Relapse Risk in Substance Abuse Disorders Pre- Determined by Psychoshysiology Functional...

Running Head: PSYCHOPHYSIOLOGY AND NEUROIMAGING PREDICT RELAPSE 1

qEEG and fMRI Predict Relapse Risk in Abstinent Substance

Abusers: Scaled Use is Advocated

Eric Ternand

University of Minnesota

PSYCHOPHYSIOLOGY AND NEUROIMAGING PREDICT RELAPSE 2

Abstract

Alcohol and other substance use disorders (SUDs) destroy the

lives of millions of people and cost society billions of dollars

every year. And yet after decades of intense and well-funded

research, the major goals of elucidating a definitive etiology,

finding effective treatments, or even just predicting the course

of the disease in any given individual remain frustratingly

elusive. It’s estimated that a third of the chemically-dependent,

treatment-seeking population will relapse immediately following

treatment completion (many for the 5th or 6th time), and that

statistic has not changed in nearly 40 years (Emrick, 1974;

Miller, Walters, & Bennet, 2001).

The purpose of this review is to demonstrate that there are

tools available to screen for relapsing subtype substance users

before they enter treatment. Further, it will be shown that this

can be done reliably and inexpensively through

psychophysiological testing (qEEG elevated fast-β and/or

reduced/delayed P300 ERP) and/or functional neuroimaging

(specifically BOLD fMRI targeting the orbital and medial

prefrontal cortex during either go-no-go or drug-cue tasks).


Finally I put forth an argument that doing so would enable a

cost savings by treating those most in need most thoroughly,

giving more intensive Tx to the relapse-prone subtype, while

simultaneously shifting the majority of treatment seekers, the

non-relapsers, out of the traditional (and expensive) inpatient

treatment centers. They don’t want to be there, and the research

consistently shows they don’t need to be (Bottlender & Soyka,

2005) so why not use those resources on the real problem?

This would free up resources to finally reclaim that long

lost third, and also open up exciting new possibilities for the

next step forward as well.


Alcoholism abuse and dependence, and other substance use

disorders (SUDs) are devastating diseases with enormous costs for

society and for afflicted individuals and their loved ones. It is

conservatively estimated that alcohol (EtOH) abuse alone cost:

“$43,000,000,000 annually in lost production, medical and public

assistance expenditures, police and court costs, and motor

vehicle and other accidents” (109th Congress of the United

States, 2005) just in the United States. It is further estimated

that approximately 7% of the adult population of the country are

abusers of alcohol or alcohol dependent based on DSM-IV

definitions- (Congress, 2005). More than 30% of the automobile

accident fatalities in this country involve alcohol in some

fashion, and perhaps most tragically, more than half of kids

killed in car accidents (910 of them last year) involved parental

intoxication (Insurance Institute for Highway Safety [IIHS],

2012). While clearly the economic toll of this disease is

staggering, the human costs are far greater.

Children of chemically dependent parents are nine times more

likely to end up in prison, and are 13 times more likely to get

pregnant before the age of 18 (Congress, 2005). At least 49% of


domestic abuse cases involve alcohol or other drug use (Congress,

2005). Those who have ever had a diagnosed SUD are 15 times more

likely to be in jail or on probation at some point, and are 27

times more likely to commit suicide than their non-substance-

abusing peers (Congress, 2005). The effects of this dependency

syndrome are wide ranging and extremely destructive, and while

advances in understanding SUD causation and treatment have indeed

occurred (Miller, Walters, & Bennett, 2001), it remains a disease

in many cases almost defined by its chronic relapses and

revolving door treatment centers.

Miller, Walters, & Bennet (2001) analyzed seven large,

multi-site studies of alcoholism treatment (ntotal =8,389) for

efficacy, all of which included intensive and long-term

longitudinal follow-up (a feature lacking in many of the self-

conducted “studies” of efficacy performed and promoted by

treatment providers). Though the metrics are variable and the

definitional questions continue to nag (when has a “relapse”

occurred? Should drinking days or drinks per drinking day be the

primary determinant of “non-abstinent improvers?”), the final

conclusion reached is that about one third of those exiting


primary alcoholism treatment remain abstinent a year after

discharge, while another third show “substantial improvement.”

This is substantially unchanged from a similar study done in the

early 1970s (Emrick, 1974) which literally reported exactly the

same proportions almost 30 years prior.

