The difference between smokers and healthy controls in glutamate concentration, impulsivity and smoking severity

The difference between smokers and healthy

controls in glutamate concentration, impulsivity and

smoking severity

Student Steffie Szegedi

Student number 5781892

Daily supervisor Mieke ter Mors – Schulte

Abstract

Background: Tobacco smoking is one of the leading preventable causes of death. Unfortunately, only 3-5 percent of the smokers stay abstinent one year after quitting. There is accumulating evidence for a crucial role of glutamate in the reward system of the brain. Malfunction of the dorsal anterior cingulate cortex (dACC) may contribute to the vulnerability of relapse in tobacco smoking addiction. Another factor that may contribute to relapse is impulsivity. We hypothesized that smokers and healthy controls would differ in their glutamate concentration in the dACC. Moreover we hypothesized that smokers would be more impulsive than healthy controls.

Methods: Proton magnetic resonance spectroscopy (1_{H-MRS) was used to compare the}

glutamate concentration between smokers (n=26) and healthy controls (n=14). The Baratt Impulsiveness Scale 11 (BIS-11), the behaviour inhibition system and behavioural activation system (BIS/BAS) and the delay discounting task (DDT) were administered to test whether smokers were more impulsive than healthy controls and to examine the relationship between dACC glutamate levels and impulsivity.

Results: The glutamate concentration did not differ between smokers and healthy controls. Smokers were not more impulsive than healthy controls except for the subtype of impulsivity that encompasses self-control and cognitive flexibility. No significant correlation was present between glutamate and impulsivity.

Conclusion: Our findings do no support the crucial role of the glutamate concentration in the dACC in tobacco smoking addiction. More research is needed to gain more understanding of the glutamate homeostasis.

Introduction

The attributable mortality of cigarette smoking between the ages of 30-69 is estimated around 12.5% in males and 2.5% in females in western European countries. This percentage fluctuates around regions in the world in men between 23% and 1.5% and in women between 7% and 1% (Ezzati & Lopez, 2004). The prevalence of smoking in the Netherlands in 2012 was 27% in men and 25% in women (Stivoro, 2012). Despite all the health risks associated with smoking, relapse is a great problem, since only 3-5% of the smokers stay abstinent one year after quitting (Hughes et al., 2003). Addiction is characterized by a pattern of maladaptive behavior which leads to severe impairment and/or distress. According to the DSM IV-TR people have a substance use disorder when they meet three or more of the following criteria: 1) tolerance, 2) withdrawal, 3) more of the substance is used or the duration of using is increased than initially intended, 4) craving, 5) spending large amounts of time to obtain, use the substance or to recover from its effects, 6) social life, occupation or recreational activities are given up or reduced because of the substance abuse, 7) usage is continued despite being aware of the negative consequences (Schippers & Van den Brink, 2008). Both the pleasurable effects and the withdrawal symptoms are drug specific. In the case of cigarette smoking the pleasurable effects are mainly arousal, relaxation, improved mood (Benowitz, 2008), and cognitive enhancing effects (Jasinska et al., 2013). While withdrawal in cigarette smoking dependence is characterized by anger, anxiety, depression, concentration difficulties, impatience, insomnia and restlessness (Hughes, 2007).

It has been proposed, that addiction encompasses features of both impulse control disorders (ICD) and compulsive disorders (Koob & Volkow, 2010) which is in accordance with some of the DSM IV symptoms of addiction mentioned above. ICD’s are characterized by a buildup of tension or arousal before acting out the impulsive behavior and pleasure and/or satisfaction at the time of performing the behavior. Therefore ICD’s are associated with positive reinforcement. To the contrary compulsive disorders are characterized by anxiety and stress before acting out the compulsive repetitive behavior and relief from the stress by committing the compulsive behavior. Therefore compulsive disorders are associated with negative reinforcement and automaticity. The earlier stages of addiction are characterized by ICD like symptoms and as the disorder progresses it becomes more compulsive (Koob & Volkow, 2010). Appendix 1 presents a more detailed explanation.

Impulsivity seems to play an important role in addiction. Impulsivity is a very broad construct which probably consists of multiple components, but what these components are, is still heavily debated (Evenden et al., 1999; Moeller et al., 2001). However, impulsivity is often divided in two components: 1) impulsive action which is characterized by a lack of inhibitory control which implies acting rapidly without appropriate forethought, 2) impulsive choice which is characterized by a preference for smaller more immediate reward over a greater but more delayed reward (Broos et al., 2012; Diergaarde et al., 2008; Mitchell, 1999; Winstanly et al., 2006). Impulsive choice is the focus in this research. A common paradigm to study impulsive choice is a delay discounting task (DDT; Mitchell, 1999), which is used in both animals and humans. An earlier study has shown that adolescent smokers, who are able to stay abstinent from smoking, were less impulsive at treatment onset on an experimental discounting task and continuous performance task compared to adolescent smokers who were unable to achieve abstinence (Krishnan-Sarin et al., 2007). A trend towards significance has been found for smokers to discount delayed rewards more than healthy controls (Mitchell, 1999). High impulsivity in rats seems to be a predictor of drug escalation and this is comparable to one of the symptoms of substance abuse in humans mentioned above (taking more of the substance initially intended). High impulsive rats were also more likely to exhibit drug seeking behaviour after a period of abstinence and this is comparable to relapse in humans (Everitt et al., 2008).

Top-down control could be important for not giving into impulsive behaviors as choosing the smaller reward (substance of abuse) over the bigger more delayed reward (healthier and higher quality of life). The anterior cingulate cortex (ACC) is a subregion of the ventromedial frontal cortex and consists of the cingulate sulcus and gyrus (Gasquoine, 2013). The ACC is an area in the brain that has been implicated in many different functions as regulation of emotional behavior, executive functioning, reward based decision making (Bush et al., 2002), (effortful) cognitive control (Claus et al., 2013; Gasquoine, 2013), focusing attention towards relevant stimuli and is also involved in resolving conflict from distracting stimuli (Weissman et al., 2005). That the dACC plays a role in smoking addiction has been shown in prior research, because activity in the dACC was associated with more attentional bias towards smoking cues in smokers compared to healthy controls (Luijten et al., 2010) and an attentional bias towards smoking is a predictor of (early) relapse in a smoking cessation attempt (Waters et al., 2003). And because an inverse association has been found between

nicotine addiction severity and the strength of the functional connectivity between the dACC and the striatum (Hong et al., 2009).

