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Retraining the Brain for Substance Abuse

Retraining the Brain for Substance Abuse

Monday, September 16, 2013 Author: Anonym Categories: Neuroscience
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The focus of this article is to explore and introduce recent work in the field of addiction recovery as it relates to the neuro-cognitive dimension. This article is the first of a series of articles focused on identifying and then developing a treatment program for addiction based disorders. Part of the intent of this article is to help those new to the field of substance abuse to understand that individuals suffering from Substance Abuse Disorder (SUD) must be both viewed and treated from a Biopsychosocial model of healing. This requires us to consider not only the personality traits and behaviors of the individual, but also to treat many of the underlying physical causes, which are driving the addictive cycle.

The author’s experience, within the Crossroads Institute Centers, has shown that by including traditional based treatment programs in combination with body health recovery and a valid brain-retraining program using the latest neuro-imaging studies, can help overcome measurable losses of cognitive functional ability, due to substance use disorder. (Gunkelman, Cripe, 2008) We’ve further found this approach gives these individuals the ability to not only abstain, but also to reintegrate back into their society in a manner of their choosing.

Today, substance use disorder (SUD) still remains one of the major social issues which plague our culture.  It affects all walks of life (Mathias R., 2008).  There are many programs that attempt to address this issue; most focus on abstaining from the substance of choice. Some more modern programs attempt to teach both interpersonal and vocational skills in the hope of preventing substance abusers from relapsing. The final goal is reintegration back into normal life.

Recent findings in brain research indicate that there is a neuro-cognitive component, with an underlying brain mechanism associated with SUD (Mathias, 2008; Fahrion, 1992). These findings also indicate that a measurable loss in cognitive function (or abilities to think) occurs as a result of SUD (Mathias, 2008; Fahrion, 1992). These findings make SUD both biologically based and behaviorally based, which requires us to rethink our treatment programs.

Addiction starts in the brain not the behavior. Most people who enter addiction treatment programs started their addictions as adolescents. Current research on brain development, in typically developing humans, indicates that the full adult maturation process is not complete until after 25 years of age.  When drugs and alcohol are introduced during this maturation process, this will have an effect on the brain’s development and may explain why many individuals suffering from SUD have common issues in their cognitive function.

The interference in developmental changes offers a physiological explanation for why addicts act so impulsively, and have difficulty in recognizing that actions have consequences.  For many, this impacts their level of social maturation. Recent neuro-imaging techniques using both fMRI and fqEEG indicate a specific area of the brain, called the nucleus accumbens, processes pleasure/rewards signals. This area is supposed to mature before the brain’s prefrontal cortex, which is located behind the forehead. This maturing process isn’t complete until somewhere in the late twenties. The prefrontal cortex is involved in executive function, memory, planning, and making decisions.

Through recent neuro-imaging studies, it appears this is one of the areas that is adversely affected by SUD. It seems to be one of the areas in which the addicted brain is not able to process information effectively, and which is necessary to make responsible decisions in life. By using a neuro-imaging technique called fqEEG, a clinician is able to gain deeper insight into the addicted brain. Of equal importance, if proper biological treatment, based on identified brain dysfunction, is included in the treatment, these dysfunctions are able to be retrained and help reduce if not eliminate some of the addictive drives that are brain-based (Gunkelman & Cripe, 2008).

Brain recovery programs for addiction have a long history. Most programs stem from two therapeutic traditions: Self-help groups (mutual aid), first established in the 1840s and professional medical specialties with roots going back to the postbellum “inebriate” asylums. (Hertzberg, D., b2002) Even though individuals attending treatment centers report short term success for SUD, long term success rates for these individuals is poor (Mark, 2005; Fahrion, 1992) if the treatment is focused mainly on a psychosocial model of recovery. These programs generally focus on changing a person’s lifestyle and habits to eliminate the SUD lifestyle (Fahrion, 1992; Fagan, 1994). Even with this effort there has been little improvement in the relapse success rate. Studied relapse rates remain high, typically over 70% (Fahrion, 1992; Mark, 2005; Fagan, 1994). Gossop, Stewart, Browne and Marsden (Gossop, 2002) reported 60% of heroin addicts relapsed one year following addiction treatment. Studies which have focused on the individuals who are successful with these programs have isolated a common variable. That variable seems to be a person’s sense of self efficacy or the belief that they can overcome and break the addiction cycle. (Mark, 2005)

