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SMOKING ADDICTION
The role of the anterior cingulate cortex and insula in smoking and treatment with neurobiofeedback: A new approach.
Smoking, an addictive behavior, is the leading cause of disease and death in the developed world<sup>1</sup>. However, many smokers find it difficult to quit, even though they know the consequences can be severe.<sup>2-3</sup>
The American Psychiatric Association's DSM classifications and the US Department of Health's reports on nicotine dependence also highlight the highly addictive effects of nicotine.
Quitting smoking can be quite difficult, and even after quitting, the cravings they experience make relapse very high.
The difficulty in quitting and the high relapse rate appear to be due to long-term changes in specific deep brain subsystems. One of the most challenging aspects of quitting smoking is the intense craving and risk of relapse that arise after making the decision to quit.
Animal models have shown that changes in regions such as the amygdala, nucleus accumens, and mesothelencephalic dopamine system promote the self-administration of harmful drugs 4-5-6. Since craving for cigarettes is a significant factor leading to relapse among smokers trying to quit, controlling this craving may help in quitting smoking.
The anterior cingulate cortex (ACC) is part of the brain's limbic system. Based on lesion studies in animals and humans, this region has been shown to be associated with affect in both humans and animals. Based on EEG studies, a focal negativity develops after an error response, leading to the theory that the ACC may play a role in the brain's error detection and control.
Neuropsychological studies show that the cognitive version of Counting Stroop activates the cognitive subdivision of Emotional Counting Stroop, while activating the emotional subdivision.
The cognitive subdivision is part of the distributed attention network, maintaining strong interconnections with the lateral prefrontal cortex (BA 46/9), parietal cortex (BA 7), and premotor and complementary motor areas. On the other hand, emotional dissociation (ACad) is activated by affect-related tasks in normal voluntary work related to emotional processing and when symptoms are triggered in psychiatric disorders (anxiety, simple phobia, and obsessive-compulsive disorder).
Furthermore, it has been observed to be repeatedly reactivated by triggering sadness in both normal individuals and those with major depression. The emotional subdivision is linked to the amygdala, periaqueductal gray, nucleus accumbens, hypothalamus, anterior insula, hippocampus, and orbitofrontal cortex. Functional imaging studies show that areas such as the cingulate cortex, anterior cingulate cortex, orbitofrontal cortex, and insula are activated in the presence of drug-related treatments. 8-9
INSULA
The insula is of particular interest due to its potential role in conscious drive. It has been suggested that this area functions in conscious emotional feelings through its role in representing bodily (internal) states. 10-11-12 Subjective drug drives induced by a cue have been shown to be associated with activity within the insula on both sides of the brain during a simple decision-making task related to relapse into drug use. 13-14-15 During a simple decision-making task, a high amount of activity associated with drug use was observed in the right insula. 16
Interestingly, damage to the insulin can lead to a loss of the desire to smoke.
According to Dr. Antoine Bechara and colleagues from the University of Southern California and the University of Iowa, patients with insula damage were able to quit smoking quickly, easily, and without relapse, compared to patients with other types of brain damage.
Many methods exist for quitting smoking. These range from nicotine replacement therapy to psychotherapy, and various personal development and behavior modification programs. However, their success rates are quite low. Completely abstaining from smoking may require several attempts, and even then, those who quit may still experience problems with quitting.
Although 70% of smokers report wanting to quit, only 5% report being able to do so. The relapse rate is over 70%.<sup>17</sup>
Quitting Smoking Behaviors in US Adults 17 Most adult smokers want to quit smoking. In 2015, 68% (22.7 million) of adult smokers said they wanted to quit smoking.
More than half of adult smokers report trying to quit smoking in the past year. In 2018, 55.1% (21.5 million) of adult smokers said they had tried to quit smoking in the past year.
Fewer than one in ten adult smokers manage to quit each year. In 2018, 7.5% (2.9 million) of adult smokers successfully quit smoking. Of the nine adult smokers who consulted a health professional last year, four did not receive advice to quit.
Less than one-third of adult smokers use cessation counseling or medications approved by the Food and Drug Administration when trying to quit smoking.<sup>1</sup> In 2015, 31.2% (7.6 million) of adult smokers reported using counseling or medication when trying to quit smoking.