Besides the ACC, other areas in the brain are involved in the addiction circuit. The ventral tegmental area (VTA) is an area in the brain, that is involved in detecting and processing rewarding stimuli (Schultz, 1997), since numerous studies have shown that pleasurable activities as sex, food, drugs of abuse have in common to activate mesolimbic dopaminergic neurons in the VTA (Berrigde & Robinson, 1998; Geisler & Wise, 2008; Koob & Volkow, 2010; Leslie et al., 2013). Nicotine is often considered the addictive substance in tobacco smoking (Benowitz, 2008; Dani, 2001), however there are indications that other compounds in tobacco are important to induce the addictive effects of smoking (Cao et al., 2007; Talhout et al., 2007) or even that nicotine in itself is not addictive, since its limited ability to induce self-administration of nicotine in animals (Dar & Frenk, 2004). Therefore, this research focuses on cigarette smoking addiction and not merely on nicotine addiction, but nicotine will be used to explain a possible mechanism of addiction in cigarette smoking addiction. Nicotine binds to nicotinic acetylcholine receptors (NAChR). Each NAChR complex is composed of 5 subunits and there are 9 α-subunits (α2-10) and three β-subunits (β2-4) (Benowitz, 2008; Dani et

al., 2001; Zao-Shea et al., 2011). Different subunit combinations yield different chemical receptor properties (Zao-Shea et al., 2011). The binding of nicotine to NAChR in the VTA induces increased dopamine release in two important VTA projection pathways. The first projection pathway is the dopaminergic mesocortical pathway (PFC, ACC) and the second projection pathway is the dopaminergic mesolimbic pathway (ventral striatum, nucleus accumbens (NAc) and amygdala) (Jasinska et al., 2013; Kalivas & Volkow, 2005; Schultz, 1997) see Figure 1. The acute rewarding effects of a drug depend on the increased dopamine release in the NAc from the VTA (mesolimbic pathway) (Kalivas & Volkow, 2005). The increased dopamine release in the PFC from the VTA (mesocortical pathway) induces increased glutamate release from the PFC into the NAc (Kalivas & Volkow, 2005) see Figure 1.

Figure 1. Simplified model of a possible mechanism of cigarette smoking addiction adapted from Jasinska et al., 2013 & Kalivas & Volkow, 2005.

There are three processes which influence the glutamate homeostasis. The first process is that extracellular glutamate is derived mainly from non-synaptic glial cells, the second process is that extra-synaptic metabotropic glutamate receptors are activated by non-synaptically released glutamate at specific concentrations and third glutamate uptake mechanisms prevent two things a) extracellular glutamate to activate ionic glutamate receptors in the synaptic cleft and b) synaptically released glutamate to activate metabotropic glutamate receptors (Kalivas, 2009). Repeated drug use seems to alter these three processes and therefore disrupts the glutamate homeostasis (Kalivas, 2009). These drug induced cellular plastic changes in the PFC alters the glutamergic projection towards the NAc (Kalivas, 2009; Quintero, 2013). This in turn alters the projection from the NAc towards the limbic sub circuit which decreases the capacity of an individual to regulate drug seeking behavior and increases the risk of relapse (Kalivas & Volkow, 2005; Kalivas, 2009; Quintero, 2013).

Magnetic resonance spectroscopy is a technique that makes it possible to detect concentrations of metabolites in vivo in a non-invasive manner (Puts & Edden, 2012). This method is increasingly used to explore the role of certain metabolites including glutamate in specific brain areas in addiction research. Some prior proton magnetic resonance spectroscopy (1_{H-MRS) research focused on glutamate in different addictions.}

Previous research found a lower glx (glutamine and glutamate taken together) concentration in opiate addicts in the dACC compared to healthy controls (Yücel et al., 2007), lower glutamate in the ACC in marijuana users compared to healthy controls (Prescot et al., 2011), lower glx concentration in the ACC in opiate addicts compared to controls when only participants under the age of 34 were taken into account (Hermann et al., 2011), lower Glu/tCR ratio in chronic cocaine users in the rACC compared to healthy controls (Yang et al., 2009), lower glutamate concentration in alcohol dependent participants in remission compared to controls (Thoma et al., 2011), lower glutamine in the ACC have been found in alcohol dependent subjects compared to healthy controls (Hermann et al., 2012) and lower glu/Cr ratios in smokers who were not able to stay abstinent compared to smokers who stayed abstinent in the dACC (Mashhoon et al., 2011).

No differences in glutamate concentrations in the ACC have been found between alcohol dependent participants compared to healthy controls (Lee et al., 2007; Thoma et al., 2011), between smokers and healthy controls (Gallinat & Schubert, 2007) and no difference in glutamate and glx have been found between opiate addicts and healthy controls when participants of all ages were included (Hermann et al., 2011).

Increased glutamate/CR ratio concentrations have been found in the ACC of alcohol dependent subjects compared to healthy controls (Lee et al., 2007) and of cocaine dependent subjects compared to healthy controls (Schmaal et al., 2012b). Higher glutamine concentrations have been found in the ACC of alcohol dependent participants compared to healthy controls and a trend of higher glutamine concentrations in alcohol dependent participants in remission compared to healthy controls in the ACC (Thoma et al., 2011). Hermann et al., (2012) found increased levels of glutamate in the ACC of both alcohol dependent rats and humans during acute withdrawal, which normalizes after longer periods of abstinence. It was also found that there is a positive association between the number of

indications that glutamine and glutamate are negatively associated (Hermann et al., 2012; Thoma et al., 2011). Alterations in the glutamate concentration have also been reported in other subject groups. Higher glutamate concentrations have been found in the ACC in borderline personality disorder (BPD) patients, which is a disorder characterized amongst other things by impulsivity. These glutamate concentrations were positively correlated with self-reported impulsivity (Hoerst et al., 2010). This positive correlation between impulsivity (delay discounting task) and glutamate concentrations have also been found in healthy subjects (Schmaal et al., 2012a).