To help address this added component, there have been several major studies ranging from cognitive behavioral therapies (CBT) to EEG biofeedback, which have shown some improvement over the traditional treatment center approach. Programs, which include a form of EEG biofeedback, are beginning to report a significant improvement; from the traditional 70% relapse rate down to that of only 40%.  These classic forms of biofeedback focus on creating a hypnogogic state (a hypnotic state), using a protocol now labeled the alpha-theta Peniston protocol, named after Eugene Peniston. Peniston demonstrated significantly higher abstinence rates with alcoholics when EEG biofeedback was incorporated into the treatment protocol (Gossop, 2002; Peniston, 1989; Peniston, 1990). The Peniston protocol and its variations focus on placing a person in a highly suggestive hypnotic state. Then,  during this hyper-suggestive state, affirming suggestions are employed, which support the individual’s sense of self-efficacy as being capable of abstaining, as well as helping their ability to focus. These studies showed that eighty percent of subjects in these experiments were reported to be abstinent one-year post treatment, but not necessarily able to successfully reintegrate back into their society.

Upon interview, many abstinent SUD subjects still indicated poor cognitive performance abilities and a feeling of something ‘lacking or missing’ in their life (Gossip, 2002; Peniston, 1989; Peniston, 1990; Mathias, 2008).  Based on the clinical experience at Crossroads, and utilizing objective measures for SUD, fqEEG brain maps often indicate many areas of imbalances in brain wave patterns as they address both cognitive function and personality maturation.

When looking at the fqEEG, two specific EEG patterns called EEG phenotypes, have been identified as common patterns (Johnstone, 2005) within the SUD population. Additionally, EEG phenotypes, which affect a person’s ability to function cognitively, have also been identified in learning disabled individuals. These cognitive impairments include attention and impulse issues (Johnstone, 2005).  Cognitive function which includes cognitive abilities of learning, memory, executive function, abilitiy to inhibit impulses, proper choices and decision making are significant factors when integrating back into societal communities effectively (Mathias, 2008).

In a study published by the authors (Gunkelman, Cripe, 2008) from preliminary neuro-imaging work, based upon the fqEEG, it appears that two different neural “factors” underlie the preponderance of cases studied. This most likely represents separate pathophysiologic drives for addictive behaviors:  1)   CNS Over-arousal 2) Cingulate issues (Obsessive-Compulsive) Fig 1.

Further, the study indicated that not only can the brain based drives be identified, but also more importantly, when a full biopsychosocial-healing model is applied, results from the study indicated that these issues responded effectively to treatment. The pilot study, using clinical outcomes data, represents a non-controlled study. The study addressed observed efficacy on a large clinical case series, comprising the first 30 clients who completed therapy at Crossroads Institute in their BrainRecovery Program™ for substance abuse disorder.

The program uses a biopsychosocial model and includes:

  • Phenotype-based neuro-cognitive target
  • EEG-assisted cognitive rehabilitation program
  • Nutrition
  • Counseling

The case series illustrates characterization of the pathophysiology associated with addiction, and generally shows the impact of this approach on various outcome measures as it relates to neuro-cognitive functional performance on pilot data acquired on subjects as it related to fqEEG measurements. Fig 2. Illustrates the changes that resulted from the treatment program.

Our outcomes are not merely abstinence/sobriety from the client’s drug of choice, but reflect a more fundamental improvement in neuro-cognitive function, as seen in our routine pre-post testing.  Some quantification of the “neuropsych” changes associated with our phenotype based neurotherapy approach are seen in measures which were taken both before and after treatment.  Measurements are expressed as standard scores, with 100 being a normal performance.  The following table lists the change in the addiction client’s group mean values comparing the various measurements from before to after treatment.