In the intervening four decades it is essentially impossible

to overstate the amount of time, effort, money, and brain power

thrown at the problem of better understanding SUDs and of more

effectively treating the victims thereof. The NIH’s “Research

Portfolio Online Reporting Tools (RePORT)” shows that the Federal

Government alone, has spent somewhere between $5 and $10 billion

on SUD research and treatment annually over the last 5 years; a

rate which is extremely sizable in the research world, and has

held stable and even seen increases in relative, inflation-

adjusted terms for many years (National Institutes of Health

[NIH], 2012). All of that money spent on such research and

clinical treatment also translates into tens or hundreds of

thousands of scientists and clinicians devoting their

professional lives to solving this problem. And yet for all of


this effort and all of this money over 40 years, a third of

patients still chronically relapse.

Certainly progress has been made in a number of areas, and

by no means is research into alcoholism a black hole or waste of

resources. There have been great strides in casting light on an

incredibly elusive etiology and doing the basic research that

will ultimately lead to better outcomes for all patients.

Advances in genetics, genomics, neuroscience, and brain-imaging,

psychopharmacology, and physiology, all show promise for altering

the decades old treatment paradigm and ultimately overcoming this

problem of chronic relapse and addressing the issue of that

stubborn, forsaken, final third.

If it is possible to predict, a priori, which treatment-

seeking addicts are most likely to relapse, then it should also

be possible to devote to that group the most significant

resources and effect a better outcome than the difficult

prognosis the relapse-prone third now faces. It should also be

possible to pre-determine individualized treatment options, and

apply scarce resources more efficiently – devoting the most

expensive, longest lasting interventions to those most in need of


them and utilizing more efficient, outpatient-type treatment for

those likely to obtain significant benefit from any intervention

(Bottlender and Soyka, 2005 showed that for non-chronic-relapsing

AUD patients, inpatient vs. outpatient was not correlated to

outcome at all. This finding has shown up over and over again

throughout the literature).

It may also be the case that different subtypes of SUDs can

be identified, each responding optimally to different treatments.

The ideal situation would be simple, inexpensive, and

determinative pre-treatment testing that would give practitioners

insights into what would work best for any given patient, much as

we would culture an infection to know which antibiotic to

prescribe, or biopsy and genotype a tumor before deciding on

radiation vs. chemotherapy vs. surgery. This review will

demonstrate that we are very close to that style of treatment for

SUDs.

Predictors of Relapse

Traditional Psychological Metrics- Demographic, Behavioral,

Personality-Based, Etc.


Craving. The word itself almost makes one think addiction.

And the research literature is littered with references to this

somewhat ephemeral concept. But it turns out that years ago,

decades even, no one could really agree on an operational

definition and that none of the definitions put forward seemed

to have predictive validity anyway (Stanovich, 2010; Ciraulo,

Piechniczek-Buczek & Iscan, 2003). Defining craving as (self-

reported) drug-cue reactivity in 1994, Rohsenow et al. observed

no significant effect (a negative result) when they tried to

correlate feelings of “craving” upon seeing drug-cued stimuli

(pictures of bottles, or needles, etc… as appropriate to the

individual) with later relapse risk. Defining craving via self-

reported “urge diaries” also consistently lead to non-results and

frustrating refutations of the obvious face validity craving

seemed to enjoy (Miller & Gold, 1990, among others). Even when

formally operationalized with a questionnaire, as for example by

Miller et al. in 1996, the correlation with relapse was barely

significant and the effect size miniscule. And despite the fact

that these studies – showing no significant link between

“craving” (however defined) and relapse risk, treatment outcome,


or any other metric – were completed 20+ years ago in some cases,

the term continues to pepper the literature (to be sure not all

the craving studies have led nowhere, but an overwhelming

majority “…fail to demonstrate significance”).

This is true actually of more than just craving; other

metrics like disease onset/age of first drink, socio-economic

status, marital status, age at treatment entrance, education

level, and even gender show very inconsistent results in terms of

predicting relapse (Porjesz et al., 2005. Walton et al., 2003;

Durazzo et al., 2008).

There are a number of factors that have long been thought to

predict outcomes in substance use disorder patients that can

usually be obtained on simple medical history forms, with

interviews, or with various psychological assessment instruments.

