BIOFEEDBACK ARTICLES

Alexey B. Skok, MD, Ph.D., Senior Researcher at the Biofeedback Computer Systems Laboratory of the Institute of Molecular Biology and Biophysics of the Siberian Department of the Russian Academy of Medical Sciences.
Mark B. Shtark, MD, Ph.D., Academician of the Russian Academy of Medical Sciences, doctor of sciences (biology), Deputy Director for Researche of the Institute of Molecular Biology and Biophysics of the Siberian Department of the Russian Academy of Medical Sciences.
Olga Shubina, MD, Ph.D., Leading Researcher at the Biofeedback Computer Systems Laboratory of the Institute of Molecular Biology and Biophysics of the Siberian Department of the Russian Academy of Medical Sciences.

SIBERIAN BRANCH of the RUSSIAN ACAD. MED. SCI.
INSTITUTE for MOLECULAR BIOLOGY and BIOPHYSICS
2, Acad. Timakova St., Novosibirsk, 630117, Russia
Tel/Fax: +7 (3832) 321-256
E-mail: jafarova@cyber.ma.nsc.ru
http://www.nsk.su/~cyber
http://www.bfbgames.com

It is widely known that most frequently, EEG detects alcohol abstinence as a decrease of brain bioelectric activity in the alpha range. Peniston and Kulkosky [1, 2, 3] have shown that alcohol continence leads to the cessation of a typical modulated alpha-rhythm and the dominance of beta activity in patients with brain injuries. According to these authors, EEG phenomena strongly correlated with the emotional condition of patients and endorphine production. The shift of bioelectric activity to the left as a result of alpha and theta biofeedback treatment (alpha-theta stimulating training) produced the improvement of psychoemotional condition of patients. The long-term course of alpha-theta training allowed to achieve remission in the majority of patients.

There is a substantial number of papers devoted to EEG correlation with emotional condition. On the whole, one may think that the dominance of alpha-rhythms is accompanied by the condition of pshychoemotional comfort. With the certain type of initial EEG, the dominance of beta-rhythms compared to alpha-rhythms can indicate high anxiety levels. In 1990s, some publications described the interrelationship of cerebral asymmetry and emotional condition of controls and depressive patients.

It was demonstrated that healthy people responded to visual emotional stimuli by frontal asymmetry of bioelectric activity in the range of 8-12 Hz. (The question of whether frontal asymmetry in this range is alpha-rhythm is still open. However, for the sake of shortness, we will further use the term "alpha-activity" or "alpha-rhythm" for this phenomenon, as it is accepted in special literature on biofeedback). Negative stimuli cause the dominance of left-sided frontal alpha-rhythms. On the contrary, positive images cause the dominance of right-sided frontal alpha-rhythms. It was confirmed that depressive patients with dominating left-sided frontal alpha-rhythms can learn to redistribute frontal alpha-rhythms to make right frontal lobule more alpha-active than the left one. In this case, depression becomes less massive and the need of antidepressant drugs decreases.

There is a number of works describing electrogenesis of EEG rhythms. The existing data allow to consider that the deficiency of endorphine and some other neuromediators occurring in abstinent drug addicts is tied to malfunctioning CNS structures (primarily amygdala and hyppocampus) responsible for the starting and sustaining of drug lure. The abated functioning of these structures shows itself in decreasing power of alpha-rhythms. The increase of alpha-rhythms which is achieved during biofeedback treatment is accompanied by the restructuring of pathological cortex and subcortex ties, mobilization of endorphines and, possibly, changes in electrosensitivity of opiate mediators as a result of changing brain's "electric condition" [8].

2.2. Psychological and behavioral aspects of the problem.

As it was shown above, Neurofeedback can be used for learning drug addicts to change their psychoemotional condition. On the one hand, biofeedback's clinical effect is achieved through its influence on CNS mechanisms responsible for the beginning and sustaining of the addictive condition. On the other hand, this influence is produced by the patient independently, with the computer being essentially only the controller responsible for assessing correctness and effectiveness of the process of learning new skills. These new skills are the attributes of effective behavior. Considering the process of alpha-stimulating training (which is basic for drug addict therapy) as a form of learning, certain key function systems can be determined. These systems are presented at the scheme:

1. Receiving authentic information on one's own condition;
2. Planning the activity (selecting a strategy of achieving the training purpose);
3. The activity - alpha-stimulating training;
4. Assessing results: subjective assessment of the achieved emotional condition and objective stating of neurophysiological changes (based on BOSLAB records);
5. Assessment-based planning of more effective behavior aimed at developing greater comfort.

