Skip to main content
NCBI home page
PMC Canada Author Manuscripts logoLink to PMC Canada Author Manuscripts
. Author manuscript; available in PMC: 2015 Jul 24.
Published in final edited form as: Genes Brain Behav. 2015 Jul 15;14(6):466–476. doi: 10.1111/gbb.12226

COMT polymorphism modulates the resting-state EEG alpha oscillatory response to acute nicotine in male non-smokers

H Bowers †, D Smith ‡, S de la Salle §, J Choueiry ‡, D Impey §, T Philippe , H Dort , A Millar ‡,**, M Daigle **, P R Albert ‡,**, A Beaudoin , V Knott ‡,§,¶,*
PMCID: PMC4514526  CAMSID: CAMS4922  PMID: 26096691

Abstract

Performance improvements in cognitive tasks requiring executive functions are evident with nicotinic acetylcholine receptor (nAChR) agonists, and activation of the underlying neural circuitry supporting these cognitive effects is thought to involve dopamine neurotransmission. As individual difference in response to nicotine may be related to a functional polymorphism in the gene encoding catechol-O-methyltransferase (COMT), an enzyme that strongly influences cortical dopamine metabolism, this study examined the modulatory effects of the COMT Val158Met polymorphism on the neural response to acute nicotine as measured with resting-state electroencephalographic (EEG) oscillations. In a sample of 62 healthy non-smoking adult males, a single dose (6 mg) of nicotine gum administered in a randomized, double-blind, placebo-controlled design was shown to affect α oscillatory activity, increasing power of upper α oscillations in frontocentral regions of Met/Met homozygotes and in parietal/occipital regions of Val/Met heterozygotes. Peak α frequency was also found to be faster with nicotine (vs. placebo) treatment in Val/Met heterozygotes, who exhibited a slower α frequency compared to Val/Val homozygotes. The data tentatively suggest that interindividual differences in brain α oscillations and their response to nicotinic agonist treatment are influenced by genetic mechanisms involving COMT.

Keywords: Alpha, catechol-O-methyltransferase, cognition, dopamine, electroencephalography, genotype, nicotine, oscillations, polymorphism, resting state

The cognitive enhancement properties of nicotinic acetylcholinergic receptor (nAChR) agonists such as nicotine (Heishman et al. 2010) are associated with their moderating effects on the dopamine (DA) pathway connecting the ventral tegmental area (VTA) with cortical regions, including the prefrontal cortex (PFC). Agonists effect this enhancement by binding to nAChRs on VTA DA projection neurons, increasing dopamine signaling and processing in cortical networks (Jasinska et al. 2013; Livingstone & Wonnacott 2009; Mansvelder et al. 2006). A range of evidence has further shown that nicotinic stimulation increases dopamine concentrations in the PFC, where stimulation of presynaptic nAChRs elevates dopamine levels and influences cognitive processes (Wallace & Bertrand 2013).

Studies of cognitive performance (Newhouse et al. 2004; Perkins 1999) and patterns of activation of task-specific neural networks (Bentley et al. 2011; Newhouse et al. 2011) show considerable intersubject response variability to nicotine and nicotinic agonists, often resembling an ‘inverted-U’ shaped function (persons exhibiting suboptimal performance prior to drug challenge tend to show performance benefits and normalized neural activity with nicotine, while those performing at optimal level or exhibiting task-related neural efficiency tend to show nil or diminished cognitive and neural response to nicotine). While the neurobiological causes underlying this heterogeneity are not well understood, there is an increasing trend to use molecular genetic approaches to assay individual differences in cognitive functions (Greenwood & Parasuraman 2003) and response to pharmacological treatments (Apud & Weinberger 2006; Goldstein et al. 2007), including nicotine (Herman & Sofuoglu 2010). Such approaches focus on allelic variations in the pharmacodynamic and pharmacokinetic properties of neurotransmitter genes involved in the different aspects of cognition (Parasuraman 2009). Of the likely candidate genes influencing response heterogeneity to nicotine, those regulating DA neurotransmission show promise as pharmacological studies in animals (Grannon et al. 2000) and humans (Kimberg et al. 1997; Mattay et al. 2000; Mehta et al. 2000) indicate that the effects of amphetamine and other dopamimetic drugs are baseline dependent. Relatively poor performers on prefrontal cognitive tasks have improved with treatment, whereas high performers have shown no response or response deterioration.

Catabolic flux of synaptic dopamine in the cortex is controlled primarily by the enzyme catechol-O-methyltransferase (COMT) (Huotari et al. 2002). The COMT gene contains a single nucleotide polymorphism that produces a valine-to-methionine (Val/Met) substitution at position 158 (Val158Met), producing a trimodal distribution of enzyme activity (Floderus et al. 1981; Lachman et al. 1996). Met158 homozygotes biotransform dopamine less than Val carriers, showing one third less COMT enzymatic activity in brain. Hence, there are higher extracellular dopamine levels with Met homozygotes (Chen et al. 2004). Val/Met heterozygotes exhibit intermediate levels of COMT activity (Weinshilboum et al. 1999).

Thus, COMT is an excellent candidate gene for modulating dopamine levels and function in the cortex and for determining where on the inverted-U shaped curve of dopamine function an individual lies (Tunbridge et al. 2006). However, inconsistent behavioral data and performance meta-analyses support only a weak association between COMT polymorphisms and individual differences in PFC function (Barnett et al. 2007; Munafo et al. 2005). Nevertheless, investigations of individual differences with intermediate brain-based phenotypes, more sensitive for detecting gene effects on the brain (Green et al. 2008; Parasuraman & Jiang 2012), have found greater cortical processing efficiency in Met158 homozygotes compared to Val158 homozygotes, with heterozygotes displaying intermediate activation levels (Egan et al. 2001; Heinz & Smolka 2006). Acute dosing with amphetamine, which elevates synaptic dopamine levels, increased PFC task-evoked cortical efficiency in individuals with the Val/Val genotype, who have presumed low prefrontal synaptic dopamine, and reduced PFC processing efficiency compared to low-activity Met/Met genotypes (Mattay et al. 2003).

The limited functional magnetic resonance imaging (fMRI) investigations of the COMT polymorphism’s effects on the cerebrovascular activational response to nicotine in smokers have shown mixed results. For example, Val/Val smokers were more prone to cognitive impairment and reduced prefrontal activation during smoking abstinence (Loughead et al. 2009) but another study showed Met/Met smokers with significant activation reduction of frontal executive control regions during cessation (Ashare et al. 2013). Val/Val genotypes experienced more severe withdrawal symptoms following cessation, with greater subjective effects from acute intravenous nicotine (Herman et al. 2013; Lee et al. 2013). Because these studies in chronic smokers may simply reflect a ‘remediation’ of a cortical deficiency during nicotine withdrawal (Ashare et al. 2014; Beaver et al. 2011; Cole et al. 2010), a clearer picture of COMT-mediated response differences to nicotine may be obtained using nicotine-naïve volunteers examined with electrophysiological probes that permit direct, instantaneous detection of neuronal activity.

Electroencephalographic (EEG) studies have linked neuronal oscillations at low and high frequency ranges with specific cognitive functions (Lopes da Silva 2013; Uhlhaas et al. 2009; Wang 2010). These oscillations are key to sculpting temporal coordination of neural networks governing cognitive functions such as perception, attention and working memory (Cantero & Atienza 2005; Kaiser & Lutzenberger 2003; Lisman & Buzsaki 2008). The basic building blocks defining these oscillations can be probed with the spectral profiling of EEG recordings during a resting state (Narayanan et al. 2014), and have shown that distinct changes in oscillatory activity in low and high frequencies are associated with different drug classes (Knott 2000; Saletu et al. 2002), including cognitive-enhancing drugs (Ahnaou et al. 2014; Leiser et al. 2011) and nicotine (Knott 1990).

In acute smoking, EEG studies have shown a characteristic, stimulant-like pharmaco-EEG profile – accelerating the dominant (α) oscillatory frequency (PAF), increasing power of α2, β, decreasing power of δ, θ, α1 (Knott 2001; Knott & Venables 1977). Similar patterns are observed in smokers using nicotine replacement products (Knott et al. 1999; Lindgren et al. 1999; Pickworth et al. 1986, 1988; Teter et al. 2002). Individual differences in these profiles are reported, with power variations in accord with performances on frontal lobe tasks (Knott et al. 1995), presmoking arousal level (Shikata et al. 1995), hemisphere dominance (Domino et al. 1995a) and personality (Tatsuno 1995). In non-smokers, oscillatory changes due to acute nicotine administration are limited primarily to α rhythms, with increases observed in both PAF (Foulds et al. 1994; Harkrider et al. 2001) and frontal upper frequency α2 power (Fisher et al. 2012a) during resting states, and increases in anterior α2 during working memory tasks (Fisher et al. 2012b, 2013). Nicotine-induced oscillatory response differences between smokers and non-smokers may reflect genetic factors, including COMT (Beuten et al. 2006; Colilla et al. 2005; Guo et al. 2007), involved in smoking initiation and progression to dependence (Kendler et al. 1999; Maes et al. 2004) or individual differences in EEG. Little is known about the genetics underlying EEG traits or pharmacologically modulated EEG, but twin studies show that heritability of resting EEG oscillations is substantial (Stassen et al. 1987), particularly for PAF (Posthuma et al. 2001; Smit et al. 2005, 2006) and α-band oscillations with 80–90% heritability estimates (Van Beijsterveldt & van Baal 2002; Van Beijsterveldt et al. 1996). The COMT polymorphism contributes to individual differences in brain α oscillations, with Val homozygotes exhibiting reduced α2 and PAF compared to Met/Met carriers (Bodenmann et al. 2009a; Enoch et al. 2003), who exhibited greater delta, theta and beta (Solis-Ortiz et al. 2015).

We have examined the COMT polymorphism’s moderating effects on the EEG oscillatory response to acute nicotine administration in non-smokers with upper alpha (α2) and PAF as primary endpoints. Assuming that DA neuro-transmission innervates the α oscillatory component of the nicotine-modulated EEG response, and that COMT impacts prefrontal cortical DA signaling, we hypothesized that a single dose of nicotine to non-smokers would act as a pharmacologic probe of dopaminergic tone, enhancing α2 and accelerating PAF, with the strongest effects in Met/Met individuals with higher levels of cortical dopamine and the weakest effects in Val/Val individuals. These same oscillatory changes should also differentiate COMT polymorphisms per se, with Met/Met homozygotes registering greater α2 power and a higher PAF than Val/Val homozygotes that exhibit higher COMT activity. Secondary study endpoints included low frequency (δ, θ, α1) and β oscillations as they are consistently modulated by smoking/nicotine (Knott 2001). Given the increasing attention of cortical oscillatory synchrony in the γ frequency range and its association with cognitive processes (Basar 2013; Herrmann et al. 2010; Merker 2013), we studied, for the first time in humans, resting-state γ oscillation response to nicotine and its moderation by the COMT polymorphism.

