Clinical Applications of Electroencephalography Research Paper

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1. Introduction

The electrical activity of the brain, as manifested in the power spectrum of the electroencephalogram (EEG), is precisely regulated by complex homeostatic systems in the brain. Numerous quantitative electroencephalographic (QEEG) descriptors of spontaneous brain electrical activity can be readily extracted by simple computing algorithms. Measurements made from any individual can be objectively evaluated using statistical parameters which can be obtained from standardized, age-regressed, normative databases, and are: (a) highly stable and replicable within the individual, with few false positive findings in healthy, normally functioning persons; (b) independent of ethnic influences or cultural background of the individual; and (c) highly sensitive to many forms of developmental dysfunction, neurological disorders, or psychopathology.

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Invaluable assessments of the structural and functional integrity of the brain are accessible from quantitative analyses of the scalp EEG. QEEG methods provide sensitive and precise measures for study of normal as well as abnormal brain functions. Contemporary research suggests that synchronization of oscillatory activity among distant brain regions, as well as localized coherent activity, may play an important role in the processing of afferent information, particularly in ‘binding’ the diverse features of a complex stimuli into a perceptual whole, and in the storage of memories. Such processes are reflected in quantitative measures of the EEG power spectrum, symmetry, and coherence (synchronization) within and between the cerebral hemispheres. In most studies to date, results have been visualized as topographic maps. Validated methods for three-dimensional QEEG tomography have very recently become available and provide statistical parametric ‘virtual functional magnetic resonance imaging (MRI)’ images, upon which the statistical significance of deviations relative to reference data can be encoded by colors assigned to each voxel. The importance of assessing the possible contribution of such objective neurophysiological and neuroanatomical measures to better understanding of the abnormal behaviors exhibited in psychopathology cannot be overemphasized.

QEEG methods have been used in numerous studies of patients with psychiatric disorders. This research paper will be restricted to QEEG applications in psychiatric patients. Due to the limitation of space, only illustrative articles can be cited here. A more comprehensive survey has recently been published (Hughes and John 1999), to which this research paper is indebted.

2. QEEG Sensitivity To Brain Dysfunctions In Psychopathology

A voluminous literature, using subjective visual pattern recognition, attests to the utility of the conventional EEG in neurological and psychiatric disorders. While the astute electroencephalographer is capable of a high level of recognition of pathophysiological patterns, test–retest reliability between as well as within clinical EEG practitioners is poor. Algorithms for computer pattern recognition, the computer’s unsurpassed capacity to measure and calculate, together with the availability of normative databases across the human life span, have added an important additional dimension to electroencephalography. QEEG objectively supplements the EEGer’s trained eye with literally many hundreds of reliable quantitative measures which can be computed automatically from a brief recording (John and Prichep 1993). Precise statistical comparisons of each of these numerous measures from an individual can be generated relative to a ‘self-norm’ reference state or to mean values from databases of age-matched normal subjects or of patients with different diagnoses. A broad consensus exists on the high proportion of abnormalities found in drug-free patients with different psychiatric disorders and their distinctive nature. A brief overview of these findings follows.

3. Cerebrovascular Disease And Multi-Infarct Dementia

In cerebrovascular disease, several QEEG parameters are highly correlated with regional blood flow or metabolism. Sensitivity and specificity are high for detection of ischemia-related diffuse or focal impairment (Nuwer 1994). These studies show sensitivity generally greater than 80 percent, false positive rates below 5–10 percent, and correlations of r>0.7 between EEG and regional cerebral blood flow (rCBF) as measured using Xenon133 or SPECT. EEG slowing (especially increased theta activity) is highly correlated with decreased rCBF (Passero et al. 1992) or diminished regional cerebral glucose metabolism.

QEEG can be quite abnormal even when the CT is still normal, such as in the first 1–3 days after a stroke or when the degree of ischemia is mild enough to cause dysfunction without infarction.

Differentiation between multi-infarct dementia (MID) and Alzheimer’s dementia (AD) has been reported in several studies evaluating asymmetries (Clark et al. 1991) and coherence (Leuchter et al. 1992). Focal asymmetries and hypocoherences are more prevalent in MID.

