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Int. J. Environ. Res. Public Health 2010, 7(10), 3760-3791; doi:10.3390/ijerph7103760

Chronic Cigarette Smoking: Implications for Neurocognition and Brain Neurobiology
Timothy C. Durazzo 1,2,*, Dieter J. Meyerhoff 1,2 and Sara Jo Nixon 3,4
Department of Radiology and Biomedical Imaging, University of California, 505 Parnassus Avenue, M-391, San Francisco, CA 94143, USA
Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center, 4150 Clement St., San Francisco, CA 94121, USA
Department of Psychiatry, University of Florida, P.O. Box 100256, Gainesville, FL 32611, USA
Department of Psychology, University of Florida, P.O. Box 112250, Gainesville, FL 32611, USA
Author to whom correspondence should be addressed; Tel.: +1-415-221-4810 Ext. 4157; Fax: +1-415-668-2864.
Received: 9 September 2010; in revised form: 29 September 2010 / Accepted: 9 October 2010 / Published: 21 October 2010


Compared to the substantial volume of research on the general health consequences associated with chronic smoking, little research has been specifically devoted to the investigation of its effects on human neurobiology and neurocognition. This review summarizes the peer-reviewed literature on the neurocognitive and neurobiological implications of chronic cigarette smoking in cohorts that were not seeking treatment for substance use or psychiatric disorders. Studies that specifically assessed the neurocognitive or neurobiological (with emphasis on computed tomography and magnetic resonance-based neuroimaging studies) consequences of chronic smoking are highlighted. Chronic cigarette smoking appears to be associated with deficiencies in executive functions, cognitive flexibility, general intellectual abilities, learning and/or memory processing speed, and working memory. Chronic smoking is related to global brain atrophy and to structural and biochemical abnormalities in anterior frontal regions, subcortical nuclei and commissural white matter. Chronic smoking may also be associated with an increased risk for various forms of neurodegenerative diseases. The existing literature is limited by inconsistent accounting for potentially confounding biomedical and psychiatric conditions, focus on cross-sectional studies with middle aged and older adults and the absence of studies concurrently assessing neurocognitive, neurobiological and genetic factors in the same cohort. Consequently, the mechanisms promoting the neurocognitive and neurobiological abnormalities reported in chronic smokers are unclear. Longitudinal studies are needed to determine if the smoking-related neurobiological and neurocognitive abnormalities increase over time and/or show recovery with sustained smoking cessation.
chronic cigarette smoking; neurocognition; neurobiology; neuroimaging; genetics

1. Introduction

Approximately 2 billion people worldwide use tobacco products, mostly in the form of cigarettes, with tobacco smoking-related diseases resulting in 4 million deaths per year [1]. Among the approximately 64.5 million active smokers in the USA, smoking-related disease results in approximately 440,000 preventable annual deaths [2]. The enormous healthcare expenditures and mortality associated with chronic cigarette smoking results in an estimated $92 billion annual productivity loss in the US. Internationally, the greatest smoking related mortality is increasingly apparent among economically disadvantaged groups, which, in the US includes a disproportionate number of ethnic minorities and those with psychiatric and substance use disorders [3,4]. An extensive body of research thoroughly describes the deleterious effects of chronic cigarette smoking on human cardiac and pulmonary function, peripheral vascular systems as well as its carcinogenic properties [58]. Recent research indicates chronic cigarette smoking is associated with increased risk for numerous biomedical conditions that may directly or indirectly compromise brain neurobiology and neurocognition [912]. However, compared to the substantial volume of research on the cardiovascular, pulmonary and cancer-related health consequences associated with chronic smoking, surprisingly little research has been specifically devoted to the investigation of its effects on human neurocognition and brain neurobiology.
This review summarizes the peer-reviewed literature on the neurocognitive and neurobiological repercussions of chronic cigarette smoking in cohorts and population-based samples that were not specifically seeking treatment for substance use or psychiatric disorders. Prospective or retrospective studies that expressly assessed the neurocognitive or neurobiological consequences of chronic smoking are targeted. Research employing proton magnetic resonance-based studies of brain morphology and metabolites that specifically evaluated the neurobiological consequences of chronic smoking are emphasized. In this review, non-smoking control groups are referred to as NSC and individuals comprising these groups generally were indicated to be never smokers or consumed less than 100 cigarettes over lifetime. NSC were equivalent in age to smoking cohorts unless otherwise specified. The research reviewed was generally conducted with individuals in one of three age ranges: 18–30, 40–59 and 60–90. Individuals 18–30 years of age are referred to as “young adults”, 40–59 as “middle-aged adults” and 60–90 years of age as “older adults”. In studies where the participants do not conform to the above defined age groups, specific age ranges are provided. For reviews on the effects of chronic smoking on brain neurobiology and function in alcohol and substance use disorders see [1315]. Please refer to [1620] for thorough reviews on the acute effects of nicotine administration and nicotine withdrawal on brain neurobiology and neurocognition (although not the focus of this review, these topics are briefly addressed in Section 4). For inclusive reviews on functional MRI and nuclear imaging findings in chronic smokers see [21,22].