Methods

Study participants

The sample of volunteers consisted of 62 right-handed, healthy, non-smoking males between 18 and 34 years of age (mean age = 22.4 years) who were recruited primarily from local universities. Male, and not female, volunteers were chosen to avoid any potential confounding effects of menstrually related hormonal changes on nicotine response. All were screened for medical history, personal psychiatric history using the structured Clinical Interview, Non-Patient version for DSM-IV (SCID-NP; First et al. 2002) and family psychiatric history (first-degree biological relatives) with the Family Interview for Genetic Studies (FIGS; Maxwell 1992). Volunteers were included in the study if they were Caucasian, reported no personal or family psychiatric history including substance/alcohol abuse or dependence and had no significant medical issues and were medication free. Non-smokers were defined as those who had consumed no more than 100 cigarettes in their lifetime and had not smoked a cigarette over the past year. Non-smoking status was confirmed by expired carbon monoxide levels, which were <3 parts per million, a level consistent with that of non-smokers (Cropsey et al. 2006). All volunteers signed a consent form prior to participation in the study, which was approved by the Research Ethics Board of the Royal Ottawa Health Care Group. Each participant received $60 CAD for his involvement in the study.

Experimental design

Each participant was assessed in two test sessions within a randomized, double-blind, placebo-controlled design. The two test sessions, involving nicotine or placebo treatment, were counterbalanced so that half of the participants received nicotine in their first session and placebo in their second session, while the remaining half received treatments in the reverse order. A minimum 2-day interval separated tested sessions.

Testing procedures

Testing was carried out between 0900 and 1630 h, with participants being required to abstain from caffeine, alcohol, drugs and medication and food for a minimum of 8 h prior to their scheduled testing, and abstain from liquids (with the exception of water) for 2 h prior in order to avoid interference with nicotine absorption. Sessions were carried out in a dimly lit, sound-attenuated chamber situated adjacent to the control room housing the monitoring and testing computers. Nicotine/placebo was administered concurrently with EEG electrode hook-up while participants were seated in a large semi-reclining chair. Electroencephalogram was recorded 30 min after nicotine/placebo administration, the time for nicotine to reach peak level in the blood. Vital signs and adverse events were assessed before and after nicotine administration.

Nicotine administration

Oral administration of nicotine was in the form of two pieces (4 mg + 2 mg) of cinnamon-flavored Nicorette® gum (Johnson & Johnson Inc., Markham, Ontario, Canada). Administering a 6 mg dose was intended to result in a similar nicotine level as achieved by smokers smoking a single cigarette of average nicotine yield, producing a nicotine blood concentration of approximately 15–30 ng/ml (Hukkanen et al. 2005). Peak blood nicotine levels are achieved approximately 30 min after the beginning of the gum chewing, and the elimination half-life of nicotine is ~120 min. Gum chewing was in accordance with the manufacturer’s guidelines, which specified a chewing time of 25 min, biting twice every minute (as cued by an audio recoding) and ‘parking’ the gum between teeth and cheeks between bites. Placebo gum pieces were cinnamon-flavored and were similar in size, color and texture. Participants were blindfolded and wore nose plugs throughout the gum administration in order to reduce any possible sensory differences between nicotine and placebo. Prior to removing the nose plug after the chewing period, participants chewed a mint-flavored gum for 1–2 min in order to remove any lingering taste differences.

Electroencephalographic acquisition

Electroencephalograms were recorded during a vigilance-controlled, 3-min eyes-closed resting-state condition. Electrical activity was sampled from an electrode cap (Electro-Cap International, Eaton, OH, USA) that positioned Ag+/Ag+ CI− electrodes at eight scalp sites; frontal midline (Fz), left (F3) and right (F4); central midline (Pz), left (C3) and right (C4); midline parietal (Pz) and midline occipital (Oz). An electrode on the nose served as a reference, and an electrode positioned anterior to the Fz site was the ground. Additional electrodes were placed on the supraorbital and suborbital ridges of the right eye, and on the external canthus of both eyes to monitor vertical (VEOG) and horizontal (HEOG) electro-oculographic activity. Electrode impedances were kept below 5 kΩ. Electroencephalograms were acquired (500 Hz sampling rate) with the eight-channel BrainVision V-Amp® amplifier (bandpass filters set at 0.1–120 Hz) and BrainVision Recorder® software (v1.1, Brain Products, Gilching, Germany). Digital recordings were stored for later off-line analysis.

Electroencephalographic processing

Electroencephalographic analysis was carried out with BrainVision Analyzer® software (Brain Products). This included bandpass filtering (0.1–70 Hz; 24 dB/octave roll-off), epoch segmentation (2000 ms), ocular correction (Gratton et al. 1983) and artifact rejection (excluding ocular-corrected EEG epochs with voltages exceeding ±100 μV). For each test session, a minimum of 45 2-second artifact-free epochs were subjected to a fast fourier transform (FFT) algorithm (with a high-pass, autoregressive filter, weighted by a 5% cosine taper) for computation of absolute amplitude (μV) at each scalp site for: δ (0.5–5.5 Hz), θ (6–8 Hz), α1 (8.5–10 Hz), α2 (10.5–12 Hz), α total (8.5–12 Hz), β1 (12.5–18 Hz), β2 (18.5–20.5 Hz), β3 (21–30 Hz) and γ (40–60 Hz) frequency bands. Relative amplitude (%) was also computed for each band by expressing amplitude in each band as a percent of total amplitude across all bands. As with Bodenmann et al. (2009b), PAF was determined visually from each individual’s average spectra and was defined as the frequency bin (0.5 Hz resolution) in the α range with the maximal power (0.5 Hz resolution).

COMT genotyping

A sample of each participant’s saliva was collected using Oragene DNA Self-Collection Kits (DNA Genotek Inc., Ottawa, Ontario, Canada). The genetic analysis was blinded to the results and provided by an external lab (Dr Paul Albert, Ottawa Hospital Research Institute). Extracted genomic DNA was assessed by real-time polymerase chain reaction (PCR) (Rotor-Gene RG-3000) to determine allele frequencies of the COMT Val158Met polymorphism (rs#4680), using 0.1× Taqman Drug Metabolism Genotyping Assay Kit (Applied Biosystems, USA, Assay ID# C_25746809_50) and template DNA with 1× Taqman master mix (4304437). The Rotor-gene 3000 (Corbett Research) real-time PCR apparatus was used with PCR cycling parameters, which included an initial 10-min denaturation at 95°C, 40 cycles of denaturation (15 seconds at 92°C) and annealing/extension (90 seconds at 60°C).

Statistical analysis

Statistical analysis of the data was carried out with the Statistical Package for Social Sciences (SPSS Inc., Chicago, IL, USA). Mixed analysis of variance (ANOVA) was used to analyze each band, with separate ANOVAs for log-transformed absolute and relative power in each band consisting of two within-subject factors – treatment (two levels: nicotine and placebo) and scalp region (eight levels: Fz, F3, F4, Cz, C3, C4, Pz and Oz), and one between-subject factor, genotype (three levels: Val/Val, Val/Met and Met/Met curriers). Similar ANOVAs were run for PAF but as peak spectral frequency was less apparent at lateral hemisphere recordings, the site factor in the ANOVA contained only four levels (Fz, Cz, Pz and Oz). Regardless of ANOVA significance for α-band measures, planned comparison testing (correlated t-tests) study hypotheses were carried out and were mainly limited to placebo–nicotine contrasts within each genotype, as well as genotype contrasts in the placebo condition. For non-α-band measures, significant main or interaction effects (P < 0.05) were followed up with Bonferroni-corrected pairwise comparisons. Significant region effects were not examined in any of these comparisons unless they interacted with treatment and/or genotype.

Results

Allelic distribution

Catechol-O-methyltransferase genotyping resulted in the following distribution within the study sample: Met/Met = 24.19% (N = 15); Val/Met = 48.38% (N = 30) and Val/Val = 27.42% (N = 17). As evident with χ2 statistics, no significant deviation from Hardy–Weinberg equilibrium was shown in the sample (χ2 = 3.76, P = 0.05). No significant age differences were observed between genotypes.

Alpha oscillations

Analysis of absolute amplitude in total α (F = 183.16, df = 7/413, P < 0.0001) and α1 bands (F = 121.12, df = 7/413, P < 0.0001) yielded significant effects for region but not for treatment, genotype or their interaction. Upper alpha (α2) analysis resulted in significant treatment (F = 6.75, df = 1/2, P < 0.015), region (F = 160.15, df = 7/413, P < 0.0001) and treatment × region × genotype interaction effects (F = 2.93, df = 14/143, P < 0.020). Genotypes did not differ with respect to absolute α2 amplitude during the placebo session but treatment comparisons within each genotype group (Fig. 1) found increases in absolute α2 amplitude with nicotine (vs. placebo) in Met/Met carriers at left (F3: P < 0.05), right (F4: P < 0.03) and mid-frontal (Fz: P < 0.015) regions as well as at left (C3: P < 0.03), right (C4: P < 0.03) and mid-central (Cz: P < 0.02) regions. In carriers of the Val/Met allele, nicotine-induced absolute α2 amplitude increases were limited to mid-parietal (Pz: P < 0.01) and mid-occipital (Oz: P < 0.003) recording regions.

Figure 1.

Figure 1

Open in a new tab

Grand averaged topographic EEG maps of α2 during placebo and nicotine treatment in Met/Met (M/M), Val/Met (V/M) and Val/Val (V/V).

Relative amplitude analysis for total (F = 66.23, df = 7/413, P < 0.0001) and lower (α1) alpha (F = 42.34, df = 7/413, P < 0.0001) showed only significant region effects, whereas relative upper alpha (α2) amplitude was significantly affected by treatment (F = 3.86, df = 1/2, P < 0.05), region (F = 68.95, df = 7/413, P < 0.0001) and a treatment × region × genotype interaction (F = 2.44, df = 14/413, P < 0.05). Similar to the effects shown with absolute amplitude, no significant genotype differences were observed in the placebo session but in the Met/Met carriers, nicotine increased amplitude in all frontal (F3: P < 0.03; F4: P < 0.04; Fz: P < 0.02) and central scalp regions (C3: P < 0.02; C4: P < 0.03; Cz: P < 0.03) but not at posterior regions.

The PAF analysis resulted in significant treatment (F = 5.92, df = 1/2, P < 0.02), region (F = 18.59, df = 73/197, P < 0.0001) and treatment × genotype (F = 3.41, df = 2/59, P < 0.04) interaction effects. The PAF was shown to be progressively higher from frontal to posterior regions and in the placebo session, Val/Met carriers displayed the slowest PAF at Fz, which was significantly different from PAF of Val/Val (P < 0.04) but not Met/Met carriers (Fig. 2). Treatment comparisons within genotypes found significant PAF acceleration in the Val/Met carriers with nicotine (vs. placebo) at frontal (Fz: P < 0.01), central (Cz: P < 0.02) and occipital (Oz: P < 0.01) regions, and at the parietal (Pz: P < 0.05) region in Met/Met carriers (Fig. 3).

