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208 http://neuro.psychiatryonline.org J Neuropsychiatry Clin Neurosci 18:2, Spring 2006

Variation inNeurophysiologicalFunction and Evidenceof QuantitativeElectroencephalogramDiscordance: PredictingCocaine-Dependent

Treatment AttritionSandy Venneman, Ph.D.Andrew Leuchter, M.D.George Bartzokis, M.D.Mace Beckson, M.D.Sara L. Simon, Ph.D.Melodie Schaefer, Psy.D.Richard Rawson, Ph.D.Tom Newton, M.D.Ian A. Cook, M.D.Sebastian Uijtdehaage, Ph.D.Walter Ling, M.D.

Received September 2, 2004; revised April 19, 2005; accepted May 16,2005. From the Medication Development Unit, Research Service, andPsychiatryService, West Los AngelesVA MedicalCenter, LosAngeles,California; the Quantitative EEG Laboratory, University of Califor-nia, Los Angeles Neuropsychiatric Institute and Hospital, Los An-geles, California; Department of Psychiatry and Biobehavioral Sci-ences, University of California Los Angeles School of Medicine, LosAngeles, California; and Tarzana Treatment Centers, Inc. Addresscorrespondence to Dr. Venneman, University of Houston-Victoria,Departments of Biology & Psychology, 3007 Ben Wilson, Victoria, TX77901; [email protected] (E-mail).

Copyright 2006 American Psychiatric Publishing, Inc.

Cocaine treatment trials suffer from a high rate of attrition. We examined pretreatment neurophysio-logical factors to identify participants at greatestrisk. Twenty-ve participants were divided intoconcordant and discordant groups following elec-troencephalogram (EEG) measures recorded priorto a double-blind, placebo-controlled treatmenttrial. Three possible outcomes were examined: suc-cessful completion, dropout, and removal. Concor-dant (high perfusion correlate) participants hadan 85% rate of successful completion, while dis-cordant participants had a 15% rate of successfulcompletion. Twenty-ve percent of dropouts and50% of participants removed were discordant (lowperfusion correlate), while only 25% of those whocompleted were discordant. Failure to complete thetrial was not explained by depression, craving,

benzoylecgonine levels or quantitative electroen-cephalogram (QEEG) power; thus cordance mayhelp identify attrition risk.

(The Journal of Neuropsychiatry and ClinicalNeurosciences 2006; 18:208216)

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PREDICTING COCAINE-DEPENDENT TREATMENT ATTRITION

trial were also excluded, as were women of childbearingage not practicing medically-accepted birth control.

Screening and Evaluation ProceduresParticipants meeting the above criteria underwent a com-

plete medical history, physical examination, psychologi-cal evaluation, blood chemistry panel (Chem 25), com-plete blood count (CBC) and differential, urine drugscreening, and for women, a serum pregnancy test. Afterthis second evaluation, participants still meeting entrycriteria were enrolled in a week-long baseline complianceevaluation that included a single-blind placebo wash-inand three clinic visits. During this week, we conrmeddiagnosis with the Structured Clinical Interview forDSMIIIR, and evaluated participants using a brief clini-cal mental status examination, the Beck Depression In-ventory (BDI), the Hamilton Depression Rating Scale

(HAM-D), and the Addiction Severity Index (ASI). Wealso performed urine tests for benzoylecgonine, canna- binoids, amphetamines, opioids and benzodiazepines.

QEEG ProcedureParticipants received a quantitative EEG (QEEG), per-formed by a registered technologist using a 19-channelreferential montage based upon the International 1020System and applied according to standard clinical pro-cedure. Recordings were performed in a sound-attenu-ated room, while participants rested in the eyes-closed,maximally alert state. 29 The signal from each EEG chan-

nel was digitized at 128 samples/sec/channel, with ahigh-frequency lter of 35 Hz and a low-frequency lterof 0.3 Hz. A technologist reviewed each recording andselected the rst 2032 seconds of artifact-free data forprocessing. I (the rst author) then reviewed these se-lections to conrm the absence of artifact in the chosendata and processed them by fast Fourier transform toobtain absolute and relative power values for four Hz bands (0.54, 48, 812, and 1220Hz) using the QNDsystem (Neurodata, Inc., Pasadena, CA). A reattributionmontage 30 was employed that provides good correlation between QEEG power values and cortical perfusion.

The resulting montage is similar to the source derivationdescribed by Hjorth, 31 but differs in that averaging isperformed on power values after transformation to thefrequency domain, as opposed to averaging being per-formed on signal voltages in the time domain.

Cordance CalculationsI calculated cordance values for each recording site inthe four frequency bands using the algorithm summa-

rized below. 28 Cordance has been shown to havestronger correlation with perfusion values than with ei-ther absolute or relative power values used indepen-dently. 28 Cordance is also more strongly associated withSPECT and PET than power alone. 32

A three-step algorithm28

was employed to derivecordance values for each of the sites recorded. In the rststep, absolute power was calculated for all the bipolarpairs of nearest-neighbor electrodes and reattributedfrom the electrode pairs to individual electrodes. Thisreattribution was accomplished by averaging EEGpower for all pairs that included that electrode. 30 Theaveraged values were then square-root transformed tominimize both kurtosis and skewness. 33 Relative powerwas then calculated by dividing the absolute power ineach frequency band for the total power for the entirespectrum.

The second step consisted of performing spatial nor-malization of both absolute and relative power across brain areas. This process involved averaging across allelectrode sites in each frequency band f to derive meanabsolute power and mean relative power value for the band. Z-scores were then calculated for each electrodesite s in each frequency band. This resulted in normal-ized absolute and relative power values for each indi-vidual electrode site [Anorm(s,f) and Rnorm(s,f), re-spectively] with values expressed in z-score (SD) units.

The third step characterized electrodes by the associ-ation between Anorm (s,f) and Rnorm(s,f), in compari-son with their means. Each individual electrode in eachfrequency band could then be examined in terms of therelationship between absolute and relative power. If both absolute and relative power were above or belowthe mean value for that electrode, the relationship waspositively associated and the electrode was identied asconcordant. If either absolute or relative power wasabove the mean value for that individual electrode whilethe other was below its mean, the association was neg-ative and the electrode was identied as discordant.

Because it is often useful to examine a global sum-mary of an individual participants brain activity, aglobal brain state was then calculated in a fourth step.Using the cordance value calculated for each electrodesite, the total proportion of electrodes displaying dis-cordance for each individual is calculated. Two-thirds ofnormal control participants, as well as most depressedparticipants we examined, have fewer than 30% of elec-trodes in the discordant condition. 34 Based upon an ex-amination of the frequency distribution of the propor-

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J Neuropsychiatry Clin Neurosci 18:2, Spring 2006 http://neuro.psychiatryonline.org 211

VENNEMAN et al.

FIGURE 1. Cordance Calculation

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2

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0

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1 1 2 3 3 2 1 00 1 2

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tion of discordant electrodes in the normal anddepressed populations, we can divide study partici-pants empirically into two categories that characterizethe predominant global brain state: concordant and dis-cordant. An individual may be designated with the dis-

cordant global characterization in two ways: by havinga large number of electrodes showing discordance, or by having a few electrodes that are highly discordant(far from the mean values). Participants with a percent-age of discordant electrodes of 30% or more are termedto exhibit the discordant state; those with a percentageof less than 30% are generally characterized as concor-dant. However, if a participant has two or more dis-cordant electrodes that are highly deviant from themean values (i.e., |Anorm| | Rnorm| 2.36 z-scoreunits, or extremely discordant), then that subject isalso termed to exhibit the discordant state. Data fromsimultaneous QEEG and PET studies 28 revealed that theconcordance/discordance category for each electrode isrelated to perfusion: brain regions with concordance(electrodes in the upper right quadrant) showed the high-est perfusion values (Figure 1a), while discordant brainregions (electrodes in the upper left quadrant) showedlower perfusion values (in Figure 1b). In this project weconned our examination to cordance measures in thetheta band for two reasons: our pilot data indicated thatchanges in brain electrical activity in this frequency bandwere most prominent among cocaine dependent partici-

pants, and theta band EEG activity has been associatedwith response outcomes in depression. 34,35

Experimental ProcedureAll potential participants signed an informed consent

form after experimental procedures were explained.Participants who then successfully completed a week-long compliance baseline underwent a random double- blinded assignment to receive either daily selegiline (10mg) or a matching placebo for 8 weeks. All participantswere required to complete three visits per week duringwhich they took their medications under observation,provided urine samples for drug monitoring, and com-pleted rating scales. The participants received one hourof individual counseling and three sessions of grouptherapy per week. We examined clinical ratings col-lected during the baseline compliance assessment andWeek 1 of the trial as possible predictors of study reten-tion. QEEG data were collected during the rst 10 daysof the study from all but three participants who werenot available. One of these participants was recorded inWeek 2 and two were recorded in Week 3 of the study.All three were characterized with the discordant brainstate, but were evenly divided among the three outcomegroups, so their data were included in the analyses. Weexamined retention data from the entire 8 weeks of thestudy to chart successful completion or early departurefrom the trial.

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TABLE 1. Demographic Characteristics of Participants

GroupMean Age

YearsGender Male to

Female (M/F)BDI

Week 1% of

Concordant SubjectsBenzoylecgonine

ng/ml

Completers N 14 (SD) 33.2 (7) 3.7/1 6.8 (5) 85% 45,505 (53,310)Drop outs N 5 (SD) 31.4 (5) 1.5/1 4.3 (4) 15% 91,000 (36,735)Dismissed N 6 (SD) 40.5 (7)* 6/0 16 (5)** 0% 49,983 (50,948)All subjects N 25 (SD) 34.6 (7) 4/1

*signicantly different from completers p 0.05** signicantly different from completers p 0.01

Data AnalysisWe examined variables of age, depression level, depen-dence, and QEEG power collected during the baselinecompliance week and Week1 of the study using analysisof variance (ANOVA) and analysis of covariance (AN-

COVA) to determine usefulness in predicting subject re-tention. Retention was dened as a participant remain-ing in the trial for the entire 9 weeks (1 week of baselinecompliance and 8 weeks of drug trial). Dropouts weredened as participants who nished the baseline com-pliance week, but voluntarily withdrew while still meet-ing eligibility requirements. Dismissed participantswere those who were removed involuntarily from thestudy after baseline for activities that violated the pro-tocol (such as missing visits). We also examined cord-ance employing a Kruskal-Wallis ANOVA with a Mann-Whitney U-Wilcoxon Rank Sum W Test post hoc

analysis to elucidate the ability of cordance to predictsubject retention.

RESULTS

Of the 25 participants examined, 14 successfully com-pleted the study, ve dropped out and six were re-moved. There was a statistically signicant difference inage (F 3.54 (2, 24) p 0.04) which was accounted for by the difference between those participants who re-mained in the study and those who were dismissed (Tu-key-B p 0.05). Three of the ve women enrolled in thestudy remained, while 11 of the 20 men enrolled re-mained (Table 1). There was only a weak association between some of the clinical factors and attrition fromthe study. The BDI, Ham-D scores, and urine levels of benzoylecgonine from the baseline compliance weekwere not signicant predictors of completion. There wasa statistical trend toward a difference in urine levels of benzoylecgonine between participants who completedand those who dropped out of the study. Voluntary re-ports of wanting (F 3.26 (2, 24) p 0.06) and crav-

ing (F 2.62 (2, 24) p 0.10) cocaine were higher inthose participants who were dismissed from the study, but were not signicant predictors of retention afterurine levels of benzoylecgonine were controlled for byANCOVA. There was a difference among the groups in

BDI scores at Week 1 (F 8.20 (2, 21) p 0.003), attrib-utable to a difference between those who were dis-missed and the other two groups employing a Tukey-Bpost hoc (p 0.05). The difference remained signicantafter controlling for the effects of age and benzoylec-gonine by ANCOVA with variables entered simulta-neously (F 5.48 (2, 21) p 0.02). Neither absolute norrelative QEEG power differed among those who com-pleted and those who did not complete the medicationtrial when examined by ANOVA. Cordance measuresdid, however, detect a difference between those whocompleted and those who did not complete the study.A Kruskal-Wallis ANOVA showed that discordant par-ticipants were signicantly less likely to complete thetreatment trial than participants exhibiting the concor-dant global brain state (Chi-Square Likelihood Ratio13.34 (2), p 0.006). Figure 2 left illustrates a pattern ofcordance, and right, a pattern of discordance. There wasa signicant difference between those who completed,those who dropped out, and those who were dismissed by a Mann-Whitney-Wilcoxon post hoc test (Z 2.26,p 0.02) and (Z 2.76, p 0.006), respectively (Figure3). The rate of attrition according to the baseline level ofcordance is shown in Figure 4. Concordant participantsexhibited 92% retention through Week 2 of the study,and a nal attrition rate of only 15%, as opposed to 83%and 100%, respectively, for discordant participants.

DISCUSSION

These results suggest that there is variation in the neu-rophysiological function of potential cocaine-dependentstudy participants prior to enrollment in a medication

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VENNEMAN et al.

FIGURE 3. Cordant vs. Discordant, Number of Participants byGroup

12

10

Concordant

Discordant

8

6

4

2

0

N u m

b e r o

f S u b j e c t s

Completers Drop Outs Dismissed

FIGURE 2. Electrode Dispersion

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1

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2 1 0 1 2 2 1 0 1 2

N o r m a

l i z e d

R e l a

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P o w e r

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Cz

F3F4

F7

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FP1FP2

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T4T5

T6

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vOC3

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C3

treatment trial for dependency. In addition, this varia-tion may be related to the probability of trial completion,dismissal of or dropout by the participant. A signicantpercentage of participants who successfully completedthe trial were classied as concordant in their brain stateas opposed to those who did not complete the trial. Theneurophysiological differences reported here are signi-

cant even after controlling for benzoylecgonine levels,suggesting that there are fundamental differences in brain function which are independent of recent druguse. The differences in outcome among these partici-pants cannot be explained on the basis of the many com-mon clinical factors that contribute to heterogeneity inclinical trials. 36 Consistent with previous research, thelevels of depression or craving 23 were not powerful fac-tors in explaining participant retention. Those who wereremoved from the study did have slightly higher levelsof depression than either those who remained ordropped out, but there was considerable overlap in therange of depression scores among the three outcomegroups. Congruent with Kampman et al.s work, 24 weobserved a statistical trend toward a difference in levelsof benzoylecgonine between those who completed andthose who dropped out; however, this indicator did notreach the level of signicance. Because of themoderatelystrong association between cordance and cerebral per-fusion, 28 one may hypothesize that the participants whocompleted the study had higher levels of cerebral per-fusion globally, and therefore may have suffered less brain dysfunction than those participants who droppedout or were dismissed. Studies of cerebral perfusion incocaine-dependent participants without apparent neu-rological defects have shown widespread perfusion de-fects.37 It is important to note, however, that no directmeasurements of perfusion were performed on any of

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FIGURE 4. Pretreatment Cordance and Treatment Retention

00 1 2 3 4 5 6 7 8

10

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50

60

70

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90

100

P e r c e n

t a g e o

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i n S t u d y

Weeks in Treatment

Concordant

Discordant

the participants in this study. Futurestudiesof treatmentretention should examine other indicators of brain dys-function in cocaine dependent trial participants, includ-ing brain structure using magnetic resonance imaging(MRI) as well as neuropsychological test results. For ex-ample, cocaine dependence has been associated with anarrest of normal white matter volume increase as wellas evidence of premature white matter damage withage. 18,38 The participants that were dismissed were sig-nicantly older, more dysfunctional (as seen in their in-ability to follow protocol), and more depressed. Furtherexamination with measures such as single-photon emis-sion computed tomography (SPECT) or functional mag-

netic resonance imaging (fMRI) could elucidate specic brain structures giving rise to concordant and discor-dant brain states. No conclusions can be drawn aboutthe relationship between baseline cordance characteris-tics and effectiveness of the medication examined in the

treatment trial. This study used only retention, a crudeindicator of treatment outcome, as the measure of inter-est. The small sample size precludes our examining in-dices of treatment effectiveness, and the medicationswere assigned randomly and not according to the neu-rophysiological characteristics of the participants. Inter-nal validity would have been increased if participant baseline QEEG measures were all obtained at identicalabstinence length. Our inability to collect baseline mea-sures at identical times may have confounded our mea-sure, with differences in brain activity dependent uponlength of abstinence or initiation of treatment. Future

studies with more participants should examine the re-lationship between neurophysiological measures andclinical indicators of outcome such as frequency and/orintensity of use, periods of abstinence, functional im-pairment, or derived measures such as the treatment ef-fectiveness score (TES). 39 Examination of these types ofoutcome measures will help determine whether neuro-physiological heterogeneity is related to the overallfunctional outcome in cocaine-dependent participants.

This work was supported in part by Medication Develop-ment Research Unit contract #1 Y01 DA 50038 from the Na-

tional Institute on Drug Abuse to the Department of Veteran Affairs, research grant 1RO1 40705-09, and Research Sci-entist Development Award 1 KO2 MH 01165 from the Na-tional Institute on Mental Health. The views in this manu-script represent those of the authors, and do not necessarilyrepresent those of the Department of Veteran Affairs.

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