Conventional and Quantitative Electroencephalography in Psychiatry
John R. Hughes, M.D., Ph.D. and E. Roy John, Ph.D.

SOURCE: J Neuropsychiatry Clin Neurosci 11:190-208, May 1999 © 1999 American Psychiatric Press, Inc.

Schizophrenia

Numerous qualitative studies indicate abnormal conventional EEG findings in 20% to 60% of schizophrenic patients.A more specific finding in schizophrenia is a relatively low mean alpha frequency,although some patients may show a fast alpha rhythm. Catatonic patients often present with paroxysmal activity.

Conclusion:

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.

Across a large number of EEG and QEEG studies, there is a broad consensus that schizophrenia shows a high incidence of EEG and QEEG abnormalities. Most often, the reported abnormalities have been delta and/or theta excesses in frontal areas, a decreased mean frequency and lower power in the alpha band, and increased beta power. Increased anterior coherence also has often been reported. Coherence measures may contribute to distinguishing bipolar disorder from schizophrenia.

Mood Disorders: Unipolar and Bipolar Depression

The incidence of abnormal conventional EEG findings in mood disorders appears to be substantial, ranging from 20% to 40% higher in 1) manic than depressed patients, 2) female than male bipolar patients, and 3) nonfamilial cases with late-age onset. Whether an "abnormal" EEG is a necessary correlate of a clinically effective series of ECT treatment is controversial. This suggestion, like that made above regarding clozapine in schizophrenia, will require further study.

Conclusion:

Both EEG and QEEG studies report that a high proportion of patients with mood disorders display abnormal brain electrical activity. EEG studies report that small sharp spikes and paroxysmal events are often found, especially on the right hemisphere, and that abnormal sleep studies are common.
There is broad consensus in QEEG studies that increases in alpha or theta power, as well as asymmetry and hypocoherence in anterior regions, appear most often in unipolar depressed patients. Bipolar patients often display decreased alpha but increased beta activity.

Mood Disorders: Anxiety, Panic, Obsessive-Compulsive, and Eating Disorders

Several studies suggest a high incidence of EEG abnormalities in patients with anxiety disorders, panic disorders, and obsessive-compulsive disorder (OCD).Diminished alpha activity has been found in anxiety disorder by using QEEG, 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%) to serotonergic antidepressants, while the second, with increased theta relative power, failed to improve (80%). 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% of patients, possibly related to the effect of starvation on cerebral metabolism. Paroxysmal abnormalities are seen in about 12% of these patients. In intractable binge eating, "soft" neurological and EEG signs can appear. Both anticonvulsant and antidepressant drugs have been helpful in some of these patients. Patients with eating disorders frequently give 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.

Conclusion:

Although abnormalities have been reported repeatedly in EEG and QEEG studies of patients in the above categories, consistent patterns have not yet been discerned.

Developmental Learning Disorders, Attention Deficit Disorders, and Autism

Specific developmental learning disorders (SDLD) are estimated to affect 4% to 6% of all school-age children.21,185 Attention deficit disorders with or without hyperactivity (ADHD or ADD) have a prevalence of 6% to 9% in school-age children.186,187 Although ADD/ADHD 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 versus SDLD can be of critical importance in avoiding the potentially devastating impact of these disorders on children and their families. EEG and QEEG can contribute usefully to this distinction as well as to 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% to 60% of children with ADHD or with specific learning disability (SDLD or LD), as reviewed by several authors. Reported abnormalities have 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, with theta or alpha excess often seen in children with ADD or ADHD. The types of QEEG abnormality found in SDLD children are related to academic performance. A large percentage of children with attention deficit problems (more than 90%) show QEEG signs of cortical dysfunction, the majority displaying frontal theta or alpha excess, hypercoherence, and a high incidence of abnormal interhemispheric asymmetry. Using QEEG measures, it has been possible to discriminate replicably ADD/ADHD versus normal children, with a specificity of 88% and a sensitivity of 94%, and ADD versus SDLD children, with a sensitivity of 97% and a specificity of 84.2%.
The EEG is frequently abnormal in autism. In 14 studies encompassing approximately 800 patients, the mean incidence of abnormal EEGs was 50% (median 47%), but the range of values for the incidence of abnormalities was considerable (10%–83%). This large range undoubtedly arose from differences both in the populations under study and, more important, the criteria used for abnormality. EEG abnormality can help predict a poorer outcome with regard to intelligence, speech, and educational achievement. Although clinical seizures are uncommon in autism, epileptiform activity sometimes occurs.