The first measure is taken from the Woodcock-Johnson III as a “general intellectual ability” measure, is considered a measure of the Fluid Intelligence of the individual and is crudely equivalent to an “IQ” score.   The Thinking Ability metric measures thinking processes used when short term memory information cannot be processed automatically.  Cognitive Efficiency measures the ability to process information automatically, with tasks requiring that information be held in working memory, and also visual perceptual speed. Audio-Visual-Learning Ability measures learning in situations where information is presented both orally and visually.  Delayed Recall is a measure of both auditory and visual recall after a 30 min delay.  Working Memory measures the ability to hold information in immediate awareness while performing mental operations on the information. The following table represents the groups’ pre- and post average scores.

“IQ” (Woodcock-Johnson III) Pre 99 Post 120
Thinking ability 103 122
Cognitive efficiency 94.7 118
Audio-Visual Learning ability 88 112
Delayed Recall 65.8 103.6
Working Memory 93 122

Abstained time group average over 18 months

GIA SCORES -  General Intellectual Abilities – IQ score as measured by Woodcock Johnson III

Thinking Ability Scores – Is a measure of different thinking processes that can be used when information in short-term memory cannot be processed automatically.

Cognitive Efficiency – Is an index on the patient’s ability to process information automatically. This is a measure of two types of tasks 1) Task requiring one to attend to information held in immediate awareness while working on it; and 2) Tasks that require visual perceptual speed.

Audio-Visual-Learning Ability – Is a measure of how one learns in situations where information is present both orally and visually.

Audio-Visual-Learning Delayed Recall Ability – Is a measure of how one recalls oral and visual learned information that is recalled after a 30 min delay

Working Memory – is a measure of the ability to hold information in immediate awareness while performing mental operations on the information.

References

  1. Fabiani M. Gratton G. and Coles MGH. Event-related brain potentials: methods, theory and applications, in JT.
  2. Fagan J. Women and drugs revisited: female participation in the cocaine economy. Special Issue: drugs and crime revisited. J Drug Issues 1994; 24:179-225.
  3. Fahrion SL, Walters ED, Coyne L, Allen T Alterations in EEG amplitude, personality factors, and brain electrical mapping after alpha-theta brainwave training: a controlled case study of an alcoholic in recovery. Alcohol Clin Exp Res. 1992 Jun;16(3):547-52
  4. Gossop M, Stewart D, Browne N, Marsden J. Factors associated with abstinence, lapse or relapse to heroin use after residential treatment: protective effect of coping responses. Addiction 2002; 97(10): 1259-1267.
  5. Johnstone, J., Gunkelman, J., &  Lunt, J., Clinical database development: Characterization of EEG Phenotypes. Clinical EEG and Neuroscience, 36(2); 99-107, 2005.
  6. Mark Ilgen, John McKellar, and Quyen Tiet, Abstinence Self-Efficacy and Abstinence 1 Year After Substance Use Disorder Treatment Journal of Consulting and Clinical Psychology In the public domain 2005, Vol. 73, No. 6, 1175–1180
  7. Mark Ilgen, John McKellar, and Quyen Tiet, Abstinence Self-Efficacy and Abstinence 1 Year After Substance Use Disorder Treatment Journal of Consulting and Clinical Psychology In the public domain 2005, Vol. 73, No. 6, 1175–1180
  8. Mathias Robert , (2008), Studies show cognitive impairments linger in heavy marijuana user; Retrieved January 19, 2008 from www. NIDA.gov
  9. Peniston EG, Kulkosky PJ. Alpha theta brainwave training and beta-endorphin levels in alcoholics. Alcoholism: Clin Exp Res 1989; 13(2):271-279.
  10. (8.) Peniston EG, Kulkosky PJ. Alcoholic personality and alpha-theta brainwave training. Med Psychother: Int J 1990; 3:37-55.

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