In 2015, 6.8% (1.7 million) of adult smokers reported using counseling, 29% (7.1 million) reported using medication, and 4.7% (1.1 million) reported using both counseling and medication while trying to quit smoking.
In 2015, 57.2% (18.8 million) of adult smokers who consulted a health professional the previous year reported receiving advice to quit smoking.
Even a short (<3 minute) recommendation from a doctor to quit smoking increases quitting rates and is extremely cost-effective. More than three out of five adults who have smoked have quit. In 2018, 61.7% of adult smokers (55.0 million adults) quit smoking.
Tobacco Cessation Behaviors Among American Youth
About two-thirds of young tobacco users report wanting to quit smoking in the past year, and almost two-thirds say they tried to quit.
In 2021, 65.3% of young people (middle and high school students) who were currently using tobacco products reported that they were seriously considering quitting all tobacco use.
In 2021, 60.2% of young people who were currently using tobacco products reported that they had stopped using all tobacco products for one day or more in the past year because they were trying to quit.
Daily pressures, environmental factors such as the smell of cigarette smoke, and other environmental factors and triggers can cause strong cravings as well as pleasant memories, making it difficult to resist smoking.
This is why most smoking cessation programs encourage people to avoid triggers, reduce stress, and find alternatives to smoking.
NEUROFEEDBACK
Instead of saying "Don't smoke," regulate the brain cycle that produces the urge to smoke.
Neurofeedback (NF) is one of the methods found to be effective in changing addictive behavior. NF is an operant conditioning paradigm in which patients are given conditioned auditory/visual rewards to produce specific patterns of brainwave activity.
Since the 1960s, studies have shown that NF patients can be taught to improve normal brain function by normalizing dysfunctional brainwave patterns characterized by excessively slow wave activity, or by normalizing patterns that deviate from age-related norms.
18-19 NF provides the user with real-time feedback about brainwave activity, usually in the form of video and audio. The goal is to provide the Central Nervous System (CNS) with real-time information about its current activity.
For example, people are asked to increase their beta or sensorimotor rhythm (SMR) and decrease their delta and theta rhythms. When the desired paradigm is achieved, the patient is rewarded with a moving image and/or sound. This is operant conditioning.
Studies using EEG neurofeedback have shown positive effects on medication use, treatment adherence, and signal reactivity in patients with cocaine and alcohol dependence.
Neurological feedback provided via real-time functional magnetic resonance imaging (rtfMRI) can facilitate self-regulation of internal states by providing individuals with feedback from localized regions of interest (ROIs) while performing a task.
Son yıllarda, rtfMRI geri bildirimi, ağrı 30 , depresyon 31 ve Dikkat Eksikliği ve Hiperaktivite Bozukluğu (DEHB) 32-33 ile ilişkili beyin aktivasyonunun modülasyonunu kolaylaştırarak terapötik potansiyelini ortaya koymuştur.
Cantebery ve ark. 34, önemli bir istek bölgesi olan anterior singulat korteksten (ACC) gelen nörogeribildirim (NF) kullanarak, bir seans da beyin aktivasyonuyla ilgili arzuyu ve kendi bildirdiği arzuyu azaltmayı başardılar 35 .
Başka bir çalışmada, ACC'deki istekle ilgili ilgi alanından (ROI) gelen geri bildirim, medial prefrontal korteks üzerindeki bir ROI'nin eşzamanlı dirençle ilgili birden fazla seanslık geri bildiriminden daha etkiliydi.
36 Bu, sigara içenlerin sigarayı bırakmak için beyinlerinin tepkilerini ve sigara içme ipuçlarına yönelik davranışlarını etkili bir şekilde modüle etmek için geri bildirim yöntemlerini kullanabileceğini ve istekle ilgili alanlardaki (örn. ventral anterior singulat korteks (vACC)) aktiviteyi azaltmanın, aktiviteyi arttırmaktan daha etkili olduğunu göstermektedir.
Bunlara direnç gösteren bölgelerde (örn. dorsal medial prefrontal korteks (dmPFC)) bağımlılığın şiddeti, şiddetli istek ve sigarayı bırakma sonuçlarıyla ilişkilidir 37-38 ve sigara içme ipuçlarına maruz kalma sırasında ACC aktivasyonuyla bağlantılıdır.
