The status of spectral EEG abnormality as a diagnostic test for schizophrenia

Abstract

Objective

A literature review was conducted to ascertain whether or not EEG spectral abnormalities are consistent enough to warrant additional effort towards developing them into a clinical diagnostic test for schizophrenia.

Methods

Fifty three papers met criteria for inclusion into the review and 15 were included in a meta-analysis of the degree of significance of EEG deviations as compared to healthy controls. Studies were classified based on a 4-step approach based on guidelines for evaluating the clinical usefulness of a diagnostic test.

Results

Our review and meta-analysis revealed that most of the abnormalities are replicated in the expected directions with the most consistent results related to the increased preponderance of slow rhythms in schizophrenia patients. This effect remained consistent in un-medicated patients. Only a small number of studies provided data on the sensitivity and specificity of the findings in differentiating among the psychiatric disorders that frequently appear on the same differential diagnostic list as schizophrenia (Step 3 studies). No multicenter studies using standardized assessment criteria were found (Step 4 studies).

Conclusions

Additional Step 3 and Step 4 studies are needed to draw conclusions on the usefulness of EEG spectral abnormalities as a diagnostic test for schizophrenia

Introduction

Laboratory tests are an essential part of the practice of modern medicine. Laboratory tests can be used to confirm a diagnosis, provide supportive evidence for one diagnosis vs. another, or rule out a specific disorder. The last fifty years of biological research into the pathophysiology of psychiatric disorders have yielded a number of highly replicable abnormalities. These abnormalities have the potential for being developed into clinically useful diagnostic tests. While psychiatrists do use lab tests to rule out general medical conditions as causes for mental disorders, there is no tradition for using laboratory tests in differentiating among primary psychiatric disorders. As a field, psychiatry has lagged behind in developing lab tests according to well-defined epidemiological principles.

Laboratory tests in psychiatry tend to either not be developed into diagnostic tools (e.g., P300 evoked response in schizophrenia) or to be disseminated before their validity is fully documented (e.g., Quantified EEG) (Nuwer, 1989). The premature release of such tests could lead to disappointment of the medical community and premature abandonment of the test. Moreover, when tests are used out of context they may hinder the diagnostic and treatment process and increase the cost of management unnecessarily (Steffens and Krishnan, 2003). On the other hand, an APA task force published a report in 1991 indicating that quantified EEG (QEEG) is particularly useful in detecting slow wave abnormalities and concluded that clinical replications and sharing of normative and patient data bases are necessary for the advancement of this field. They further stated that standards for training and for use of the technology in psychiatry are urgently needed. In fact, the situation has not changed appreciably since then (Quantitative elecroencephalography, 1991).

The development of ancillary diagnostic procedures is important to help the field move forward as diagnosis in psychiatry remains the major limiting step in biological research and treatment studies (van Praag, 1997). In order to promote a standard approach we have recently proposed a four-step process for developing laboratory-based diagnostic tests for use in aiding the diagnostic process in psychiatry (Boutros and Struve, 2002, Boutros et al., 2005, Boutros and Arfken, 2007). The Four-Step approach proposed is based on the guidelines for deciding the clinical usefulness of diagnostic tests published by Sackett et al. (1991) and the more recently published criteria specified by the Standard for Reporting Diagnostic tests (STARD) (Bruns, 2003, Bossuyt et al., 2003).

For Step 1, a biological variable is observed to be deviant from healthy controls in a particular patient population. The demonstration of test–retest reliability of the finding using blinding procedures is an essential component of this early step. Replication of the finding by the same or collaborating groups is important but confirmation by independent groups is essential for this particular test to move into the next step of development.