Figure 2.

Figure 2

Open in a new tab

Mean (±SE) placebo peak alpha frequency in Val/Val (V/V), Val/Met (V/M) and Met/Met (M/M) genotypes at each electrode site.

Figure 3.

Figure 3

Open in a new tab

Mean (±SE) peak alpha frequency of placebo and nicotine in Val/Val (V/V), Val/Met (V/M) and Met/Met (M/M) carriers at each midline electrode site.

Non-alpha oscillations

Absolute amplitude analysis yielded significant region effects for δ (F = 80.56, df = 7/413, P < 0.0001), θ (F = 43.54, df = 7/413, P < 0.0001), β1 (F = 55.51, df = 7/413, P < 0.0001), β2 (F = 15.72, df = 7/413, P < 0.0001), β3 (F = 12.72, df = 7/413, P < 0.0001) and γ (F = 6.29, df = 7/413, P < 0.0001) but no treatment, genotype or interaction effects were observed.

Similar region effects were found for relative amplitude in δ (F = 9.36, df = 7/413, P < 0.0001), θ (F = 10.92, df = 7/413, P < 0.0001), β1 (F = 5.53, df = 7/413, P < 0.0001), β2 (F = 6.12, df = 7/413, P < 0.0001), β3 (F = 69.68, df = 7/413, P < 0.0001) and δ (F = 63.76, df = 7/413, P < 0.0001) bands, which were not affected by treatment, genotype or their interaction.

Discussion

Our results indicate that the COMT Val158Met polymorphism contributes to individual differences in resting-state EEG oscillations and their response to acute nicotine. This points to a heritable dopaminergic mechanism in electrocerebral rhythmic activities that may reflect the cognitive response variability to nicotinic agonists. Earlier EEG research showed that in non-smokers only resting- and task-associated oscillations in the α band varied with COMT polymorphism (Bodenmann et al. 2009a) and were sensitive to nicotine (Fisher et al. 2012a, 2012b, 2013; Foulds et al. 1994; Harkrider et al. 2001). Our EEG findings were limited to PAF and α2 oscillations, thus linking dopamine signaling and COMT variation with α-mediated oscillatory functions, possibly supporting cognitive enhancement associated with nicotine. Although cognitive processing was not directly assessed in this study, the case that resting neural changes affected by COMT polymorphism and nicotine interaction may meaningfully influence cognition is based on studies showing significant correlations between EEG during rest (pretask baseline) and behavioral performance. With respect to alpha, resting oscillatory activity in this band has detected both trait and state differences in cognitive functioning (Angelakis et al. 2004a,2004b) and interindividual differences in resting-state PAF and alpha power have been shown to be linearly associated with behavioral performance in healthy (Zhou et al. 2012; Zunini et al. 2013) and pathological populations (Dubbelink et al. 2013; Dubovik et al. 2012, 2013; Velikova et al. 2011).

Nicotine effects on EEG oscillations were evident only in the α-frequency range and, consistent with our study hypothesis and previous non-smoker studies, PAF was accelerated and amplitude in α2 was enhanced with single-dose treatment. A sizable body of research on the functional roles of brain oscillations has implicated α in sensory processing and cognition (Basar 2012; Basar & Guntekin 2012), and during working memory (Roux & Uhlhaas 2014), where it may play a role in maintaining information through active inhibition of task-irrelevant information (Klimesch et al. 2007; Sauseng 2009). Acetylcholine is thought to modulate the efficiency of the cortical processing of sensory or associational input (Basar & Guntekin 2012; Sarter et al. 2005); damage to the basal forebrain, the main source of acetylcholine, reduces resting EEG α, which also diminishes in a number of cognitive disorders (schizophrenia and Alzheimer’s disease) which, as our lab demonstrated, exhibit α increases with smoking (Knott et al. 1995) and nicotine (Knott et al. 2000).

This is, to our knowledge, the first time that nicotine influences on resting EEG have been shown to be modulated by COMT polymorphism. Because these effects were assessed in non-smokers, they cannot be attributed to a ‘remediation’ of withdrawal-induced alterations in neural networks observed in smokers during abstinence (Ashare et al. 2014; Beaver et al. 2011; Cole et al. 2010). The Val158Met polymorphism of COMT was shown to modulate the resting-state response to nicotine, with α-band oscillatory changes confirming our hypothesis, namely that such modulations occurred with carriers of the Met allele and not Val/Val. For α2, nicotine was found to increase oscillatory power in both Met/Met and Val/Met carriers but in different brain regions, with Met/Met in the frontocentral and Val/Met in the posterior cortical region. These COMT polymorphisms also exhibited PAF acceleration following nicotine administration but this time with Met/Met showing PAF increments in the posterior and Met/Val in the frontocentral regions. Because faster PAF and greater upper-α activity are linked to greater cognitive and memory performance (Klimesch 1997, 1999), altered PAF and α2 oscillatory states induced by nicotine might be expected to preferentially enhance associated cognitive processes, presumably by increasing dopamine signaling in a regionally specific manner.

In line with these resting-state findings, previous fMRI studies on acute nicotine dosing in healthy volunteers (Herman et al. 2013), dopamimetics (Hamidovic et al. 2010; Mattay et al. 2003) and COMT inhibitors (Apud et al. 2007; Farrell et al. 2012; Giakoumaki et al. 2008) generally support the inverted-U model of dopamine function. By enhancing PFC dopamine in the higher COMT activity of homozygotes (Val/Val, positioned left of Met genotypes on the curve) these drugs shifted Val genotypes rightward, closer to the increased subjective responses as well as enhanced cortical efficiency and performance, while reducing cortical processing in Met/Met genotypes, presumably beyond the peak of the inverted-U. The actions of DA in the PFC are concentration- and receptor subtype-dependent, and while a balance is assumed between D1 and D2 dopamine receptor activation during resting states, working and memory tasks produce low to moderate increases in dopamine (Phillips et al. 2004) and D1 dopamine receptor activation (thought to enhance PFC glutamatergic and GABAergic currents) (Seamans et al. 1998). This is in contrast to high phasic dopamine levels, which are thought to activate D2 receptors and reduce these currents (Seamans & Yang 2004). Interestingly, in our study with smokers, smoking-induced β2 increases appear to be mediated by D2 receptor activation as they are blocked by haloperidol, a D2 receptor antagonist (Walker et al. 2001).

COMT studies involving fMRI have typically assessed Blood-oxygen-level dependent (BOLD) activation during executive (e.g. working memory) task performance. There is increasing evidence, however, that the resting-state activity and deactivation of these neural networks, such as the default mode network (DMN) (regions that are active during non-task conditions and are suppressed by goal-directed cognitive demands; Raichle et al. 2001), determine the ability of task-positive networks to perform tasks, as measured by task-related fMRI (De Luca et al. 2006) and vary across individuals to predict behavioral performance (Kelly et al. 2008). There are a number of studies of dopaminergic modulation of task-induced changes in the DMN with transient dopamine depletion (Carbonell et al. 2014; Nagano-Saito et al. 2008), administration of dopamine receptor agonists and antagonists (Cole et al. 2013), levodopa (Delaveau et al. 2010), apomorphine (Nagano-Saito et al. 2009) and with pharmacological blockade (Minzenberg et al. 2011) and natural variation in dopamine transporter binding (Tomasi et al. 2009). All of these suggest that higher dopamine transmission is associated with augmented task-induced DMN deactivation. The COMT gene variation has similarly affected the fMRI–DMN response (Liu et al. 2010) with intermediate levels of COMT activity associated with increased medial PFC connectivity, which in turn correlates with increased task-induced DMN deactivation (reduced BOLD) and better performance (Dang et al. 2013).

Although there are inconsistencies (Neuner et al. 2014), both EEG (Cannon & Baldwin 2012; Chen et al. 2008, 2013; Knyazev 2013; Knyazev et al. 2011) and simultaneous EEG–fMRI studies (Chang et al. 2013; Jann et al. 2009; Liu et al., 2014; Mo et al. 2013) suggest that alpha and upper α, in particular (Jann et al. 2009), positively correlate with the fMRI–DMN. As ongoing oscillatory alpha activity is associated with processing internal stimuli and can determine stimulus detection and attention (Hanslmayr et al. 2011; Klimesch 1999, 2012; Klimesch et al. 2007), the apparent nicotine-induced enhancement of the EEG–DMN network (evidenced by α2 increases in frontocentral regions in Met/Met carriers and in parietal/occipital regions in Val/Met carriers) may reflect the differential effects of dopamine neurotransmission on inhibitory and selection processes in anterior and posterior hubs of the DMN. Both of these are sensitive to individual differences (Knyazev et al. 2012) but appear to exhibit distinct relationships with dopamine transporter availability (Tomasi et al. 2009) and levodopa treatment (Asanuma et al. 2006).

Genotype differences in the placebo condition were expressed only with PAF, which, contrary to our hypothesis, was found to be faster in Val homozygotes than heterozygotes. This is in direct contrast to a previous report of a slower PAF in Val/Val (vs. Met/Met) genotype (Bodenmann et al. 2009a) and another showing no effect of COMT polymorphism on PAF (Veth et al. 2014). Although this may be due to differences in EEG methodology or PAF measurement techniques (Bazanova & Vernon 2014), COMT–PAF relationships require further study as this electrophysiological index is thought to reflect the influence of individual genes on the underlying neural mechanisms generating α activity (Hughes et al. 2011; Lopes da Silva 1991; Steriade & Timofev 2003; Steriade et al. 1990). The behavioral significance of PAF is not clear but does provide a mechanism for searching and identifying encoded information (Angelakis et al. 2007; Bazanova & Aftanas 2006, 2008; Bazanova & Vernon 2014; Bodenmann et al. 2009b; Klimesch et al. 1993; Zoefel et al. 2011). Posterior PAF increases with increasing cognitive demands (Haegens et al. 2014) and artificially induced increases in α power above one’s PAF have resulted in improved cognitive performance (Hanslmayr et al. 2005; Klimesch et al. 2003). Interestingly, given the impaired executive performance in Val allele carriers, performance and brain activation are more efficient and emotional (anxiety) disorders less prevalent in this same genetic group compared to Met allele carriers (Heinz & Smolka 2006; Mier et al. 2010). The negative emotionality associated with low COMT activity (high cortical/subcortical tonic dopamine) in the Met allele may be related to the inflexibility of neural networks in processing information related to emotion, which in the Val allele is more flexible due to decreased tonic dopamine cortically and subcortically (Bilder et al. 2004). Alpha oscillations are particularly sensitive to the processing of negative emotional stimuli (Guntekin & Basar 2007) and as PAF is reduced in patients with anxiety disorder and increased with treatment (Saunders et al. 2015), the faster PAF in the Val carriers may be a neural marker of the dopaminergic regulation of the stability/flexibility of brain networks related to emotional processing.