Co-morbid psychiatric illness, for example, is widely believed to

have a strong correlation with relapse risk, and this has to a

certain extent been borne out in the literature (notably by

Ciralao, 2003; Durazzo, 2008), but again, it varies considerably

from study to study.


For example, Cadoret and Winkour (1974) found that

depression symptoms increased risk of relapse in men, but were

actually protective for female subjects, lowering their risk of

relapse. Rounsaville et al. (1987) found exactly the opposite to

be true; depression protected men and caused relapse in women!

Heath et al., in two separate studies (1997 & 2001) found that

both depression and anxiety histories predicted poor treatment

outcomes, while Kushner (2005) found that neither had any effect

and Brown (1990) showed that anxiety did not correlate with

outcomes. In sum it seems that it cannot be reliably stated

whether or not co-morbid “minor” psychopathology (mood and

anxiety disorders) can predict treatment outcome with any

accuracy or reliability (Ciralao, 2003).

It seems obvious that at the very least PTSD and bi-polar

disease should be predictive of relapse, but the literature here

is just as unclear (Kushner et al., 2005; Ouimette, Brown &

Najavits, 1998). This seems to be another example of face

validity being confused with empirical predictive validity - a

stumbling block social scientists must constantly be watching out

for (Stanovich, 2010).


Some of the traditional metrics do “work” at predicting

relapse, to a limited degree, and have held up well under

rigorous study. These include severity of disease (directly correlated

with relapse-risk and severity of relapse - Heath et al., 1997;

Poling, Kosten & Sofuoglu, 2007; Bauer, 2012), family history of SUDs

(Milne et al., 2009), self-efficacy (definitely inversely correlated

with relapse risk, but causality arrow very much in doubt,

however. Durazzo et al. 2008; Heath et al., 1997), cognitive ability

(also inverse, Alterman et al., 1990), increased psychological stress

(directly correlated, Brown et al., 1990), and co-morbid major

thought disorders (schizophrenia has a near perfect correlation with

treatment failure and subsequent relapse, see Cuffel & Chase,

1994; Iacono, 1998) have held up well under empirical study, but

other seemingly “obvious” psychological correlates of treatment

outcome have demonstrated little predictive validity.

As pointed out by Bauer in 2001 and again in 2012, most of

these potential psychological correlates suffer from some

combination of poor test-retest and inter-rater reliability,

self-report study designs, potentially confounding state

variables (like medication, intoxication, or withdrawal) during


the pre-screening process, and differing definitions of “relapse”

itself during the main study phase.

Biological and Psychophysiological Relapse Predictors

Bauer (2012) criticizes the demographic and traditional

psychological metrics as being inadequate in the ways described

above, but he also lists a set of criteria that make for useful,

relapse-predictive metrics that seems worth repeating here.

According to his paper, for a pre-screen to be clinically useful,

it must be not only predictive of relapse, but also be:

A) Sensitive – avoiding false negatives;

B) Selective – simultaneously avoiding false positives;

C) Reliable – scored objectively and with minimal inter-

rater discrepancy;

D) Stable – remaining constant barring intervention (also

called test-retest reliable);

E) Cost Effective and Practical (i.e. Portable) – these all

relate to scalability issues;

He also states that there must be some “value added,” defining

this to mean that either the screening examines a variable that’s

predictive on its own, or else that “…the predictive validity…


should exceed that of other variables which are more easily and

inexpensively measured.” (Bauer, 2012)

These rules seem to define well what the goal is, and

clearly the psychosocial traits above are too inconsistent across

studies to come close to hitting this high bar. Therefore,

biological and psychophysiological traits will now be examined

and tested against this list for utility as relapse predicting

metrics.

Psychophysiological Metrics

Quantitative electroencephalography (qEEG)

Quantitative Electroencephalography (qEEG) builds on the

technique of EEG, which dates to the 1920s. Both involve placing

electrodes on the scalp and measuring voltage changes in the

cerebral neocortex (the outermost brain layers) trans-cranially,

then processing and interpreting the

electric signal generated by the

brain and transduced by the

electrodes. The quantitative portion

of the term indicates that data are

being pooled by computer from many different

Figure 1. Old-style, 4-lead EEG vs modern high-resolution qEEG

head gear


leads, and even many different subjects. These can then be used

to make more accurate, higher-powered statistical claims and even

to make inferences about groups.