Conclusion:

Numerous EEG as well as QEEG reports agree that a high proportion of children with developmental disorders—among which learning disabilities and attention-deficit hyperactivity have received the most attention—display abnormal brain electrical activity.
There is a wide consensus that delta or theta excess and alpha and beta deficits are commonly encountered in children with learning disorders and that theta or alpha excesses are often seen in children with ADD/ADHD.

Alcohol and Substance Abuse

The changes during acute alcoholic intoxication include the slowing of the EEG, seen in the form of decreased alpha frequency and abundance, an increased amount of theta, and even some generalized delta rhythms. These slow waves have a relationship with the degree of intoxication. The extent of the disturbance of consciousness is related to the amount of slow activity.
For chronic alcoholism, as in the acute stage, an increase in slow activity is often seen. This change appears as a decrease in alpha frequency and abundance, related to the typical blood alcohol level of a given patient, and also an increase in the theta rhythm, especially on the temporal areas. Temporal and frontal areas may also display an increase in fast activity related to the neuropsychological impairment, which must be distinguished from muscle artifact and often characterizes these records. Family history of alcoholism plays a prominent role in the risk of the disease. In the subacute encephalopathy associated with alcoholism, not only are slow waves noted, but epileptiform activity can also be seen, even as periodic lateralized epileptiform discharges (PLEDS).
Recent studies of substance abuse have largely relied on QEEG. Replicated reports have appeared of an increased beta (relative power) in alcohol dependence. Increased alpha power, especially in anterior regions, has been reported in withdrawal, as well as after acute exposure to cannabis. Increased alpha and decreased delta and theta have been reported in crack cocaine users in withdrawal.

Conclusion:

 There is a broad consensus that both EEG and QEEG reveal marked abnormalities in alcoholics and substance abusers. The effects vary depending on the drug. Either increased slow activity with lower alpha and beta or the converse have been reported; this reflects the diversity of substances or states focused upon.
However, among numerous QEEG studies, there is a consensus of increased beta relative power in alcoholism and increased alpha in cannabis or crack cocaine users.

SUMMARY
Both conventional EEG and QEEG studies provide valuable information to the psychiatrist regarding diagnosis and treatment responsiveness.
Conventional EEG is most useful in the following:

Conventional EEG assessments should be included in the diagnostic workup for the following:

Quantitative EEG studies are particularly well suited to identifying subtle changes in the topographic distribution of background activity. They can aid difficult differential diagnoses, such as:

Quality of Evidence Ratings

Class I:
Evidence provided by one or more well-designed, prospective, blinded, controlled clinical studies.
Class II:
Evidence provided by one or more well-designed clinical studies, such as case control or cohort studies.
Class III:
Evidence provided by expert opinion, nonrandomized historical controls, or case reports of one or more.
Strength of Recommendation Ratings
Type A:
Strong positive recommendation, based on Class I evidence or overwhelming Class II evidence.
Type B:
Positive recommendation, based on Class II evidence.
Type C:
Positive recommendation, based on strong consensus of Class III evidence.
Type D:
Negative recommendation, based on inconclusive or conflicting Class II evidence.
Type E:
Negative recommendation, based on evidence of ineffectiveness or lack of efficacy.
Learning and Attention Disorders:
On the basis of multiple Class II studies and abundant Class II evidence, Type B recommendation.
Mood Disorders:
On the basis of multiple Class II studies, Type B recommendation.
Schizophrenia:
On the basis of conflicting Class II and III evidence, Type D recommendation.
Substance Abuse:
On the basis of conflicting Class II and III evidence, Type D recommendation.

Clinical Implications

In view of the accumulation of positive findings surveyed in this article, more psychiatrists may wish to explore the utility of these methods for themselves and begin to apply them in their clinical practice. EEG, first clinically applied in 1929 by the neuropsychiatrist Hans Berger, promises to have greatly expanded use as psychiatrists become more familiar with its many applications.

QEEG profiles of psychiatric disorders.

New York University Medical Center, Department of Psychiatry, NY 10016.
Using this approach, we have demonstrated high discriminant accuracy in independent replications separating many populations of psychiatric patients from normal as well as from each other, including major affective disorder, schizophrenia, dementia, alcoholism, and learning disabilities, as well as high accuracy of discrimination between known subtypes of depression (unipolar vs bipolar). The use of classification accuracy curves (CACs) which allow one to assess the sensitivity and specificity achieved by the discriminant functions is discussed. In addition, using cluster analysis, neurometric subtypes can be identified in several clinically homogenous populations. Preliminary results suggest that baseline membership in some neurometric subtypes may be highly correlated with response to treatment.

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