The drug addict enters a special virtual environment in which he or she faces the problem of planning his or her independent activity, makes efforts to achieve psychological comfort and objectively assesses his or her own activity, or does all things which people with addictive disorders normally cannot do. Thus, alpha-stimulating training leads not only to improving the emotional condition of patients but to radical changes in their behavior. The ratio of addictive aspects of behavior and medical effects of virtual environment is illustrated by Table 1.

Table 1. Improving addictive behavior as a result of virtual environment.

3. Materials and methods.

As this paper illustrates a relatively new method of drug addiction therapy, we decided to waver the traditional plan used when writing papers on this subject and start from describing the biofeedback method of opiate addiction therapy.

3.1. Neurofeedback technology for addictive disorders treatment

Neurofeedback for treating addictive disorders consists of several components. The most important of them will be described below.

3.1.1. The assessment of the initial psychoemotional condition of the patient, the defining of his or her resources and the planning of the training course’s duration and its potential effectiveness in the concrete clinical case.

We believe that Neurofeedback is a peculiar instrument in the hands of a drug addict therapist or psychologist. This instrument can be introduced in any personal approach to addiction therapy. Therefore, we will not impose on clinics any specific set of techniques of pathopsychological study. In our work, we use the so-called Szondi’s test [9] which allows quantitative assessment of a series of subconscious motives in people with opiate addiction, such as the self-destruction motive, the oral condition level, etc. More detail on this item can be found in our previous papers [10, 11, 12].

Currently, one can speak of existing «ineffectiveness predictors» of the use of Neurofeedback in treating opiate addicts. Factors influencing its effectiveness include neurological case history and specific premorbid personal traits of the patient. These factors are described in detail in the section «Biofeedback effectiveness in treating opiate drug addiction»

The duration of treatment of opiate addicts can not be lesser than 20-30 takes. As a rule, first 15-20 training sessions are conducted on a daily basis, and then in alternate days. Usually we sign an agreement providing for a 6-month program within which the patient receives 60, 80 or more takes. In the heaviest cases it is sensible to have two training sessions daily. The number of sessions in the treatment of alcohol addicts in the drinking period is somewhat less; normally we instruct people to take first 10-15 sessions on a daily basis and the following 15-25 sessions on alternate days. In many cases, patients with high motivation level and retained self-criticism insist on amassing the number of sessions. In general, the therapist must take a flexible approach to the treatment schedule as he or she ought to consider psychoemotional condition of the patient (for example, offer the patient to meet more frequently before the expected drinking period).

3.1.2. Brain mapping (quantitative EEG graphs) and determining the optimal scheme of assembling sensors.

The assessment of space-power characteristics of brain before initiating treatment allows to determine optimal position of sensors. Working with the patient abstaining from drug-taking for at least 3 or 5 days we expect to see the typical picture of distribution of bioelectric activity. Power of alpha-rhythms in the right semisphere is less than or equal to power of alpha-rhythms in the left semisphere (Fig. 3).

Fig.3. Results of brain mapping in patients with opiate addiction before and after treatment. Maps show changes of weighted power (mkV2/Hz) of alpha-rhythms in a patient with the 4-year span of heroin addiction before the beginning and after 60 sessions of Neurofeedback. Maps are drawn as a result of averaging five 10-sec epochs. Arrows show sensors F4 and O2 (according to the accepted EEG 10-20 scheme). The left map shows distribution of alpha-rhythms typical of depressive patients. As a result of treatment, redistribution of alpha-rhythms occurs and the right semisphere becomes more alpha-active. Distribution of alpha-rhythms presented in Fig. 3 dictates using bipolar probes on F4 and O2 or monopolar probes on O2 and F4. Most frequently, Neurofeedback is conducted in one of these two ways. Bipolar probes are preferable as this method provides for achievement of the minimal number of deviations during the training.