The inconsistent findings in glutamate concentration in the ACC in addicted subjects make it interesting to explore whether there is a difference in glutamate concentration in the dACC between smokers and healthy controls. Because of the inconsistent findings, we do not expect the glutamate concentrations to differ in a particular direction, but that there is a difference. As mentioned above, impulsivity seems to be an important component of addiction, so it is expected that smokers are more impulsive than healthy controls. Furthermore, it is expected that smokers score higher on the smoking severity scales than healthy controls and that healthy controls score zero or close to zero. Associations are expected between glutamate concentration in the dACC and impulsivity, between glutamate concentration in the dACC and smoking severity and between impulsivity and smoking severity, since all these factors seem to be related to addiction.

Methods

Participants

The participants consisted of 26 regular smokers (age range of 18-53, M = 36.85, SE = 1.871) and 14 healthy controls (age range of 22-54, M = 37.93, SE = 3.589), who were matched on age and educational level and were all males. These subjects were recruited on websites, advertisements, postings in hospitals and referrals. To be included as a smoker in this study, the subjects were required to smoke more than 15 cigarettes a day for two or more consecutive years and were required to have a result of four points or higher on the Fagerström Test for Nicotine Dependence (FTND; Fagerström, 1978; Heatherton et al., 1991). Other requirements for both smokers and healthy controls were: not to drink more than 30 alcoholic beverages a week, to not have a score of 12 or more on the Alcohol Use Disorder

Identification Test (AUDIT; Saunders et al. 1993), to have no neurological, psychological and or psychiatric disorders, no regular drug use (in the case of soft drugs this means no more than ten occasions a year and in the case of hard drugs no more than one or two occasions a year), to be between 18 and 55 years of age, not to use certain psychoactive medications, not to have severe stomach problems and to not have irremovable metal objects in or on their body.

MRI protocol

The MRI images were obtained by the Phillips Achieva XT 3T head only scanner equipped with a SENSE 32 channel receiver head coil at the Spinoza Centre Amsterdam. The participants were scanned in the supine position. The three-D T1 weighted images were collected using the following parameters; TR=8.2ms, TE=3.7ms, number of slices= 20, voxel size=1mm x 1mm x 1mm, field of view=240mm x 188mm x 220mm and matrix size=240mm x 187mm. MRS characteristics in the ACC are; TR=2000ms, TE=73ms, voxel size 35mm x 20mm x 15mm, the number of acquisitions = 384 (Figure 2 presents the voxel placement in the dACC).

Figure 2. Voxel placement in the dACC during the H – MRS scan

Questionnaires

The smoking habits were assessed with the FTND. This questionnaire consisted of six multiple choice questions with two or four answer options regarding smoking behavior. The scores ranged between 0 and 10, in which zero is an indication for low dependency and 10 for high dependency on nicotine.

The AUDIT screens for harmful and risky alcohol consumption/behavior (Saunders et al., 1993). It consists of ten multiple choice questions; each item is scored on a 0 to 5 or 0 to 4 scale. More points on this test are an indication for problematic alcoholic consumption.

The thoughts and feelings about smoking were assessed with the Obsessive Compulsive Smoking Scale (OCSS; Hitsman et al., 2010). This questionnaire consists of ten multiple choice questions with five answer options. This test is an adaptation of the Obsessive Compulsive Drinking Scale (OCDS; Anton et al., 1995; Roberts et al., 1999). Each answer option is worth 0, 1, 2, 3, 4, or 5 points. The higher the score on this test, the more obsessive thoughts and compulsive behaviors are present.

The intelligence is measured with the Dutch reading test for adults (NLV; Schmand et al., 1991). This is a test for vocabulary in which subjects need to pronounce the words correctly. All words are “imported” words from other languages and the difficulty and rarity of use in everyday language increases with each word. This test is an adaptation of the American NART.

The Baratt Impulsiveness Scale 11 (BIS-11; Patton et al., 1995) is a widely used questionnaire to asses self-reported impulsivity. This questionnaire consists of 30 questions in which each question is rated on a four point scale, more points are indicative for higher impulsivity.

The behavioural inhibition system/behavioural approach system (BIS/BAS) is a questionnaire which consists of two scales and attempts to assess these two systems. The first system is activated by punishment or omission of reward of conditioned stimuli and is also activated by novel stimuli. The second system is activated by reward or omission of punishment (Franken et al., 2005).

Delay discounting Task

The delay discounting task (DDT) is a task in which participants have to choose between a (smaller) reward in the more immediate future or a (bigger) reward in the more delayed future. The subjective value of a reward becomes less when it is delayed in time. The steepness of this discounting is an individual trait and the more rewards are discounted in the future the more impulsive an individual is (Mitchell, 1999; Wittman et al., 2007). The DDT

we used consisted of 138 questions in which participants have to choose between a variable reward now (€10.50, €10.00, €9.50, €9.00, €8.50, €8.00, €7.50, €7.00, €6.50, €6.00, €5.50, €5.00, €4.50, €4.00, €3.50, €3.00, €2.50, €2.00, €1.50, 1.00, €0.50, €0.25 or €0.00) or a fixed €10 with a variable amount of delay (0, 7, 30, 90, 180 or 365 days; Mitchell, 1999). The questions are alternated in a random order whether the fixed €10 (with a variable amount of delay) is presented left or right to the variable reward. Participants were instructed to click with the mouse on the answer they prefer. To calculate the discounting the following formula is used :

V = A

1+k∗D

V represents the subjective value of the delayed reward (indifference point), A represents the amount of money of the delayed reward, k represents the discounting rate and D is the delay (Bickel et al., 1999; Mazur, 2006; McKerchar et al., 2009; Mitchell, 1999). The indifference point is at what amount of delay of the fixed €10 participants subjectively value it equally to one of the smaller immediate rewards.

