Electroencephalographic features of alcohol use disorders with different decision-making efficiency in risk conditions

封面

如何引用文章

全文:

开放存取 开放存取
受限制的访问 ##reader.subscriptionAccessGranted##
受限制的访问 订阅存取

详细

In order to identify the neurophysiological mechanisms underlying the violation of decision-making in risk conditions, we conducted a comparative analysis of spectral EEG indicators of patients with alcohol use disorders with different effectiveness of their decision-making in a number of cognitive tasks. As a result of the cluster analysis, two subgroups of patients were identified: with “moderate” and with “pronounced” decision-making deficit, which did not differ in socio–demographic and clinical indicators (p > 0.05). The subgroup of patients with a “pronounced” decision-making deficit differed statistically significantly lower values of the spectral power of θ- and α-rhythm in the central (p = 0.018 for θ-rhythm and p = 0.017 for α-rhythm), parietal (p = 0.031 for θ-rhythm and p = 0.014 for α-rhythm), occipital (p = 0.029 for θ-rhythm and p = 0.016 for α-rhythm) and temporal (p = 0.022 on the left and p = 0.043 on the right for α-rhythm) leads compared with patients with “moderate” decision-making deficit. Thus, in a subgroup of patients with a “pronounced” deficit of decision-making, a certain deficit of the brain’s inhibitory systems was noted.

全文:

受限制的访问

作者简介

S. Galkin

Mental Health Research Institute, Tomsk National Research Medical Center, RAS

编辑信件的主要联系方式.
Email: s01091994@yandex.ru
俄罗斯联邦, Tomsk

参考

  1. Glantz M.D., Bharat C., Degenhardt L. et al. The epidemiology of alcohol use disorders cross-nationally: Findings from the World Mental Health Surveys // Addict. Behav. 2020. V. 102. P. 106128.
  2. Yen F.S., Wang S.I., Lin S.Y. et al. The impact of heavy alcohol consumption on cognitive impairment in young old and middle old persons // J. Transl. Med. 2022. V. 20. № 1. P. 155.
  3. Galkin S.A., Bokhan N.A. [Features of the reward-based decision-making in patients with alcohol use disorders] // Zh. Nevrol. Psikhiatr. Im. S.S. Korsakova. 2023. V. 123. № 2. P. 37.
  4. Peshkovskaya A.G., Galkin S.A., Bokhan N.А. [Cognition in alcohol dependence: Review of concepts, hypotheses and research methods] // Sibirskiy Psikhol. Zh. — Siberian J. Psychol. 2023. № 87. P. 138.
  5. Maksimova I.V. [Cognitive and electroencephalographic changes in patients with alcohol dependence who suffered a seizure] // Siberian Herald of Psychiatry and Addiction Psychiatry. 2018. № 2. P. 89.
  6. Arts N.J., Walvoort S.J., Kessels R.P. Korsakoff’s syndrome: A critical review // Neuropsychiatr. Dis. Treat. 2017. V. 13. P. 2875.
  7. Brevers D., Bechara A., Cleeremans A. et al. Impaired decision-making under risk in individuals with alcohol dependence // Alcohol. Clin. Exp. Res. 2014. V. 38. № 7. P. 1924.
  8. Galkin S.A., Bokhan N.A. [Disorders of cognitive decision-making mechanisms related to reward in alcohol use disorders] // Zh. Nevrol. Psikhiatr. Im. S.S. Korsakova. 2023. V. 123. № 4. P. 98.
  9. Brevers D., Cleeremans A., Goudriaan A.E. et al. Decision making under ambiguity but not under risk is related to problem gambling severity // Psychiatry Res. 2012. V. 200. № 2-3. P. 568.
  10. Levin I., Weller J., Pederson A., Harshman L. Age-related differences in adaptive decision-making: sensitivity to expected value in risky choice // Judgm. Decis. Mak. 2007. V. 2. № 4. P. 225.
  11. Bowden-Jones H., McPhillips M., Rogers R. et al. Risk-taking on tests sensitive to ventromedial prefrontal cortex dysfunction predicts early relapse in alcohol dependency: a pilot study // J. Neuropsychiatry Clin. Neurosci. 2005. V. 17. № 3. P. 417.
  12. Iznak A.F., Medvedeva T.I., Iznak E.V. et al. Disruption of neurocognitive decision-making mechanisms in depression // Human Physiology. 2016. V. 42. № 6. P. 598.
  13. Gomez P., Ratcliff R., Perea M. A model of the go/no–go task // J. Exp. Psychol. Gen. 2007. V. 3. № 3. P. 389.
  14. Lejuez C.W., Read J.P., Kahler C.W. et al. Evaluation of a behavioral measure of risk taking: the Balloon Analogue Risk Task (BART) // J. Exp. Psychol. Appl. 2002. V. 8. № 2. P. 75.
  15. Romeu R.J., Haines N., Ahn W.Y. et al. A computational model of the Cambridge gambling task with applications to substance use disorders // Drug Alcohol Depend. 2020. V. 206. P. 107711.
  16. Bull P.N., Tippett L.J., Addis D.R. Decision making in healthy participants on the Iowa Gambling Task: New insights from an operant approach // Front. Psychol. 2015. V. 6. P. 391.
  17. Iznak A.F., Iznak E.V., Medvedeva T.I. et al. Features of EEG spectral parameters in depressive patients with different efficiencies of decision-making // Human Physiology. 2018. V. 44. № 6. P. 627.

补充文件

附件文件
动作
1. JATS XML
2. Fig. 1. Profile of the selected variants of decision-making efficiency. I — errors on the Go signal in the Go/NoGo task, II — errors on the NoGo signal in the Go/NoGo task, III — risk propensity in the BART test, IV — "alogism" in CGT, V — decision-making time in CGT, VI — choice of high-risk decks in IGT.

下载 (110KB)
3. Fig. 2. Topographic maps of the background spectral power of the electroencephalogram (EEG) in 3-frequency ranges in groups of patients with alcohol dependence, included in the 1st cluster (A) and 2nd cluster (B) according to the results of cluster analysis.

下载 (137KB)

版权所有 © Russian Academy of Sciences, 2024