Persistent impacts of smoking on resting-state EEG in male chronic smokers and past-smokers with 20 years of abstinence

Abstract

Smoking is a severe addictive health risk behavior and notorious for the high likelihood of relapse after attempted cessation. Such an addictive pattern in smoking has been associated with neurobiological changes in the brain. However, little is known whether the neural changes associated with chronic smoking persist after a long period of successful abstinence. To address this question, we examined resting state EEG (rsEEG) in chronic smokers who have been smoking for 20 years or more, past-smokers who have been successfully abstaining for 20 years or more, and never-smokers. Both current-smokers and past-smokers showed significantly decreased relative theta power than never-smokers, showcasing persistent effect of smoking on the brain. Other rsEEG features in alpha frequency band demonstrated distinctive patterns associated with active smoking, such that compared to never-smokers, only current-smokers, but not past-smokers, showed significantly higher relative power, EEG reactivity-power changes between eyes-closed and eyes-open conditions-, and coherence between channels. Furthermore, individual variabilities across these rsEEG biomarkers were accounted for by individuals' self-reported smoking history and nicotine dependence in current- and past- smokers. These data suggest the persistent effect of smoking on the brain even after sustained remission for 20 years.

Figure 1

Relative EEG band power (eyes-closed condition). (A) Each row illustrates the average relative band powers calculated in each group. (B) Statistical group differences in relative band powers were observed in theta and alpha bands, particularly between the Smoker and the Control groups. Specifically, never-smoker controls showed larger relative power in theta band compared to the smoker and past-smoker groups, while smaller power in alpha band than the smoker group. The channels that showed a significant group difference are marked with larger unfilled markers (P_FDR < 0.05).

Figure 2

EEG reactivity between the eyes-closed and the eyes-open conditions. (A) Each row illustrates average EEG reactivity measures calculated in each group, for each frequency band. (B) Statistical group differences in EEG reactivity were observed in alpha band. Particularly, smokers showed larger alpha reactivity compared with past-smokers or non-smoking controls. The channels that showed a significant group difference are marked with larger unfilled markers (P_FDR < 0.05).

Figure 3

Group differences in alpha band coherence. (A) Alpha band coherence between EEG channels was calculated in each group. (B) Significant group differences in alpha band coherence were observed most distinctively between the Smoker and the Control groups. (C) For an illustrative purpose, all significant group differences in inter-channel coherences are depicted with three different thickness levels (P_FDR < 0.05, 0.01, and 0.001).

Figure 4

Correlations between EEG features and individuals’ smoking-related characteristics. Pearson’s correlation coefficients were calculated to examine the association between EEG characteristics (relative EEG band powers and coherence) and individuals’ smoking history measures (cigarettes per day, FTND, and number of quit attempts). (A) Across smokers and past-smokers, individuals who have smoked more (or had smoked more in the past for past-smokers) showed lower theta band power, (B) but higher alpha coherence. (A) In addition, individuals who self-reported higher nicotine dependence (FTND score) showed lower theta band power, and who had more quit attempts showed higher theta power. Each dot represents an individual participant, and the color-coded lines are the regression line between the corresponding measures.