Analysis

The k-values from the DDT of the participants were calculated with the program GraphPad Prism and these k-values were used for further analysis with IBM SPSS statistics version 21.

The MRS spectrum, with a range of 4.3-1.9 in concentration, was analysed with the program LC-model (Linear Combination of Model Spectra; Provencher, 1993). With this program metabolite concentrations were estimated by fitting our in vivo spectra to already in vitro acquired spectra (Figure 3 is an example of the acquired spectra). The reliability of the fit is measured by the Cramér-Rao lower bounds (CRLB) in which an SD of less than 20 percent is considered reliable (Provencher, 1993). The glutamate CRLB’s ranged between 3% and 57% (two participants had more than 20 percent; 23% which was a smoker and 57% which was a healthy control participant). Both were excluded in the analysis for reliability reasons. The glutamate concentrations we used in our analysis was measured as follows: (Glutamate/(Naa + Naag)) / ((Naa+Naag)/(PCr+Cre)).

Figure 3. Chemical shift spectra created in LC-model in which metabolites were measured

The data that met the criteria for parametric testing, the t-test and Pearson’s correlations have been used and for the non-parametric distributed data the Mann-Whitney test and Kendell’s tau have been used. In the case of the k-values, a natural logarithm transformation has been performed to make the data normally distributed. The data has been analysed in IBM SPSS statistics version 21. The data was checked for outliers, data with a z score of +/- 3.29 were removed from the analysis.

Procedure

This research consisted of a screening on the telephone and one session at the Spinoza centre. In the telephone screening general questions were asked as: age, educational level, contact information and place of residence. Regarding their smoking behaviour the following questions were asked, the number of years they have been smoking regularly, the number of cigarettes they smoke a day and the FTND questionnaire was assessed. Alcohol consumption was assessed with the AUDIT questionnaire, they were asked about their recent and past drug usage in frequency, the amount and what type of drug. They were asked about their physical

and mental health and whether they have to take medications. The last part consisted of questions whether they were able to go in to the MRI scanner. This screening took around 20 minutes for smokers and 15 minutes for healthy controls, since the questions regarding smoking were not assessed.

The participants filled out and signed the informed consent. Then they filled out the fMRI screening list. The carbon mono oxide levels were measured in parts per million with the smokelyzer. Then they filled out three questionnaires: the OCSS, the BIS-11 and the BIS/BAS. The delay discounting task followed upon the questionnaires which is a task assessed on the computer. The general intelligence (IQ) was estimated with the Nederlandse Leestest voor Volwassenen (NLV), which is the Dutch translation of the National Adult Reading Test (NART). Psychopathology was assessed with the MINI, which is a diagnostic interview based on the DSM IV. Alcohol, drug and smoking consumption was quantified with the TLFB for the past 6 months. Then the participants underwent the MRI protocol.

Results

Demographic characteristics

Age did not differ between smokers and healthy controls, U = 168.5, p =0.071, r =-0.061. The participants did not differ on IQ either, t(38) = -2,009, p =0.052. Smokers did not differ on the AUDIT questionnaire with the healthy controls, t(38) = 1.917, p =0.063 see Table 1.

Table 1. Statistics of the demographic characteristics. Non-parametric distributed variables are denoted with an asterisk * and the median is shown instead of the mean in the M/Mdn column.

Variables Smokers Controls Statistic Test M/Mdn SE Q1 Q3 N M/Mdn SE Q1 Q3 N T or U* df p value Age* 38.50 1.87 28.75 43.50 26 41.00 3.59 23.50 52.25 14 168.5 - 0.071 IQ (NART) 103.58 1.59 26 109.36 2.58 14 -2.01 - 0.052 FTND 6.12 0.38 26 - - - -Years smoking 18.08 1.89 26 - - - -Cigarettes a day * 20.00 1.31 19.00 25.00 26 - - - -AUDIT 6.50 0.78 26 4.29 0.62 14 1.92 38 0.063

Glutamate concentrations

The glutamate concentrations in the dACC did not differ significantly between smokers and healthy controls, t(35) = 1.170, p =0.250 see Table 3.

Impulsivity

The smokers (M = 67.12, SE = 1.86) and healthy controls (M = 62.15, SE = 2.87) did not differ significantly on the BIS-11 total scale, t(36) = 1.50, p =0.14. On the BIS-11 cognitive subscale there is also no significant difference between smokers (M = 17.88, SE = 0.84) and healthy controls (M = 16.77, SE = 1.340), t(36) = 0.733, p =0.468. The smokers (M = 23.56, SE = 0.866) and healthy controls (M = 22.85, SE = 1.085) did not differ t(36) = 0.498, p

=0.622 on the motor subscale. The smokers (Mdn = 26.00, Q1 = 22.50, Q3 = 27.00) and

healthy controls (Mdn=22.00, Q1=20.00, Q3=23.50) did differ significantly on the non-planning subscale U = 79.50, p =0.009, r =-0.418. On the BIS scale of the BIS/BAS the smokers (M = 19.20, SE = 0.821) and healthy controls (M = 17.23, SE = 1.336) did not differ, t(36) = 1.323, p =0.194. The smokers (M = 11.64, SE = 0.45) and healthy controls (M = 10.85, SE = 0.767) did not differ on the BAS drive scale, t(36) = 0.953, p =0.347. The smokers (M = 11.84, SE = 0.415) and healthy controls ( M =11.23, SE = 0.508) did also not differ on the BAS fun scale, t(36) = 0.892, p =0.378. There was also no significant difference between smokers (M = 17.08, SE = 0.424) and healthy controls (M = 17.23, SE = 0.496), on the BAS reward scale, t(36) = -0.219, p =0.828. The smokers (M = -5.40, SE = 0.76) did not significantly differ from the healthy controls (M = -4.5979, SE = 0.54986), on the delay discounting task, t(36) = -0.76, p =0.454.