This technology has actually been around for several

decades, though computers powerful enough to process enough data

to obtain adequate spatial resolution were prohibitively

expensive for a time. It is now not uncommon for a research-

grade, high-resolution qEEG setup to involve 96, 128, or even 256

leads connected to the scalp (see Figure 1), each lead recording

a different voltage several times every second, all integrated

into a meaningful pattern by the computer to which they are

connected (Ray & Cole, 1985).

Older rigs, like the one in Figure 1 on the left, required

taping the eyes and also the use of conductive gel – messy, time-

consuming, and long necessary to make an adequate electrical

contact with the scalp. This is no longer needed due to an

improved dry-electrode design which became available in 2006,

dramatically cutting experiment times. This new generation of

high-resolution, gel-less transducers enables a researcher to get

a much more accurate picture (spatial resolution has improved but


remains marginal) of what brain regions are active at a given

time (temporal resolution is excellent) and to detect the neural

correlates of stimulus perception events, all with no

confinement, no messy gel, and in under an hour.

These “neural correlates of stimulus” are called Evoked

Potentials (EPs) if they are a direct result of the stimulus (a

loud noise, say, or a light flash) or else Event Related

Potentials (ERPs) if they are indirectly caused by the event, and

represent the cognition that occurs after the event itself. In

either case, these are named for their polarity (N for negative,

increased polarity peaks, and P for positive, depolarizing

valleys) followed by a number which indicates the characteristic

time in milliseconds after the triggering event when the

particular ERP appears. Some of the most

well described EPs are the N100/P200

complex – a negative (hyperpolarized)

peak followed by a positive

(depolarized) valley, at approximately

100 and 200 ms after the stimulus

respectively (See Figure 3) which follows any unexpected event,


whether or not the subject is even attending to the area where it

appears (Fabiani, Gratton, & Coles, 2000). And by far the most

widely studied ERP is the E300, a long depolarizing wave-form

present only if the triggering event is both attended-to and

novel.

Alterations in the P300 ERP have been repeatedly

demonstrated to be strongly and specifically correlated with

relapsing subtype alcohol use disorders (Enoch et al., 2006;

Saletu-Zyhlarz, 2004; Bauer, 1997; Iacono, 1998; and Carlson,

Iacono, & McGue, 2002). Specifically, in relapsing type

alcoholics, the onset of the P300 waveform is delayed and the

amplitude is dramatically decreased, even after long–term

abstinence. Furthermore, it has been demonstrated that this

alteration is in fact a heritable endophenotype for alcohol use

disorders, that is to say it is present in many of the sons of

alcoholic fathers, long before they could possibly have had their

first drink. If they too show this characteristic alteration,

their already elevated risk for AUD roughly quintuples, to > 95%

(Bauer, 1997; Iacono, 1998; and Carlson, Iacono & McGue, 2002).

. Graph of time (ms) vs. EEG (and cell membrane) voltage for ERP


This is tragic, of course, but it’s also crucial for

researchers. Since it amounts to the closest thing in existence

to a pre-alcohol-use test for alcoholism, pre-exposure altered

P300 gives researchers a means of pointing the causal arrow, a

way to determine causality instead of just correlation in

substance use studies, previously considered impossible.

For example, one may know that there are 6 or 8

physiological, neurochemical, and epigenetic differences between

an alcoholic and a non-alcoholic adult, but without the a priori

knowledge provided by the endophenotype (the highly predictive

test-trait), it becomes all but impossible to determine the

direction of the causation. Were the neuro-physiological-

epigenetic differences found in research what caused the drinking

problem? Or did the heavy alcohol exposure cause the differences?

Or was some as yet undiscovered force at work that caused both

drinking and differences? By knowing, in advance, which pre-teens

are likely to become alcoholics, it becomes possible for the

first time to begin to unravel that mess.

Bauer et al. (1993), along with Winterer et al. (1998,

2001), and Bauer (2001) have also identified a second qEEG


predictor of elevated relapse risk. In this case a characteristic

elevated pattern of fast-β wave activity during resting states

which shows positive and negative predictivity values for time to

relapse of .75 and .74, respectively (Bauer, 2001). It should be

noted that these selectivity and sensitivity numbers are more

than triple the next closest correlate amongst the putative

traditional psychological metrics (this was seen in cognitive

ability, which showed + predictivity/selectivity at .21 in the

Alterman study from 1990). Replication and scaling of this work

is taking place, and even higher predictive validity has been

shown in subsequent experiments using the same protocol

(Ragnasamy et al., 2002, 2004; Porjesz et al., 2002, 2005).

