Inhibition control impairments in adolescent smokers - Springer Link

Brain Imaging and Behavior DOI 10.1007/s11682-015-9418-0

ORIGINAL RESEARCH

Inhibition control impairments in adolescent smokers: electrophysiological evidence from a Go/NoGo study Junsen Yin 1,2 & Kai Yuan 1,2,3 & Dan Feng 1,2 & Jiadong Cheng 1,2 & Yangding Li 1,2 & Chenxi Cai 1,2 & Yanzhi Bi 1,2 & Shi Sha 1,2 & Xiaomin Shen 1,2 & Ben Zhang 4 & Ting Xue 3 & Wei Qin 1,2 & Dahua Yu 3 & Xiaoqi Lu 3 & Jie Tian 1,2,5

# Springer Science+Business Media New York 2015

Abstract Smoking during adolescence may promote nicotine dependence later on in life. Therefore, it is extremely important to study the neural mechanisms of adolescent smokers. As inhibition control is emphasized in several contemporary theoretical models of addiction, in the current study, we focused on the electrophysiological evidence of inhibition control deficits in adolescent smokers. By using relatively homogenous groups of adolescent smokers (n = 18) and matched nonsmokers (n = 18), we employed event-related potentials (ERP) to investigate the N200 and P300 amplitude and latency differences during a Go/NoGo task between the adolescent smokers and nonsmokers. Relative to nonsmokers, more NoGo response errors, reduced NoGo P300 amplitude, and longer P300 latency were observed in adolescent smokers. Correlation analysis revealed that the NoGo P300 amplitudes were significantly correlated with NoGo errors in both

* Kai Yuan [email protected] * Dahua Yu [email protected] 1

School of Life Science and Technology, Xidian University, Xi’an Shaanxi 710071, People’s Republic of China

2

Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, Xi’an, People’s Republic of China

3

Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, People’s Republic of China

4

School of Mechanical, Sanjiang University, Nanjing, Jiangsu 210012, People’s Republic of China

5

Institute of Automation, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China

adolescent smokers and nonsmokers. Our findings provided direct electrophysiological evidence for inhibitory control impairments in adolescent smokers. It is hoped that our results may enhance understanding of the pathology of inhibitory control in adolescent smokers. Keywords Adolescent smokers . Inhibition control . Go/NoGo task . Event-related potentials (ERP) . N200 . P300

Introduction Cigarette smoking is associated with serious medical problems, as a long-term use of tobacco exerts toxic effects on the brain, heart, and lungs (Glantz and Parmley 1991; Keylock et al. 2009; Swan and Lessov-Schlaggar 2007). As the Chinese Center for Disease Control and Prevention reported (May, 2014), the smoking rate of youth in junior high school students is 10.6 % for males and 1.8 % for females (http://www.chinacdc.cn/). In the United States, people between 18 and 25 years old have the highest smoking rate compared to other age groups (US Department of Health and Human Services 2012, http:// www.surgeongeneral.gov/library/reports/preventing-youthtobacco-use/full-report.pdf), with most smokers first using cigarettes when they were adolescents (Sussman 2002). People who start smoking at an early age are more likely to become life-long smokers and have higher levels of nicotine dependence (O’Loughlin et al. 2003; Taioli and Wynder 1991; White et al. 2009; Galván et al. 2011). Moreover, adolescence is a special period in transition from childhood to adulthood and encompassed by alterations in physical, psychological, and social function (Casey et al. 2005; Li et al. 2015). It has been proposed that smoking exerts neurotoxic effects during this special period, when

