By Arline Kaplan © 2002 (All Rights Reserved)
The quantitative electroencephalogram (QEEG) may prove to be a sensitive and specific screening test for identifying patients with substance use disorders at the highest risk for relapse, according to a recent published study.
Lance O. Bauer, Ph.D., professor of psychiatry at the University of Connecticut School of Medicine and director of the Neural Dynamics Laboratory, has found that QEEG is a reliable tool to predict which patients with histories of dependence on alcohol, alcohol and cocaine, cocaine or opiates are most prone to relapse (Bauer, 2001).
The study involved 107 substance dependent patients living in a residential treatment program in the Hartford, Connecticut area. The patients were recruited after they had verifiably maintained abstinence for a period ranging from one and five months. The average was three months. Structured clinical interview data and a 5-minute recording of the resting, eyes-closed electroencephalogram were obtained. The patients were then monitored for relapse or successful abstinence by the research staff for an ensuing six-month period.
"Most of the patients remained in the residential treatment program. During the six-month follow-up period, we were able to obtain frequent breath and urine samples from them to confirm either relapse or abstinence. A small fraction of patients did leave the residential treatment program, but in those cases we were able to follow people after they returned home," Bauer said.
In the initial analyses, Bauer found that 48 patients who returned to alcohol or other drug abuse, exhibited an enhanced amount of high frequency beta activity within their EEG at the baseline time point, compared to the 59 patients who maintained abstinence and 22 control subjects.
"In secondary analysis, we asked whether the ability of this subtle EEG abnormality to predict relapse would vary as a function of the preferred substance of abuse; it did not," Bauer said. Enhanced EEG beta activity was useful for predicting relapse regardless of whether the patients were abusing alcohol, alcohol and cocaine, cocaine alone, or opiates.
"This suggests that the measure might eventually have clinical or, at least more immediately, some research utility, because it generalizes across various substances of abuse," Bauer added.
Since the elevated beta activity did not vary as a function of the preferred substance of abuse, Bauer then wondered whether it might be related to some premorbid factor that is common across many different substances of abuse.
"Among the most commonly identified risk factors for substance abuse are a family history of alcoholism and a childhood history of conduct disorder," Bauer said. We, therefore, conducted a third set of analyses examining the interaction between those two premorbid risk factors and treatment outcomes, and found that there was a relationship. In other words, the neurophysiological abnormality that predicted return of substance abuse was most likely present even before the substance abuse career began. It is probably a premorbid trait.
"Finally, in the fourth set of analyses, we asked whether this particular EEG measure would add value, would it better predict relapse than other risk factors, including severity of dependence and comorbid depression. In fact when we put all of the data into a logistic regression analysis, we found this objective EEG measure, which could be obtained in about five minutes of time, was superior to all the other measures predicting relapse."
Several other studies employing objective and quantitative EEG techniques have demonstrated an association between the beta (i.e. > 13 Hz) activity in the spontaneous EEG and relapse to alcohol or cocaine abuse (Bauer, 1994; Prichep et al., 1999; Winterer et al., 1998).
"There are a number of groups working on the question, and the results across various laboratories seem rather consistent. What we hope to do next is to evaluate the predictive accuracy of this measure in a more heterogeneous population of substance abusers," Bauer said.
"To date, the equation has been developed with a group of patients who, for example, were not receiving psychoactive medication, and the age range of the sample was somewhat restricted. If this measure is going to have some ultimate clinical utility, it needs to be tested in a typical clinic population, which might be much more variable in terms of their medication status, in terms of their age and in terms of other factors."
Once the reliability of QEEG screening is found to predict relapse in heterogeneous populations, then Bauer believes it can be used as a basis for assigning relapse-prone people to a more intensive treatment.
"Obviously," he said, "if one could identify patients with high risk for relapse, then those individuals might be assigned to a more intensive psychotherapy or intensive pharmacotherapy."
Bauer LO (1994), Electroencephalographic and autonomic predictors of relapse in alcohol-dependent patients. Alcohol Clin Exp Res 18(3):755-760.
Bauer LO (2001), Predicting relapse to alcohol and drug abuse via quantitative electroencephalography. Neuropsychopharmacology 25(3):332-340.
Prichep LS, Alper KR, Kowalik SC et al. (1999), Prediction of treatment outcome in cocaine dependent males using quantitative EEG. Drug Alcohol Depend 54(1):35-43.
Winterer G, Kloppel B, Heinz A et al. (1998), Quantitative EEG (QEEG) predicts relapse in patients with chronic alcoholism and points to a frontally pronounced cerebral disturbance. Psychiatry Res 78(1-2):101-113.
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