and the ludicrous situation has arisen wherein, even after purchasing one major system, the buyer finds no such listing in the user manual. The situation may be changing since the most recently approved system is much better described. Lawsuits between commercial vendors similarly constrain the information they make available.

Applications to Specific Disorders
When reading the following sections, an important point must be kept in mind. Each section contains comparisons between standard, visually analyzed EEG and qEEG. Preceding parts of this article have stressed repeatedly that the indispensable first step in qEEG analysis is a standard visual reading of the raw EEG by a qualified electroencephalographer, and that qEEG is used responsibly only as a supplementary and complementary technique for post hoc analysis, serving to draw the physician’s attention to aspects of the original EEG that may have been overlooked. It is always the physician who performs the diagnosis or makes other relevant clinical decisions, not the machine. However, in the sections to follow, these fundamental dicta are consistently violated in order to assess the ability of the qEEG system to classify individuals independent of the clinical expertise of the user. In this manner the comparisons are artificially weighted against qEEG since the critical first step, evaluation of the raw EEG by an electroencephalographer, and the critical last step, the integration of all clinical information into the physician’s decision-making process, are omitted—omissions which would violate the most basic requirements of qEEG in actual clinical practice.

Learning Disorders, Attention Deficit Disorders
Nuwer,¹⁰ in his AAN/ACNS position paper, gave a negative recommendation for qEEG’s clinical use in learning disabilities or attention disorders, basing his recommendation on "inconclusive or conflicting evidence from well designed clinical studies, such as case control, cohort studies, etc." (p. 286). Hughes and John,⁷ applying standards similar to those of Nuwer to a more extensive review of the literature, gave positive recommendations for qEEG in learning and attention disorders, citing many relevant (as well as some irrelevant) studies. More measured and focused reviews and evaluations of the technique and its underlying clinical literature have been provided subsequently by Chabot et al.^4,51

Although the taxonomy and diagnostic criteria for attention disorders and especially for the various types of learning disabilities are often problematic, it is clear that clinically significant EEG and statistically significant qEEG abnormalities increase in rough proportion to the severity of the problem.^52–60

Learning Disorders
One of the seminal works in the qEEG literature was John et al.’s 1977 Science paper¹⁸ describing the Neurometric approach and applying it to, among other conditions, learning disorders. Used to evaluate a mixed group of 118 healthy and 57 children with learning disorders, an initial discriminant accuracy of 93% was obtained (versus 76% for standard psychometric evaluation), and a jackknife replication (or "leave one out" replication in which each individual of the original sample is classified according to a discriminant formed using all other members of the sample) produced classification accuracies of 77% and 71%, respectively. Another early attempt to use neurometrics for classification of children with learning disorders exhibiting borderline normal intelligence and generalized learning disabilities, and children with specific learning disabilities with normal intelligence⁶¹ also showed promising results. The strong points of the report were the large sample sizes and the very low false positive rates for both healthy comparison groups, which made the 46% to 58% true positive rates for the clinical groups useful. A subsequent multivariate qEEG classification study found that children with learning disorders could be discriminated from healthy children with 72% sensitivity and 80% specificity,¹⁵ using a multivariate discriminant function derived from (and optimized for) only those two types of children. An independent replication produced lower sensitivity at 65% but higher specificity at 87%. Broadening the discriminant function by including children suffering from a variety of neurological disorders decreased the sensitivity to learning disorders, but also detected children with specific learning disabilities whose learning problems stemmed from a much wider range of etiologies. Most of the classification accuracies shown in Table 1 arguably are high enough to have practical clinical utility, and replicate well with independent samples, as shown. Similarly, Lubar et al.⁶² studied the qEEGs of children with learning disorders and healthy subjects using exploratory discriminant analyses to assess the ability of various frequency bands, scalp sites, and analysis procedures to classify the children into their respective groups. Although methodological problems are apparent, the authors were able to classify children with learning disorders with sensitivities as high as 79% and specificities as high as 81% for their healthy counterparts using discriminant analyses based on 20 variables. (Using 672 variables an overall classification accuracy of 98% was attained.)