Brain Imaging and Behavior

the neural circuit of inhibition control undergoes significant development (Silveri et al. 2004; Lydon et al. 2014), and that this would result in subsequent inhibition control deficits in adulthood (Galván et al. 2011). Therefore, it is extremely important to study smoking mechanisms in this critical period. The role of inhibition control in addictive behavior, which is associated with the ability to implement the inhibition of inappropriate behavior (Groman et al. 2009), has been emphasized in several contemporary theoretical models (Lubman et al. 2004; Jentsch and Taylor 1999; Goldstein and Volkow 2002; Verdejo-García et al. 2008; Goldstein and Volkow 2011). The failure to develop adequate inhibition control can have a profound impact on the ability of an individual to gate propotent, yet inappropriate and dangerous, behaviors such as tobacco use (Kaufman et al. 2003). Researchers had argued that nicotine dependence is underpinned by a failure of the brain’s inhibition control mechanisms (Lubman, Yücel et al. 2004); impaired inhibition control in tobacco users would mean that nicotine dependents cannot suppress their craving for tobacco use and fail to quit smoking. Especially in adolescents, the imbalance of maturing status exists between the limbic regions implicated in risk seeking and the prefontal regions critical for inhibition control (Casey 2015; Lydon et al. 2014), which made adolescents more vulnerable to smoking (Lydon et al. 2014). Additionally, the ability of inhibition control would be compromised due to the neurotoxic effects of nicotine. During this special period, smoking behavior would result in subsequent inhibition control deficits and higher levels of nicotine dependence in adulthood (Galván et al. 2011; Eissenberg 2004). Understanding the neural mechanisms of inhibition control in this special period may offer obvious promise for pharmacological treatments and behavioral treatment programs for tobacco users (VerdejoGarcía et al. 2008). Inhibition control is often measured with the Go/NoGo task, in which participants respond to frequent ‘Go’ stimuli as quickly as possible, but inhibit their responses to infrequent ‘NoGo’ stimuli (Jonkman 2006; Kirmizi-Alsan et al. 2006). Owing to the precise temporal resolution that allows neural processes to be tracked in milliseconds, the timing of brain mechanisms underlying inhibition control has been extensively examined using ERPs in a Go/NoGo task (Littel et al. 2012; Albert et al. 2013). Two major components of ERPs have been consistently linked with the inhibition control in Go/NoGo tasks (Littel et al. 2012). The first component is the N200, a negative-going wave arising 200-300 ms after stimulus presentation which is believed to relate to conflict detection during early stages of the inhibition process (Nieuwenhuis et al. 2003; Michael Falkenstein 2006). The second component is the P300, a positive-going wave arising 300-500 ms after stimuli onset, appears to reflect a later stage of the inhibition

control process closely related to the actual inhibition (Huster et al. 2010). However, few studies have used ERPs to investigate inhibition control in smokers (Luijten et al. 2011; Evans et al. 2009), and the results are not consistent. For instance, Evan et al. had found reduced P300 amplitude during Go/ NoGo task in adult smokers, but no difference was found in performance. In contrast, Luijten et al., found a higher error rate in NoGo trials and reduced N200 amplitude in adult smokers compared to nonsmokers. Additionally, in previous ERP studies in substance dependence (SUD) or behavioral addiction, such as alcohol (Cohen et al. 1997; Kamarajan et al. 2005), cocaine (Sokhadze et al. 2008), internet addiction (Zhou et al. 2010; Dong et al. 2010), and impaired inhibition control reduced N200 and/or P300 in Go/NoGo task were widely observed. With regard to adolescent smokers, there are few ERP studies focusing on the electrophysiological mechanism of inhibition control impairment and the relationship between ERP and behavioral performance. Therefore, in present study, we employed ERPs to investigate more precisely whether N200 and P300 were specifically related with inhibition control measured by the Go/NoGo task in adolescent smokers. As impaired inhibition control was widely observed in previous ERP and fMRI addiction studies including alcohol (Cohen et al. 1997; Kamarajan et al. 2005), opioid (Fu et al. 2008), cocaine (Kaufman et al. 2003; Sokhadze et al. 2008) and behavioral addiction (Yuan et al. 2015; Xing et al. 2014; Yuan et al. 2013b; Yuan et al. 2013a), and less pronounced N200 and/or P300 amplitudes in addicted populations relative to controls can be considered markers for neural deficits in inhibition control (Luijten et al. 2014). Rationally, we hypothesised that adolescent smokers might make more errors in Go/NoGo task and show reduced N200 and P300 amplitude compared with healthy nonsmokers.

Methods Ethics statement All research procedures were approved by the Medical Ethical Committee of the First Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology, and were conducted in accordance with the Declaration of Helsinki. All participants and their legal guardians gave written informed consent after totally understanding the purposes of our study. Participants Thirty-six (18 smokers and 18 nonsmokers) male righthanded students, with an age range of 15–18 years (smokers,


16.9 ± 0.9; nonsmokers, 17.3 ± 0.48) and recruited at the local high school, participated in our study. All nonsmokers had less than 5 cigarettes consumption in their lifetime. In order to avoid the effect of second hand smoke exposure, the roommates and parents of nonsmokers all were required to be nonsmokers. All participants had normal or corrected-to-normal vision. The inclusion criteria for adolescent smokers were as follows: 1) reported more than 10 cigarettes a day in the last 6 months. 2) met the DSM-V criteria for current nicotine dependence. 3) expired air carbon monoxide (CO) > 6 ppm (ppm) (by Smokelyzer, Bedfont Scientific, Kent, UK). 4) no period of smoking abstinence longer than 3 months. Additionally, nicotine dependence levels were assessed with the Fagerstrom Test of Nicotine Dependence (FTND) (Fagerstrom and Schneider 1989). At the same time, all participants had no physical illness or neurological or psychiatric disorders as assessed by clinical evaluations and medical records, or alcohol or drug abuse. All participants received financial compensation (100RMB) for participation, and all procedures were carried out with the adequate understanding and written informed consent of subjects. More detailed information can be found in Table 1.