2. Neurocognitive Consequences of Chronic Cigarette Smoking (see Table 1)

The vast majority of research investigating the neurocognitive consequences of chronic cigarette smoking is cross-sectional in design and focused primarily on middle-aged and older adults. In the sole study of adolescents, daily smokers (mean age = 17 ± 1) showed deficits in accuracy of working memory relative to NSC, with individuals who began smoking at a younger age demonstrating greater impairment than those who began smoking at a later age [23]. In the few studies with young adults, smokers were inferior to NSC on measures of sustained attention and impulse control [24], auditory-verbal memory, oral arithmetic, and receptive and expressive vocabulary [25], information processing speed [26] and general intelligence [27]. On an experimental behavioral measure of risk-taking (Balloon Analogue Risk Task [28]), young adults smokers demonstrated higher levels of risk-taking [29]. In cross-sectional studies specifically comparing NSC to middle-aged and/or older adult smokers, poorer performance in smokers was reported for auditory-verbal learning and/or memory [3034], working memory [26,35,36], executive functions [33,37,38], general intellectual abilities [39], visual search speed [40], processing speed and cognitive flexibility [3032,38,41,42] and global cognitive function (e.g., brief mental status examinations such as the MMSE) [41]. In a middle-aged cohort of combined current and former smokers, any history of smoking was associated with increased risk for abnormal auditory-verbal memory [43]. Some studies observed the performance of former smokers fell between that of current smokers and NSC in young [25], middle-aged and older adults [31,35,39]. Other studies found no differences between former smokers and NSC [25,30]. The inconsistencies among these studies may be related to the substantial variety of measures used across studies to evaluate the domain of functioning in question as well as inconsistency in the magnitude of neurocognitive dysfunction in the smoking study cohort.
In cross-sectional population-based studies with community-dwelling older adults, where smoking status (i.e., current smoker, past smoker, never smoker) was used as a prospective or retrospective predictor, current smoking [4446] and any history of smoking [47] were associated with poorer performances on measures of global cognitive function. Any previous smoking history (with variable lengths of smoking cessation) was associated with poorer cognitive flexibility [45], and impaired general cognitive function [44] or, conversely, decreased risk of global cognitive impairment [46]. Chronic smoking in older adults has also been associated with a diminished ability to execute some activities of daily living [48] and compromised postural stability [49].
Longitudinal research with non-demented, population-based samples found that current cigarette smokers demonstrated an abnormal rate of decline on indices of reasoning [43] and auditory-verbal memory [40] in middle aged adults, and abnormal decline on measures of global cognitive function [47,5053] and auditory-verbal memory in older adults [54].
Sex effects in neurocognition among smokers have also been addressed in some studies. Edelstein et al. [34] found no differences between older adult male smokers and male NSC on measures of global cognitive functioning, set-shifting, semantic fluency and auditory-verbal and visuospatial learning and memory, while older adult female smokers demonstrated poorer global cognitive functioning and auditory-verbal memory than female NSC. Jacobsen and colleagues [23] reported that male adolescent smokers performed more poorly than did female smokers on measures of selective and divided attention. Similarly, Razani and colleagues [37] observed that sex was a significant predictor of non-verbal abstraction in currently smoking older adults, however, no interactions were reported among sex, smoking status or smoking severity. Other studies, however, have found no sex effects on neurocognition in middle aged and older adults [32,33,39].
The level and chronicity of smoking, as reflected in the number of cigarettes smoked per day, duration of smoking over lifetime, and/or dose-duration (i.e., pack-years) were inversely related to various domains of neurocognition in adults across a wide age range [25,30,32,45,52,55,56]. Several reports indicate chronic smoking is associated with increased risk for various forms of dementia, in particular Alzheimer’s Disease and vascular dementia [5761]. This risk may be modulated through the apolipoprotein E ɛ4 (ApoE4) genotype, a known genetic risk for the development of Alzheimer’s Disease [52,54]. Interestingly, some studies have reported that risk for development of Alzheimer’s Disease was greater in smokers who were not ApoE4 carriers [52,54,58,59].
Several studies, smoking status (i.e., smoker or non-smoker) or measures of smoking consumption (e.g., pack years), showed weak or no relationships to specific neurocognitive functions (e.g., measures of learning and memory, mental arithmetic, verbal fluency, processing speed), global neurocognitive function (e.g., MMSE) and neurocognitive decline in young and middle aged adults [62,63] and in large community-based samples consisting of middle-aged and older adults [6470].

3. Neurobiological Consequences of Chronic Cigarette Smoking (See Table 2)

The specific neurobiological factors underlying the reported smoking-related cognitive deficits are not established. However, there are a few of computed tomography (CT) and magnetic resonance (MR)-based studies that suggest the reported neurocognitive deficiencies in smokers may be, in part, mediated by abnormalities in brain morphology, perfusion and/or neurochemistry. The majority of these studies are cross-sectional in design.

3.1. Brain Morphology

Computed tomography (CT) studies with cohorts ranging from middle-aged to older adults report that chronic smoking is associated with an abnormal increase of global brain atrophy with advancing age [7477]. These CT studies assessed whole brain volumes and did not report major anatomical subdivisions (e.g., frontal gray matter/white matter). An early MRI study with older adults examined global brain atrophy over a 5-year-interval and found higher pack years was related to increased ventricular volume in men and was associated with increased sulcal volume in women, after controlling for age and vascular risk factors [78]. More recent MRI studies have employed voxel-based morphological measures to assess the regional brain volumes and densities of the cortical gray matter (GM). Smokers aged 39.5 ± 10.3 years evidenced smaller volumes and lower tissue densities than did NSC in bilateral anterior frontal lobe regions; smokers also had smaller volume of the left dorsal cingulate cortex and lower GM density in the cerebellum. Anterior frontal cortex density was inversely related to pack-years [79]. Smokers aged 30.8 ± 7.5 years demonstrated widespread GM volume and density reductions relative to NSC [80], particularly in the bilateral frontal lobes, cingulate gyrus and insula. Non-demented older adult smokers (75.0 ± 3.4 years of age) exhibited reduced GM density in right precuneus, left posterior cingulate gyrus, right thalamus and bilateral precentral and middle frontal gyri compared to NSC [81].
A MR-based study employed diffusion tensor imaging (DTI) and voxel based morphometry to assess microstructural integrity and morphology, respectively, of the corpus callosum in middle aged chronic smokers [82]. Contrary to expectations, smokers demonstrated higher fractional anisotropy (FA; higher FA values are considered to reflect greater microstructural integrity [83,84]) in the body and total corpus callosum than did NSC and no volume differences were observed between smokers and NSC in the corpus callosum. However, smokers with high levels of nicotine dependence (as reflected by scores on the Fagerstrom Test for Nicotine Dependence) had significantly lower FA values than both smokers with low levels of nicotine dependence and NSC. It is widely recognized from population-based studies, with middle-aged and older adults, that chronic smoking is associated with increased incidence of regional white matter (WM) signal hyperintensities on standard MR imaging (e.g., T2-weighted and FLAIR) [8589]. WM hyperintensities are associated with decreased cerebral blood perfusion [90,91] and neurocognitive dysfunction [92,93]. Overall, the degree of smoking-related morphological changes observed appears to be contingent on the method and brain region under consideration.