Limitations

The present findings on COMT–nicotine interactions with EEG need to be interpreted within the limitations of the study, which include the assessment of a relatively small sample and one that was all male and was nicotine naive, thus limiting generalization to smokers – individuals who comprise a proportion of the population with psychiatric disorders (e.g. schizophrenia) and are purported to use smoking/nicotine for their cognitive enhancing properties (Kumari & Postma 2005). Dose- and time-response effects were not examined with nicotine and the neural effects associated with the relatively slow absorption of nicotine via gum may be dissimilar from those obtained with the rapid nicotine delivery associated with cigarette smoking. Blood nicotine levels were not assessed and as α2 increments with smoking are not observed with blood levels of nicotine below 10 ng/ml (Domino et al. 1995b), it is uncertain whether our observed genotype differences reflect different nicotine absorption levels. Our EEG recording montage, containing only eight recording sites, did not allow for a comprehensive assessment of electrocerebral activity and the use of larger electrode arrays in future studies would not only permit the localization of sources contributing to COMT and nicotinic influences on scalp EEG activity but also on inter-regional connectivity, which has been shown to be modulated by COMT polymorphism (Lee et al. 2011). Oscillatory measures included power and frequency but not phase characteristics, which, for α rhythms, is associated with unique sensory and cognitive processes (Bazanova & Vernon 2014). Electroencephalogram was assessed only during rest and not during task engagement which, if incorporated in future studies, would allow for clearer understanding of the cognitive implications of the COMT-modulated resting electrocortical response to nicotine.

Conclusion

This study tentatively suggests that COMT polymorphism, nicotine and their interaction affect resting-state electro-cerebral rhythms. That the nicotine-induced electrocortical changes were evident in individuals with the COMT Met allele infers that the acute, nicotine-modulated spontaneous oscillations reflect tonic cortical dopamine level and its potential functional role in cognitive tasks. These spectral EEG findings were observed with respect to α oscillatory activity, which is associated with cognitive processes and therefore may be of relevance to people diagnosed with schizophrenia – a disorder invariably linked with COMT polymorphism (Gupta et al. 2011; Lewandowski 2007; Sagud et al. 2010; Williams et al. 2007), and is also associated with excessive smoking (Winterer 2010), aberrant dopamine neuro-transmission (Brisch et al. 2014), abnormalities in α rhythms (Knott et al. 2001; Narayanan et al. 2014; Venables et al. 2009; Wix-Ramos et al. 2014), fMRI– (Whitfield-Gabrieli et al. 2009) and electrophysiologic–DMN (Kim et al. 2014) and cognition (Nuechterlein et al. 2014), with the latter being shown to improve both with smoking and nicotine administration (D’Souza & Markou 2012). Schizophrenia patients have shown increases in α2 with the smoking of a single cigarette (Knott et al. 1995), but the role of COMT in these oscillatory changes is not yet known and the cognitive changes accompanying these cortical rhythms require addressing.

Acknowledgments

This work was supported in part by a grant to V.K. from NSERC (Natural Sciences and Engineering Research Council of Canada – #210572-152799-2001); and a grant to P.R.A from CIHR (Canadian Institutes of Health Research).