Procedure After providing informed consent, participants filled out questionnaires on demographics (Table 1). In order to reduce the acute effects of nicotine, a short period of smoking deprivation of one hour was applied to smokers before the start of the experiment. Then participants were seated confortably and the electrodes were attached. The Go/NoGo task was explained and before the formal experiment, participants had an initial practice block consisting of 30 trials. Participants were told that errors are inevitable, but they should try their best to accomplish the task as quickly and accurately as possible. E-prime software (Psychology Tools, Pittsburgh, PA,

Table 1 Demographic Characteristics of Adolescent Smokers and Nonsmokers in The Present Study

USA) was used for stimulus presentation and collecting the behavior of data (Table 2). Task paradigm After introducing the procedures to participants, a Go/NoGo task (Littel et al. 2012) was accomplished. In this task, there were four blocks, each block consisting of 159 letters (e.g. A B C D). Seventy-four NoGo trials (11.6 %) were presented randomly during this task, and NoGo trials were never presented in succession. Stimuli were placed at the center of a screen. Letters were presented for 700 milliseconds, preceded by a white cross against a black ground for 300 milliseconds. Participants were instructed to press a button with their right index finger as fast and accurately as possible for a letter which was not repeated (Go trials) and withhold their response for a repeated letter (NoGo trials). Between blocks, there were 60 s for rest. EEG recording and data reduction The electroencephalogram (EEG) recording was made in a dimly lit and sound-attenuated room. Participants were positioned about 100 cm away from a screen with horizontal and vertical visual angles of less than 5°. EEG data was recorded using the BrainAmp MR plus (Brain Products GmbH. Munich. Germany) with the electrodes at 64 scalp sites (positioned following the 10–20 International System) with additional electrode at FCz (reference electrode). The vertical electrooculogram (EOG) was recorded with two electrodes at locations above the left eye and outer canthus of the right eye. All signals were digitalized with a sample rate of 1000 Hz with a frequency band from 0.10-250 Hz, and impedances were reduced to less than 10kΩ. Offline data were processed with Brain Vision Analyzer 2 (Brain Products GmbH. Munich. Germany). EEG data was re-referenced to the average of mastoids (TP9 and TP10), and interpolated the FCz

Smokers (n = 18)

Nonsmokers (n = 18)

P-value

Age(years) Age range (years) Levels of Education (years) Cigarettes Per Day (CPD) Age at Start of Smoking Years of Smoking Pack-Years

16.92 ± 0.91 15–18 9.71 ± 0.82 12.29 ± 4.21 11 ± 1.7 5.92 ± 1.81 3.72 ± 1.76

17.23 ± 0.59 16–18 10 ± 0.7 NA NA NA NA

0.32

FTND

4.71 ± 2.3

NA

Values are expressed as means ± standard deviations FTND: Fagerström Test for Nicotine Dependence Pack-years: smoking years × Daily consumption/20

0.34

Brain Imaging and Behavior Table 2

Mean amplitudes and peak latencies in Go/NoGo conditions in different groups Mean amplitude

Peak latencies

Go

N200 P300

NoGo

Go

NoGo

Nonsmoker

Smoker

Nonsmoker

Smoker

Nonsmoker

Smoker

Nonsmoker

Smoker

−1.34 ± 0.52 0.58 ± 0.28

−1.21 ± 0.51 0.41 ± 0.25

−1.59 ± 0.54 1.57 ± 0.57

−1.68 ± 0.54 0.80 ± 0.36

228.55 ± 15.44 338.05 ± 25.69

222.38 ± 22.78 357.33 ± 22.37

242.11 ± 20.22 332.55 ± 27.71

222.38 ± 22.78 351.94 ± 20.64

Values are expressed as means ± standard deviations

electrode at the same time. EEG signals were band pass filtered using a 0.1-35 Hz (IIR filter 12 dB/octave roll off, 50 Hz notch) band-pass filter. Eye movements and eye blinks were removed using an independent component analysis (ICA). Artifact rejection procedures were applied to all epochs (−200 ms pre-stimulus to 800 ms post-stimulus). The rejection criteria were: maximal allowed voltage step (gradient) is 50 μv for each sample point, maximal allowed amplitude −75-75μv, maximal allowed value difference 100μv in a 200 ms interval and activity below 0.5μv in a 100 ms interval were rejected. All ERPs were baseline corrected −200 to 0 ms pre-stimulus. Epochs were averaged using only correct trials according to the condition (Go, NoGo). The N200 and P300 components were analyzed at FCz and Cz (Stock et al. 2014). It is well known that the N200 component is typically largest over anterior sites (Folstein and Van Petten 2008) while the P300 component is usually largest over more posterior sites (Kok et al. 2004; Polich 2007; Stock et al. 2014). The N200 and P300 amplitude were defined as the global maximal value to baseline at signal subject level (N200, 200-300 ms post stimulus; P300, 300-500 ms post stimulus). The number of analyzable epochs was 522.03 (SD = 21.24) for Go trials and 36.66 (SD = 5.24) for NoGo trials.