Ironically, EEG results have historically been seen as

unreliable themselves, showing little test-retest or inter-rater

reliability, and having very poor spatial resolution (by Lehmann,

1984; Jervis, 1983; and Seyal, Emerson, & Pedley, 1983, amongst

others) and certainly this was true of the old analogue 4-channel

units with their paper rolls and waggling needles. But the tool

has come a long way since then, and these new results cannot be

ignored.


The test-retest reliability question was addressed and

answered by Bauer in 1993 and again in 1997, where he

demonstrated that provided it’s held in the same orientation,

with the same equipment and following the same protocol, there is

no detectable difference in fast-β score or P300 variance across

trials and across examiners. The question should have been

settled once and for all by Iacono et al. in 1996, when they

demonstrated such high concordance of scores among separated

adoptees in a blinded study conducted at multiple sites that the

researchers initially thought they had a participant double

dipping for the stipend at both centers.

They were even more amazed when MZ twins, tested at

different locations on the same day, actually had slightly higher

concordance with each other than with their own scores a week later

(all 4 sets were near identical, but this screen has been called

the psychophysiological fingerprint because individual subjects’

signature waveforms are so unique and personal).

The Iacono (1996) study does however bring up one more

powerfully positive point associated with more widespread

adoption of qEEG as the tool of choice; the fact that like


altered p300 waveforms, heightened fast-β wave activity during

resting states is not just a phenomenally good predictor of

relapse in abstinent alcoholics, it is also a genetically

determined heritable trait, and can function just as well in the

capacity of endophenotype for prospective SUD risk studies of all

kinds.

It is true that spatial resolution in qEEG will never be

that of fMRI, because of the way the process works- signals have

to penetrate the skull, the intervening (electrically active)

brain matter, etc. to reach a limited number of voltage dipoles,

and they get spread out and malformed in this process. These

messy data are then analyzed and reconstructed inside the

computer and then turned into a map by complex algorithmic

analysis. This has gotten much, much less necessary with the

introduction of the high-resolution 128 and 256 electrode caps,

to the point where it is now used to actually create imagery as

well as numerical data even, a process called LORETA EEG, and

these advances are coming at the same moment when the powerful

computing equipment necessary for the signal processing component

is falling precipitously in price. The spatial resolution has


improved by a factor of 100 in the last decade alone, qEEG is

about 1/10,000th the price of fMRI, and the temporal resolution

is 100x that of even that powerful imaging technique (Mahmood,

2013).

Functional Magnetic Resonance Imaging (fMRI).

Functional magnetic resonance imaging (fMRI) is a technique

that takes advantage of the slightly ferrous character of

hemoglobin to measure blood flow. First the researcher obtains a

detailed structural image of the brain using traditional MRI

techniques, then adjusts the sensitivity of the magnet and

sensors to detect the miniscule magnetic difference in oxygenated

vs. de-oxygenated blood (called BOLD or Blood Oxygen Level

Dependant) in each three dimensional “voxel” (an abbreviation for

“volume-pixel,” a voxel is a tiny rectangular solid region within

the brain, on the order of ~1 mm3) as it varies over time. By

measuring each voxel and re-combining them in a computer, the

researcher is able to get a pixelated 3-dimensional picture of

the blood flow in the brain once he maps it back on to the 3-D

structural model obtained earlier with traditional structural

MRI. Actually, since fMRI is analogous to taking a 3-D video of


blood flow, and each voxel is constantly changing with time, it’s

appropriate to say that a 4-dimensional model of the blood is

what’s ultimately created (Stevens, 2005).

Since active brain regions and structures use far more

oxygen than dormant or baseline regions, it’s possible to see the

different areas of the brain activate as the subject does

whatever task he is assigned while inside the magnet (looking at

pictures, playing “computer games” designed to measure some

psychologically significant variable or elicit a certain

cognitive or emotional response, etc…) Differences between these

patterns of activation in meaningfully different groups are what

one is hoping to find in an fMRI study- the group selection is

usually the independent, manipulated variable, and blood flow is

usually the dependent, observed variable (Stevens, 2005).