Serious doubt was cast upon the utility of neurometric methods for learning disorder detection by Yingling et al.,⁶³ and since the dispute is frequently cited by those opposing the clinical use of qEEG, it is reviewed briefly here. These authors assembled a group of very carefully screened children suffering from "pure dyslexia" without accompanying neurological abnormalities, and an equally well-screened group of healthy control children. They then used the neurometric methods described by Ahn et al.⁶¹ to assess both groups. As expected, Yingling’s healthy group was classified as normal when compared with the neurometric normative database. However, Yingling’s pure dyslexia group also was found to be within normal limits (no attempt was made to classify individual subjects). Noting that Ahn et al.’s specific learning disabled group contained many subjects with coexisting neurological and/or sensory deficits, Yingling attributed Ahn’s findings to the presence of such deficits rather than to learning disorders per se. This view was supported by Fein et al.⁶⁴ who also found no differences between groups of dyslexic and healthy control children (again, no attempt was made to classify individual subjects). Diaz de Leon et al.,⁶⁵ however, reported significant qEEG differences between learning disordered and healthy groups of children, all lacking neurological symptoms, paralleling Ahn’s findings and calling into question Yingling’s and Fein’s view that pure dyslexia is unaccompanied by qEEG abnormalities. In the Diaz de Leon et al. study, learning disorders were shown to exert effects independent of neurological risk factors, such as prolonged labor and perinatal asphyxia. Similarly, Flynn and Deering⁶⁶ published group qEEG evidence of distinct learning disorder subtypes and Harmony et al.⁵⁸ found that qEEG abnormalities increase in parallel with reading and writing difficulties.

To account for the Yingling/Fein results, John et al.¹⁹ and Duffy et al.⁹ noted that Ahn’s learning disorder and specific learning disabled groups contained a wider range of etiologies than did Yingling’s. Ahn’s learning disorder and specific learning disabled children did not meet the rigorous screening criteria applied by Yingling for pure dyslexia, so comparing the two studies directly is inappropriate. Operationally defined, pure dyslexia is neither a learning disorder nor a specific learning disability, and great care must be taken to ensure that group membership criteria and allowable comparisons are uniform across applications. That argument, however, is a double-edged sword. On the one hand, the work of Yingling serves as a caution against the cavalier application of neurometric and other techniques to inappropriate groups and places the responsibility on the user to ensure that the patient being evaluated meets the same selection criteria as the subjects in the clinical patient group used to form the discriminant. But on the other hand, the point of the Yingling study was not that the Neurometric learning disorder and specific learning disabled discriminants failed to classify purely dyslexic children accurately (no individual classification was attempted), but rather that the purely dyslexic children as a group fell within normal limits. An interesting parallel is found in a study by Matsuura et al.⁶⁷ These authors assembled a normative healthy qEEG database of children from Japan, China, and Korea. The healthy children from these countries fell within normal limits, and children diagnosed with attention deficit hyperactivity disorder (ADHD) fell outside these limits, as expected. However, children with "deviant behavior" (apparently a rough equivalent of conduct disorder), operationally defined by the Rutter Child Questionnaires, also fell within normal limits. The take-home message appears to be that some clinically defined disorders do not manifest strongly in the qEEG. That is a good reality check and a valuable caution when interpreting negative findings.

On the other hand, in order to be clinically useful the neurometric discriminants must be applicable to the range of patients seen in clinical practice, placing a responsibility on the commercial vendor to ensure that a reasonably wide clinical spectrum of a disorder is represented in the patient sample used to form the discriminant. If discrete subtypes exist, it will be important for future studies to identify and characterize them in terms of more focused discriminants. In any case, it is important to establish and make clear to the user the parameters limiting valid application of existing discriminants to individual patients.

Heuristically, it may make little difference whether the qEEG abnormalities detected by the studies of Ahn, John, and Lubar derive from learning disorders themselves or from associated neurological disorders. From the studies reviewed it appears that most children diagnosed with a learning disorder will be found to have abnormal qEEGs, and the vast majority of healthy children will have normal qEEGs. If the clinical question can be structured as a discrimination between healthy and learning disordered, then qEEG may aid the clinician in diagnosis. If other diagnoses, such as learning disorder versus ADHD versus healthy are considered, the question becomes more complex, as discussed below under Attentional Disorders.