Statistics The behavioral outcomes of performance on the Go/NoGo task as well as ERP data were analyzed using repeated measures analyses of variance (ANOVAs; with GreenhouseGeisser adjusted p-values) with ‘condition’ (Go, NoGo) as within-subject factors and ‘group’ (smokers, nonsmokers) as a between-subjects factor. This results in group (smokers, nonsmokers) × condition (Go, NoGo) ANOVAs for N200 and P300 amplitudes and latencies related to inhibition. Post hoc tests and Bonferroni-correction were used whenever necessary. Finally, Pearson correlation coefficients were calculated for the number of NoGo errors and smoking status (pack- years, FTND, Cigarettes Per Day (CPD)) and amplitudes of ERPs (N200, P300).

Results Behavioral data A robust main effect was found for ‘condition’, F = 134.19, p = 9.52E-18, showing that participants in general made more errors on NoGo than on Go trials, and a significant group × condition interaction was found, F = 11.92, p = 0.00096. Furthermore adolescent smokers made more errors in response to NoGo trials (38.6 %,28.55 ± 9.12) than controls (25.5 %, 18.88 ± 6.76; p = 0.001), whereas the groups did not differ on number of errors in response to Go trials (p = 0.89). No significant difference was found in Go reaction time (RT)(smokers:398.77 ± 56.86,controls:418.13 ± 65.39) between the two groups, F = 0.89, p = 0.35. EEG data N200 For the N200 component, the main effect of ‘group’ was not significant, F = 0.02, p = 0.88, indicating that the N200 amplitudes were not different between smokers and nonsmokers. A significant main effect was found in ‘condition’ factor, F = 8.13, p = 0.0058, indicating that the N200 amplitudes were higher in NoGo trials than Go trials. Furthermore, no group × condition interaction was found, F = 0.737, p = 0.394. With regard to N200 latency, no significant main effect of ‘group’ was found, F = 2.73, p = 0.10. The main effect of ‘condition’ was found, F = 5.47, p = 0.022, indicating that the N200 latency in NoGo trials are longer than Go trials. No group × condition interaction was found, F = 0.16, p = 0.68. P300 As we expected, significant main effects were found for ‘condition’, F = 55.41, p = 2.24E-10 and ‘group’, F = 25.79, p = 0.000003, indicating NoGo trials eliciting larger NoGo P300 amplitudes than Go trials and the P300 amplitudes were larger in nonsmokers than smokers. Importantly, only in NoGo trials the P300 was significantly reduced in smokers


compared with nonsmokers (p = 5.01086E-05). Also, a group × condition interaction, F = 10.42, p = 0.0019 was found. With regard to P300 latency, no significant main effect of ‘condition’ was found, F = 0.90, p = 0.34. However, the main effect of ‘group’ was found, F = 11.42, p = 0.0012, indicating that the P300 latency in smokers was longer than nonsmokers. No group × condition interaction was found, F = 0.0001, p = 0.99. Correlations Pearson correlation coefficients were calculated for the error number and smoking status (pack- years, FTND, CPD) and amplitudes of the NoGo N200 and P300. Significant correlation was found between error number and NoGo P300 amplitudes (Fig. 3, nonsmokers, r = −0.647, p = 0.004; smokers, r = −0.694, p = 0.001) and the difference between the correlation coefficients was not significant, p = 0.82.No significant correlation was found between the electrophysiological measurements (N200, P300 amplitude) and the clinical measures, pack-years, cigarettes per day, FTND.

Discussion Due to few studies being focused on the inhibition control characteristic in adolescent smokers using the high temporal resolution ERPs, the purpose of this study was to investigate inhibition control deficits between adolescent smokers and nonsmokers both on the behavioral and electrophysiological level during a Go/NoGo task. In the current study, more NoGo errors were found in adolescent smokers than nonsmokers (Fig. 1), which is consistent with previous smoker studies (Nestor et al. 2011; Luijten et al. 2011; Verdejo-García et al. 2008). The poor performance in the Go/NoGo task confirmed the inhibition control deficits in adolescent smokers.