Smoking scales

The smokers (Mdn = 19.00, Q1 = 14.50, Q3 = 22.00) and healthy controls (Mdn < 0.0001) did differ significantly on the OCSS total scale, U < 0.0001, p < 0.0001, r =-0.83. On the OCSS preoccupation subscale the smokers (Mdn = 10.00, Q1 = 7.00, Q3 = 13.00) and the healthy controls (Mdn < 0.0001) differed significantly, U = 11.00, p < 0.0001, r =-0.79. The smokers (Mdn = 8.00, Q1 = 7.00, Q3 = 10.00) and healthy controls (Mdn <0.0001) differed significantly on the OCSS compulsion subscale, U < 0.0001, p < 0.00001, r =-0.83. The smokers (Mdn = 17.50) and healthy controls (Mdn < 0.0001) differed significantly on the CO ppm measured with the smokelyzer, U = 3.00, p < 0.0001, r =-0.77.

distributed are denoted with an asterisk* and the median is shown instead of the mean in the M/Mdn column.

Variables Smokers Controls Statistic Test M/Mdn SE Q1 Q3 N Mean SE Q1 Q3 N T or U* df p value Glutamate 0.22 0.02 24 0.167 0.02 13 1.17 35 0.250 BIS – 11 total 67.12 1.86 25 62.15 2.87 13 1.50 36 0.140 BIS – 11 Cognitive 17.88 0.84 25 16.77 1.34 13 0.73 36 0.468 BIS – 11 Motor 23.56 0.87 25 22.85 1.09 13 0.50 36 0.622 BIS – 11 * Non – planning 26.00 22.50 27.00 25 22.00 20.00 23.50 13 79.50 0.009 BIS/BAS BIS 19.20 0.82 25 17.23 1.34 13 1.32 36 0.194 BIS/BAS BAS drive 11.64 0.45 25 10.85 0.77 13 0.95 36 0.892 BIS/BAS BAS fun 11.84 0.42 25 11.23 0.51 13 0.89 36 0.378 BIS/BAS BAS reward 17.08 0.42 25 17.23 0.50 13 -0.22 36 0.828 DDT -5.4 0.76 25 4.60 0.55 13 0.76 36 OCSS Total* 19.00 14.50 22.00 26 <0.001 <0.001 <0.001 14 <0.0001 <0.0001 OCSS Preoccupation* 10.00 7.00 13.00 26 <0.001 <0.001 <0.001 14 11.00 <0.0001 OCSS Compulsion* 8.00 7.00 10.00 26 <0.001 <0.001 <0.001 14 <0.0001 <0.0001 Smokelyzer CO* 17.50 11.00 28.00 26 <0.001 <0.001 1.00 11 3.00 <0.0001

Correlations between glutamate concentration in the dACC and impulsivity

p =0.413, BIS, r =-0.056, p =0.749, BAS drive subscale, r =0.022, p =0.900, BAS fun

subscale, r =-0.086, p =0.623, BAS reward subscale, r =-0.029, p =0.869, DDT, r =0.176, p

=0.304.

Smoke group

There were no significant correlations between the glutamate concentrations in the dACC and the BIS-11, r =-0.112, p =0.612, BIS-11 cognitive subscale, r =-0.089, p =0.685, BIS-11

of the BIS/BAS, r =-0.080, p =0.718, BAS drive subscale r =0.109, p =0.622, BAS fun subscale, r =-0.063, p =0.744, BAS reward subscale, r =0.004, p =0.986, DDT, r =0.285, p

=0.188.

Control group

There were no significant correlations between the glutamate concentration in the dACC and the BIS-11 total scale, r =-0.329, p =0.297, BIS-11 cognitive subscale, r =-0.065, p =0.841, BIS-11 non-planning subscale, τ = -0.110, p =0.627, the BIS of the BIS/BAS, r =-0.099, p

=0.761, BAS drive subscale, r =-0.289, p =0.363, BAS fun subscale, r =-0.268, p =0.401,

BAS reward subscale, r =-0.186, p =0.563 and the DDT, r =-0.125, p =0.685. There was a significant correlation between the glutamate concentration in the dACC and the BIS motor subscale, r =-0.580, p =0.048.

Table 3. Correlation table of the glutamate concentration in the dACC and impulsivity of all subjects, only smokers, and only controls. Kendall’s tau (τ) is used instead of Pearson’s rho (r) for the variables which are denoted with an asterisk* .

Glutamate Glutamate smoke Glutamate control

r or τ p r or τ p r or τ p

BIS-11 total -0.11 0.53 -0.11 0.61 -0.33 0.3

BIS-11 cognitive -0.06 0.73 -0.09 0.69 -0.06 0.84

BIS-11 motor -0.04 0.83 0.08 0.73 -0.58 0.048

BIS-11 Non - planning * -0.1 0.41 -0.2 0.2 -0.11 0.63

BIS/BAS BIS -0.06 0.75 -0.08 0.72 -0.01 0.76

BIS/BAS BAS drive 0.02 0.9 0.11 0.62 -0.29 0.36

BIS/BAS BAS fun -0.09 0.62 -0.06 0.74 -0.27 0.4

BIS/BAS BAS reward -0.03 0.87 0.004 0.1 -0.19 0.56

DDT 0.18 0.3 0.29 0.19 -0.13 0.69

Correlation between the glutamate concentration in the dACC and smoking

scales in the smoke group

There were no significant correlations between glutamate concentrations in the dACC and the FTND, r =0.264, p =0.213, OCSS total scale, r =0.060, p =0.781, OCSS preoccupation subscale, r =0.063, p =0.771, OCSS compulsion subscale, r =0.025, p =0.906, CO measured with the smokelyzer, r =0.232, p =0.275.