29-40 Bu nikotin bağımlılığının şiddetinin, düşük ila orta derecede nikotin bağımlısı sigara içenlerin, istekle ilişkili aktivasyonu azaltmak için ACC 'yi hedefleyen nörogeribildirim kullanabileceği nörogeribildirim yanıtını etkileyebileceğini göstermektedir.
18-19-20 Biofeedback (BF) 41-42 ve/veya NF eğitimi, öğrenilen koşullanmayı davranışa aktaran otonom ve/veya merkezi sinir sistemi aktivitesinin modülasyonunu kolaylaştırmaktadır. Verilen eğitime bağlı nöroplastik değişiklikleri araştıran çalışmaların çoğu, esas olarak azaltma veya kontrolü amaçlayan sigara içme isteğine verilen yanıtın modülasyonuna odaklanmıştır. Böylece davranışla ilgili sonuçlar arasında istek azalması, nikotin bağımlılığı şiddetinin azalması ve psikiyatrik semptomların azalması görülmektedir. 43-44
STUDIES SHOW THAT NEUROFEEDBACK THERAPY HAS A SUCCESS RATE OF 76-80% IN NICOTINE ADDICTION.
The clinical value of neurofeedback here is that instead of simply telling the person "don't smoke," it helps reorganize the brain cycle that produces the urge to smoke. The goal is for the brain to learn to produce a more regulated and controlled response when the person encounters the trigger, instead of automatically reverting to their old smoking pattern.
Therefore, neurofeedback can be considered an important neurophysiological intervention option in smoking addiction for craving, impulse control, stress management, attention regulation, and relapse prevention.
In conclusion, nicotine addiction is not merely a chemical dependence on nicotine; it is a complex brain pattern involving the combined action of the anterior cingulate cortex, insula, prefrontal control networks, reward system, and bodily sensations.
Without a change in this pattern, it can be difficult for a person to quit permanently through willpower alone. Neurofeedback, however, offers a new and promising approach to treating smoking addiction by allowing the brain to learn its own activity and develop healthier regulation patterns.
Source
Kaynakça 1. Peto R, Lopez AD, Boreham J, Thun M, Heath C Jr. Lancet. 1992; 339:1268. [PubMed: 1349675] 2. American Psychiatric Association (A.P.A.). Diagnostic and Statistical Manual of Mental Disorders Text Revision: DSM-IV-TR. 4. A.P.A; Washington, DC: 2000. p. 191-296. 3. U.S. Department of Health and Human Services. 1988 Surgeon General’s Report: The Health Consequences of Smoking: Nicotine Addiction. Vol. chap 6. U.S. Government Printing Office;Rockville, MD: 1988. p. 377-458. 4. Allen, S. S., Bade, T., Hatsukami, D., & Center, B. (2008).Craving, withdrawal, and smoking urges on days immediately prior to smoking relapse. Nicotine & Tobacco Research, 10, 35–45. 5. Ferguson, S. G., & Shiffman, S. (2009). The relevance and treatment of cue-induced cravings in tobacco dependence.Journal of Substance Abuse Treatment, 36, 235–243. 6. Killen, J. D., & Fortmann, S. P. (1997). Craving is associated with smoking relapse: Findings from three prospective studies. Experimental and Clinical Psychopharmacology, 5, 137–142. 7. Vogt, B.A. et al. (1992) Functional heterogeneity in cingulate cortex:the anterior executive and posterior evaluative regions. Cereb. Cortex 2, 435–443 8. Grant S, et al. Proc Natl Acad Sci USA. 1996; 93:12040. [PubMed: 8876259] 9. Myrick H, et al. Neuropsychopharmacology. 2004; 29:393. [PubMed: 14679386] 10. Damasio AR, et al. Nat Neurosci. 2000; 3:1049. [PubMed: 11017179] 11. Damasio, AR. The Feeling of What Happens: Body and Emotion in the Making of Consciousness. Harcourt; Chicago: 2000 12. Craig AD. Nat Rev Neurosci. 