Additionally, the ERP results revealed reduced NoGo P300 amplitude and longer NoGo P300 latency in adolescent smokers compared with nonsmokers. Furthermore, a correlation between NoGo error number and NoGo P300 amplitude was found in both adolescent smokers and nonsmokers. On the electrophysiological level, P300 amplitude is associated with the late stage of the inhibition process, closely related to the actual inhibition in a Go/NoGo task (Kok et al. 2004; Band and Van Boxtel 1999). Reduced NoGo P300 amplitude and longer P300 latency was found in adolescent smokers (Fig. 2), which is consistent with a previous smoker study (Evans et al. 2009). In addition to these results, the previous SUD studies (Singh and Basu 2009; Euser et al. 2012) had also reported reduced P300 amplitude in inhibition control tasks and they suggested reduced P300 amplitude as a marker of inhibition control deficits in SUD individuals. Researchers had reported that P300 involves a circuit pathway between the frontal and temporal/parietal brain areas and suggested P300 reflected a top-down inhibition (Pires et al. 2014; Enriquez-Geppert et al. 2010). Moreover, as P300 amplitude increased with inhibition load, it has been believed to index inhibition control process (Dimoska and Johnstone 2008; Pires et al. 2014). In addition, researchers had reported that anterior cingulate cortex (ACC), orbitofrontal cortex (OFC) and pre-supplementary area (pre-SMA) were the main regions of the generators of P300 in Go/NoGo tasks (Pires et al. 2014; Kiefer et al. 1998; Bokura et al. 2002; Albert et al. 2013). Complemented by fMRI studies, the ACC, OFC activation and preSMA had been suggested as core regions associated with inhibition control during the Go/NoGo task in healthy controls (Luijten et al. 2014; Albert et al. 2013; Bokura et al. 2002; Kiefer et al. 1998). Especially, hypoactivation in ACC and OFC had been widely reported in smokers during inhibition control tasks (Luijten et al. 2014). It had been suggested that P300 latency is an indicator of inhibition control processing speed, which is associated with cognitive efficiency

Fig. 1 Adolescent smokers made more errors in response to NoGo trials (38.6 %, 28.55 ± 9.12) than controls (25.5 %,18.88 ± 6.76; p = 0.001). No significant difference was found in Go reaction time (RT) (smokers: 398.77 ± 56.86,controls:418.13 ± 65.39) between the two groups, F = 0.89, p = 0.35


Fig. 2 Reduced NoGo P300 amplitude and longer NoGo P300 latency of Cz was found in adolescent smokers. The NoGo N200 amplitudes and latency were not different between smokers and nonsmokers

(McEvoy et al. 2001; Polich and Criado 2006; Dong et al. 2010). Longer P300 latency was found in the adolescent smoker group compared with the healthy control group, which suggested that adolescent smokers had less efficient information processing function in the Go/NoGo task, which may be related to impaired inhibition control(Bokura et al. 2005; Shucard et al. 2008). Together with previous studies, our findings, i.e. reduced P300 amplitude, longer P300 latency and P300 amplitude correlation with NoGo error number of Go/ NoGo task in adolescent smokers (Fig. 3), demonstrated that compromised P300 amplitude is associated with inhibition control impairments in adolescent smokers, and that may make it more difficult for them to quit smoking.

Fig. 3 Significant correlation was found between NoGo error number and NoGo P300 amplitudes (nonsmokers, r = −0.647, p = 0.004,, smokers, r = −0.694, p = 0.001). No significant correlation was found between the N200 amplitudes and NoGo error number (nonsmokers, r = −0.205, p = 0.461, smokers, r = 0.148, p = 0.557)

Previous studies had revealed reduced NoGo N2 amplitude in smokers (Luijten et al. 2011) and suggested that the NoGo N2 amplitude is related to conflict detection during early stages of the inhibition process (Nieuwenhuis et al. 2003; Michael Falkenstein 2006). Unfortunately, in our study, the N200 amplitudes were not significantly different between these two groups (adolescent smokers vs. nonsmokers). The following reasons may contribute to our results. First, the small size of the sample and the severity of smoking in our study are not enough to classify the difference between adolescent smokers and healthy nonsmokers. Second, the Go/NoGo task (11.6 % NoGo trials) used in our study (Littel et al. 2012) is more difficult than traditional


versions (NoGo trials 20 %-50 %) (M Falkenstein et al. 1999; Dong et al. 2010; Luijten et al. 2011), which is confirmed by high error rate in NoGo trials in both groups. Evidently, in the future, more specific tasks designed for adolescent smokers and relatively larger homogenous samples with heavier severity adolescent smokers are necessary to better explore the NoGo N200 amplitude differences implicated with inhibition control in adolescent smokers and nonsmokers.

Limitation First, the sample size of our study is too small for us to explore the inhibition control characteristics sufficiently in adolescent smokers. Only male participants were enrolled, which may not be a representative sample of the whole population. In a future study, expanding the sample size is important for us to explore smoking mechanisms in adolescent smokers. Second, our cross sectional design does not allow us to determine the causality of inhibition control deficits and adolescent smoking. Whether reduced inhibition control ability is the result of prolonged nicotine dependence or what urges the adolescent students to smoke needs to be explored by a longitudinal design in the future.