FTND OCSS Total scale OCSS Preoccupation OCSS compulsion Smokelyzer CO r p r p r p r p r p Glutamate 0.2 6 0.2 1 0.06 0.78 0.06 0.77 0.0 3 0.91 0.23 0.28

Correlations between the smoking scales and the impulsivity measurements

Smoke group

There were no significant correlation between the FTND, 11, r =-0.110, p =0.617, BIS-11 cognitive subscale, r =-0.193, p=0.377, BIS-BIS-11 motor subscale, r =0.084, p =0.702, BIS of the BIS/BAS, r =-0.228, p =0.295, BAS fun subscale, r =-0.017, p =0.937, BAS reward subscale, r =0.390, p =0.066 and the DDT, r =0.185, p =0.398, BIS-11 non-planning subscale, τ = -0.133, p =0.425. There was a significant correlation between the FTND and the BAS drive subscale, r =0.475, p =0.022.

There were no significant correlations between the OCSS total scale and the BIS-11, r

=0.179, p=0.415, BIS-11 cognitive subscale, r =-0.068, p =0.758, BIS-11 motor scale,

r =-0.211, p =0.335, BIS-11 non-planning subscale, τ = -0.034, p =0.829, BIS of the

BIS/BAS, r =-0.055, p =0.801, BAS drive subscale, r =0.277, p =0.201, BAS fun subscale, r

=-0.196, p =0.371, BAS reward subscale, r =0.301, p =0.163 and DDT, r =0.312, p =0.147.

There were no significant correlations between the OCSS preoccupation subscale and the 11 total scale, r =-0.238, p =0.275, 11 cognitive subscale, r =-0.102, p =0.643, BIS-11 motor subscale, r =-0.262, p =0.227, BIS-BIS-11 non-planning subscale, τ = -0.031, p =0.849, BIS of the BIS/BAS, r =-0.242, p =0.265, BAS drive subscale, r =0.196, p =0.371, BAS fun subscale, r =-0.203, p =0.352, BAS reward subscale, r =0.162, p =0.459 and DDT, r =0.240,

p =0.270.

There were no significant correlations between the OCSS compulsion subscale and the BIS-11 total scale, r =0.032, p =0.884, BIS cognitive subscale, r =0.038, p =0.864, BIS motor, r

=-0.002, p =0.994, BIS-11 non-planning subscale, τ = 0.118, p =0.476, BIS of the BIS/BAS, r =0.377, p =0.076, BAS drive subscale r =0.326, p =0.129, BAS fun subscale, r =-0.089, p =0.686 and the DDT, r =0.314, p =0.144. There was a significant correlation between the

There were significant correlations between the CO concentration and the BIS-11 total scale,

r =-0.468, p =0.024, BIS-11 non-planning subscale, τ = -0.0491, p =0.002, BIS of the

BIS/BAS, r =-0.460, p =0.027, BAS drive, r =-0.06, p =0.79, BAS fun subscale, r =-0.462, p

=0.027. There were no significant correlations between the CO concentrations and the BIS-11

cognitive subscale r =-0.278, p =0.199, BIS-motor scale, r =-0.305, p =0.158, BAS reward subscale, r =-0.038, p =0.864 and DDT, r =-0.039, p =0.859.

Table 5. Correlation table of the impulsivity scales and the smoking severity scales. Kendall’s tau (τ) is used instead of Pearson’s rho (r) for the variables denoted with an asterisk*.

FTND OCSS Total scale OCSS Preoccupation OCSS Compulsion Smokelyzer CO r or τ p r or τ p r or τ p r or τ P r or τ p BIS-11 Total scale -0.11 0.6 2 0.18 0.4 2 -0.24 0.28 0.03 0.8 8 -0.47 0.02 BIS-11 Cognitive -0.19 0.3 8 -0.07 0.7 6 -0.10 0.64 0.04 0.8 6 -0.28 0.20 BIS-11 Motor 0.08 0.7 0 -0.21 0.3 4 -0.26 0.23 -0.002 0.9 9 -0.31 0.16 BIS-11 * Non-planning -0.13 0.4 3 -0.03 0.8 3 -0.03 0.85 0.118 0.4 8 -0.05 0.002 BIS/BAS BIS -0.23 0.3 0 -0.06 0.8 0 -0.24 0.27 -0.24 0.2 7 -0.46 0.03 BIS/BAS BAS drive 0.48 0.0 2 0.28 0.2 0 0.20 0.37 0.20 0.3 7 -0.06 0.79 BIS/BAS BAS fun -0.02 0.9 4 -0.20 0.3 7 -0.20 0.35 -0.20 0.3 5 -0.46 0.03 BIS/BAS BAS reward 0.39 0.0 7 0.30 0.1 6 0.16 0.46 0.162 0.4 6 -0.04 0.86 DDT 0.185 0.4 0 0.31 0.1 5 0.24 0.27 0.24 0.2 7 -0.04 0.86

Discussion

The current study addressed one main research question and fsive related sub research questions. The main question was to investigate whether smokers have different glutamate concentrations in the dACC compared to healthy controls. The second question was to determine whether smokers are more impulsive than healthy controls. The third question was to check whether smokers and healthy controls would differ in smoking severity. The fourth question was to find out whether there is an association between the glutamate concentration

between the glutamate concentration in the dACC and smoking severity. And the last question was to determine whether there is an association between impulsivity and smoking severity.

The current study did not find a significant difference between the smokers and healthy controls, with respect to the glutamate concentration in the dACC. It was expected that smokers and healthy controls would differ in their glutamate concentration (Hermann et al., 2011; Hermann et al., 2012; Lee et al., 2007; Thoma et al., 2011; Yang et al., 2009; Yücel et al., 2007). Second, we also expected that smokers would be more impulsive than healthy controls. Overall, this has not been supported by the current data, however a subcomponent of impulsivity, which encompasses self-control and cognitive complexity, shows a significant difference between smokers and healthy controls. In accordance to our hypothesis, the smokers and healthy controls differed in smoking severity. And the healthy controls scored close to zero in smoking severity. Furthermore, there was no association between the glutamate concentration in the dACC and impulsivity. This was also in contrast to our hypothesis. However, a negative association has been found in healthy controls between the glutamate concentration in the dACC and one of the subtypes of impulsivity, which encompasses acting in the spur of the moment in healthy controls. There was no association between the glutamate concentration in the dACC and smoking severity. This was in contrast to our a priori hypothesis. Overall, there were no associations between smoking severity and impulsivity, but on some subtypes there were negative correlations and positive correlations. So based on these results most of the hypotheses could not be confirmed.