References

  1. Ahnaou A, Hysmans H, Jacobs T, Drinkenburg W. Cortical EEG oscillations and network connectivity as efficiency indices for assessing drugs with cognition enhancing potential. Neuropsychopharmacology. 2014;86:362–377. doi: 10.1016/j.neuropharm.2014.08.015. [DOI] [PubMed] [Google Scholar]
  2. Angelakis E, Lubar J, Stathopoulou S, Kounios J. Peak alpha frequency: an electroencephalographic measure of cognitive preparedness. Clin Neurophysiol. 2004a;115:887–897. doi: 10.1016/j.clinph.2003.11.034. [DOI] [PubMed] [Google Scholar]
  3. Angelakis E, Lubar J, Stathopoulou S. Electroencephalographic peak alpha frequency correlates of cognitive traits. Neurosci Lett. 2004b;371:60–63. doi: 10.1016/j.neulet.2004.08.041. [DOI] [PubMed] [Google Scholar]
  4. Angelakis E, Stathopoulou S, Frymiare J. EEG neurofeedback: a brief overview and an example of peak alpha frequency training for cognitive enhancement in the elderly. Clin Neurophysiol. 2007;21:110–129. doi: 10.1080/13854040600744839. [DOI] [PubMed] [Google Scholar]
  5. Apud J, Weinberger D. Pharmacogenetic tools for the development of target-oriented cognitive-enhancing drugs. J Am Soc Exp Neurother. 2006;3:106–116. doi: 10.1016/j.nurx.2005.12.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Apud J, Mattay V, Chen J, Kolachana B, Callicott J, Ragetti R. Toliopone improves cognition and cortical information processing in normal human subjects. Neuropsychopharmacology. 2007;32:1011–1020. doi: 10.1038/sj.npp.1301227. [DOI] [PubMed] [Google Scholar]
  7. Asanuma K, Tang C, Ma Y, Dhawan V, Mattis P, Edwards C. Network modulation in the treatment of Parkinson’s disease. Brain. 2006;129:2667–2678. doi: 10.1093/brain/awl162. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Ashare R, Valdez J, Ruparel K, Albelda B, Hopson R, Kiefe J, Loughead J, Lerman C. Association of abstinence-induced alterations in working memory function and COMT genotype in smokers. Psychopharmacology (Berl) 2013;230:653–662. doi: 10.1007/s00213-013-3197-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Ashare R, Falcone M, Lerman S. Cognitive function during nicotine withdrawal: implications for nicotine dependence treatment. Neuropharmacology. 2014;76:581–591. doi: 10.1016/j.neuropharm.2013.04.034. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Barnett J, Heron J, Ring S. Gender-specific effects of the catechol-O-methyltransferase Val 108/158 Met polymorphism on cognitive function on children. Am J Psychiatry. 2007;164:142–149. doi: 10.1176/ajp.2007.164.1.142. [DOI] [PubMed] [Google Scholar]
  11. Basar E. A review of alpha activity in integrative brain function: fundamental physiology, sensory coding, cognition and pathology. Int J Psychophysiol. 2012;86:1–24. doi: 10.1016/j.ijpsycho.2012.07.002. [DOI] [PubMed] [Google Scholar]
  12. Basar E. A review of gamma oscillations in healthy subjects and in cognitive impairments. Int J Psychophysiol. 2013;90:99–117. doi: 10.1016/j.ijpsycho.2013.07.005. [DOI] [PubMed] [Google Scholar]
  13. Basar E, Guntekin B. A short review of alpha activity in cognitive processes and in cognitive impairment. Int J Psychophysiol. 2012;86:25–38. doi: 10.1016/j.ijpsycho.2012.07.001. [DOI] [PubMed] [Google Scholar]
  14. Bazanova O, Aftanas L. Relationship between learnability and individual indices of EEG alpha activity. Ann Gen Psychiatry. 2006;5:74–75. [Google Scholar]
  15. Bazanova O, Aftanas L. Individual measures of electroencephalogram alpha activity and non-verbal creativity. Neurosci Behav Physiol. 2008;38:227–235. doi: 10.1007/s11055-008-0034-y. [DOI] [PubMed] [Google Scholar]
  16. Bazanova O, Vernon D. Interpreting EEG alpha activity. Neurosci Biobehav Rev. 2014;44:94–110. doi: 10.1016/j.neubiorev.2013.05.007. [DOI] [PubMed] [Google Scholar]
  17. Beaver J, Long C, Cole D, Durcan M, Bannon L, Michara R, Mathews P. The effects of nicotine replacement on cognitive brain activity during smoking withdrawal studies with simultaneous fMRI/EEG. Neuropsychopharmacology. 2011;36:1792–1800. doi: 10.1038/npp.2011.53. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Bentley P, Driven J, Dolan R. Cholinergic modulation of cognition: insights from human pharmacological functional neuroimaging. Prog Neurobiol. 2011;94:360–388. doi: 10.1016/j.pneurobio.2011.06.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Beuten J, Payne T, Ma J, Li M. Significant association of catechol-O-methyltransferase (COMT) heliotypes with nicotine dependence in male and female smokers of two ethnic populations. Neuropsychopharmacology. 2006;31:675–684. doi: 10.1038/sj.npp.1300997. [DOI] [PubMed] [Google Scholar]
  20. Bilder R, Volauka J, Lachman H, Grace A. The catechol-O-methyltransferase polymorphism: relations to the tonic-phasic dopamine hypothesis and neuropsychiatric phenotypes. Neuropsychopharmacology. 2004;29:1943–1961. doi: 10.1038/sj.npp.1300542. [DOI] [PubMed] [Google Scholar]
  21. Bodenmann S, Rusterholz T, Durr R, Stoll C, Bachmann V, Geissler E, Jaggi-Schwarz K, Landolt HP. The functional Val158Met polymorphism of COMT predicts interindividual differences in brain α oscillations in young men. J Neurosci. 2009a;29:10858–10862. doi: 10.1523/JNEUROSCI.1427-09.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Bodenmann S, Yu S, Luhmann U, Arand M, Berger W, Jung H, Landolt H. Pharmacogenetics of Modafinil after sleep loss: catechol-O-methyltransferase genotype modulates waking functions but not recovery sleep. Clin Pharmacol Ther. 2009b;85:296–304. doi: 10.1038/clpt.2008.222. [DOI] [PubMed] [Google Scholar]
  23. Brisch R, Saniotis A, Wolf R, Bielau H, Bernstein H, Steiner T, Bogerts B, Braun K, Jankowski Z, Kumaratilake J, Hennenberg M, Gos T. The role of dopamine in schizophrenia from a neurobiological evolutionary perspective: old fashioned, but still in vogue. Front Psychiatry. 2014;5:47. doi: 10.3389/fpsyt.2014.00047. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Cannon R, Baldwin D. EEG current source density and the phenomenology of the default network. Clin EEG Neurosci. 2012;43:257–267. doi: 10.1177/1550059412449780. [DOI] [PubMed] [Google Scholar]
  25. Cantero J, Atienza M. The role of neural synchronization in the emergence of cognition across the wake-sleep cycle. Rev Neurosci. 2005;16:69–83. doi: 10.1515/revneuro.2005.16.1.69. [DOI] [PubMed] [Google Scholar]
  26. Carbonell F, Nagano-Saito A, Loyton M, Cisek P, Benkelfat C, He Y, Dagher A. Dopamine precursor depletion impairs structure and efficiency of resting state brain functional networks. Neuropharmacology. 2014;84:90–100. doi: 10.1016/j.neuropharm.2013.12.021. [DOI] [PubMed] [Google Scholar]
  27. Chang C, Liu Z, Chen M, Liu X, Doyn J. EEG correlates of time-varying BOLD functional connectivity. Neuroimage. 2013;15:227–236. doi: 10.1016/j.neuroimage.2013.01.049. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Chen J, Lipska B, Halim N, Ma Q, Matsumoto M, Melhem S. Functional analysis of genetic variation in catechol-O-methyltransferase (COMT): effects on mRNA, protein, and enzyme activity in postmortem human brain. Am J Hum Genet. 2004;75:807–821. doi: 10.1086/425589. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Chen A, Feng W, Zhao H, Yin Y, Wang P. EEG default mode network in the human brain: spectral regional field powers. Neuroimage. 2008;41:561–574. doi: 10.1016/j.neuroimage.2007.12.064. [DOI] [PubMed] [Google Scholar]
  30. Chen JL, Ros T, Gruzelier J. Dynamic changes of ICA-derived EEG functional connectivity in the resting state. Hum Brain Mapp. 2013;34:852–868. doi: 10.1002/hbm.21475. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Cole D, Bechman C, Long C, Mathews P, Durcan M, Beaver J. Nicotine replacement in abstinent smokers improves cognitive withdrawal symptoms with modulation of resting brain network dynamics. Neuroimage. 2010;52:590–599. doi: 10.1016/j.neuroimage.2010.04.251. [DOI] [PubMed] [Google Scholar]
  32. Cole D, Beckman C, Oei N, Both S, van Gerven J, Rombouts S. Differential and distributed effects of dopamine neuro-modulators on resting-state network connectivity. Neuroimage. 2013;78:59–67. doi: 10.1016/j.neuroimage.2013.04.034. [DOI] [PubMed] [Google Scholar]
  33. Colilla S, Lerman C, Shields P, Jepson C, Rukstalis M, Berlin J. Association of catechol-O-methyltransferase (COMT): effects on mRNA, protein and enzyme activity in postmortem human brain. Am J Hum Genet. 2005;75:807–821. doi: 10.1086/425589. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Cropsey K, Eldridge E, Weaver M, Vilalobos F, Stitzer M. Expired carbon monoxide levels in self-reported smokers and non-smokers in prison. Nicotine Tob Res. 2006;8:653–659. doi: 10.1080/14622200600789684. [DOI] [PubMed] [Google Scholar]
  35. D’Souza M, Markou A. Schizophrenia and tobacco smoking comorbidity: nAChR agonists in the treatment of schizophrenia-associated cognitive deficits. Neuropharmacology. 2012;62:1564–1573. doi: 10.1016/j.neuropharm.2011.01.044. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Dang L, O’Neil J, Jagust W. Genetic effects on behaviour are mediated by neurotransmitters and large-scale neural networks. Neuroimage. 2013;66:203–214. doi: 10.1016/j.neuroimage.2012.10.090. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. De Luca M, Beckmann C, De Stafano M, Mathews P, Smith S. fMRI resting state networks define distinct modes of long-distance interactions in the human brain. Neuroimage. 2006;29:1359–1367. doi: 10.1016/j.neuroimage.2005.08.035. [DOI] [PubMed] [Google Scholar]
  38. Delaveau P, Salgado-Pineda P, Fossati P, Witjas T, Azulay J, Bliu O. Dopamineric modulation of the default mode network in Parkinson’s disease. Eur Neuropsychopharmacol. 2010;20:784–792. doi: 10.1016/j.euroneuro.2010.07.001. [DOI] [PubMed] [Google Scholar]
  39. Domino E, Kadoya C, Matsuoka S. Effects of tobacco smoking on the topographic electroencephalogram. In: Domino E, editor. Brain Imaging of Nicotine and Tobacco Smoking. NPP Books; Ann Arbor, MI: 1995a. pp. 253–262. [Google Scholar]
  40. Domino E, Matsuoka S, Kadoya C. Variable EEG brain localization effects of tobacco smoking in relationship to plasma nicotine levels. In: Domino E, editor. Brain Imaging of Nicotine and Tobacco Smoking. NPP Books; Ann Arbor, MI: 1995b. pp. 263–273. [Google Scholar]
  41. Dubbelink O, Stoffers D, Deijen J, Twisk J, Stam C, Berendie H. Cognitive decline in Parkinson’s disease is associated with slowing of resting-state brain activity: a longitudinal study. Neurobiol Aging. 2013;34:408–418. doi: 10.1016/j.neurobiolaging.2012.02.029. [DOI] [PubMed] [Google Scholar]
  42. Dubovik S, Pignat J, Ptak R, Aboulafia T, Allet L, Gillabert N, Magnin C, Albert F, Momjian-Mayor I, Nahum L, Lascano A, Michel C, Schnider A, Guggisberg A. The behavioral significance of coherent resting-state oscillations after stroke. Neuroimage. 2012;61:249–257. doi: 10.1016/j.neuroimage.2012.03.024. [DOI] [PubMed] [Google Scholar]