Conclusion In the current study, we focused on inhibition control deficits in adolescent smokers. The reduced NoGo P300 amplitude, longer P300 latency and the NoGo P300 amplitude’s correlation with NoGo errors during Go/NoGo task suggested that P300 amplitude is associated with inhibition control impairments in adolescent smokers. Our findings provide direct electrophysiological evidence for inhibition control impairments in adolescent smokers.

Acknowledgments This paper is supported by the Project for the National Key Basic Research and Development Program (973) under Grant nos. 2014CB543203, 2011CB707700, 2012CB518501, the National Natural Science Foundation of China under Grant nos. 81401478, 81401488, 81271644, 81271546, 81271549, 81470816, 81471737, 81301281, the Natural Science Basic Research Plan in Shaanxi Province of China under Grant no. 2014JQ4118, and the Fundamental Research Funds for the Central Universities under the Grant nos. 8002–72135767, 8002– 72145760, the Natural Science Foundation of Inner Mongolia under Grant no. 2012MS0908. General Financial Grant the China Post- doctoral Science Foundation under Grant no. 2014 M552416. Conflict of Interest Junsen Yin, Kai Yuan, Dan Feng, Jiadong Cheng, Yangding Li, Chenxi Cai, Yanzhi Bi, Shi Sha, Xiaomin Shen, Ben Zhang, Ting Xue, Wei Qin, Dahua Yu, Xiaoqi Lu, and Jie Tian declare that they have no conflicts of interest.

References Albert J., López-Martín S., Hinojosa J. A., & Carretié L. (2013). Spatiotemporal characterization of response inhibition. NeuroImage, 76, 272–281. Band G., & Van Boxtel G. (1999). Inhibitory motor control in stop paradigms: review and reinterpretation of neural mechanisms. Acta Psychologica, 101(2), 179–211. Bokura H., Yamaguchi S., & Kobayashi S. (2005). Event-related potentials for response inhibition in Parkinson’s disease. Neuropsychologia, 43(6), 967–975. Bokura, H., Yamaguchi, S., Matsubara, M., & Kobayashi, S. (2002). Frontal lobe contribution to response inhibition process—an ERP study and aging effect. In International Congress Series, 2002 (Vol. 1232, pp. 17–20): Elsevier Casey B. (2015). Beyond simple models of self-control to circuit-based accounts of adolescent behavior. Annual Review of Psychology, 66, 295–319. Casey B., Tottenham N., Liston C., & Durston S. (2005). Imaging the developing brain: what have we learned about cognitive development? Trends in Cognitive Sciences, 9(3), 104–110. Cohen H., Porjesz B., Begleiter H., & Wang W. (1997). Neurophysiological correlates of response production and inhibition in alcoholics. Alcoholism: Clinical and Experimental Research, 21(8), 1398–1406. Dimoska A., & Johnstone S. J. (2008). Effects of varying stop-signal probability on ERPs in the stop-signal task: do they reflect variations in inhibitory processing or simply novelty effects? Biological Psychology, 77(3), 324–336. Dong G., Lu Q., Zhou H., & Zhao X. (2010). Impulse inhibition in people with internet addiction disorder: electrophysiological evidence from a go/NoGo study. Neuroscience Letters, 485(2), 138–142. Eissenberg T. (2004). Measuring the emergence of tobacco dependence: the contribution of negative reinforcement models. Addiction, 99(s1), 5–29. Enriquez-Geppert S., Konrad C., Pantev C., & Huster R. J. (2010). Conflict and inhibition differentially affect the N200/P300 complex in a combined go/nogo and stop-signal task. NeuroImage, 51(2), 877–887. Euser A. S., Arends L. R., Evans B. E., Greaves-Lord K., Huizink A. C., & Franken I. H. (2012). The P300 event-related brain potential as a neurobiological endophenotype for substance use disorders: a metaanalytic investigation. Neuroscience & Biobehavioral Reviews, 36(1), 572–603. Evans D., Park J., Maxfield N., & Drobes D. (2009). Neurocognitive variation in smoking behavior and withdrawal: genetic and affective moderators. Genes, Brain and Behavior, 8(1), 86–96. Fagerstrom K.-O., & Schneider N. G. (1989). Measuring nicotine dependence: a review of the fagerstrom tolerance questionnaire. Journal of Behavioral Medicine, 12(2), 159–182. Falkenstein M. (2006). Inhibition, conflict and the nogo-N2. Clinical Neurophysiology, 117(8), 1638–1640. Falkenstein M., Hoormann J., & Hohnsbein J. (1999). ERP components in go/nogo tasks and their relation to inhibition. Acta Psychologica, 101(2), 267–291. Folstein J. R., & Van Petten C. (2008). Influence of cognitive control and mismatch on the N2 component of the ERP: a review. Psychophysiology, 45(1), 152–170. Fu, L.-p., Bi, G.-h., Zou, Z.-t., Wang, Y., Ye, E.-m., Ma, L., et al. (2008). Impaired response inhibition function in abstinent heroin dependents: an fMRI study. Neuroscience Letters, 438(3), 322– 326. Galván A., Poldrack R. A., Baker C. M., McGlennen K. M., & London E. D. (2011). Neural correlates of response inhibition and cigarette