In the case when multi-channel brain mapping is impossible for some reasons, similar data can be obtained using BOSLAB. Evidently, while mapping the drug addict’s brain, we must be confident that the patient has not taken the drug. To make sure of it, we use standard monoclonal brief tests

3.1.3. Bioelectric activity monitoring (behavior supervision)

Any session begins with monitoring bioelectric activity of brain. The values of power of rhythms recorded by means of monitoring allow, on the one hand, assess psychoemotional condition of the patient, and, on the other hand, obtain information on whether the patient has taken drugs or not. Typical change of monitoring results during the alpha-stimulating training session can be seen in Fig. 4.

3.1.4. Alpha-stimulating training

Alpha-stimulating training is the basic component of treating addictive disorders in general and opiate addiction in particular.

Fig. 7. During the session of alpha-stimulating training at the 6th and 7th minutes, the ratio of power of alpha and beta-rhythms rapidly changes which indicates the emerging condition of psychoemotional comfort. At the 10th minute the patient falls asleep.

The training is conducted as follows. The patient sits in the chair with closed eyes and follows the therapist’s instructions to achieve greater frequency of biofeedback audio signals. When alpha-rhythm power exceeds the level fixed by the therapist before the beginning of the take, the biofeedback signal emerges. Examples of effective and ineffective alpha-training sessions can be found in Figs. 6 and 7.

Fig. 6. The effective session of the alpha-stimulating training. Alpha-activity increases up to the 20th minute of the session. Power of theta and beta-rhythms remains unchanged.

That the therapist recommends patients to imagine certain pictures in order to stimulate alpha-rhythms and achieve comfortable condition is an important aspect of alpha-training. Working with these images is directly connected with the psychotherapeutic work in which the patient is involved.

3.1.5. EMG training and computer relaxation games.

Let’s discuss in greater detail some more components of this addictive disorder treatment technology. EMG control and relaxation computer games play a substantial role in addiction therapy. When starting the work with the addict we feel it important to show the patient that he or she is capable of at least slightly changing his or her emotional condition. It is widely accepted that psychoemotional tension changes tonus of muscle groups and stimulates the sympatho-adrenal system leading to greater sistolic frequency. Myographic training aimed at short-cutting muscular tonus (primarily face) and our computer games place the patient in a specific environment where one should either weaken the tension of a certain group of muscles (myographic training) or decrease sistolic frequency. Practice has shown that training drug addicts in relaxation skills with the help of these components is easier than exposing them to EEG. Therefore, games and myographic training in the beginning of the course can take up to 50 and more per cent of time of the treatment session. Whereas the patient develops confidence that he or she can change psychoemotional condition, alpha-stimulating training starts playing the leading role in the treatment program as it can learn the patient to optimize CNS functions responsible for the emergence and sustaining of drug-taking.

3.2. Patients.

The authors faced the problem of developing the addictive treatment technology and testing its effectiveness. To achieve this goal, we needed a relatively solid slab of addictive patients. We selected opiate addicts.

Under survey were 191 people (154 males and 46 females) who volunteered for help at the outpatient department of the Institute of Molecular Biology and Biophysics of the Siberian Department of the Russian Academy of Medical Sciences. The age of the patients averaged 24.1 years. In 175 cases, abstinence was cut by the beginning of biofeedback treatment. Before 1999, the basic technique of abstinence-cutting was efferent therapy (primarily plasmopheresis) combined with the use of moderate doses of neuroleptics and amitriptillin. Starting from 1999, it changed to general controlled hyperthemy which allowed to undertake rehabilitation within 48-72 hours after the last drug take.

The necessary precondition of biofeedback was full refusal from drugs, alcohol, and any psychothropic aids.

Patients with opiate addiction enrolled in the study were not a solid group, primarily because of differing drug-taking span and type of the drug which caused addiction. Difference between the patients who asked help in 1996 and 2000 are shown in Table 2.

Table 2. Age and anamnesis differences of opiate addicts who voluntarily asked help at the Institute of Molecular Biology and Biophysics in 1996 and 2000

All patients were divided into groups depending on specific premorbid personal traits and discernible CNS pathology. For the latter, we considered cases of perinatal injuries which required durable treatment at neuropathologist’s in early years, concussion and brain bruises, and post-overdose coma. The patient was put into a certain group based on a clinical questionnaire filed by him or her and family. We believe that the attempt of identifying premorbid personal peculiarities in patients with 6-year-long span is ineffective.