  43. Dubovik S, Ptak R, Aboulafia T, Magnin C, Gillabert N, Allet L, Pignat J, Schnider A, Gugginberg A. EEG alpha band synchrony predicts cognitive and motor performance in patients with ischemic stroke. Behav Neurol. 2013;26:187–189. doi: 10.3233/BEN-2012-129007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Egan M, Goldberg T, Kioluchang B, Callicott J, Mazzanti C, Straub R. Effect of COMT Val 108/158 Met genotype on frontal lobe function and risk for schizophrenia. Proc Natl Acad Sci USA. 2001;98:6917–6922. doi: 10.1073/pnas.111134598. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Enoch M, Xu K, Ferro E, Harris C, Goldman D. Genetic origins of anxiety in women: a role for a functional catechol-O-methyltransferase polymorphism. Psychiatr Genet. 2003;13:33–41. doi: 10.1097/00041444-200303000-00006. [DOI] [PubMed] [Google Scholar]
  46. Farrell S, Tunbridge E, Braeutigam S, Harrison P. COMT Val (158) Met genotype determines the direction of cognitive effects produced by catechol-O-methyltransferase inhibition. Biol Psychiatry. 2012;71:538–544. doi: 10.1016/j.biopsych.2011.12.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. First M, Spitzer R, Gibbon M, Williams J. Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Non-Patient Version. American Psychiatric Association; Washington, DC: 2002. [Google Scholar]
  48. Fisher D, Daniels R, Jaworska N, Knobelsdorf A, Knott V. Effects of acute nicotine administration on resting EEG in non-smokers. Exp Clin Psychopharmacol. 2012a;20:717s. doi: 10.1037/a0025221. [DOI] [PubMed] [Google Scholar]
  49. Fisher D, Daniels R, Jaworska N, Knobelsdorf A, Knott V. Effects of acute nicotine administration on behavioural and neural (EEG) correlates of working memory in non-smokers. Brain Res. 2012b;1429:72–81. doi: 10.1016/j.brainres.2011.10.029. [DOI] [PubMed] [Google Scholar]
  50. Fisher D, Knobelsdorf A, Jaworska N, Daniels R, Knott V. Effects of nicotine on electroencephalographic (EEG) and behavioural measures of visual working memory in non-smokers during a dual-task paradigm. Pharmacol Biochem Behav. 2013;103:494–500. doi: 10.1016/j.pbb.2012.09.014. [DOI] [PubMed] [Google Scholar]
  51. Floderus Y, Ross S, Wetterberg L. Erythrocyte catechol-O-methyltransferase activity in a Swedish population. Clin Genet. 1981;19:389–392. doi: 10.1111/j.1399-0004.1981.tb00731.x. [DOI] [PubMed] [Google Scholar]
  52. Foulds J, McSorley K, Sneddon J, Feyerabend C, Jarvis M, Russell M. Effect of subcutaneous nicotine injections on EEG alpha frequency in non-smokers: a placebo-controlled pilot study. Psychopharmacology (Berl) 1994;115:163–166. doi: 10.1007/BF02244767. [DOI] [PubMed] [Google Scholar]
  53. Giakoumaki S, Roussos P, Bitsios P. Improvement of pre-pulse inhibition and executive function by the COMT inhibitor Tolcapone depends on COMT Val(158)Met polymorphism. Neuropsychopharmacology. 2008;33:3058–3068. doi: 10.1038/npp.2008.82. [DOI] [PubMed] [Google Scholar]
  54. Goldstein D, Need A, Singh R, Sisodiya S. Potential genetic causes of heterogeneity of treatment effects. Am J Med. 2007;120:S21–S25. doi: 10.1016/j.amjmed.2007.02.004. [DOI] [PubMed] [Google Scholar]
  55. Grannon S, Passetti F, Thomas K, Dalley T, Everitt B, Robbins T. Enhanced and impaired attentional performance after infusion of D1 dopaminergic receptor agents into the rat prefrontal cortex. J Neurosci. 2000;20:1208–1215. doi: 10.1523/JNEUROSCI.20-03-01208.2000. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Gratton G, Coles M, Donchin E. A new method for off-line removal of ocular artifact. Electroencephalogr Clin Neurophysiol. 1983;55:468–484. doi: 10.1016/0013-4694(83)90135-9. [DOI] [PubMed] [Google Scholar]
  57. Green A, Munafo M, DeYoung C, Fossella J, Fan J, Gray J. Using genetic data in cognitive neuroscience: from growing pains to genuine insights. Nat Rev Neurosci. 2008;9:710–720. doi: 10.1038/nrn2461. [DOI] [PubMed] [Google Scholar]
  58. Greenwood P, Parasuraman R. Normal genetic variation, cognition and aging. Behav Cogn Neurosci Rev. 2003;2:278–306. doi: 10.1177/1534582303260641. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Guntekin B, Basar E. Emotional face expression are differentiated with brain oscillations. Int J Psychophysiol. 2007;64:91–100. doi: 10.1016/j.ijpsycho.2006.07.003. [DOI] [PubMed] [Google Scholar]
  60. Guo S, Chen D, Zhou D, Sun H, Wu G, Halle C, Kosten T, Zhang X. Association of functional catechol-O-methyltransferase (COMT) Val108Met polymorphism with smoking severity and age of smoking initiation in Chinese male smokers. Psychopharmacology (Berl) 2007;190:449–456. doi: 10.1007/s00213-006-0628-4. [DOI] [PubMed] [Google Scholar]
  61. Gupta M, Kaur H, Jajodia A, Jain S, Satyamoorthy K, Mukerji M, Thirthalli J, Kuhaeti R. Diverse facets of COMT: from a plausible predictive marker to a potential drug target for schizophrenia. Curr Mol Med. 2011;2:732–743. doi: 10.2174/156652411798062386. [DOI] [PubMed] [Google Scholar]
  62. Haegens S, Cousin H, Wallis G, Harrison P, Nobre A. Inter- and intra-individual variability in peak alpha frequency. Neuroimage. 2014;92:46–55. doi: 10.1016/j.neuroimage.2014.01.049. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Hamidovic A, Dlugos A, Palmer A, de Wit H. Polymorphisms in dopamine transporter (S2C6A3) are associated with stimulant effects of D-amphetamine: an exploratory pharmacogenetic study using healthy volunteers. Behav Genet. 2010;40:255–261. doi: 10.1007/s10519-009-9331-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Hanslmayr S, Sav Seng P, Doppelmayr M, Schabus M, Klimesch W. Increasing individual upper alpha power by neurofeedback improves cognitive performance in human subjects. Appl Psychophysiol Biofeedback. 2005;30:1–10. doi: 10.1007/s10484-005-2169-8. [DOI] [PubMed] [Google Scholar]
  65. Hanslmayr S, Gross J, Klimesch W, Shapiro K. The role of alpha oscillations in temporal attention. Brain Res Rev. 2011;67:331–343. doi: 10.1016/j.brainresrev.2011.04.002. [DOI] [PubMed] [Google Scholar]
  66. Harkrider A, Champlin C, McFadden D. Acute effect of nicotine on non-smokers: III. LLRs and EEGs. Hear Res. 2001;160:99–110. doi: 10.1016/s0378-5955(01)00347-1. [DOI] [PubMed] [Google Scholar]
  67. Heinz A, Smolka M. Effects of catechol-O-methyltransferase genotype on brain activation elicited by affective stimuli and cognitive tasks. Rev Neurosci. 2006;17:359–367. doi: 10.1515/revneuro.2006.17.3.359. [DOI] [PubMed] [Google Scholar]
  68. Heishman S, Kleykamp B, Singleton E. Meta-analysis of the acute effects of nicotine and smoking on human performance. Psychopharmacology (Berl) 2010;210:453–469. doi: 10.1007/s00213-010-1848-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Herman A, Sofuoglu M. Cognitive effects of nicotine: genetic mediators. Addict Biol. 2010;15:250–265. doi: 10.1111/j.1369-1600.2010.00213.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Herman A, Jatlow P, Gelernter J, Listman J, Sofuoglu M. COMT Val158Met modulates subjective response to intravenous nicotine and cognitive performance in smokers. Pharmacogenomics. 2013;13:490–497. doi: 10.1038/tpj.2013.1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Herrmann C, Frund I, Lenz D. Human gamma-band activity: a review on cognitive and behavioural correlates and network models. Neurosci Biobehav Rev. 2010;34:981–992. doi: 10.1016/j.neubiorev.2009.09.001. [DOI] [PubMed] [Google Scholar]
  72. Hughes S, Lorincz M, Blethyn K, Kekesi K, Juhas G, Turmaine M, Parnavelas J, Crunello V. Thalamic gap junctions control local neuronal synchrony and influence macroscopic oscillation amplitude during EEG alpha rhythms. Front Psychol. 2011;2:193. doi: 10.3389/fpsyg.2011.00193. [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Hukkanen J, Jacob P, Benowitz N. Metabolism and disposition kinetics of nicotine. Pharmacol Rev. 2005;57:79–115. doi: 10.1124/pr.57.1.3. [DOI] [PubMed] [Google Scholar]
  74. Huotari M, Gogos J, Karayiorgov M, Koponen O, Forsberg M, Rassmaja A. Brain catecholamine metabolism in catechol-O-methyltransferase (COMT)-deficient mice. Eur J Neurosci. 2002;15:246–256. doi: 10.1046/j.0953-816x.2001.01856.x. [DOI] [PubMed] [Google Scholar]
  75. Jann K, Dierks T, Boesch C, Kottlow M, Strik W, Koenig T. BOLD correlates of EEG alpha phase-locking and the fMRI default mode network. Neuroimage. 2009;45:903–916. doi: 10.1016/j.neuroimage.2009.01.001. [DOI] [PubMed] [Google Scholar]
  76. Jasinska A, Zorick T, Brody A, Stein ET. Dual role of nicotine in addiction and cognition: a review of neuroimaging studies in humans. Neuropharmacology. 2013;84:111–122. doi: 10.1016/j.neuropharm.2013.02.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Kaiser J, Lutzenberger W. Induced gamma-band activity and human brain function. Neuroscientist. 2003;9:475–484. doi: 10.1177/1073858403259137. [DOI] [PubMed] [Google Scholar]
  78. Kelly A, Liddin B, Biswal F, Castellanos M, Milham M. Competition between functional brain networks mediates behavioural variability. Neuroimage. 2008;39:527–537. doi: 10.1016/j.neuroimage.2007.08.008. [DOI] [PubMed] [Google Scholar]
  79. Kendler K, Neale M, Sullivan P, Corey L, Gardner C, Prescott C. A population-based twin study in women of smoking initiation and nicotine dependence. Psychol Med. 1999;29:299–308. doi: 10.1017/s0033291798008022. [DOI] [PubMed] [Google Scholar]
  80. Kim J, Shin K, Jung W, Kim S, Kwon J, Chung C. Power spectral aspects of the default mole network in schizophrenia: an MEG study. BMC Neurosci. 2014;15:104. doi: 10.1186/1471-2202-15-104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Kimberg D, D’Esposito M, Farah M. Effects of bromocriptine on human subjects depend on working memory capacity. Neuroreport. 1997;8:3581–3585. doi: 10.1097/00001756-199711100-00032. [DOI] [PubMed] [Google Scholar]
  82. Klimesch W. EEG-alpha rhythms and memory processes. Int J Psychophysiol. 1997;26:319–340. doi: 10.1016/s0167-8760(97)00773-3. [DOI] [PubMed] [Google Scholar]
  83. Klimesch W. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Rev. 1999;29:169–195. doi: 10.1016/s0165-0173(98)00056-3. [DOI] [PubMed] [Google Scholar]
  84. Klimesch W. Alpha-band oscillations, attention, and controlled access to stored information. Trends Cogn Sci. 2012;16:606–617. doi: 10.1016/j.tics.2012.10.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Klimesch W, Schimke H, Pfurtscheller G. Alpha frequency, cognitive load and memory performance. Brain Topogr. 1993;5:241–251. doi: 10.1007/BF01128991. [DOI] [PubMed] [Google Scholar]
  86. Klimesch W, Sausang P, Gerloff C. Enhancing cognitive performance with repetitive transcranial magnetic stimulation at human individual alpha frequency. Eur J Neurosci. 2003;17:1129–1133. doi: 10.1046/j.1460-9568.2003.02517.x. [DOI] [PubMed] [Google Scholar]
  87. Klimesch W, Sauseng P, Hanslmayer S. EEG alpha oscillations: the inhibition–timing hypothesis. Brain Res Rev. 2007;53:63–88. doi: 10.1016/j.brainresrev.2006.06.003. [DOI] [PubMed] [Google Scholar]