Brain Imaging and Behavior smoking in late adolescence. Neuropsychopharmacology, 36(5), 970–978. Glantz, S. A., & Parmley, W. W. (1991). Passive smoking and heart disease. Epidemiology, physiology, and biochemistry. Circulation, 83(1), 1–12. Goldstein R. Z., & Volkow N. D. (2002). Drug addiction and its underlying neurobiological basis: neuroimaging evidence for the involvement of the frontal cortex. American Journal of Psychiatry, 159(10), 1642–1652. Goldstein R. Z., & Volkow N. D. (2011). Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications. Nature Reviews Neuroscience, 12(11), 652–669. Groman S. M., James A. S., & Jentsch J. D. (2009). Poor response inhibition: at the nexus between substance abuse and attention deficit/ hyperactivity disorder. Neuroscience & Biobehavioral Reviews, 33(5), 690–698. Huster R., Westerhausen R., Pantev C., & Konrad C. (2010). The role of the cingulate cortex as neural generator of the N200 and P300 in a tactile response inhibition task. Human Brain Mapping, 31(8), 1260–1271. Jentsch J. D., & Taylor J. R. (1999). Impulsivity resulting from frontostriatal dysfunction in drug abuse: implications for the control of behavior by reward-related stimuli. Psychopharmacology, 146(4), 373–390. Jonkman L. M. (2006). The development of preparation, conflict monitoring and inhibition from early childhood to young adulthood; a go/ nogo ERP study. Brain Research, 1097(1), 181–193. Kamarajan C., Porjesz B., Jones K. A., Choi K., Chorlian D. B., Padmanabhapillai A., et al. (2005). Alcoholism is a disinhibitory disorder: neurophysiological evidence from a go/no-go task. Biological Psychology, 69(3), 353–373. Kaufman J. N., Ross T. J., Stein E. A., & Garavan H. (2003). Cingulate hypoactivity in cocaine users during a GO-NOGO task as revealed by event-related functional magnetic resonance imaging. The Journal of Neuroscience, 23(21), 7839–7843. Keylock C. J. B., Galvin J. R., & Franks T. J. (2009). Sclerosing hemangioma of the lung. Archives of Pathology & Laboratory Medicine, 133(5), 820–825. Kiefer M., Marzinzik F., Weisbrod M., Scherg M., & Spitzer M. (1998). The time course of brain activations during response Inhibition: evidence From event‐Related potentials In a go/no go Task. Neuroreport, 9(4), 765–770. Kirmizi-Alsan E., Bayraktaroglu Z., Gurvit H., Keskin Y. H., Emre M., & Demiralp T. (2006). Comparative analysis of event-related potentials during go/NoGo and CPT: decomposition of electrophysiological markers of response inhibition and sustained attention. Brain Research, 1104(1), 114–128. Kok A., Ramautar J. R., De Ruiter M. B., Band G. P., & Ridderinkhof K. R. (2004). ERP components associated with successful and unsuccessful stopping in a stop‐signal task. Psychophysiology, 41(1), 9–20. Li Y., Yuan K., Cai C., Feng D., Yin J., Bi Y., et al. (2015). Reduced frontal cortical thickness and increased caudate volume within fronto-striatal circuits in young adult smokers. Drug and Alcohol Dependence. Littel M., Berg I., Luijten M., Rooij A. J., Keemink L., & Franken I. H. (2012). Error processing and response inhibition in excessive computer game players: an event‐related potential study. Addiction Biology, 17(5), 934–947. Lubman D. I., Yücel M., & Pantelis C. (2004). Addiction, a condition of compulsive behaviour? Neuroimaging and neuropsychological evidence of inhibitory dysregulation. Addiction, 99(12), 1491–1502. Luijten, M., Littel, M., & Franken, I. H. (2011). Deficits in inhibitory control in smokers during a go/NoGo task: an investigation using event-related brain potentials. PloS One, 6(4), e18898. Luijten M., Machielsen M. W., Veltman D. J., Hester R., de Haan L., & Franken I. H. (2014). Systematic review of ERP and fMRI studies