Limitations

There are a couple of possible explanations for these differences on a methodological level.

1_{H-MRS is a very interesting method to measure metabolite concentrations, since it is the only}

non-invasive manner to do so. However, the fitting of the in vivo data to the already in vitro acquired spectra in LC-model is quite subjective, so the inter-rater reliability could be a problem. Besides the inter-rater reliability, the test-retest reliability yielded conflicting results; fairly poor test-retest reliability at 3T for measuring glutamate concentrations have been reported (Gasparovic et al., 2011), but also good test-retest reliability for measuring glutamate at 3T have been reported in another study (Schubert et al., 2004). Although the 3 T field strength is increasingly used in 1_{H-MRS research and has a better spectral resolution than 1.5}

2008), and this decreases the accuracy of measuring glutamate. Another technical flaw of

1_{H-MRS is its inability to distinguish between extra- and intra-cellular glutamate (Thoma et}

al., 2011) and this differentiation is desirable, since these two pools of glutamate are thought to have different functions in the brain (Kalivas, 2009). The participants were tested at different times (for three hours) during the day, this could influence the metabolite concentrations. NAA has been reported to be higher in the morning than in the afternoon in healthy participants (Soreni et al., 2005) and circadian glutamate fluctuations have been reported in mice in the striatum and the NAc (Castañeda et al., 2004) and this circadian rhythm could be present for glutamate in the ACC in humans as well.

A factor, which may influence the measurement of the metabolites is scanner/frequency drift due to heating of the MRI scanner and this changes the B0 field. Prior research has shown that

drifts of -10 Hz were common and influenced the GABA measurements (Harris et al., 2013). The fMRI technique is known for heating up the MRI scanner, thus fMRI scans which were planned right before our scans could have induced frequency drift and this may have influenced our glutamate measurements.

Explanations

In accordance with our finding, no difference was found between smokers, former smokers and healthy controls in the glutamate concentration in the ACC (Gallinat & Schubert, 2007). This was also found in the ACC in alcohol dependent participants compared to controls (Lee et al., 2007; Thoma et al., 2011). However, lower glutamate concentrations have been found in smokers, who were unable to stay abstinent compared to smokers who successfully stayed abstinent (Mashoon et al., 2011). A possible explanation is that we used a different glutamate ratio. Another possible explanation for this difference is the different field strength that has been used. We and Gallinat & Schubert (2007) used a 3T scanner but Mashoon et al., (2011) used a 4T scanner. Higher field strengths are associated with increased spectral resolution and this increases the accuracy of quantifying glutamate and glutamine (Ramadan et al., 2013), so this may explain the conflicting results. Gender differed between the three studies (the last study consisted of only female participants (Mashoon et al., 2011), the other study of both

men and woman (Gallinat & Schubert, 2007), while our study used exclusively men) and this may account for the different results that have been found, since it has been reported that men have higher glutamate, GABA and glx concentrations than women (O’Gorman et al., 2011). The study of Mashoon (2011) consisted of a small sample size, four and five for abstinent and non-abstinent smokers respectively.

Contrary to our results, lower glutamate or glutamine or glx concentrations or glutamate glutamine or glx ratios have been found in other addictions as well, in the rACC of chronic cocaine users compared to healthy controls (Yang et al., 2009), in the ACC of marijuana users compared to healthy controls (Prescot et al., 2011), in the ACC of alcohol dependent subjects in remission compared to healthy controls (Thoma et al., 2011) in the dACC of opiate users compared to healthy controls (Yücel et al., 2007) and in the ACC of opiate users compared to healthy controls when only subjects under the age of 34 were taken into account (Hermann et al., 2011). However, contrary to our findings higher glutamate or glutamine or glx concentrations or glutamate or glutamine or glx ratios have been found as well in alcohol dependent participants compared to healthy controls (Lee et al., 2007; Thoma et al., 2011), in the ACC of cocaine dependent participants compared to healthy controls (Schmaal et al., 2012b). Higher glutamate concentrations have been reported, when early withdrawal in alcohol dependent subjects was compared to two weeks of abstinence and to healthy controls (Hermann et al., 2012).

The results among the different kinds of drugs of abuse are conflicting, but even within one type of drug of abuse the results are conflicting. A difficulty in addiction research is poly-drug use, most substance abusers use other substances of abuse as well. This makes it hard to determine which drug of abuse (or which combinations of drugs of abuse) are sufficient and/ or necessary to disrupt the glutamate homeostasis and more specifically in which direction. Besides this, prior research differs on other (confounding) factors. There are indications that these factors - such as age (Hermann et al., 2011; Schubert et al., 2004), sex (O’Gorman, 2012), duration of abstinence when scanned (Hermann et al., 2012), glutamate ratios used (Lee et al., 2007), whether glutamate and glutamine are assessed together or separate (Thoma et al., 2011), number of withdrawals (Hermann et al., 2012) and age of onset of substance use - have an influence on the glutamate concentration or an influence on the measurement of glutamate. And all these factors or combinations of factors could account for the differences