By analyzing neurological case history, we divided all patients into two groups: with signs of discernible organic brain pathology and without them.

It appeared that frequency of biofeedback rejections depended on the group. The greatest rejection frequency was showed by stenic and astenic patients, the lowest one – by hysterical and unstable persons. From 45 patients who undertook the full course, 26 people (42.6 %) had dependent personality disorders which seems to explain the high degree of willingness to be treated in this group. The group which did not have any specific premorbid personal traits dropped out in 30 % of cases. The high degree of willingness of the hysterics to engage in biofeedback most possibly could be explained by the «magical» influence of environment (computer, a number of head sensors, etc.) We analyzed the influence of neurological peculiarities on the degree of willingness to undertake treatment and showed that people with discernible CNS disorder were poor candidates for biofeedback treatment (especially when biofeedback is combined with psychotherapy without medication treatment). The reasons, in our opinion, are evident and do not require any special discussion.

4. Biofeedback effectiveness in treating drug addiction

4.1. Clinical efficiency

Table 4 shows personality influence on biofeedback effectiveness. Remission lasting for more than a year was reported in 21.9 % of patients. With patients who dropped out prior to the 10th session (i.e. before first self-regulation skills emerged) not included, general biofeedback efficiency was 31.1%.

Group 1 developed remission in 26.3 % of the number of patients who undertake the full treatment course.

Group 2 consisted mostly of dependent, schizoid and schizophrenic persons who undertake the full treatment course and developed remission in 36.8% of cases.

Table 4. Personal traits of patients and biofeedback effectiveness

As was said above, hysteric and pathologically dependent people demonstrated the greatest willingness to participate in the training course. Hysteric patients showed moderate biofeedback effectiveness (26.3 % of the total). Group 3 (unstable patients) proved to be less prospective candidates for treatment: only 11 % reported remission for over than one year. It is possible that the real effect in this group was even lesser because it was hard to track drug-addictive behavior of all three patients with asocial disorders.

In the group of patients without specific premorbid personal traits, the training effectiveness was the greatest. It equaled 29.7 % and 52.4% of the total number of patients and those who took the full course, respectively.

All this allows us to make the following conclusion: Neurofeedback is effective for treating opiate addiction when combined with psychotherapeutic technologies.

5. Clinical example

Patient O., 30 years. From case history: he periodically smoked hashish from 16 and took opiates for the first time at 24. From 25 years, he took the drug daily 2-3 times per 24 hours but daily doses were relatively small. However, he continued working as a handwork at the private enterprise governed by his mother. To cut abstinent syndrome, the patient was exposed to general controlled hyperthermy on 10.30.99.

At the meeting with the therapist on 11.01.1999, he reported general weakness and fear of the forthcoming insomnia. Also he was scared of meeting friends with whom he had taken drugs. He believed he was a weak person and said he was capable of nothing. He asked to give him soporifics or «program» him.

The results of Luscher’s test, the test of the degree of self-control, and Sondi’s test showed that the man was not confident in himself, wanted rest, comfort, and protection, experienced somatic discomfort, wanted understanding and cooperation of kin, showed low level of internal features, needed linkage with the object symbolizing Mother, and showed the presence of the so-called «undefining Ego».

Real initial positions of the patient were assessed as weak because of personal traits (linkage with the object symbolizing Mother and the presence of «undefining Ego»), purely external motivation to drop the drug, the low level of internal features, bias to «passive» treatment technologies («programming», etc), long addiction span (14 years), etc.

The first 10 sessions were conducted daily, and the following 20 – 2 or 3 times per week. To carry out the training, monopolar electrodes were attached to section F4. The initial picture which determined the gauge selection is showed in Fig. 8.

Fig. 8. Initial distribution of power of alpha-rhythms in an opiate addict registested using BOSLAB.In accordance with the current views, if the left-sided frontal power exceeds or equals the right-sided one, the brain works in a «depressive mode». In this case the patient should increase the alpha-rhythm power in the right frontal lobule.


The progress of the ratio of power values of alpha-rhythms registered in monitoring and training sessions during neurofeedback course is demonstrated in Fig. 9.