Because all brains are different sizes, there is a series of

complicated statistical parametric modeling steps that have to be

undertaken to “process” the 4-dimensional model in order for the

group comparisons to be meaningful. The brain has to be aligned

exactly so each voxel from each subject is in the same place. It

also has to be resized to fit the “standard” brain, so that for


each subject, each voxel corresponds to the same brain region. It

also needs to be split into gray, white and CSF matter, and

digitally “smoothed” to even out the hard voxel edges. The

different groups of brain models are then averaged together and

differences in blood flow during a task can be analyzed across

groups. This process remains somewhat specialized and expensive,

but like all technologies prices are dropping rapidly (Stevens,

2005).

Some of these differences in blood flow correlate to the

variables under study. First off, in a 2007 study by Sinha & Li,

the procedure just described was performed on 40 subjects (newly

sober, detoxified alcoholics and cocaine abusers seeking

treatment at a local hospital) and 39 non-smoking, light-drinking

age, socio-economic, and ethnically matched controls (healthy

adults from the community, either paid or volunteering). While in

the scanner they were shown pictures designed to elicit cravings

alternating with neutral imagery, and asked each to self-report

their level of drug-desire or craving. After completion of

treatment he followed them for 90 days to assess treatment

outcome and monitor for relapse.


Even with a follow-up period of just 3 months, significant

differences were noted between the relapsing and abstaining

groups. Increased activity in the medial pre-frontal cortex

(MPFC), a brain area associated strongly with reward salience,

during the drug cues, correlated to p < .02 inversely with time

to relapse, and also to p < .02 directly with amount used after

relapse. Additional effects of note between relapsers and

abstainers included increased cue-associated activity in the

posterior insula correlated with post-relapse drug use frequency,

and in the posterior cingulate (PCC) with amounts of drug used,

also post relapse.

Interestingly, though there were between-groups differences

between addicts and controls on almost every other metric, on the

self-reported “craving scale” they were indistinguishable from

each other, as were the abstainers; it showed no predictive

validity whatsoever. It would seem a true operational definition

for “craving” might be “increased medial pre-frontal cortex

activation during drug cue challenges” but self-reporting is once

again shown to be lacking. A similar study conducted by Grüsser

et al. in 2004 also showed drug cues and also asked for craving


ratings on a 1-10 point scale. This time, however, a galvanic

skin response “sweat test” of the sort used in lie detection was

also included. Once again, no significant differences were noted

between controls, abstainers, and relapsers on either metric; on

the sweat test the group that would later relapse actually scored

slightly better.

But their brains told a different story.

The relapsing subgroup showed significantly elevated orbital

pre-frontal cortex (OFPC) activity during the drug cue imagery

compared to controls (p < .01) and relapsers (p < .04) both, even

as they were sweating slightly less and reporting an identical

level of subjective drug craving. The OPFC is a region

immediately adjacent to the MPFC involved in impulse control and

executive function and also long implicated in theories of

addictive etiology. Increased activity in this case could mean

more effort required to block the craving, apparently without the

subject even being aware it was happening.

Finally, a Pearson’s Correlation Coefficient was performed

to rule out any unrecognized confounding variables between the

relapsing and abstaining groups. Using this statistical method,


lifetime drinking severity, time between last drink and scan

initiation, severity of alcoholism, comorbid depression, comorbid

anxiety, family history, and age were all ruled out as significant

contributors to the abstainer-relapser differences observed in

the study.

Conclusions and Extension

There are at least three different pre-treatment screening

protocols shown by this review to pass all five tests for being

clinically useful. It is time to retire the old, ineffective,

non-predictive metrics and embrace a new paradigm. This will lead

to far more efficient resource allocation, keeping those who

don’t need expensive inpatient treatment from having to be away

from home, while guaranteeing the space, money, and personal

professional attention that might finally reach the “forsaken

third” and give them back their lives.

P300 and fast-β qEEG based predictions of relapsing subtype

are more than three times as accurate at predicting relapse than

any of the psychological methods now in use, both in terms of

specificity and selectivity. They are also cheap; at this point a

qEEG can be administered by a non-expert after a few hours


training, whereas most of the traditional methods require hours

of time from a holder of an advanced degree. The machines

themselves have gotten so inexpensive they’re sold as “toys you

can play with your mind.”

fMRI is, if anything, even more predictive than qEEG. It

does, however, have a ways to go in terms of price and

availability before it’s quite ready to be rolled out on a large

scale. But the cost of doing such pre-screening has dropped by a

factor of 25 in the last 10 years (Bauer, 2012) and they’re

getting cheaper and more predictive still every day; I certainly

wouldn’t bet against fMRI pre-treatment screening becoming the

standard within a decade.

qEEG and fMRI have been shown recently to predict relapse-

prone patients, with startling degrees of both specificity and

selectivity, and are, right now, readily available and approved

for use in human subjects. And they both add value, in several

different ways – both in terms of being the best available test

for the client, for society as a whole in terms of efficiencies,

and also in terms of enabling development of a still better

screening tool.


There are limitations in all 3 methods of prediction

described above. Spatial resolution in the qEEG tests, costs and

eligibility in the fMRI, to name a few. Also, confounding state-

variables like intoxication or withdrawal during the pre-

screening process can interfere markedly with the predictive

validity of all the methods described. (Bauer, Covault, &

Gelernter, 2012). The gold standard therefore in the quest to

predict post-treatment relapse risk prior to treatment lies in

the one test where not even acute intoxication would be a

confound – the genetics of the individual patient.

Until recently it has been far too expensive, time-delayed,

and impractical to even consider determining the genotype of each

and every patient, but this is changing rapidly – according to

Bauer (2012) if present trends continue, in 10 years genotyping

will cost no more and take no longer than an x-ray. Even now

there are several different identifiable genetic polymorphisms

that seem to predict relapse to an almost unbelievable degree; in

one study, Edenburg, et al. (2004) found a 3 SNP haplotype in the

gene encoding GABAaR (a particular receptor for ᵧ-aminobutyric

acid (GABA), an inhibitory neurotransmitter that EtOH mimics the


effects of) that correlates to relapsing-subtype alcohol

dependence at p < .000000022! In fact this gene of interest was

discovered and targeted as a direct result of the type of

psychophysiological metrics being advocated here- the qEEG was

being used as a pre-screen before genotyping areas identified in

the national COGA sample (Collaborative study Of the Genetics of

Alcoholism – a massive nationwide hunt for candidate genes.

Reich, 1996).

Thus the non-genetic methods could and should be deployed as

rapidly as possible; first because, as demonstrated in this

review, they possess enough predictive power to make an enormous

difference in relapse identification, because doing so would

likely generate a huge cost savings that could be put to work

helping the lost third of relapse-prone addicts, and also because

of the ancillary benefits that could be provided by leveraging

the data thus obtained in moving away from all subjective criteria

and toward a purely empirical, scientific approach.

The immediate and widespread use of qEEG, prior to admission

to treatment centers, as a prescreen for relapsers (who would

then receive even more help, support, and resources; non-


relapsers would be directed to outpatient programs) would be

hugely beneficial toward reaching that goal more quickly. Data

from those screens could be combined with a simple cheek swab,

which would be de-identified, and used to refine and perfect the

genetic techniques that would be the ultimate goal. This is in

fact exactly what happened with GABRA2 in the Edenburg (2004)

study.

With each treatment program admission, not only would the

client receive the best and least invasive or confining course of

treatment for her individual disease, she’d be helping to advance

the science of addictive disease characterization and relapse

prediction as well. It is shown above that all three of these

techniques are capable of sorting the treatment-seeking

population into its hidden bi-modal distribution.

The type of genome-wide data collectable on a massive scale

under such a paradigm, has already been used to identify several

promising targets for the penultimate goal – discovering the true

neurobiological etiology of alcoholism, and then targeting that

mechanism directly and putting an end to it.


Wojnar et al. got it started by looking at an individual

gene for relapse prevention in 2009 – the brain-derived

neurotrophic factor (BDNF) polymorphism they examined holds a

great deal of promise as a potential target for novel

pharmacotherapies, and it is already a superb test for relapse

likelihood. Bauer (2012) is looking at several genes as well, and

Zuo et al. (2013) in New Haven have a serine transporter gene

they’re beginning to look at which seems promising as can be.

The geneticists are waiting, they just need the data, and

the data collection will help the treatment seekers, while

simultaneously saving money throughout the system by shifting the

majority of addicts, who are not ultra-high-risk relapsers into

far less expensive outpatient programs, which have been shown to

be equally effective for that subgroup (Bottlender & Soyka,

2005). It’s past time to move beyond subjective “craving”

questionnaires and solve this problem.


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