  88. Knott V. Neuroelectric approach to the assessment of psychoactivity in comparative substance use. In: Warburton D, editor. Addiction Controversies. Harwood Academic Publishers; Churchill, UK: 1990. pp. 66–89. [Google Scholar]
  89. Knott V. Quantitative methods and measures in human psychopharmacological research. Hum Psychopharmacol. 2000;15:479–498. doi: 10.1002/1099-1077(200010)15:7<479::AID-HUP206>3.0.CO;2-5. [DOI] [PubMed] [Google Scholar]
  90. Knott V. Electroencephalographic characterization of cigarette smoking behaviour. Alcohol. 2001;24:95–97. doi: 10.1016/s0741-8329(00)00140-3. [DOI] [PubMed] [Google Scholar]
  91. Knott V, Venables P. EEG alpha correlates of non-smokers, smoking and smoking deprivation. Psychophysiology. 1977;14:150–156. doi: 10.1111/j.1469-8986.1977.tb03367.x. [DOI] [PubMed] [Google Scholar]
  92. Knott V, Hooper C, Lusk-Mikkelsen S, Kerr C. Variations in spontaneous brain electric (EEG) topography related to cigarette smoking: acute smoking, drug comparisons, cholinergic transmission, individual differences and psychopathology. In: Domino E, editor. Brain Imaging of Nicotine and Tobacco Smoking. NPP Books; Ann Arbor, MI: 1995. pp. 167–189. [Google Scholar]
  93. Knott V, Bosman M, Mahoney C, Ilivitsky V, Quirt K. Transdermal nicotine: single dose effects on mood, EEG, performance, and event-related potentials. Pharmacol Biochem Behav. 1999;63:253–261. doi: 10.1016/s0091-3057(99)00006-4. [DOI] [PubMed] [Google Scholar]
  94. Knott V, Engeland C, Mohr E, Mahoney C, Ilivitsky V. Acute nicotine administration in Alzheimer’s disease: an exploratory EEG study. Neuropsychobiology. 2000;41:210–220. doi: 10.1159/000026662. [DOI] [PubMed] [Google Scholar]
  95. Knott V, Labelle A, Jones B, Mahoney C. Quantitative EEG in schizophrenia and in response to acute and chronic clozapine treatment. Schizophr Res. 2001;50:41–53. doi: 10.1016/s0920-9964(00)00165-1. [DOI] [PubMed] [Google Scholar]
  96. Knyazev G. Extraversion and anterior vs. posterior DMN activity during self-referential thoughts. Front Hum Neurosci. 2013;6:1–10. doi: 10.3389/fnhum.2012.00348. [DOI] [PMC free article] [PubMed] [Google Scholar]
  97. Knyazev G, Slobodskoj-Plusnin J, Bocharov A, Pylkova L. The default mode network and EEG alpha oscillations: an independent component analysis. Brain Res. 2011;1402:67–79. doi: 10.1016/j.brainres.2011.05.052. [DOI] [PubMed] [Google Scholar]
  98. Knyazev G, Savostyanov A, Volf N, Liou M, Bocharov A. EEG correlates of spontaneous self-referential thoughts: a cross-cultural study. Int J Psychophysiol. 2012;86:173–181. doi: 10.1016/j.ijpsycho.2012.09.002. [DOI] [PubMed] [Google Scholar]
  99. Kumari V, Postma P. Nicotine use in schizophrenia: the self-medication hypothesis. Neurosci Biobehav Rev. 2005;29:1021–1034. doi: 10.1016/j.neubiorev.2005.02.006. [DOI] [PubMed] [Google Scholar]
  100. Lachman H, Papoulos D, Saito T, Yu YM, Szumlanski C, Weinshiboum R. Human catechol-O-methyltransferase phamacogenetics: description of a functional polymorphism and its potential application to neuropsychiatric disorders. Pharmacogenetics. 1996;6:243–250. doi: 10.1097/00008571-199606000-00007. [DOI] [PubMed] [Google Scholar]
  101. Lee TW, Younger Y, Hong CJ, Tsai SJ, Wu HC, Chen TJ. The effects of catechol-O-methyltransferase polymorphism Val158Met on functional connectivity in healthy young females: a resting EEG study. Brain Res. 2011;1377:21–31. doi: 10.1016/j.brainres.2010.12.073. [DOI] [PubMed] [Google Scholar]
  102. Lee M, Gallen C, Rois T, Kurup P, Salmeron B, Hodgkinson C, Goldman D, Stein E, Enoch M. A preliminary study suggests that nicotine and prefrontal dopamine affect corticostriatal areas in smokers with performance feedback. Genes Brain Behav. 2013;12:554–563. doi: 10.1111/gbb.12027. [DOI] [PMC free article] [PubMed] [Google Scholar]
  103. Leiser S, Dunlop J, Bowlby M, Devilbiss D. Aligning strategies for using EEG as a surrogate biomarker: a review of pre-clinical and clinical research. Biochem Pharmacol. 2011;81:1408–1421. doi: 10.1016/j.bcp.2010.10.002. [DOI] [PubMed] [Google Scholar]
  104. Lewandowski K. Relationship of catechol-O-methyltransferase to schizophrenia and its correlates: evidence for associations and complex interactions. Harv Rev Psychiatry. 2007;15:233–244. doi: 10.1080/10673220701650409. [DOI] [PubMed] [Google Scholar]
  105. Lindgren M, Molander L, Verhaan C, Lunell E, Rosen J. Electroencephalographic effects of intravenous nicotine: a dose-response study. Psychopharmacology (Berl) 1999;145:342–350. doi: 10.1007/s002130051067. [DOI] [PubMed] [Google Scholar]
  106. Lisman J, Buzsaki G. A neural coding scheme formed by the combined function of gamma and theta oscillations. Schizophr Bull. 2008;34:974–980. doi: 10.1093/schbul/sbn060. [DOI] [PMC free article] [PubMed] [Google Scholar]
  107. Liu B, Song M, Li J, Liu Y, Li K, Yu C, Jiang T. Prefrontal-related functional connectivities within the default network are modulated by COMT val158met in healthy young adults. J Neurosci. 2010;30:64–69. doi: 10.1523/JNEUROSCI.3941-09.2010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  108. Liu Y, Bengson J, Huang H, Mangun E, Ding M. Top-down modulation of neuronal activity in anticipatory visual attention: control mechanisms revealed by simultaneous EEG-fMRI. Cereb Cortex. 2014 doi: 10.1093/cercor/bhu204. Epub ahead of print 9 September 2014. pii: bhu204. [DOI] [PMC free article] [PubMed] [Google Scholar]
  109. Livingstone P, Wonnacott S. Nicotinic acetylcholine receptors and the ascending pathways. Biochem Pharmacol. 2009;78:744–755. doi: 10.1016/j.bcp.2009.06.004. [DOI] [PubMed] [Google Scholar]
  110. Lopes da Silva F. Neural mechanisms underlying brain waves: from neural membranes to networks. Clin Neurophysiol. 1991;79:81–93. doi: 10.1016/0013-4694(91)90044-5. [DOI] [PubMed] [Google Scholar]
  111. Lopes da Silva F. EEG and MEG: relevance to neuroscience. Neuron. 2013;80:1112–1128. doi: 10.1016/j.neuron.2013.10.017. [DOI] [PubMed] [Google Scholar]
  112. Loughead J, Wiley E, Valdez J, Sanborn P, Tang K, Strasser A. Effect of abstinence challenge on brain function and cognition in smokers differs by COMT genotype. Mol Psychiatry. 2009;14:820–826. doi: 10.1038/mp.2008.132. [DOI] [PMC free article] [PubMed] [Google Scholar]
  113. Maes H, Sullivan P, Bulik C, Newle M, Prescott C, Eaves L, Kendler K. A twin study of genetic and environmental influences on tobacco initiation, regular tobacco use and nicotine dependence. Psychol Med. 2004;34:1217–1261. doi: 10.1017/s0033291704002405. [DOI] [PubMed] [Google Scholar]
  114. Mansvelder H, van Aerde K, Covey J, Brussard A. Nicotinic modulation of neuronal networks: from receptors to cognition. Psychopharmacology (Berl) 2006;184:292–305. doi: 10.1007/s00213-005-0070-z. [DOI] [PubMed] [Google Scholar]
  115. Mattay V, Callicott J, Bertolino A, Heaton I, Frank J, Coppola R, Berman K, Goldberg T, Weinberger D. Effects of dextroamphetamine on cognitive performance and cortical activation. Neuroimage. 2000;12:268–275. doi: 10.1006/nimg.2000.0610. [DOI] [PubMed] [Google Scholar]
  116. Mattay V, Goldberg T, Fera F, Hariri A, Tessitore A, Egan M, Kolachana B, Callicott J, Weinberger D. Catechol-O-methyltransferase val158-met genotype and individual variation in the brain response to amphetamine. Proc Natl Acad Sci USA. 2003;100:6186–6191. doi: 10.1073/pnas.0931309100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  117. Maxwell M. Interview for Genetic Studies (FIGS). Manual for FIGS. Clinical Neurogenetics Branch, Intramural Research Program, National Institute of Mental Health; Bethesda, MD: 1992. [Google Scholar]
  118. Mehta M, Owen A, Sahakian B, Mavaddat N, Pickard J, Robbins T. Methylphenidate enhances working memory by modulating discrete frontal and parietal lobe regions in the human brain. J Neurosci. 2000;20:RC65. doi: 10.1523/JNEUROSCI.20-06-j0004.2000. [DOI] [PMC free article] [PubMed] [Google Scholar]
  119. Merker B. Cortical gamma oscillations: the functional key is activation, not cognition. Neurosci Biobehav Rev. 2013;37:401–417. doi: 10.1016/j.neubiorev.2013.01.013. [DOI] [PubMed] [Google Scholar]
  120. Mier D, Kirsch P, Meyer-Lindenberg A. Neural substrates of pleiotropic action of genetic variation in COMT: a meta-analysis. Mol Psychiatry. 2010;15:918–927. doi: 10.1038/mp.2009.36. [DOI] [PubMed] [Google Scholar]
  121. Minzenberg M, Yoon J, Carter C. Modafinil modulation of the default mode network. Psychopharmacology (Berl) 2011;215:23–31. doi: 10.1007/s00213-010-2111-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  122. Mo J, Liu Y, Huang H, Ding M. Coupling between visual alpha oscillations and default mode activity. Neuroimage. 2013;68:112–118. doi: 10.1016/j.neuroimage.2012.11.058. [DOI] [PMC free article] [PubMed] [Google Scholar]
  123. Munafo M, Bowes L, Clark T, Flint J. Lack of association of the COMT (Val 158/108 Met) gene and schizophrenia: a meta-analysis of case control studies. Mol Psychiatry. 2005;10:765–770. doi: 10.1038/sj.mp.4001664. [DOI] [PubMed] [Google Scholar]
  124. Nagano-Saito A, Leyton M, Monihi O, Goldberg Y, He Y, Dagher A. Dopamine depletion impairs frontostriatal functional connectivity during a sit-shifting task. J Neurosci. 2008;28:3697–3706. doi: 10.1523/JNEUROSCI.3921-07.2008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  125. Nagano-Saito A, Liu J, Doyon J, Dagher A. Dopamine modulates default mode network deactivation in elderly individuals during the Tower of London task. Neurosci Lett. 2009;458:1–5. doi: 10.1016/j.neulet.2009.04.025. [DOI] [PubMed] [Google Scholar]
  126. Narayanan B, O’Neil K, Berwise C, Stevens M, Calhoun V, Clementz B, Tamminga C, Sweeney J, Keshavan M, Pearlson G. Resting state electroencephalogram oscillatory abnormalities in schizophrenia and psychiatric bipolar patients and their relatives from the bipolar and schizophrenia network on intermediate phenotype studies. Biol Psychiatry. 2014;76:456–465. doi: 10.1016/j.biopsych.2013.12.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  127. Neuner I, Arrubla J, Werner C, Hitz K, Boers F, Kawohl W, Shah N. The default mode network and EEG regional spectral power: a simultaneous fMRI-EEG study. PLoS One. 2014;9:e88214. doi: 10.1371/journal.pone.0088214. [DOI] [PMC free article] [PubMed] [Google Scholar]
  128. Newhouse P, Potter A, Singh A. Effects of nicotinic stimulation on cognitive performance. Curr Opin Pharmacol. 2004;4:36–46. doi: 10.1016/j.coph.2003.11.001. [DOI] [PubMed] [Google Scholar]
  129. Newhouse P, Potter A, Dumas J, Thiel C. Functional brain imaging of nicotinic effects of higher cognitive processes. Biochem Pharmacol. 2011;82:943–951. doi: 10.1016/j.bcp.2011.06.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  130. Nuechterlein K, Ventura J, Subotnik K, Bartzokis G. The early longitudinal course of cognitive deficits in schizophrenia. J Clin Psychiatry. 2014;75(Suppl 2):25–29. doi: 10.4088/JCP.13065.su1.06. [DOI] [PMC free article] [PubMed] [Google Scholar]