2002; 3:655. [PubMed: 12154366] 13. Bonson KR, et al. Neuropsychopharmacology. 2002; 26:376. [PubMed: 11850152] 14. Brody AL, et al. Arch Gen Psychiatry. 2002; 59:1162. [PubMed: 12470133] 15. Wang GJ, et al. Life Sci. 1999; 64:775. [PubMed: 10075110] 16. Paulus MP, Tapert SF, Schuckit MA. Arch Gen Psychiatry. 2005; 62:761. [PubMed: 15997017] 17. https://www.cdc.gov/tobacco/data_statistics/fact_sheets/fast_facts/index.htm https://www.cdc.gov/tobacco/data_statistics/fact_sheets/cessation/smoking-cessation-fast- facts/index.html 18. Lubar JF. Discourse on the development of EEG diagnostics and biofeedback for attention-eficit/hyperactivity disorders. Biofeedback Self Regul. 1991;16(3):201–225. 19. Thompson L, Thompson M. Neurofeedback combined with training in metacognitive strategies: effectiveness in students with ADD. Appl Psychophysiol Biofeedback. 1998;23(4):243–263. 20. Peniston,E.G.,andKulkovsky,P.J.(1999).“Neurofeedback in the treatment of addictive disorders,”in Introduction to Quantitative EEG and Neurofeedback, eds A.Abarbarnel and J.R.Evans (London:AcademicPress),157–179.doi:10.1016/B978-012243790-8/50008-0 21. Scott,W.C.,Kaiser,D.,Othmer,S.,andSideroff,S.I. (2005). Effects of an EEG biofeedback protocol on a mixed substance abusing population. Am.J.Drug Alcohol Abuse 31, 455–469.doi:10.1081/ADA-200056807 22. Sokhadze,T.M.,Cannon,R.L.,and Trudeau,D. L. (2008).EEG-Biofeedback as a treatment for substance use disorders: review, rating of efficacy and recommendations for further research. Appl. Psychophysiol. Biofeedback 33, 1–28.doi:10.1007/s10484-007-9047-5 23.Arani FD, Rostami R, Nostratabadi M. Effectiveness of neurofeedback training as a treatment for opioid-dependent patients.Clin EEG Neurosci. 2010 Jul;41(3):170-7. 24. Dehghani-Arani F, Rostami R, Nadali H.Neurofeedback training for opiate addiction: improvement of mental health and craving. Appl Psychophysiol Biofeedback. 2013 Jun;38(2):133-41. 25. Li X, Hartwell KJ, Borckardt J, Prisciandaro JJ, Saladin ME, Morgan PS, Johnson KA, Lematty T, Brady KT, George MS. Volitional reduction of anterior cingulate cortex activity produces decreased cue craving in smoking cessation: a preliminary real-time fMRI study. Addict Biol. 2013 Jul;18(4):739-48. Epub 2012 Mar 28. 26. Hartwell KJ, Prisciandaro JJ, Borckardt J, Li X, George MS, Brady KT.Real-time fMRI in the treatment of nicotine dependence: a conceptual review and pilot studies.Psychol Addict Behav. 2013 Jun;27(2):501-9. doi: 10.1037/a0028215. Epub 2012 May 7. Review. 27. Hartwell KJ, Lematty T, McRae-Clark AL, Gray KM, George MS, Brady KT. Resisting the urge to smoke and craving during a smoking quit attempt on varenicline: results from a pilot fMRI study. Am J Drug Alcohol Abuse. 2013 Mar;39(2):92-8. doi: 10.3109/00952990.2012.750665. 28. Hanlon CA, Hartwell KJ, Canterberry M, Li X, Owens M, Lematty T, Prisciandaro JJ, Borckardt J, Brady KT, George MS. Reduction of cue-induced craving through realtime neurofeedback in nicotine users: the role of region of interest selection and multiple visits. Psychiatry Res. 2013 Jul 30;213(1):79-81. Epub 2013 May 15. 29.Weiskopf N. Real-time fMRI and its application to neurofeedback.Neuroimage. 2012 Aug 15;62(2):682-92. doi: 10.1016/j.neuroimage.2011.10.009. Epub 2011 Oct 14. Review. PMID: 22019880 30. deCharms RC, Maeda F, Glover GH, Ludlow D, Pauly JM, Soneji D, Gabrieli JD, Mackey SC.Control over brain activation and pain learned by using real-time functional MRI. Proc Natl Acad Sci U S A. 2005 Dec 20;102(51):18626-31. Epub 2005 Dec 13. 