4. Dementias

QEEG studies in dementia patients report increased delta and/or theta power, decreased mean frequency, decreased beta power, and decreased occipital dominant frequency. Many of these studies regard increased slow activity prior to reduction of alpha power as the earliest electrophysiological indicator appearing in Alzheimer’s disease (Prichep et al. 1994, 1995). The amount of theta activity shows the best correlation with cognitive deterioration and with clinical outcome in longitudinal follow-up (Prichep et al. 1995). Increased delta appears to be a correlate of severe advanced dementia, subsequent to an increase of theta. Multiple studies report accurate discrimination of AD patients from elderly patients with late onset depression and from normals using EEG or QEEG measures of slow activity (Robinson et al. 1994). Many of the distinctive features of AD, such as diffuse increased delta and/or theta power, decreased beta power and mean frequency, are absent in depression and are focal in multi-infarct dementia, enabling these disorders to be differentiated from AD.

5. Learning Disorders And Attention Deficit (ADD ADHD)

Specific developmental learning disorders (SDLD) affect 4–6 percent of all school age children. Attention deficit disorders with or without hyperactivity (ADHD or ADD) have a prevalence of 6–9 percent in school age children. While both ADD ADHD (see below) and SDLD are believed to be distinct neuropsychiatric entities, there is considerable comorbidity between the two disorders. Precise and accurate determination of the presence of ADD ADHD vs. SDLD can be of critical importance in avoiding the potentially devastating impact of these disorders on children and their families. QEEG can contribute usefully to this distinction as well as in separating children with social or motivational factors underlying school problems from those with organic dysfunction.

The conventional EEG has been reported to be abnormal in 30–60 percent of children with ADD or with specific developmental learning disorder. Abnormalities reported in that literature often included diffuse slowing and decreased alpha activity. In QEEG studies, a high incidence of excess theta or decreased alpha and/or beta activity has been reported in SDLD children (Ahn et al. 1980, Lubar 1991) with theta or alpha excess often seen in children with ADD or ADHD. The types of QEEG abnormality have been shown to be related to academic performance in SDLD children (Harmony et al. 1990) and to cognitive, emotional, and behavioral problems found in a cross-national study of children (Matsuura et al. 1993).

A large percentage of children with attention deficit problems (over 90 percent) show QEEG signs of cortical dysfunction, with the majority displaying frontal theta or alpha excess, hypercoherence, theta or alpha excess, and a high incidence of abnormal interhemispheric asymmetry. Using QEEG measures, it has been possible to discriminate replicably between ADD ADHD vs. normal children, with a specificity of 88 percent and a sensitivity of 94 percent and between ADD SDLD children with a sensitivity of 97 percent and a specificity of 84.2 percent (Chabot et al. 1996).

6. Mood Disorders

The incidence of abnormal conventional EEG findings in mood disorders appears to be substantial, ranging from 20 to 40 percent (Duffy et al. 1994). The incidence of EEG abnormalities appears to be higher in (a) manic than depressed patients, (b) female than male bipolar patients, and (c) nonfamilial cases with lateage onset. Whether an ‘abnormal’ EEG is a necessary correlate of a clinically effective series of ECT treatment is controversial. This suggestion, analogous to assertions about the prognosis of treatments with clozapine in schizophrenia, will require further study.

Numerous QEEG studies have found increased alpha and/or theta power in a high percentage of depressed patients (Alper 1995, John et al. 1988a, 1998b). It is noteworthy that antidepressants reduce alpha activity, in contrast to the increased alpha caused by neuroleptics, suggesting that appropriate medication achieves normalization of deviant QEEG features in successful pharmacotherapy of psychiatric patients (Galderisi et al. 1994, Saletu et al. 1994, Schellenberg et al. 1994).

Interhemispheric asymmetry, especially in anterior regions, has been reported repeatedly, as has decreased coherence. In bipolar illness, in contrast to unipolar depression, alpha activity is often reduced (Knott et al. 1999, Clementz et al. 1994) and beta activity increased. This difference may serve to separate unipolar from bipolar patients presenting in a state of depression, without prior history of mania (Prichep et al. 1990).

Current treatment of bipolar disorder often includes polypharmacy, with the use of the anticonvulsant medications, carbamazepine, and sodium valproate. The successful use of these agents suggests overlap between convulsive disorders and bipolar illness.