  131. Parasuraman R. Assaying individual differences in cognition with molecular genetics: theory and application. Theor Iss Ergon Sci. 2009;10:399–416. [Google Scholar]
  132. Parasuraman R, Jiang Y. Individual differences in cognition, affect, and performance: behavioural, neuroimaging, and molecular genetic approaches. Neuroimage. 2012;59:70–82. doi: 10.1016/j.neuroimage.2011.04.040. [DOI] [PMC free article] [PubMed] [Google Scholar]
  133. Perkins K. Baseline-dependency of nicotine effects: a review. Behav Pharmacol. 1999;10:597–615. doi: 10.1097/00008877-199911000-00006. [DOI] [PubMed] [Google Scholar]
  134. Phillips A, Ahn S, Floresco S. Magnitude of dopamine release in medial prefrontal cortex predicts accuracy of memory on a delayed response task. J Neurosci. 2004;24:547–553. doi: 10.1523/JNEUROSCI.4653-03.2004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  135. Pickworth W, Herning R, Henningfield J. Electroencephalographic effects of nicotine chewing gum in humans. Pharmacol Biochem Behav. 1986;25:879–882. doi: 10.1016/0091-3057(86)90401-6. [DOI] [PubMed] [Google Scholar]
  136. Pickworth W, Herning R, Henningfield J. Mecamylamine reduces some EEG effects of nicotine chewing gum in humans. Pharmacol Biochem Behav. 1988;30:149–153. doi: 10.1016/0091-3057(88)90438-8. [DOI] [PubMed] [Google Scholar]
  137. Posthuma D, Neale M, Boomsma D, de Geus J. Are smarter brains faster: heritability of alpha peak frequency, IQ and their interrelation. Behav Genet. 2001;31:567–569. doi: 10.1023/a:1013345411774. [DOI] [PubMed] [Google Scholar]
  138. Raichle M, Macleod A, Snyder A, Powers W, Gusnard P, Shulman G. A default mode of brain function. Proc Natl Acad Sci USA. 2001;98:676–682. doi: 10.1073/pnas.98.2.676. [DOI] [PMC free article] [PubMed] [Google Scholar]
  139. Roux F, Uhlhaas P. Working memory and neural oscillations: alpha-gamma versus theta-gamma codes for distinct WM information. Trends Cogn Sci. 2014;18:16–25. doi: 10.1016/j.tics.2013.10.010. [DOI] [PubMed] [Google Scholar]
  140. Sagud M, Much-Seler D, Mihaljevic-Peles A, Voksan-Cosa B, Zivkovic M, Jakoviljevic M, Pivac M. Catechol-O-methyltransferase and schizophrenia. Psychiatr Danub. 2010;22:220–224. [PubMed] [Google Scholar]
  141. Saletu B, Anderer G, Saletu-Zyhlarz G, Arndol O, Pascual-Marqui R. Classification and evaluation of the pharmacodynamics of psychotropic drugs by single-lead pharmaco-EEG, EEG mapping and tomography (LORETA) Methods Find Exp Clin Pharmacol. 2002;24:97–120. [PubMed] [Google Scholar]
  142. Sarter M, Hasselmo M, Bruno J, Givens B. Unravelling the attentional functions of cortical cholinergic inputs, interactions between signal-driven and cognitive modulation of signal detection. Brain Res Rev. 2005;35:98–111. doi: 10.1016/j.brainresrev.2004.08.006. [DOI] [PubMed] [Google Scholar]
  143. Saunders N, Downham R, Turman B, Kropotor J, Clark R, Yumash R, Szatmacy A. Working memory training with TDCS improves behavioural and neurophysiological symptoms in pilot group with post-traumatic stress disorder (PTSD) and with poor working memory. Neurocase. 2015;21:271–278. doi: 10.1080/13554794.2014.890727. [DOI] [PubMed] [Google Scholar]
  144. Sauseng P. Brain oscillatory substrates of visual short-term memory capacity. Curr Biol. 2009;29:1846–1852. doi: 10.1016/j.cub.2009.08.062. [DOI] [PubMed] [Google Scholar]
  145. Seamans J, Yang C. The principle features and mechanisms of dopamine modulation in the prefrontal cortex. Prog Neurobiol. 2004;74:1–57. doi: 10.1016/j.pneurobio.2004.05.006. [DOI] [PubMed] [Google Scholar]
  146. Seamans J, Floresco S, Phillips A. D1 receptor modulation of hippocampal-prefrontal cortical circuits integrating spatial memory with executive function in the rat. J Neurosci. 1998;18:1613–1621. doi: 10.1523/JNEUROSCI.18-04-01613.1998. [DOI] [PMC free article] [PubMed] [Google Scholar]
  147. Shikata H, Fukai H, Sakaki T. Pattern recognition study in topographic EEG changes when smoking a cigarette. In: Domino E, editor. Brain Imaging of Nicotine and Tobacco Smoking. NPP Books; Ann Arbor, MI: 1995. pp. 235–252. [Google Scholar]
  148. Smit D, Posthuman D, Boomsma D, Geuss E. Heritability of background EEG across the power spectrum. Psychophysiology. 2005;42:691–697. doi: 10.1111/j.1469-8986.2005.00352.x. [DOI] [PubMed] [Google Scholar]
  149. Smit C, Wright M, Hansel N, Geffen G, Martin N. Genetic variation of individual alpha frequency (IAF) and alpha power in a large adolescent twin sample. Int J Psychophysiol. 2006;61:235–243. doi: 10.1016/j.ijpsycho.2005.10.004. [DOI] [PubMed] [Google Scholar]
  150. Solis-Ortiz S, Parez-Luque E, Gutieriez-Munoz M. Modulation of the COMT Val158Met polymorphism on resting-state EEG power. Front Hum Neurosci. 2015;9:118. doi: 10.3389/fnhum.2015.00136. [DOI] [PMC free article] [PubMed] [Google Scholar]
  151. Stassen H, Bomben G, Propping P. Genetic aspects of the EEG: an investigation into the within-pair similarity of monozygotic and dizygotic twins with a new method of analysis. Electroencephalogr Clin Neurophysiol. 1987;66:489–501. doi: 10.1016/0013-4694(87)90095-2. [DOI] [PubMed] [Google Scholar]
  152. Steriade M, Timofev I. Neuronal plasticity in thalamocortical networks during sleep and waking oscillations. Neuron. 2003;37:563–576. doi: 10.1016/s0896-6273(03)00065-5. [DOI] [PubMed] [Google Scholar]
  153. Steriade M, Gloor P, Linas R, Lopes da Silva F, Mesulam M. Report of IFCM committee on basic mechanisms. Basic mechanisms of cerebral rhythmic activities. Electroencephalogr Clin Neurophysiol. 1990;76:481–508. doi: 10.1016/0013-4694(90)90001-z. [DOI] [PubMed] [Google Scholar]
  154. Tatsuno J. Two dimensional topographic EEG maps of cigarette smoking in healthy medical students. In: Domino E, editor. Brain Imaging of Nicotine and Tobacco Smoking. NPP Books; Ann Arbor, MI: 1995. pp. 235–252. [Google Scholar]
  155. Teter C, Asfam B, Lisong N, Lutz M, Domino E, Guthrie S. Comparative effects of tobacco smoking and nasal nicotine. Eur J Pharmacol. 2002;58:309–314. doi: 10.1007/s00228-002-0481-2. [DOI] [PubMed] [Google Scholar]
  156. Tomasi D, Volkow N, Wang R, Telang F, Wang G, Chang L, Ernst T, Fowler J. Dopamine transporters in striatum correlate with deactivation in the default mode network during visuospatial attention. PLoS One. 2009;4:e6102. doi: 10.1371/journal.pone.0006102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  157. Tunbridge E, Harrison P, Weinberger D. Catechol-O-methyltransferase, cognition, and psychosis: Val158 Met and beyond. Biol Psychiatry. 2006;60:141–151. doi: 10.1016/j.biopsych.2005.10.024. [DOI] [PubMed] [Google Scholar]
  158. Uhlhaas P, Pipa G, Lima B, Melloni L, Neuenschwander S, Nikolić D, Singer W. Neural synchrony in cortical networks: history, concept and current status. Front Integr Neurosci. 2009;3:17. doi: 10.3389/neuro.07.017.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  159. Van Beijsterveldt C, van Baal G. Twin and family studies of the human electroencephalogram: a review and a meta-analysis. Biol Psychol. 2002;61:111–138. doi: 10.1016/s0301-0511(02)00055-8. [DOI] [PubMed] [Google Scholar]
  160. Van Beijsterveldt C, Molinaar P, de Geos E, Boomsma D. Heritability of human brain functioning as assessed by electroencephalography. Am J Hum Genet. 1996;58:562–573. [PMC free article] [PubMed] [Google Scholar]
  161. Velikova S, Magnini G, Arcari C, Falautano M, Franceschi M, Comi G, Leocani L. Cognitive impairment and EEG background activity in adults with Down’s syndrome: a topographic study. Hum Brain Mapp. 2011;32:719–729. doi: 10.1002/hbm.21061. [DOI] [PMC free article] [PubMed] [Google Scholar]
  162. Venables N, Bernat E, Sponheim R. Genetic and disorder-specific aspects of resting state EEG abnormalities in schizophrenia. Schizophr Bull. 2009;35:826–839. doi: 10.1093/schbul/sbn021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  163. Veth C, Arns M, Drinkenburg W, Falloen W, Peeters P, Gordon E, Buitelaar J. Association between COMT Val158Met genotype and EEG alpha peak frequency tested in two independent cohorts. Psychiatry Res. 2014;219:221–224. doi: 10.1016/j.psychres.2014.05.021. [DOI] [PubMed] [Google Scholar]
  164. Walker D, Mahoney C, Ilivitsky V, Knott V. Effects of haloperidol pretreatment on the smoking-induced EEG/mood activation response profiles. Neuropsychobiology. 2001;43:102–112. doi: 10.1159/000054875. [DOI] [PubMed] [Google Scholar]
  165. Wallace T, Bertrand D. Importance of the nicotinic acetylcholine receptor system in the prefrontal cortex. Biochem Pharmacol. 2013;85:1713–1720. doi: 10.1016/j.bcp.2013.04.001. [DOI] [PubMed] [Google Scholar]
  166. Wang XJ. Neurophysiological and computational principles of cortical rhythms in cognition. Physiol Rev. 2010;90:1195–1268. doi: 10.1152/physrev.00035.2008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  167. Weinshilboum R, Otterness D, Szomlanska C. Methylation pharmacogenetics: catechol-O-metyltransferase, theopurine methyltransferase, and histamine N-methyltransferase. Annu Rev Pharmacol Toxicol. 1999;39:19–52. doi: 10.1146/annurev.pharmtox.39.1.19. [DOI] [PubMed] [Google Scholar]
  168. Whitfield-Gabrieli S, Thermenos H, Milanovic S, Tsvang S, Ming T, Faraone S, McCarley K, Shenton M, Green A, Nieto-Castanon A, LaViolette P, Wojcik J, Gabrieli J, Seidman L. Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia. Proc Natl Acad Sci USA. 2009;106:1279–1284. doi: 10.1073/pnas.0809141106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  169. Williams H, Owen M, O’Donovan M. Is COMT a susceptibility gene for schizophrenia? Schizophr Bull. 2007;33:635–641. doi: 10.1093/schbul/sbm019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  170. Winterer G. Why do patients with schizophrenia smoke? Curr Opin Psychiatry. 2010;23:112–119. doi: 10.1097/YCO.0b013e3283366643. [DOI] [PubMed] [Google Scholar]
  171. Wix-Ramos R, Moreno X, Capote E, Gorizaliz G, Uribe E, Ebien-Zaijur A. Drug treated schizophrenia, schizoaffective and bipolar disorder patients evaluated by qEEG absolute spectral power and mean frequency analysis. Clin Psychopharmacol Neurosci. 2014;12:48–53. doi: 10.9758/cpn.2014.12.1.48. [DOI] [PMC free article] [PubMed] [Google Scholar]
  172. Zhou G, Liu P, He J, Dong M, Yang X, Hou B, von Deneen K, Qin W, Tian J. Interindividual reaction time variability is related to resting-state network topography: an electroencephalogram study. Neuroscience. 2012;202:276–282. doi: 10.1016/j.neuroscience.2011.11.048. [DOI] [PubMed] [Google Scholar]
  173. Zoefel B, Huster R, Herrmann C. Neurofeedback training and the upper alpha frequency band is REC improves cognitive performance. Neuroimage. 2011;54:1427–1437. doi: 10.1016/j.neuroimage.2010.08.078. [DOI] [PubMed] [Google Scholar]
  174. Zunini L, Thivierge J, Kousaie S, Sheppard S, Taler V. Alterations in resting-state activity relate to performance in a verbal recognition task. PLoS One. 2013;8:e65608. doi: 10.1371/journal.pone.0065608. [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES