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

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Abstract

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.

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About the authors

S. А. Galkin

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

Author for correspondence.
Email: s01091994@yandex.ru
Russian Federation, Tomsk

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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.

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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.

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