7. Anxiety, Panic, Obsessive Compulsive, And Eating Disorders

The available studies suggest a high incidence of QEEG abnormalities in patients with anxiety disorders, panic disorders, and obsessive compulsive disorder (OCD). Diminished alpha activity has been found in anxiety disorder and increased theta activity has been reported in OCD. Two subtypes of OCD patients have been described. One, with increased alpha relative power, responded positively (82 percent) to serotonergic antidepressants, while the second, with increased theta relative power, failed to improve (80 percent) (Prichep et al. 1993). Epileptiform activity can occasionally be found in patients with tics (or stuttering), in addition to nonspecific diffuse slow activity. In patients with panic disorder, paroxysmal activity was four times more common than in depressed patients. Temporal lobe abnormalities, in particular, have been emphasized in QEEG studies in this type of patient.

In anorexia nervosa, abnormal background activity in the EEG can be seen in nearly 60 percent of patients, possibly related to the effect of starvation on cerebral metabolism. Paroxysmal abnormalities are seen in about 12 percent of these patients. In intractable binge eating, ‘soft’ neurological and EEG signs can appear. Patients with eating disorders frequently have a history of physical or sexual abuse as children, so the increase in EEG abnormalities in this group may be related to their abuse history. Alternatively, dietary and nutritional deficiencies may contribute to altered brain function. Both anticonvulsant and antidepressant drugs have been helpful in some of these patients (Grebb et al. 1984).

8. Mild Head Injury Or Postconcussion Syndrome

Patients with complaints of cognitive memory or attentional deficit after mild head injury without loss of consciousness frequently present for neuropsychological evaluation for worker’s compensation and disability benefits. Objective evidence of brain dysfunction in such cases is critical in the endeavor to separate the truly dysfunctional patient from the malingerer. QEEG evidence can play a critical role in such cases. While the absence of abnormal brain electrical activity cannot definitively exclude the possibility of brain dysfunction, the presence of abnormalities, especially those most frequently associated with unequivocal traumatic brain insult, must be considered supportive of such claims.

There is a high incidence of diffuse axonal injury in about 50 percent of the 50,000 patients year with head injury who recover, and who characteristically do not display structural lesions on CT scan or even in MRI scans early after injury. Among those who recover after moderate head injury, 90 percent have cognitive or neuropsychological deficits (Rimel et al. 1982). Among such patients, studies involving many hundreds of cases have reported normal neurologic examinations but abnormal QEEG (Jerrett and Corsak 1988).

Numerous EEG and QEEG studies of severe head injury (Glasgow Coma Scale 4–8) and moderate injury (GCS 9–12), using samples of 50–200 patients, have agreed that increased theta and decreased alpha power and/or decreased coherence and asymmetry often characterize such patients. Changes in these measures provide the best predictors of long-term outcome (Thatcher et al. 1989).

There is a consensus that the most characteristic QEEG abnormalities persisting after mild or moderate head injury are similar in type but lesser in degree to those found after severe head injury, namely increased focal or diffuse power in the theta band, decreased alpha, low coherence, and increased asymmetry. Similar abnormalities have been reported in boxers (Ross et al. 1983) correlated with the numbers of bouts or knockouts and in professional soccer players who were ‘headers’ (Tysvaer et al. 1989).

The consistency of these observations, across a wide range of different degrees and types of head trauma, lend additional credibility to the multiple reports of discriminant functions based on QEEG variables which successfully separated normal individuals from patients with a history of mild to moderate head injury, years after apparent clinical recovery (Thatcher et al. 1989).

9. Schizophrenia

Evaluation of EEG and QEEG literature on schizophrenia is complicated by the evident heterogeneity of the illness, and the diversity of medication histories and dosage levels at the time of examination. In spite of these potential sources of difference among findings, considerable agreement nonetheless appears. Numerous EEG and QEEG studies of background activity have been performed on carefully evaluated groups of schizophrenic patients. Reviews of numerous qualitative studies indicate an incidence of abnormal conventional EEG findings in 20–60 percent of schizophrenic patients (Small 1993). With few exceptions, a substantial amount of agreement emerges from this body of literature. Alpha power is consistently reported as deficient (John et al. 1988a, 1998b), as well as altered alpha mean frequency or diminished alpha responsiveness. Numerous studies have reported increased beta activity in schizophrenia (Morihisa et al. 1983, Karson et al. 1987). It was pointed out earlier that neuroleptics typically increase alpha power (Galderisi et al. 1994, Saletu et al. 1994, Schellenberg et al. 1994) and reduce beta power (Niedermeyer 1987, Herrmann and Schaerer 1986), suggesting possible normalization of deviant features by medication.

Increased delta and/or theta activity has also been reported in a large number of studies. Such increased slow activity can apparently result from neuroleptic treatment, although there are reports of increased delta in patients off medication for several weeks (Morihisa et al. 1983) and reduction of delta or theta when medication is resumed (Galderisi et al. 1992, Saletu et al. 1994).

Among the various QEEG parameters, one which serves to discriminate schizophrenic patients from controls is the presence of increased amounts of delta activity in the left anterior temporal area. A decrease in fast alpha (10–12 sec) on the frontal areas has been called ‘hypofrontality’ (Gattaz et al. 1992).

An interesting observation with therapeutic implications is that there is positive correlation between the degree of QEEG abnormality and the degree of clinical improvement (Omori et al. 1992). This observation has raised the question as to whether this ‘degradation’ of the EEG is a necessary condition for a clinical improvement with clozapine. Clinical relationships include correlations between (a) negative symptoms and delta waves in the temporal areas (Gattaz et al. 1992); (b) blink rates, alpha power, and smoking (Klein et al. 1993); and (c) a more favorable prognosis and any EEG abnormality (Omori et al. 1992).

10. Heterogeneity Within The Schizophrenic Population

Results from a small number of studies are inconsistent with the consensus of a QEEG profile with increased delta or theta, decreased alpha and increased beta in schizophrenia. For example, increased slow activity has not been found by some workers, and increased alpha and decreased beta have occasionally been reported. It should also be noted that not all of the studies reporting the increased slow activity, decreased alpha, increased beta profile found the indicated deviation in all of these frequency bands.

This inconsistency might arise from the coexistence of several subtypes with different QEEG profiles within the population of schizophrenic patients, so that observations might vary depending on the fortuitous mixture of subtypes within the samples collected for a particular study. This heterogeneity has been recently documented in a study of a large sample of medicated, nonmedicated, and never-medicated schizophrenics, using cluster analysis based on QEEG variables. Five subtypes were detected, with QEEG profiles characterized by delta plus theta excess, theta excess, theta plus alpha excess with beta deficit, theta excess and alpha excess with beta excess (John et al. 1994a, 1994b). Never-medicated patients were found in three of these subtypes. Schizophrenics with QEEG profiles corresponding to some of the groups identified by this cluster analysis have been reported to display differential responses to treatment with haloperidol (Czobor and Volavka 1991) or risperidone (Czobor and Volavka 1993). Additional evidence of heterogeneity in the schizophrenic population has been provided in QEEG studies by other groups (Roemer et al. 1991).

Findings of asymmetry in schizophrenia have been inconsistent. However, when the electrode array covered both anterior and posterior regions, power was highest over the right hemisphere in anterior but over the left hemisphere in posterior regions (Guenther and Breitling 1985, Roemer et al. 1991). This conclusion was supported by the cluster analysis just cited, in which this asymmetry pattern was found in every frequency band for all five subtypes (John et al. 1994a, 1994b).

Increased interhemispheric coherence in anterior regions has been consistently found (John et al. 1994a, 1994b, Nagase et al. 1992). Taken in conjunction with the decreased frontal coherence in depressive illness cited below, useful QEEG separation of schizophrenics from bipolar depressed patients should be attainable using measures of coherence.

11. Substance Abuse

Replicated reports have appeared of an increased beta (relative power) in alcohol dependence (John et al. 1988a, 1998b, Bauer and Hesselbrock 1993), and in children of alcoholic fathers or men at risk for alcoholism (Volavka et al. 1985). Increased alpha power, especially in anterior regions, has been reported in withdrawal, as well as after acute exposure to cannabis (Struve et al. 1994). Increased alpha, as well as decreased delta and theta, have been reported in crack cocaine users in withdrawal (Prichep et al. 1996).

12. Conclusion

The citations above document the wide applicability of QEEG as an adjunct to diagnosis, prognosis, treatment selection, and outcome evaluation in behavioral problems, developmental disorders in children, mild head injury, mood disorders, primary degenerative dementia, and schizophrenia. Familiarity with these data and with QEEG methods should enable psychologists to make major contributions to improve the management of patients in all of these categories. The EEG and Clinical Neuroscience Society (ECNS) offers training programs and board certification examinations in QEEG for psychologists, as well as for neurologists and psychiatrists. Psychologists interested in acquiring this professional qualification should contact ECNS.


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