Fig. 9. Ratio of power values of alpha-rhythms in an opiate addict registered during training sessions and monitoring of bioelectric activity.

In first treatment takes, alpha-rhythms dominated. They were recorded when monitoring bioelectric activity and were compared with alpha-rhythms during sessions of alpha-stimulating training. This resulted from the absence of the skill of redistributing bioelectric activity. The necessity of carrying out a new and completely unusual task (optimizing his own psychoemotional condition) leads to emerging psychic tension and lowering power of alpha-rhythms. During session 6, after not lengthy rise of alpha-activity (which, as was said earlier, is accompanied by the decreasing anxiety level), the patient fell asleep which led to a rapid decrease in power of alpha-rhythms. During the course, such episodes took place repeatedly. By this moment, the mood became more stable and normal sleep restored. Starting from session 9, the patient learned to increase alpha-activity while remaining awake (which is essentially the goal of the training). After session 20, the increase of background activity was reported; it exceeds the initial level which may indicate stabilization of psychoemotional condition. The results of the study of cerebral asymmetry within 1 and 4 months after the start of the treatment course are given in Fig. 10.

6. Conclusions

The wide-spread character of addictive behavior as a response to aggressive social environment requires the development of new universal approaches to addiction therapy. Attempts to treat patients «from alcohol», «from drugs» and so on in the best case lead to patients’ driving from one addiction to another. Biofeedback is one of universal approaches to addiction therapy. Currently, it is the sole technology, which makes the addict not the «passive object of treatment» but the active participant of the treatment process. Participation helps develop skills normalizing CNS functions responsible for emerging and sustaining addictive disorders. The biofeedback technology creates virtual environment in which the patient learns new skills of effective behavior independently of a specific addictive disorder he had. Combined with person-oriented psychotherapy, biofeedback is a powerful means of treating most dangerous addictive disorders, which are alcohol and drug addiction.

7. Literature

1. Peniston, E.G. & Kulkosky, P.J. Neurofeedback in the treatment of addictive disorders. In: Introduction to quantitative EEG and neurofeedback (Evans J.R., Abarbanel A., eds.) Academic Press. 1999. – P.157-179.

2. Peniston, E.G. & Kulkosky, P.J. Alcoholic Personality and alpha-theta brain wave training// Med. Psychother. 1990. 3. – P. 37-55.

3. Peniston, E.G. & Kulkosky, P.J. Alpha-theta brain wave training and beta-endorphin levels in alcoholics//Alcohol. Clin. Exp. Res. 1989. 13. – P. 271-279.

4. Davidson R.J. Cerebral asymmetry, emotion and affective style. In: Brain Asymmetry (R.J.

Davidson & Hugdahl, eds.) The MIT Press, Cambridge, MA. 1995. - P. 369-388.

5. Rosenfeld J. P. EEG biofeedback of frontal alpha asymmetry in affective disorders// Biofeedback. 1997. 25(1). – P. 8-25.

6. Baehr E., Rosenfeld J.P., Baehr R., Earnest C. Clinical use of an alpha asymmetry neurofeedback protocol in the treatment of mood disorders. Introduction to quantitative EEG and neurofeedback. 1999. – P. 181-201.

7. Robinson R. G., Kubos K.L., Starr L. B., Rao K., Prise T.R. Mood disorders in stroke patients: Importance of location of lesion// Brain. 1984. 107. – P. 81-93.

8. Джос В.В.

9. Шубина О.С. Биоуправление в лечении дистимических расстройств, сочетанных с психосоматической патологией. Автореф. дис. канд. мед. наук. Новосибирск. 1997 (in Russian).

10. Скок А.Б. Использование биологической обратной связи для целенаправленного изменения поведения пациентов с аддиктивными расстройствами. Автореф. дис. канд. мед. наук: Новосибирский медицинский институт. Новосибирск. 1999(in Russian).

11. Завьялов В.Ю., Скок А.Б., Штарк М.Б., Шубина О.С. Динамика психофизиологических аспектов аддиктивного поведения в процессе использования альфа-стимули-рующего тренинга// Бюлл. СО РАМН. Новосибирск. 1999. – С. 39-47 (in Russian).

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