investigating inhibitory control and error processing in people with substance dependence and behavioural addictions. Journal of Psychiatry & neuroscience: JPN, 39(3), 149. Lydon D. M., Wilson S. J., Child A., & Geier C. F. (2014). Adolescent brain maturation and smoking: what we know and where we’re headed. Neuroscience & Biobehavioral Reviews, 45, 323–342. McEvoy L. K., Pellouchoud E., Smith M. E., & Gevins A. (2001). Neurophysiological signals of working memory in normal aging. Cognitive Brain Research, 11(3), 363–376. Nestor L., McCabe E., Jones J., Clancy L., & Garavan H. (2011). Differences in Bbottom-up^ and Btop-down^ neural activity in current and former cigarette smokers: evidence for neural substrates which may promote nicotine abstinence through increased cognitive control. NeuroImage, 56(4), 2258–2275. Nieuwenhuis S., Yeung N., Van Den Wildenberg W., & Ridderinkhof K. R. (2003). Electrophysiological correlates of anterior cingulate function in a go/no-go task: effects of response conflict and trial type frequency. Cognitive, Affective, & Behavioral Neuroscience, 3(1), 17–26. O’Loughlin J., DiFranza J., Tyndale R. F., Meshefedjian G., McMillanDavey E., Clarke P., et al. (2003). Nicotine-dependence symptoms are associated with smoking frequency in adolescents. American Journal of Preventive Medicine, 25(3), 219–225. Pires L., Leitão J., Guerrini C., & Simões M. R. (2014). Event-related brain potentials in the study of inhibition: cognitive control, source localization and age-related modulations. Neuropsychology Review, 24(4), 461–490. Polich J. (2007). Updating P300: an integrative theory of P3a and P3b. Clinical Neurophysiology, 118(10), 2128–2148. Polich J., & Criado J. R. (2006). Neuropsychology and neuropharmacology of P3a and P3b. International Journal of Psychophysiology, 60(2), 172–185. Shucard J. L., McCabe D. C., & Szymanski H. (2008). An event-related potential study of attention deficits in posttraumatic stress disorder during auditory and visual go/NoGo continuous performance tasks. Biological Psychology, 79(2), 223–233. Silveri, M. M., Tzilos, G. K., Pimentel, P. J., & YURGELUN‐TODD, D. A. (2004). Trajectories of adolescent emotional and cognitive development: effects of sex and risk for drug use. Annals of the New York Academy of Sciences, 1021(1), 363–370. Singh S. M., & Basu D. (2009). CLINICAL STUDY: the P300 event‐ related potential and its possible role as an endophenotype for studying substance use disorders: a review. Addiction Biology, 14(3), 298–309. Sokhadze E., Stewart C., Hollifield M., & Tasman A. (2008). Eventrelated potential study of executive dysfunctions in a speeded reaction task in cocaine addiction. Journal of Neurotherapy, 12(4), 185– 204. Stock A. K., Schulz T., Lenhardt M., Blaszkewicz M., & Beste C. (2014). High‐dose alcohol intoxication differentially modulates cognitive subprocesses involved in response inhibition. Addiction biology. Sussman, S. (2002). Effects of sixty six adolescent tobacco use cessation trials and seventeen prospective studies of self-initiated quitting. Tobacco Induced Diseases, 1(1), 35–81. Swan G. E., & Lessov-Schlaggar C. N. (2007). The effects of tobacco smoke and nicotine on cognition and the brain. Neuropsychology Review, 17(3), 259–273. Taioli E., & Wynder E. L. (1991). Effect of the age at which smoking begins on frequency of smoking in adulthood. The New England Journal of Medicine, 325(13), 968–969. Verdejo-García A., Lawrence A. J., & Clark L. (2008). Impulsivity as a vulnerability marker for substance-use disorders: review of findings from high-risk research, problem gamblers and genetic association studies. Neuroscience & Biobehavioral Reviews, 32(4), 777–810.

Brain Imaging and Behavior White, H. R., Bray, B. C., Fleming, C. B., & Catalano, R. F. (2009). Transitions into and out of light and intermittent smoking during emerging adulthood. Nicotine & Tobacco Research, ntn017. Xing L., Yuan K., Bi Y., Yin J., Cai C., Feng D., et al. (2014). Reduced fiber integrity and cognitive control in adolescents with internet gaming disorder. Brain Research, 1586, 109–117. Yuan, K., Cheng, P., Dong, T., Bi, Y., Xing, L., Yu, D., et al. (2013a). Cortical thickness abnormalities in late adolescence with online gaming addiction. PloS One, 8(1), e53055. Yuan, K., Jin, C., Cheng, P., Yang, X., Dong, T., Bi, Y., et al. (2013b). Amplitude of low frequency fluctuation abnormalities in

adolescents with online gaming addiction. PloS One, 8(11), e78708. Yuan K., Qin W., Yu D., Bi Y., Xing L., Jin C., et al. (2015). Core brain networks interactions and cognitive control in internet gaming disorder individuals in late adolescence/early adulthood. Brain Structure and Function, 1-16. Zhou Z. H., Yuan G. Z., Yao J. J., Li C., & Cheng Z. H. (2010). An event‐ related potential investigation of deficient inhibitory control in individuals with pathological internet use. Acta Neuropsychiatrica, 22(5), 228–236.