role of the glutamate concentration in the ACC, the results in smoking addiction and alcohol addiction seem to be more mixed than for opiate addiction. A possible explanation for this is, that tobacco is estimated to consist of around 5000 chemicals (Talhout et al., 2011). Some of these compounds other than nicotine are identified as addictive agents, but of most of these compounds it is still unknown whether they possess addictive properties (Talhout et al., 2011). Different brands of cigarettes differ in chemical composition and therefore could influence the glutamate concentration differently. And this could lead to more mixed results, since tobacco is less pure than cocaine and opiates. A commonality between alcohol and cigarette smoking is the possible role of acetaldehyde in both addictions (Mcbride, 2002; Talhout et al., 2007). There are indications that acetaldehyde decreases the glutamate concentration in the ACC in rats (Zuo et al., 2007) and this is remarkable since no differences or higher concentrations of glutamate (ratios) have been found in alcohol dependent subjects compared to healthy controls (Lee et al., 2007; Thoma et al., 2011). A suggestion is that acetaldehyde effects are slower than alcohol and the other tobacco substance including nicotine. The initial effect of alcohol regarding glutamate is increasing the concentration in the ACC and that acetaldehyde counterbalances this effect and no differences are found as mentioned above. This could also explain why in an acute withdrawal paradigm the glutamate concentration is higher and normalizes after a longer period of abstinence (Hermann et al., 2012). In the case of cigarette smoking, the above suggestion implies that nicotine has no effect or a very minor effect on the glutamate concentration in the ACC and that acetaldehyde lowers the glutamate concentration slightly, which makes it possible to find no differences in glutamate concentration in the ACC between smokers and healthy controls as we did and the study of Gallinat & Schubert, 2007 and a lower concentration in smokers compared to healthy controls (Mashoon et al., 2011).

Another suggestion is, that systems usually seem to strive towards homeostasis and that an initial peak in a certain direction is followed by a smaller peak in the other direction; like an sinusoid with a decreasing amplitude until it reaches equilibrium and cycles around it. So drug intake induces a peak in a certain direction. This is followed by a counter reaction in the opposite direction. The glutamate concentration that is measured depends on in which phase it is in the cycle of homeostasis. Appendix 2 presents a more detailed explanation.

More research is needed to find out how the glutamate cycle/homeostasis works. A method is needed to distinguish between extra cellular and intra cellular glutamate because these pools of glutamate are thought to have different function in the brain and influence the glutamate homeostasis (Kalivas, 2009). An important step in understanding the glutamate homeostasis is the ability to distinguish between glutamate and glutamine. Higher field strengths in 1_H-MRS

increases the spectral resolution and that increases the accuracy in quantifying glutamate and glutamine separately (Ramadan et al., 2013). Determining the timeframe of the glutamate cycle would be interesting as well, because in order to map its cycle the temporal resolution in

1_{H-MRS needs to be at least smaller than ½ of one cycle. Although, homogenous addiction}

groups lack external validity (most addicts use other substances as well) it provides insight in what the influence of that drug is on the glutamate concentration. With mixed groups, there are many confounding factors when comparing one research to the other. Hopefully research with these homogenous groups will provide more insight in commonalities on a biochemical / neurological level between substances of abuse, but it may also provide insight in differences on a biochemical/ neurological level between substances of abuse.

To conclude, the current study did not find a difference in the glutamate concentration in the dACC of smokers compared to healthy controls. No differences have been found in impulsivity between smokers and healthy controls, however smokers seem to be more impulsive on subtype of impulsivity which encompasses self-control and cognitive flexibility. Furthermore, no associations have been found between the glutamate concentration and impulsivity, except for the subtype of impulsivity, which encompasses acting in the spur of the moment.

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Appendix 1

In operant conditioning there are five mechanisms to alter / influence behavior. The first, positive reinforcement, is the addition of a pleasant stimulus. The second, is negative reinforcement, which hold that an unpleasant stimulus is removed. The third, positive punishment, encompasses that a negative stimulus is added. Negative punishment is, that a positive stimulus is removed. And extinction is that a certain behavior no longer induces a certain consequence (either negative or positive), see table 1. At the earlier stages of drug addiction the drug elicit a pleasant effect (positive reinforcement). As the addiction progresses being “high” or under influence is the new default state and being abstinent of the drug for some time elicit (unpleasant) withdrawal symptoms. In order to feel better again you have to get rid of the withdrawal symptoms (negative reinforcement; Robinson & Berridge 1993).

Table 1. In operant conditioning five types of techniques are used to manipulate behavior. Positive reinforcement is the addition of a pleasant stimulus, negative reinforcement the removal/omission of an aversive stimulus, positive punishment is the addition of a aversive stimulus, negative punishment is removal/omission of a positive stimulus and extinction in which a certain behavior no longer induces a certain consequence (either negative or positive).

Techniques in operant conditioning Add Remove Positive reinforcement Negative reinforcement Positive punishment Negative punishment Extinction = Pleasant stimulus = Aversive stimulus

Appendix 2

Graph Y=1 and Y=2 are examples of homeostasis, the first one has a smaller amplitude than the second one (Figure 4).

(-2π) (-1π) (0π) (1π) (2π) (3π) (4π) -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

Homeostasis

Y1=Sinus(2x) Y2=2Sinus(2x) Equilibrium

Time (x) A m p li tu d e ( g lu ta m a te c o n ce n tr ati o n )

Figure 4. Visualization of homeostasis

Figure 5 represents healthy controls (Y=1) and two systems out of balance (Y=3 and Y=4). A drug brings a system out of balance in the upper direction and this is followed by a counter reaction in the opposite direction, until homeostasis is reached, which is shown in two

examples, graph Y=3 and Y=4. In the case of graph Y=3, after some time, the system reaches homeostasis which is equal to the homeostasis of a healthy individual (Y=1; clicking on the blue line will show that it is equal to the yellow line). When an individual takes the drug again, it will get out of balance again. In the case of graph 4 the system reaches homeostasis as well but its homeostasis is not equal to that of a healthy individual. Although graph Y3 and Y4 have more extreme values in both directions than Y1 it is possible to measure a higher value in Y1 when it is at x= -1.5 π compared to Y3 and Y4measured at x= -π. So if this homeostasis exist, it is very important to measure it at the same phase in the period. In order to do this, a sufficient temporal resolution is needed to determine the glutamate cycle.

(-2π) (-1π) (0π) (1π) (2π) (3π) (4π) -5 -4 -3 -2 -1 0 1 2 3 4 5

Out of Balance

Y1=Sinus(2x) Y3=Out of Balance Y4=Out of Balance Equilibrium

Time (x) A m p li tu d e ( g lu ta m a te c o n ce n tr ati o n )