31. Linden DE, Habes I, Johnston SJ, Linden S, Tatineni R, Subramanian L, Sorger B, Healy D, Goebel R. Real-time self-regulation of emotion networks in patients with depression. PLoS One. 2012;7(6):e38115. doi: 10.1371/journal.pone.0038115. Epub 2012 Jun 4. 32. Lévesque J, Beauregard M, Mensour B.Effect of neurofeedback training on the neural substrates of selective attention in children with attention-deficit/hyperactivity disorder: a functional magnetic resonance imaging study.Neurosci Lett. 2006 Feb 20;394(3):216-21. Epub 2005 Dec 15. 33. Beauregard M, Lévesque J.Functional magnetic resonance imaging investigation of the effects of neurofeedback training on the neural bases of selective attention and response inhibition in children with attention-deficit/hyperactivity disorder.Appl Psychophysiol Biofeedback. 2006 Mar;31(1):3-20. 34. Canterberry M, Hanlon CA, Hartwell KJ, Li X, Owens M, LeMatty T, Prisciandaro JJ, Borckardt J, Saladin ME, Brady KT, George MS. Sustained reduction of nicotine craving with real-time neurofeedback: exploring the role of severity of dependence. Nicotine Tob Res. 2013 Dec;15(12):2120-4. 35. Li, X., Hartwell, K. J., Borckardt, J., Prisciandaro, J. J., Saladin, M. E., Morgan, P. S., … George, M. S. (2013). Volitional reduction of anterior cingulate cortex activity produces decreased cue craving in smoking cessation: A preliminary real-time fMRI study. Addiction Biology, 18, 739–748. 36. Hanlon CA1, Hartwell KJ, Canterberry M, Li X, Owens M, Lematty T, Prisciandaro JJ, Borckardt J, Brady KT, George MS. Reduction of cue-induced craving through realtime neurofeedback in nicotine users: the role of region of interest selection and multiple visits. Psychiatry Res. 2013 Jul 30;213(1):79-81. doi: 10.1016/j.pscychresns.2013.03.003. Epub 2013 May 15. 37. Predicting smoking cessation and major depression in nicotine-dependent smokers. American Journal of Public Health, 90, 1122–1127. 38. Watson, N. L., Carpenter, M. J., Saladin, M. E., Gray, K. M., & Upadhyaya, H. P. (2010). Evidence for greater cue reactivity among low-dependent vs. high-dependent smokers. Addictive Behaviors, 35, 673–677. 39. McClernon, F. J., Kozink, R. V., & Rose, J. E. (2008). Individual differences in nicotine dependence, withdrawal symptoms, and sex predict transient fMRI-BOLD responses to smoking cues. Neuropsychopharmacology, 33, 2148–2157. 40. Smolka, M. N., Bühler, M., Klein, S., Zimmermann, U., Mann, K., Heinz, A., & Braus, D. (2006). Severity of nicotine dependence modulates cue-induced brain activity in regions involved in motor preparation and imagery. Psychopharmacology, 184, 577–588. 41. Grimsley D.L. Nicotine effects on biofeedback training. J. Behav. Med. 1990;13(3):321–326. 42. Pandria N. Exploring the neuroplastic effects of biofeedback training on smokers. Behav. Neurol. 2018;2018:1–19. 43. Pandria N, Athanasiou A, Konstantara L, Karagianni M, Bamidis PD. Advances in biofeedback and neurofeedback studies on smoking. Neuroimage Clin. 2020;28:102397. doi: 10.1016/j.nicl.2020.102397. Epub 2020 Aug 25. Erratum in: Neuroimage Clin. 2021;30:102642. PMID: 32947225; PMCID: PMC7502375. 44. Keilani M, Steiner M, Crevenna R. The effect of biofeedback on smoking cessation-a systematic short review. Wien Klin Wochenschr. 2022 Jan;134(Suppl 1):69-76. doi: 10.1007/s00508-021-01977-x. Epub 2021 Dec 6. PMID: 34870741; PMCID: PMC8825623. 45. https://cordis.europa.eu/article/id/123716-new-tools-and-practices-against-smoking- smokefreebrain-research-and-innovation-project