Attentional Disorders
Regarding attention deficits, a study published by Mann et al.⁶⁸ recorded qEEG from boys manifesting ADHD of the inattentive type (without hyperactivity, conduct disorder, dyslexia, or specific learning disability). Discriminant function analysis allowed correct classification of 80% of the purely attention disordered ADHD subjects and 74% of healthy subjects. Expanding on this theme, Monastra et al.⁶⁹ conducted a broadly based study drawing 397 ADHD patients and 85 healthy subjects from eight study centers nationwide. The study sample was unusual in that it included ADHD patients of both the inattentive and hyperactive-combined type, spanning an age range of 6 to 30 years. Using a very simple neurometric measure (theta/beta ratio measured at the vertex) to classify subjects into healthy or attention disordered groups, the authors reported 86% sensitivity and 98% specificity. The overall positive predictive value of the measure was 99%, meaning that only 1% of the individuals testing positive did not have an attentional disorder.

Casting an even wider clinical net, Chabot and Serfontein⁷⁰ recorded qEEG from 407 attention disordered children of various subtypes. A two-way discriminant analysis correctly classified healthy children and those with attentional problems (and normal IQs) into their respective groups with 93% sensitivity and 95% specificity (94% and 88% respectively upon independent replication). When this discriminant was applied to low IQ children with attentional problems it also classified them with 95% sensitivity. Similar two-way discriminant analyses differentiating normal IQ from low IQ children with attentional problems and differentiating attention disorder children with or without hyperactivity were less accurate, though clearly better than chance. These results are instructive because they show that a discriminant formed using one clinical group may be applicable to similar clinical groups, but that as the groups become increasingly dissimilar the discriminant accuracy also decreases. These results additionally illustrate a general finding that original patient samples tend to be more accurately classified than subsequent samples, pointing to the need for replications, particularly independent replications, of any discriminants offered for clinical use.

Later that same year Chabot et al.⁷¹ published a further analysis of data originally published by Chabot and Serfontine⁷⁰ and by John et al.¹⁵ Their primary aim was to assess the sensitivity and specificity of qEEG in the classification of children with attention deficit or specific developmental learning disorders. The secondary aim was to assess the ability of qEEG to predict treatment response of ADHD children at a 6-month follow-up. Results showed that a three-way discriminant classified with moderate accuracy healthy, ADD/ADHD and children with specific developmental learning disorders. This discriminant also classified ADD/ADHD children with low IQs into the appropriate category but was less successful in classifying children with specific developmental learning disorders. However, when a two-way discriminant was used to distinguish between ADD/ADHD and children with learning disorders it produced excellent accuracy, and additionally was very accurate in classifying specific learning disabilities and ADD/ADHD low IQ children. Furthermore, 6-month responsiveness to dextroamphetamine or methylphenidate could be predicted with accuracies high enough to be useful in guiding initial medication decisions. Independent replication confirmed the original findings, as shown in the Table.

The researchers then published a third paper assessing qEEG prediction of treatment response in the same attention disordered children after a slightly longer treatment period of 6 to 15 months.⁵³ They found that pre-treatment qEEG successfully predicted a favorable medication response (collapsed across specific diagnoses and medications) with a sensitivity of 83% and a specificity of 88%. An independent replication using a split half design yielded identical results. Similarly high accuracies for predicting methylphenidate response had been reported earlier by Prichep,⁷² though the replication used a jackknife procedure rather than an independent sample. Chabot et al.⁵³ view qEEG as a useful adjunct to behavioral testing and clinical evaluation in the differential diagnosis of ADD/ADHD and specific learning disabilities. They note that while (univariate) individual qEEG abnormalities lack sensitivity and specificity, discriminant functions based on (multivariate) combinations of qEEG features can distinguish individual patients from each other and from healthy children with a high degree of accuracy. They further argue that treatment selection may be aided by qEEG analysis and call for the development and validation of additional discriminant functions to incorporate a wider range of medications. An expanded discussion of these themes can be found in Chabot et al.,⁴ who recommend qEEG’s routine use as an aid to diagnosis and treatment evaluation of children suffering from learning and attention disorders.

Recommendations
On the basis of several large, independently replicated studies, qEEG has been shown to be capable of providing accurate probability estimates of the likelihood that a given patient is suffering from any of a variety of attentional or learning disabilities, though extraordinary care must be taken to ensure that the individual patient being assessed matches the selection criteria of the patient group used to form the discriminant. The work of Yingling et al.⁶³ and Fein et al.⁶⁴ serve as an instructive lesson in that regard. Applied conservatively, its demonstrated classification accuracies suggest that qEEG may be a useful adjunct to behavioral testing and clinical evaluation in the diagnosis of children suffering from learning or attentional problems. Furthermore, there is preliminary evidence that children’s medication responses can be predicted with accuracies sufficient to influence initial treatment decisions, although the caveat must be added that this aspect of qEEG is in its infancy. Future studies are needed to gather normative clinical data from carefully diagnosed patient groups suffering from a wider spectrum of specific subtypes of attention disorders and learning disabilities, as well as other conditions, such as mood or personality disorders, that can complicate the clinical picture. For example, the excellent studies of ADHD subtypes by Clarke et al.^54–57 appear to be ripe for extension from their present between-groups design to a predictive between-subjects design. Using normative clinical data, discriminant functions can be developed to better assist clinicians in the differential diagnosis of these disorders. Also badly needed are studies extending such normative clinical data into the adult years in order to assess the 60% to 70% of individuals with ADHD or a learning disorder who continue to present with some symptoms of these disorders in adulthood. Finally, the prediction of medication response is a promising and potentially very valuable use of qEEG that should be explored further.

Dementia
Nuwer’s review¹⁰ of qEEG frequency analysis recommended it as possibly useful as an adjunct to routine EEG in dementia. Hughes and John⁷ gave it a much stronger positive recommendation, citing a correspondingly much wider range of literature.

In the visually analyzed clinical EEG literature it is found generally that dementing illnesses are accompanied by increased rates of EEG abnormalities, though the details of such abnormalities differ between dementia etiologies. Alzheimer’s disease, the most common etiology, primarily affects broad posterior cortical regions of the temporal and parietal lobes and generally produces diffuse slowing of the EEG characterized by increased delta and theta, with slowing of the alpha rhythm and reduced beta (see Coburn et al.⁷³ for expanded discussion). These changes tend to be progressive and roughly parallel the clinical deterioration of the patient.⁷⁴ But with the exception of beta reduction, these changes are in the same direction as are those accompanying normal aging, making the identification of early Alzheimer’s disease from the visually analyzed EEG problematic. In multi-infarct vascular dementia, a common dementia etiology, more easily identified focal EEG abnormalities are sometimes present, following the distribution of discrete cortical lesions. But in cases of deep lesions or widely distributed white matter changes the EEG abnormalities may take the form of diffuse slowing similar to that seen in normal aging and Alzheimer’s disease. Complicating the picture still further, Alzheimer’s disease and multi-infarct vascular dementia not only can coexist, but do so more often than would be expected by chance.^75,76 Clinical diagnosis of these comorbid Alzheimer’s disease/multi-infarct vascular dementia patients is especially problematic.⁷⁷ Fronto-temporal dementias, such as Pick’s disease, involve frontal and anterior temporal cortical areas, but again the accompanying EEG abnormalities may be diffuse and difficult to characterize in the visually analyzed record. Indeed, a normal visually analyzed EEG is among the supportive criteria for fronto-temporal dementia diagnosis.⁷⁸ Although in principle fronto-temporal dementias, such as Pick’s disease, may be distinguished from Alzheimer’s disease by CT or MRI showing the distribution of structural changes, EEG showing the distribution of functional changes, and clinical presentation showing behavioral and personality changes in fronto-temporal dementia as opposed to memory and visuospatial changes in Alzheimer’s disease, in practice the vast majority of Pick’s patients are erroneously diagnosed as suffering from Alzheimer’s disease.⁷⁹

By virtue of its quantitative statistical comparisons, qEEG offers a solution to the problem of determining age-normal limits. John’s Neurometric Analysis System controls for normal aging by means of statistical age regression, while Thatcher’s system and most others use age stratified normative data. Either quantitative method appears superior to the impressions gained by visual inspection of the raw EEG. The qEEG literature shows clearly that abnormalities tend to increase in parallel with the clinical stage of dementia in senile dementia of the Alzheimer’s type,⁸⁰ Alzheimer’s disease,^81,82 and cognitive decline.^83–85 For example, Prichep et al.⁸⁶ published a cross-sectional neurometric study of 319 subjects showing either normal aging or signs and symptoms compatible with dementia of the Alzheimer’s type. On the basis of their Global Deterioration Scale (GDS) level the subjects were divided into groups of 40 healthy (GDS 1), 91 with subjective cognitive deficits (GDS 2), 48 with subjective + objective deficits (GDS 3), 60 with mild dementia of the Alzheimer's type (GDS 4), 55 with moderate dementia of the Alzheimer's type (GDS 5), and 25 with moderately severe dementia of the Alzheimer's type (GDS 6). Abnormalities (relative theta showed the clearest results) began in the GDS 2 group and increased in parallel with increasing deterioration through the GDS 3–6 groups. The authors make the point that the high sensitivity of neurometrics to the earliest presence of qEEG abnormalities in subjects with only subjective cognitive dysfunction suggests that the technique might be clinically useful in the initial evaluation of patients with suspected dementia. However, no sensitivity, specificity, or other similar accuracy measures were reported so the applicability of these general results to individual patients is unclear. A more recent qEEG study⁸⁷ used equivalent dipole analysis to investigate mild cognitive impairment and Alzheimer’s disease, and found that although the technique could not discriminate mild cognitive impairment patients from healthy subjects, Alzheimer’s disease patients could be distinguished from both mild cognitive impairment patients (78% accuracy) and controls (84% accuracy). Perhaps more importantly, it was found that eight of the 10 mild cognitive impairment patients misclassified as Alzheimer’s disease went on to develop Alzheimer’s disease during the 48 month study. Retrospectively comparing those mild cognitive impairment patients who progressed with those who did not progress to Alzheimer’s disease, the authors reported a classification accuracy of 77%. All of these classification accuracies are compared with those obtained using traditional FFT (Table 1). qEEG, in contrast to visually analyzed conventional EEG, also has been reported to show abnormalities in fronto-temporal dementia that are clearly distinguishable from those in Alzheimer’s disease and which do not represent healthy aging.⁷⁸ Unfortunately, little work has been published in this area.

Dementia Detection
In the evaluation of individual patients suspected of suffering from dementia, conventional or quantitative EEG can be used for several purposes, the most basic of which is detection of abnormal patterns of brain activity characteristic of dementing disorders. Since dementias of most etiologies are accompanied by EEG changes above and beyond those seen in healthy aging, EEG can be of practical utility if the clinical question can be structured as a choice between only two alternatives: either dementia or normal aging. For example, Prinz and Vitiello⁸⁸ used visually analyzed alpha slowing to distinguish between early stage Alzheimer’s disease patients and healthy subjects, attaining 71% sensitivity and 82% specificity. This accuracy is surprisingly high since the Alzheimer’s disease sample was restricted to early stage patients and included those with possible as well as probable Alzheimer’s disease diagnoses. Dementias of other etiologies also can be differentiated from healthy aging. Robinson et al.⁸⁹ visually analyzed EEGs from Alzheimer’s disease and comorbid Alzheimer’s disease and multi-infarct vascular dementia patients (all histopathologically confirmed), and from healthy subjects, achieving sensitivities of 87% for Alzheimer’s disease patients with 63% specificity, and 77% for Alzheimer’s disease and multi-infarct vascular dementia patients with 65% specificity. They also found a 36% false positive rate for healthy subjects, suggesting that specificity may have been sacrificed in the service of sensitivity, though nearly identical sensitivities of 89% for Alzheimer’s disease patients with 79% specificity, and 76% for multi-infarct vascular dementia patients with 79% specificity, were found by Sloan et al.⁴⁹ Yener et al.⁹⁰ reported a similar sensitivity of 85% for Alzheimer’s disease patients with 93% specificity and a more comforting 7% false positive rate for healthy subjects, and additionally found 69% sensitivity for fronto-temporal dementia patients with 93% specificity. These studies indicate that when the diagnostic question can be structured as a discrimination between healthy aging and a specific dementia (Alzheimer’s disease, multi-infarct vascular dementia, Alzheimer’s disease and multi-infarct vascular dementia, fronto-temporal dementia), then on the basis of the visually analyzed EEG moderate to high accuracies may be obtained.

A comparison between visual and quantitative EEG analysis was published by Mody et al.⁹¹ They used both conventional and quantitative EEG from Alzheimer’s disease patients and scrupulously screened healthy elderly controls to investigate the changes accompanying Alzheimer’s disease and to classify patients and healthy subjects into their respective diagnostic categories. For diagnostic classification qEEG was found to have 98% sensitivity and 100% specificity, while a conventional reading of the same EEGs had 16% sensitivity and 100% specificity. However, in fairness it must be noted that while visual analysis found only 16% of the Alzheimer’s disease patients to show the specific constellation of changes defined by the authors as indicative of Alzheimer’s disease, fully 98% of the Alzheimer’s disease EEGs were clinically abnormal, with 76% showing generalized abnormalities. In this regard it is important to emphasize the complementary nature of visual and qEEG analysis, and to point out once again that a visual reading by a trained electroencephalographer is the first step in clinical qEEG analysis. Perhaps a better comparison between visual and quantitative analyses for dementia is provided by Yener et al.,⁹⁰ who found that qEEG sensitivities and specificities were lower for Alzheimer’s disease but higher for fronto-temporal dementia.

Turning to purely quantitative techniques, an early study by John et al.¹⁵ noted that certain qEEG abnormalities tend to increase in parallel with dementia symptoms, and assessed the discriminant classification accuracy for different stages of dementia. Although senile dementia patients with mild symptoms were difficult to distinguish from those with moderate/severe symptoms, overall demented patients could be separated from their healthy subjects with about 75% sensitivity and 60% specificity, confirmed by a jackknife replication. A small study by Duffy et al.,⁹² published the following year, found that qEEG could discriminate senile dementia (age 65) and pre-senile dementia (age <65) from age appropriate healthy subjects with sensitivities and specificities of 89% or better, and Besthorn et al.⁹³ attained 76% overall classification accuracy, with 72% sensitivity for Alzheimer’s disease patients and 81% specificity for healthy subjects. Schreiter-Gasser et al.⁹⁴ produced a slightly higher diagnostic classification sensitivity of 93% and specificity of 100% for Alzheimer’s disease patients versus healthy subjects. Although their sample sizes were rather small, the Schreiter-Gasser et al. study is noteworthy for using "street-normal" controls, most of whom complained of subjective memory problems although none showed frank dementia. In this manner their study closely resembles actual clinical practice.

Of course, discriminations can be weighted in favor of either sensitivity or specificity,^16,95 depending on the consequences of false negatives and false positives. Brenner et al.⁹⁶ used qEEG to classify Alzheimer’s disease patients and healthy subjects, weighting the discrimination to minimize false positives. Alzheimer’s disease patients could be distinguished from their healthy counterparts with a specificity of 93% accompanied by a sensitivity of 66%, and somewhat greater sensitivity was obtained for lower functioning (79%) than for higher functioning demented patients (36%). Restricting their patient group to those showing only mild dementia (CDR 1), Coben et al.⁹⁷ also found that patients and healthy subjects could be classified with low sensitivity (24%; 57% upon independent replication) but with 100% specificity. The authors note that although the low sensitivity puts constraints on the usefulness of qEEG for this application, when dementia is in the mild stage low sensitivity would be useful if the prevalence and specificity were high, and if a high false negative rate were acceptable.

Other dementing conditions also have been included in qEEG discriminations from normal aging. A small study by Leuchter et al.⁹⁸ separated a combined sample of dementia of the Alzheimer's type and multi-infarct vascular dementia patients from healthy subjects with 83% sensitivity and 100% specificity, and in a larger study using more adequate samples of dementia of the Alzheimer's type patients, multi-infarct vascular dementia patients, and healthy sub