Step 2 involves demonstrating the potential clinical usefulness of the specific finding. The two most important objectives at this step are demonstration of difference between the target patient population and appropriate comparison groups (these should be groups of patients with diagnoses that commonly appear on the differential diagnostic list of the target disorder). This is an important point as a biological abnormality may be common to two disorders that hardly ever appear on the same differential diagnostic list (e.g., schizophrenia and dementia in a young adult). While such finding would be of considerable scientific interest, it would not particularly decrease the diagnostic potential of the finding. On the other hand, an abnormality that is equally common to disorders that frequently need to be differentiated from one another (e.g., Bipolar Disorder and Schizophrenia) is not likely to be useful clinically. Abnormalities with significant differential prevalence among disorders to be differentiated are likely to be able to significantly contribute to the differential diagnostic process and should progress to Step 3. Estimation of the effect size of the finding could be a reasonable guide to which findings should be considered good candidates for Step 3 studies.

During Step 3 the performance characteristics of the test should be established. Specifically, the sensitivity, specificity, positive and negative predictive values of the biological marker should be examined. These data should allow the estimation of the added diagnostic value resulting from incorporating the test into the work-up of a particular patient. The choice of the “gold standard” or reference test is an essential component of this step. This is the standard against which the test being developed will be measured. The currently accepted gold standard in psychiatric diagnosis is the “Best Estimate Diagnosis” (Kosten and Rounsaville, 1992). Best Estimate Diagnosis is reached by agreement among a number of experts relying on multiple sources of information and with a standardized scale with demonstrated validity and reliability. At this step, the clinical characteristics of the patient group identified by the test are usually further delineated. Due to the heterogeneous nature of psychiatric disorders, it would be naïve to expect any one biological test to be able to identify all patients that are classified into a certain DSM-based category (e.g., schizophrenia). It is much more likely that a particular test will be able to identify one or more sub-groups within these categories. Defining the clinical characteristics of the sub-group that is identifiable by a particular test would be very important for the test to be considered for clinical use. Factors such as effects of illness duration, severity, and the effects of medications should also be defined during step three. At this step, the test would be considered “promising” for development as a diagnostic test (Boutros et al., 1993).

Step 4 defines the clinical application of the test and helps standardize the technique used in large and multicenter clinical trials. Multicenter trials should pave the road towards standardization of laboratory procedures used to conduct the test as well as providing data regarding cost effectiveness and impact on both short-term and long-term clinical outcomes. Studies in earlier steps depend on smaller samples of control subjects that are usually locally formed. On the other hand, Step 4 studies should begin to develop larger normative databases that can eventually be used to examine an individual's data. Development of such databases can be challenging and will require collaboration among research groups concerned with the specific test being developed.

We have previously documented that the four-step approach can be useful in determining the stage of development of a biological finding into a clinically utilizable laboratory test (Boutros et al., 2005). In that report, the reported increased theta activity in the resting EEGs of individuals with ADHD is a highly promising finding for development into a clinical test and that step 4 studies (large and multicenter studies) are still needed for the actual clinical dissemination of the test. The purpose of the current report is to examine, in a similar manner, the status of development of spectral EEG deviations as a diagnostic tool for schizophrenia. This is important because Fink et al. (1965) provided evidence that spectral analysis of the resting EEG of schizophrenia patients could differ significantly from that of patients with depressive disorders. Subsequently, an extensive EEG in schizophrenia literature has accumulated (Shagass et al., 1984, Sponheim et al., 2003).

Choice of spectral EEG as the focus of this review was based on the fact that it is the simplest quantifiable EEG measure that has long been studied in schizophrenia. Given its long history of proven applicability in clinical neurology, it is of considerable interest to appraise the status of this literature for its potential as a diagnostic aid for schizophrenia. While the focus of the current review is not on the physiological mechanisms underlying EEG abnormalities in schizophrenia, an extensive literature addressing this aspect does exist. Most prominently, slowing of the EEG has been linked to an impaired subcortical synchronization system including the mesencephalic reticular formation, nucleus reticularis and the thalamus (Kirino, 2004). Other EEG derived measures, such as event related potentials and evoked gamma oscillations, that may have similar utility, have been recently reviewed elsewhere (Van der Stelt and Belger, 2007).