eISSN: 1689-3530
ISSN: 0867-4361
Alcoholism and Drug Addiction/Alkoholizm i Narkomania
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3/2019
vol. 32
 
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Letter to the Editor

Can EEG predict the outcome of dependence treatment?

Hamidreza Famitafreshi
,
Morteza Karimian

Alcohol Drug Addict 2019; 32 (3): 243-244
Online publish date: 2019/12/18
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Despite many advances in molecular mechanism of relapse and compulsive psychoactive substance use, there is still a lack of an effective clinical method for assessing dependence and treatment. That is important because despite implementing of many effective treatments, there is still lack of an effective method for predicting the course of disease [1].
Electroencephalogram (EEG), that is the recording of electrical activity of brain neurons, can be considered as an effective diagnosing method in this regard [2]. EEG abnormalities have been observed with biofeedback modulation and without modulation. These studies use neurofeedback and then recording EEG [3]. In another study without application of modulation in abstinence period, EEG alternation as the result of cue reactivity was studied [4]. Also, learning that is important in addiction period has been monitored in treated dependent persons [5]. EEG changes have been observed in other types of addiction like food and internet addiction [6, 7].
EEG can be also used to evaluate comorbid conditions like anxiety and depression that occur in the abstinence period [8, 9]. The absence of these symptoms is associated with better prognosis.
EEG can be considered as an effective method for helping dependent persons. It is suggested more studies are conducted to describe the mechanisms that may be altered in dependence.

Conflict of interest/Konflikt interesów

None declared./Nie występuje.

Financial support/Finansowanie

None declared./Nie zadeklarowano.

Ethics/Etyka

The work described in this article has been carried out in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki) on medical research involving human subjects, EU Directive (210/63/EU) on protection of animals used for scientific purposes, Uniform Requirements for manuscripts submitted to biomedical journals and the ethical principles defined in the Farmington Consensus of 1997.
Treści przedstawione w pracy są zgodne z zasadami Deklaracji Helsińskiej odnoszącymi się do badań z udziałem ludzi, dyrektywami UE dotyczącymi ochrony zwierząt używanych do celów naukowych, ujednoliconymi wymaganiami dla czasopism biomedycznych oraz z zasadami etycznymi określonymi w Porozumieniu z Farmington w 1997 roku.

References/Piśmiennictwo

1. Vaillant GE. What can long‐term follow‐up teach us about relapse and prevention of relapse in addiction? Br J Addict 1988; 83(10): 1147-57.
2. Bauer LO. Predicting relapse to alcohol and drug abuse via quantitative electroencephalography. Neuropsychopharmacol 2001; 25(3): 332-40.
3. Scott WC, Kaiser D, Othmer S, Sideroff SI. Effects of an EEG biofeedback protocol on a mixed substance abusing population. Am J Drug Alcohol Abuse 2005; 31(3): 455-69.
4. Horrell T, El-Baz A, Baruth J, Tasman A, Sokhadze G, Stewart C, et al. Neurofeedback effects on evoked and induced EEG gamma band reactivity to drug-related cues in cocaine addiction. J Neurother 2010; 14(3): 195-216.
5. Doborjeh MG, Wang GY, Kasabov NK, Kydd R, Russell B. A spiking neural network methodology and system for learning and comparative analysis of EEG data from healthy versus addiction treated versus addiction not treated subjects. IEEE T Bio Med Eng 2015; 63(9): 1830-41.
6. Choi JS, Park SM, Lee J, Hwang JY, Jung HY, Choi SW, et al. Resting-state beta and gamma activity in Internet addiction. Int J Psychophysiol 2013; 89(3): 328-33.
7. Imperatori C, Fabbricatore M, Innamorati M, Farina B, Quintiliani MI, Lamis DA, et al. Modification of EEG functional connectivity and EEG power spectra in overweight and obese patients with food addiction: An eLORETA study. Brain Imaging Behav 2015; 9(4): 703-16.
8. Simkin DR, Thatcher RW, Lubar J. Quantitative EEG and neurofeedback in children and adolescents: anxiety disorders, depressive disorders, comorbid addiction and attention--deficit/hyperactivity disorder, and brain injury. Child Adolesc Psychiatric Clin N Am 2014; 23(3): 427-64.
9. Lee J, Hwang JY, Park SM, Jung HY, Choi SW, Lee JY, et al. Differential resting-state EEG patterns associated with comorbid depression in Internet addiction. Prog Neuro-Psychopharmacol Biol Psychiatry 2014; 50: 21-6.
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