3.2. Brain Biochemistry

A single volume proton (1H) MR spectroscopy study with chronic smokers (36 ± 11 years of age) observed lower N-acetylaspartate (NAA) concentration (surrogate marker of neuronal integrity [94,95]), in the left hippocampus relative to NSC. No group differences were observed for NAA in the anterior cingulate cortex (ACC), but choline-containing compound (Cho) levels (a marker of cell membrane turnover/synthesis [94,96]) were positively related to greater pack years in this region [97]. A single voxel 1H spectroscopy study of glutamate levels in the left hippocampus and ACC observed no differences among current smokers (35 ± 10 years of age), former smokers (42 ± 10 years of age) abstinent for 17 ± 3 years and NSC (33 ± 10 years of age) [98]. In the sole 1H spectroscopy study investigating gamma aminobutyric acid levels (GABA; neuromodulator involved in the development and maintenance of substance use disorders [99101]) in chronic smokers, cortical GABA concentrations were lower in female smokers (and modulated by menstrual cycle phase), but GABA levels were not different between male smokers and NSC [102].

3.3. Brain Perfusion

The vast majority of neuroimaging research on brain perfusion has investigated the effects of acute nicotine exposure, rather than the consequences of chronic cigarette smoking [18]. The few published reports specifically investigating chronic smokers indicate globally decreased brain perfusion relative to NSC, as measured by CT 133Xe inhalation [103,104] in older adults and single proton emission computed tomography (SPECT) [105] in adults aged 35.5 ± 8.4 years; perfusion was inversely related to cigarette pack-years [105]. In a Xe-CT-based longitudinal study with community-dwelling older adults, decreases in global cerebral perfusion were independently associated with chronic smoking controlling for other vascular risk factors [106,107].

4. Neurocognitive and Neurobiological Effects of Acute Nicotine Exposure and Withdrawal

When investigating chronic cigarette smoking-induced neurobiological and neurocognitive dysfunction alone, or in conjunction with AUD and other conditions, it is important to distinguish the effects of acute nicotine ingestion and withdrawal from the potential consequences of chronic exposure to the multitude of noxious compounds contained in cigarette smoke. While not the focus of this review, the general findings and implications are discussed regarding the effects of acute nicotine on neurocognition and brain neurobiology, as measured with functional neuroimaging methods [i.e., functional MRI (fMRI), positron emission tomography (PET), single positron emission tomography (SPECT)].

4.1. Acute Nicotine Consumption, Nicotine Withdrawal and Neurocognition

Acute nicotine administration has been found to transiently improve some areas of neurocognition in NSC and individuals with attention deficit hyperactivity disorder and schizophrenia-spectrum disorders, most substantially on measures of sustained attention and working memory [17,19,108]. Acute nicotine administration in nicotine deprived smokers is associated with improved cognitive task performance [109,110], whereas several studies report decrements in neurocognitive performance with nicotine administration to NSC (see [19] for review). A recent meta-analysis conducted by Heishman and colleagues [111] suggests that acute smoking or nicotine consumption, independent of withdrawal effects, is associated with enhanced function in the following domains of function: fine motor skills, alerting attention accuracy and response time, orienting attention reaction time, short-term episodic memory accuracy and working memory reaction time (but not accuracy). In non-clinical chronic smokers, the adverse effects of nicotine withdrawal are not typically apparent on neurocognitive function until 8–12 hours after last nicotine dose [17,19,109,112]. Protracted duration from last cigarette smoked/nicotine administration to onset of withdrawal mediated disturbances in neurocognition is likely attributable to the maintenance of relatively high levels of plasma nicotine during waking hours due to repeated dosing of nicotine (via cigarettes) [113].

4.2. Acute Nicotine Consumption, Nicotine Withdrawal and Neurobiological Function

Several functional neuroimaging (PET, SPECT, fMRI) studies in active chronic smokers (see [21,22] for review) and a few functional MRI studies addressed the acute effects of nicotine administration on brain activity during task activation in healthy non-smokers [17,18,20]. The effects of acute cigarette smoking on functional neuroimaging modalities in non-smokers have not been investigated [18,20]. In chronic smokers, functional neuroimaging studies investigating responses to acute smoking or nicotine administration have shown are that acute nicotine administration is associated with decreased global cerebral blood flow, increased activity in the dorsolateral, inferior and mesial frontal and orbitofrontal regions, thalamus and visual processing regions (see [21,22]). In chronic smokers deprived of tobacco for more than 2 hours, acute cigarette smoking elicits different patterns of relative perfusion responses, with increases of the order of 6–8% in the anterior frontal and cingulate cortices as well as decreases in cerebellum and occipital lobes that were associated with plasma nicotine levels [18,114,115]. Some studies report a 7–10% decrease in global glucose utilization following acute nicotine administration in chronic smokers deprived of nicotine for 8 hours or more [116,117]. Depending on the nature of the task, results suggest acute nicotine administration in smokers and non-smokers is associated with increased regional blood flow/brain activity and improves task performance or decreases blood flow/oxygenation level-dependent activity and task performance [18,20]. As discussed by Sharma and Brody [22], the reported regionally specific findings may be influenced by whether or not activity was standardized to whole brain blood flow.
Overall, the effects of acute nicotine administration on neurocognition and functional imaging measures appear to depend on duration of nicotine deprivation, the brain region studied, resting versus activation conditions, and the neurocognitive domain investigated [18].

5. Potential Biological Mechanisms Contributing to Chronic Cigarette Smoking-Induced Neurocognitive and Neurobiological Dysfunction

Nicotine is one of more than 4000 compounds composing the particulate and gas phases of cigarette smoke [5,8,118]. In addition to nicotine, scores of these compounds are bioactive and may affect tissue locally in the oral cavity, the upper and lower respiratory systems, and distally via the systemic circulation. The many potentially cytotoxic compounds in cigarette smoke (e.g., carbon monoxide, aldehydes, ketones, nitrosamines, dihydroxybenzenes) [119] may directly compromise neuronal and cellular membrane function of cerebral tissue. There are several potential mechanisms that may contribute independently, or in concert, to the neurobiological and neurocognitive abnormalities in chronic smokers. These mechanisms may operate in a direct and/or indirect manner. The following overview is based on in vivo and in vitro studies of animals and humans.

5.1. Direct Mechanisms

A significant number of potentially cytotoxic compounds (e.g., carbon monoxide, free radicals and their precursors, nitrosamines, phenolic compounds, and other polynuclear aromatic compounds [119]), are found in the gas and particulate phases of cigarette smoke, which may be directly cytotoxic, damage neuronal or glial cell organelles and promote oxidative damage ([120], Muscat, 2004 #13479, [121,122]). For example, carbon monoxide (CO) levels are significantly higher in smokers [123], and this elevation is associated with decreased effective hemoglobin concentrations, diminished oxygen carrying capacity of erythrocytes [124], as well as a diminished efficiency of the mitochondrial respiratory chain [125]. Furthermore, cigarette smoke also contains high concentrations of free radical species (e.g., reactive nitrogen species; reactive oxygen species, ROS) known to promote oxidative damage or stress to cellular structures as well as to macromolecules including membrane lipids, proteins, carbohydrates and DNA [126]. The radical species in the particulate matter of cigarette smoke are long-lived (i.e., hours to months) compared to those in the gas phase [5], and can compromise organs other than the lungs [120,127]. In vivo chronic exposure of rat brain tissue to cigarette smoke significantly decreases membrane-bound ATPases, which alters ion homeostasis, and leads to increased Ca2+ and Na+ levels in the cytosol of various cell types [128], as well as increased Ca2+ in mitochondria [122], which is associated with neuronal injury or death [129]. Increased mitochondrial Ca2+ secondary to cigarette condensate exposure is associated with damage to the inner mitochondrial membrane (e.g., membrane swelling) and vacuolization of the matrix. Importantly, nicotine delivered independently of cigarette smoke does not appear to produce these adverse affects [122]. Nicotine administration in adolescent rats does, however, evoke cell injury and loss throughout the brain, with significant effects in the hippocampus of female rats but not males [130,131]. In general, the mechanisms underlying the observed nicotine-induced cell injury remain to be fully explicated.

5.2. Indirect Mechanisms

In vivo chronic cigarette smoke exposure is also associated with decreased enzyme-based free radical scavenger (e.g., superoxide dismutase, catalase, glutathione reductase) and non-enzyme-based radical scavenger (e.g., glutathione and vitamins A, C and E) concentrations in rat brains [132,133]. This may render brain tissue more vulnerable to oxidative damage by radical species generated by cellular metabolism or other exogenous sources. The brain, in general, is exceedingly susceptible to oxidative damage because of high levels of unsaturated fatty acids in the composition of cell membranes and myelin. Additionally, chronic cigarette smoking is related to nocturnal hypoxia [7] as well as chronic obstructive pulmonary disease and other conditions that may impair lung function [8]. Decreased lung function is associated with poorer neurocognition and increased subcortical atrophy in older adults [134]. Chronic smoking increases the risk for atherosclerosis [9], as well as abnormalities in vascular endothelial morphology and function [135138], which may alter cerebral perfusion. Additionally, nicotine administered through means other than cigarette smoke may alter or impair vasomotor reactivity of cerebral arterioles through upregulation of Ca2+ channels and/or modulation of nitric oxide [136]. These processes may impact the functional integrity (e.g., vasomotor reactivity/responsivity) of the cerebrovasculature and may, at least partially, contribute to the decreased regional cerebral blood flow [114,115,139] and/or white matter disease [85,8789,140,141] observed in chronic smoking. Both the neocortex and underlying WM are vulnerable to the effects of diffuse ischemia (see [142] and references therein). Correspondingly, it has been suggested that late-myelinating areas such as the frontal and temporal lobes may be particularly vulnerable to increased oxidative stress and cerebral hypoperfusion [143,144], both of which have been described in chronic smokers.
Chronic smoking is also associated with central obesity (often reflected in increased body mass index; BMI) and/or insulin resistance [145], which, in turn, are reported to adversely affect brain neurobiology [146149] and neurocognition [146,150].
In summary, although nicotine is likely the principal bioactive agent that underlies the addictive properties of tobacco smoke [19,151154], the reviewed literature suggests that the majority of adverse neurobiological and neurocognitive effects of chronic cigarette smoking are a function of the direct and indirect consequences of continual exposure of the cardiopulmonary system, cerebrovascular system and brain parenchyma to the combination of non-nicotine combustion products contained in cigarette smoke [13,14,155]. However, a significant amount of data regarding potential mechanisms contributing to the neurobiological and neurocognitive abnormalities observed in humans is derived from in vitro and animal studies. Consequently, it is unclear if all potential mechanisms are generalizable to humans.

6. Discussion

The cumulative body of research reviewed suggests chronic cigarette smoking is associated with deficiencies in auditory-verbal learning and/or memory, general intellectual abilities, visual search speeds, processing speed, cognitive flexibility, working memory and executive functions, across a wide age range. With advancing age, chronic smoking is related to abnormal decline in reasoning, memory and global cognitive function, and may increase the risk for both vascular dementia and Alzheimer’s Disease. However, several studies showed a weak or no association with smoking status and neurocognition. Chronic smoking is related to structural and biochemical abnormalities in multiple brain regions, particularly the anterior dorsolateral, mesial frontal cortex, limbic system and underlying WM. A dose-response relationship is suggested between cigarette smoking, neurocognition and neurobiological function. The reviewed literature suggests the adverse neurobiological and neurocognitive effects of chronic cigarette smoking in humans may be related to the direct and indirect consequences of continual exposure of the cardiopulmonary system, cerebrovascular system and/or brain parenchyma to the combustion products of cigarette smoke. However, the potential mechanisms contributing to the neurobiological abnormalities observed are derived from in vitro and animal studies. Consequently, it is unclear if these mechanisms are actually operational in humans. Furthermore, it is uncertain to what extent, if any, the reported neurocognitive and neurobiological abnormalities reported in smokers are influenced by premorbid or comorbid factors. Overall, the following methodological limitations are present in the reviewed literature:

6.1. Confounding Variables

Potentially confounding medical conditions (e.g., hypertension, diabetes, insulin-resistance, chronic obstructive pulmonary disease, atherosclerosis, neurodegenerative diseases) and comorbid alcohol use/misuse, substance use/misuse, and psychiatric conditions (particularly mood disorders) were not consistently screened or statistically accounted for in many studies. Several psychiatric disorders known to have adverse effects on brain neurobiology and neurocognition are highly prevalent in chronic smokers, including anxiety disorders [156], attention deficit/hyperactivity disorder [157,158], alcohol and substance use disorders [13,157,159], mood disorders [160,161], and schizophrenia-spectrum disorders [162,163]. Additionally, the potential influence of sex, exercise, diet, body mass index, exposure to secondary/environmental smoke, nicotine withdrawal and genetic predispositions [e.g., ApoE4 genotype, single nucleotide polymorphisms in nicotinic acetlycholinergic receptors (nAChr), brain derived neurotrophic factor (BDNF), dopamine receptor D2 (DRD2), catechol-O-methyl transferace (COMT)] were not considered. The aforementioned factors are likely mediators or moderators of brain neurobiology and neurocognition in controls and addictive disorders [146,147,149,164181]. Finally, the potential effects of nicotine withdrawal on the primary measures of interest were not addressed in many studies.

6.2. Limited Scope of Neurocognitive Assessment

Overall, there are a limited number of studies in each age group that conducted a comprehensive assessment of neurocognition. Additionally, measures of executive function (e.g., Categories Test, Wisconsin Card Sorting Test, Wechsler Adult Intelligence Scale-III Matrix Reasoning) were seldom administered. In older adults, many of the population-based research used single screening measures of global cognitive function (e.g., MMSE), or employed a composite score based on a limited number of tests primarily used to assess the severity of cognitive dysfunction in neurodegenerative diseases. Additionally, only two studies [29,63] investigated the effects of chronic smoking on tasks specifically assessing decision making, risk taking and impulsivity. Consequently, the full scope of the neurocognitive consequences associated with chronic smoking remains unclear.

6.3. Limited Number of Neurocognitive Studies in Young Adults

The vast majority of studies investigating the neurocognitive consequences of chronic cigarette smoking have been conducted in middle aged and older adults. There is a particular shortage of studies in the 30–40 years of age range.

6.4. Limited Number of Neuroimaging Studies

Previous neuroimaging research assessing the chronic effects of cigarette smoking has been primarily restricted to a few CT and MR-based studies of brain morphology, metabolites or blood flow, which primarily targeted neocortical and subcortical GM. Only one study investigated WM integrity via DTI. Prospective multimodal neuroimaging studies thoroughly examining WM morphology, biochemistry and perfusion of regional cerebral WM have not been conducted. Assessment of the cerebral WM is vital to better understand the extent of potential neurobiological dysfunction associated with chronic cigarette smoking.

6.5. Limited Longitudinal Research

The vast majority of studies assessing the neurocognitive and neurobiological consequences of chronic smoking are cross-sectional in design. The few longitudinal neurocognitive and neuroimaging-based studies were conducted with older adult cohorts.

6.6. Absence of MR-based Studies Examining Relationships between Brain Neurobiology and Neurocognition

No study has concurrently combined MR-based neurobiological measures with comprehensive neurocognitive assessment in order to study the correspondence between brain function and neurocognition. Studies relating MR-based brain volumetric and metabolite measures to neurocognition in substance dependent populations have observed different patterns/relationships for smokers and non-smokers [182,183] suggesting a differential use of compensatory functions in smokers and non-smokers to accomplish the same task.

7. Conclusions

Increasing evidence suggests that chronic smoking in community-dwelling participants is associated with diminished function of multiple neurocognitive abilities and neurobiological abnormalities. The cumulative pattern of neurocognitive findings suggests dysfunction prominently in neurocircuitry implicated in decision making, impulse control, judgment, planning and reasoning skills, and in the initiation and maintenance of substance use disorders [184187]. Specifically, the pattern of the neurocognitive and neurobiological findings in chronic smokers points to abnormalities in the brain reward system [186188]. Major components of the brain reward system include (but are not limited to) the dorsolateral prefrontal cortex, orbitofrontal cortex, insula, anterior cingulate cortex, hippocampus, amygdala, nucleus accumbens, ventral tegmental area and other nuclei in the basal forebrain and ventral pallidum [186,189191]. Plastic changes in the brain reward system are implicated in the development and maintenance of all substance use disorders, including nicotine dependence, and other maladaptive behaviors [186188,192194]. However, the actual mechanisms promoting the neurocognitive and neurobiological abnormalities reported in chronic smokers are unclear and premorbid variables(e.g., genetic vulnerabilities) must also be considered as potential contributing factor. More specifically, the neurobiological and neurocognitive abnormalities reported in the reviewed studies may represent premorbid risk factors for the development and maintenance of nicotine dependence and/or premorbid vulnerabilities that were compounded by the effects of chronic smoking. Additionally, as many studies of the neurocognitive consequences of chronic smoking were conducted with older adults, the reported findings may be influenced by a survivor effect [43].
To assist in clarifying the factors contributing to the reported neurocognitive and neurobiological dysfunction, studies are needed that:
  • Concurrently assess cohorts of males and females ranging from young to older adults.
  • Employ prospective multi-modality neuroimaging studies (i.e., combining brain morphology, biochemistry, perfusion, and metabolism in the same cohort), with particular attention to the brain reward system.
  • Employ comprehensive neurocognitive testing including behavioral measures of impulsivity, decision-making and risk taking [24,195,196].
  • Consider genetic factors (e.g., ApoE genotype, single nucleotide polymorphisms in BDNF, nAChr, DRD2, COMT, glutamate receptors) implicated in the development and maintenance of substance use disorders (see [197200]). Such an approach would better delineate the extent and magnitude of the neurobiological and neurocognitive consequences of chronic cigarette smoking, the roles of common genetic variations in vulnerability to nicotine dependence and their inter-relationships.
  • Employ prospective serial longitudinal studies to assess changes in neurobiology and neurocognition over extended periods in chronic smokers (e.g., >5 years). Additionally, it is vital to conduct prospective pre-and-post neuroimaging and neurocognitive studies with individuals engaging in smoking cessation programs to determine if smoking-related neurobiological and neurocognitive abnormalities recover with smoking cessation, and to assess the effect of pharmacologic interventions (e.g., nicotine replacement, varenicline) on neurobiological and neurocognitive changes. Such longitudinal studies will assist in determining if the neurocognitive and/or neurobiological abnormalities observed in cross-sectional studies are related to premorbid factors.
In conclusion, chronic cigarette smoking appears to be associated with demonstrable abnormalities in brain neurobiology and neurocognition in cross-sectional research across the lifespan, and is related to abnormal rates of brain volume loss in the elderly. However, the mechanisms promoting these abnormalities have yet to be explicated in humans. To better understand the factors associated with the reported neurocognitive and neurobiological abnormalities, longitudinal research combining comprehensive neurocognitive assessment with neuroimaging of brain metabolites, microstructure, macroscopic morphology, brain function and genetic vulnerabilities are necessary. Such longitudinal studies are required to inform the development of more effective pharmacological and behavioral interventions to reduce the ever-increasing worldwide mortality and morbidity associated with the modifiable health risk that is chronic cigarette smoking.


This material is the result of work supported by NIH DA 024136 (TCD), AA10788 (DJM) and DA 13677 and U54 RR025208 (Nelson, PI; SJN, Co-Investigator) with resources and the use of facilities at the San Francisco Veterans Administration Medical Center, San Francisco, CA, USA.

References and Notes

  1. DeMarini, DM. Genotoxicity of tobacco smoke and tobacco smoke condensate: A review. Mutat Res 2004, 567, 447–474. [Google Scholar]
  2. Giovino, GA. Epidemiology of tobacco use in the United States. Oncogene 2002, 21, 7326–7340. [Google Scholar]
  3. Jha, P; Peto, R; Zatonski, W; Boreham, J; Jarvis, MJ; Lopez, AD. Social inequalities in male mortality, and in male mortality from smoking: Indirect estimation from national death rates in England and Wales, Poland, and North America. Lancet 2006, 368, 367–370. [Google Scholar]
  4. Dome, P; Lazary, J; Kalapos, MP; Rihmer, Z. Smoking, nicotine and neuropsychiatric disorders. Neurosci. Biobehav. Rev 2010, 34, 295–342. [Google Scholar]
  5. Ambrose, JA; Barua, RS. The pathophysiology of cigarette smoking and cardiovascular disease: An update. J Am Coll Cardiol 2004, 43, 1731–1737. [Google Scholar]
  6. Boudreaux, ED; Francis, JL; Carmack Taylor, CL; Scarinci, IC; Brantley, PJ. Changing multiple health behaviors: Smoking and exercise. Prev. Med 2003, 36, 471–478. [Google Scholar]
  7. Casasola, GG; Alvarez-Sala, JL; Marques, JA; Sanchez-Alarcos, JM; Tashkin, DP; Espinos, D. Cigarette smoking behavior and respiratory alterations during sleep in a healthy population. Sleep Breath 2002, 6, 19–24. [Google Scholar]
  8. Bartal, M. Health effects of tobacco use and exposure. Monaldi. Arch. Chest Dis 2001, 56, 545–554. [Google Scholar]
  9. Bolego, C; Poli, A; Paoletti, R. Smoking and gender. Cardiovasc. Res 2002, 53, 568–576. [Google Scholar]
  10. Garey, KW; Neuhauser, MM; Robbins, RA; Danziger, LH; Rubinstein, I. Markers of inflammation in exhaled breath condensate of young healthy smokers. Chest 2004, 125, 22–26. [Google Scholar]
  11. Hawkins, BT; Brown, RC; Davis, TP. Smoking and ischemic stroke: A role for nicotine. Trends Pharmacol. Sci 2002, 23, 78–82. [Google Scholar]
  12. Tsushima, Y; Tanizaki, Y; Aoki, J; Endo, K. MR detection of microhemorrhages in neurologically healthy adults. Neuroradiology 2002, 44, 31–36. [Google Scholar]
  13. Durazzo, TC; Meyerhoff, DJ. Neurobiological and neurocognitive effects of chronic cigarette smoking and alcoholism. Front Biosci 2007, 12, 4079–4100. [Google Scholar]
  14. Swan, GE; Lessov-Schlaggar, CN. The Effects of Tobacco Smoke and Nicotine on Cognition and the Brain. Neuropsychol. Rev 2007, 17, 259–273. [Google Scholar]
  15. Ceballos, NA. Tobacco use, alcohol dependence, and cognitive performance. J. Gen. Psychol 2006, 133, 375–388. [Google Scholar]
  16. Waters, AJ; Sutton, SR. Direct and indirect effects of nicotine/smoking on cognition in humans. Addict. Behav 2000, 25, 29–43. [Google Scholar]
  17. Sacco, KA; Bannon, KL; George, TP. Nicotinic receptor mechanisms and cognition in normal states and neuropsychiatric disorders. J Psychopharmacol 2004, 18, 457–474. [Google Scholar]
  18. Brody, AL. Functional brain imaging of tobacco use and dependence. J. Psychiatr. Res 2006, 40, 404–418. [Google Scholar]
  19. Mansvelder, HD; van Aerde, KI; Couey, JJ; Brussaard, AB. Nicotinic modulation of neuronal networks: From receptors to cognition. Psychopharmacology (Berl) 2006, 184, 292–305. [Google Scholar]
  20. McClernon, FJ; Gilbert, DG. Human functional neuroimaging in nicotine and tobacco research: Basics, background, and beyond. Nicotine Tob. Res 2004, 6, 941–959. [Google Scholar]
  21. Azizian, A; Monterosso, J; O’Neill, J; London, ED. Magnetic resonance imaging studies of cigarette smoking. In Nicotine Psychopharmacology; Springer-Verlag Berlin Heidelberg: Berlin, Germany, 2009; pp. 113–143. [Google Scholar]
  22. Sharma, A; Brody, A. In vivo brain imaging of human exposure to nicotine and tobacco. In Nicotine Psychopharmacology; Springer-Verlag Berlin Heidelberg: Berlin, Germany, 2009; pp. 145–171. [Google Scholar]
  23. Jacobsen, LK; Krystal, JH; Mencl, WE; Westerveld, M; Frost, SJ; Pugh, KR. Effects of smoking and smoking abstinence on cognition in adolescent tobacco smokers. Biol. Psychiat 2005, 57, 56–66. [Google Scholar]
  24. Yakir, A; Rigbi, A; Kanyas, K; Pollak, Y; Kahana, G; Karni, O; Eitan, R; Kertzman, S; Lerer, B. Why do young women smoke? III. Attention and impulsivity as neurocognitive predisposing factors. Eur. Neuropsychopharmacol 2007, 17, 339–351. [Google Scholar]
  25. Fried, PA; Watkinson, B; Gray, R. Neurocognitive consequences of cigarette smoking in young adults—A comparison with pre-drug performance. Neurotoxicol. Teratol 2006, 28, 517–525. [Google Scholar]
  26. Spilich, GJ; June, L; Renner, J. Cigarette smoking and cognitive performance. Br. J. Addict 1992, 87, 1313–1326. [Google Scholar]
  27. Weiser, M; Zarka, S; Werbeloff, N; Kravitz, E; Lubin, G. Cognitive test scores in male adolescent cigarette smokers compared to non-smokers: A population-based study. Addiction 2010, 105, 358–363. [Google Scholar]
  28. Wallsten, TS; Pleskac, TJ; Lejuez, CW. Modeling behavior in a clinically diagnostic sequential risk-taking task. Psychol. Rev 2005, 112, 862–880. [Google Scholar]
  29. Lejuez, CW; Aklin, WM; Jones, HA; Richards, JB; Strong, DR; Kahler, CW; Read, JP. The Balloon Analogue Risk Task (BART) differentiates smokers and nonsmokers. Exp. Clin. Psychopharmacol 2003, 11, 26–33. [Google Scholar]
  30. Hill, RD; Nilsson, LG; Nyberg, L; Backman, L. Cigarette smoking and cognitive performance in healthy Swedish adults. Age Ageing 2003, 32, 548–550. [Google Scholar]
  31. Starr, JM; Deary, IJ; Fox, HC; Whalley, LJ. Smoking and cognitive change from age 11 to 66 years: A confirmatory investigation. Addict. Behav 2006, 32, 63–68. [Google Scholar]
  32. Kalmijn, S; van Boxtel, MP; Verschuren, MW; Jolles, J; Launer, LJ. Cigarette smoking and alcohol consumption in relation to cognitive performance in middle age. Am. J. Epidemiol 2002, 156, 936–944. [Google Scholar]
  33. Paul, RH; Brickman, AM; Cohen, RA; Williams, LM; Niaura, R; Pogun, S; Clark, CR; Gunstad, J; Gordon, E. Cognitive status of young and older cigarette smokers: Data from the international brain database. J Clin. Neurosci 2006, 13, 457–465. [Google Scholar]
  34. Edelstein, SL; Kritz-Silverstein, D; Barrett-Connor, E. Prospective association of smoking and alcohol use with cognitive function in an elderly cohort. J. Womens Health 1998, 7, 1271–1281. [Google Scholar]
  35. Ernst, M; Heishman, SJ; Spurgeon, L; London, ED. Smoking history and nicotine effects on cognitive performance. Neuropsychopharmacology 2001, 25, 313–319. [Google Scholar]
  36. George, TP; Vessicchio, JC; Termine, A; Sahady, DM; Head, CA; Pepper, WT; Kosten, TR; Wexler, BE. Effects of smoking abstinence on visuospatial working memory function in schizophrenia. Neuropsychopharmacology 2002, 26, 75–85. [Google Scholar]
  37. Razani, J; Boone, K; Lesser, I; Weiss, D. Effects of cigarette smoking history on cognitice functioning in healthy older adults. Am J Geriatr Psychiatry 2004, 12, 404–411. [Google Scholar]
  38. Caspers, K; Arndt, S; Yucuis, R; McKirgan, L; Spinks, R. Effects of alcohol- and cigarette-use disorders on global and specific measures of cognition in middle-age adults. J. Stud. Alcohol Drugs 2010, 71, 192–200. [Google Scholar]
  39. Deary, IJ; Pattie, A; Taylor, MD; Whiteman, MC; Starr, JM; Whalley, LJ. Smoking and cognitive change from age 11 to age 80. J. Neurol. Neurosurg Psychiatry 2003, 74, 1003–1007. [Google Scholar]
  40. Richards, M; Jarvis, MJ; Thompson, N; Wadsworth, ME. Cigarette smoking and cognitive decline in midlife: evidence from a prospective birth cohort study. Am. J. Public Health 2003, 93, 994–998. [Google Scholar]
  41. Whalley, LJ; Fox, HC; Deary, IJ; Starr, JM. Childhood IQ, smoking, and cognitive change from age 11 to 64 years. Addict. Behav 2005, 30, 77–88. [Google Scholar]
  42. Hill, RD. Residual effects of cigarette smoking on cognitive performance in normal aging. Psycho. l Aging 1989, 4, 251–254. [Google Scholar]
  43. Sabia, S; Marmot, M; Dufouil, C; Singh-Manoux, A. Smoking history and cognitive function in middle age from the Whitehall II study. Arch. Intern. Med 2008, 168, 1165–1173. [Google Scholar]
  44. Huadong, Z; Juan, D; Jingcheng, L; Yanjiang, W; Meng, Z; Hongbo, H. Study of the relationship between cigarette smoking, alcohol drinking and cognitive impairment among elderly people in China. Age Ageing 2003, 32, 205–210. [Google Scholar]
  45. Stewart, MC; Deary, IJ; Fowkes, FG; Price, JF. Relationship between lifetime smoking, smoking status at older age and human cognitive function. Neuroepidemiology 2006, 26, 83–92. [Google Scholar]
  46. Galanis, DJ; Petrovitch, H; Launer, LJ; Harris, TB; Foley, DJ; White, LR. Smoking history in middle age and subsequent cognitive performance in elderly Japanese-American men. The Honolulu-Asia Aging Study. Am. J. Epidemiol 1997, 145, 507–515. [Google Scholar]
  47. Fischer, P; Zehetmayer, S; Bauer, K; Huber, K; Jungwirth, S; Tragl, KH. Relation between vascular risk factors and cognition at age 75. Acta Neurol. Scand 2006, 114, 84–90. [Google Scholar]
  48. Wang, L; van Belle, G; Kukull, WB; Larson, EB. Predictors of functional change: A longitudinal study of nondemented people aged 65 and older. J. Am. Geriatr. Soc 2002, 50, 1525–1534. [Google Scholar]
  49. Iki, M; Ishizaki, H; Aalto, H; Starck, J; Pyykko, I. Smoking habits and postural stability. Am. J. Otolaryngol 1994, 15, 124–128. [Google Scholar]
  50. Ott, A; Andersen, K; Dewey, ME; Letenneur, L; Brayne, C; Copeland, JR; Dartigues, JF; Kragh-Sorensen, P; Lobo, A; Martinez-Lage, JM; Stijnen, T; Hofman, A; Launer, LJ. Effect of smoking on global cognitive function in nondemented elderly. Neurology 2004, 62, 920–924. [Google Scholar]
  51. Cervilla, JA; Prince, M; Mann, A. Smoking, drinking, and incident cognitive impairment: A cohort community based study included in the Gospel Oak project. J. Neurol. Neurosurg. Psychiatry 2000, 68, 622–626. [Google Scholar]
  52. Dufouil, C; Tzourio, C; Brayne, C; Berr, C; Amouyel, P; Alperovitch, A. Influence of apolipoprotein E genotype on the risk of cognitive deterioration in moderate drinkers and smokers. Epidemiology 2000, 11, 280–284. [Google Scholar]
  53. Launer, LJ; Feskens, EJ; Kalmijn, S; Kromhout, D. Smoking, drinking, and thinking. The Zutphen Elderly Study. Am. J. Epidemiol 1996, 143, 219–227. [Google Scholar]
  54. Reitz, C; Luchsinger, J; Tang, MX; Mayeux, R. Effect of smoking and time on cognitive function in the elderly without dementia. Neurology 2005, 65, 870–875. [Google Scholar]
  55. Heffernan, TM; Ling, J; Parrott, AC; Buchanan, T; Scholey, AB; Rodgers, J. Self-rated everyday and prospective memory abilities of cigarette smokers and non-smokers: A web-based study. Drug Alcohol Depend 2005, 78, 235–241. [Google Scholar]
  56. Cerhan, JR; Folsom, AR; Mortimer, JA; Shahar, E; Knopman, DS; McGovern, PG; Hays, MA; Crum, LD; Heiss, G. Correlates of cognitive function in middle-aged adults. Atherosclerosis Risk in Communities (ARIC) Study Investigators. Gerontology 1998, 44, 95–105. [Google Scholar]
  57. Launer, LJ; Andersen, K; Dewey, ME; Letenneur, L; Ott, A; Amaducci, LA; Brayne, C; Copeland, JR; Dartigues, JF; Kragh-Sorensen, P; Lobo, A; Martinez-Lage, JM; Stijnen, T; Hofman, A. Rates and risk factors for dementia and Alzheimer’s disease: results from EURODEM pooled analyses. EURODEM Incidence Research Group and Work Groups. European Studies of Dementia. Neurology 1999, 52, 78–84. [Google Scholar]
  58. Ott, A; Slooter, AJ; Hofman, A; van Harskamp, F; Witteman, JC; van Broeckhoven, C; van Duijn, CM; Breteler, MM. Smoking and risk of dementia and Alzheimer’s disease in a population-based cohort study: the Rotterdam Study. Lancet 1998, 351, 1840–1843. [Google Scholar]
  59. Merchant, C; Tang, MX; Albert, S; Manly, J; Stern, Y; Mayeux, R. The influence of smoking on the risk of Alzheimer’s disease. Neurology 1999, 52, 1408–1412. [Google Scholar]
  60. Anstey, KJ; von Sanden, C; Salim, A; O’Kearney, R. Smoking as a risk factor for dementia and cognitive decline: A meta-analysis of prospective studies. Am. J. Epidemiol 2007, 166, 367–378. [Google Scholar]
  61. Tyas, SL; White, LR; Petrovitch, H; Webster Ross, G; Foley, DJ; Heimovitz, HK; Launer, LJ. Mid-life smoking and late-life dementia: The Honolulu-Asia Aging Study. Neurobiol. Aging 2003, 24, 589–596. [Google Scholar]
  62. Sakurai, Y; Kanazawa, I. Acute effects of cigarettes in non-deprived smokers on memory, calculation and executive functions. Hum Psychopharmacol 2002, 17, 369–373. [Google Scholar]
  63. Businelle, MS; Apperson, MR; Kendzor, DE; Terlecki, MA; Copeland, AL. The relative impact of nicotine dependence, other substance dependence, and gender on Bechara Gambling Task performance. Exp. Clin. Psychopharmacol 2008, 16, 513–520. [Google Scholar]
  64. Schinka, JA; Vanderploeg, RD; Rogish, M; Graves, AB; Mortimer, JA; Ordoric, PI. Effects of the use of alcohol and cigarettes on cognition in elderly adults. J. Int. Neuropsychol. Soc 2002, 8, 811–818. [Google Scholar]
  65. Schinka, JA; Belanger, H; Mortimer, JA; Graves, AB. Effects of the use of alcohol and cigarettes on cognition in elderly African American adults. J. Int. Neuropsychol. Soc 2003, 9, 690–697. [Google Scholar]
  66. Chen, WT; Wang, PN; Wang, SJ; Fuh, JL; Lin, KN; Liu, HC. Smoking and cognitive performance in the community elderly: A longitudinal study. J. Geriatr. Psychiatry Neurol 2003, 16, 18–22. [Google Scholar]
  67. Schinka, JA; Vanderploeg, RD; Rogish, M; Ordorica, PI. Effects of alcohol and cigarette use on cognition in middle-aged adults. J. Int. Neuropsychol. Soc 2002, 8, 683–690. [Google Scholar]
  68. Ford, AB; Mefrouche, Z; Friedland, RP; Debanne, SM. Smoking and cognitive impairment: A population-based study. J. Am. Geriatr. Soc 1996, 44, 905–909. [Google Scholar]
  69. Hebert, LE; Scherr, PA; Beckett, LA; Albert, MS; Rosner, B; Taylor, JO; Evans, DA. Relation of Smoking and Low-to-moderate Alcohol Consumption to Change in Cognitive Function: A Longitudinal Study in a Defined Community of Older Persons. Am. J. Epidemiol 1993, 137, 881–891. [Google Scholar]
  70. Elwan, O; Hassan, AA; Abdel Naseer, M; Elwan, F; Deif, R; El Serafy, O; El Banhawy, E; El Fatatry, M. Brain aging in a sample of normal Egyptians cognition, education, addiction and smoking. J. Neurol. Sci 1997, 148, 79–86. [Google Scholar]
  71. Spilich, GJ; June, L; Renner, J. Cigarette smoking and cognitive performance. Br. J. Addict 1992, 87, 1313–1326. [Google Scholar]
  72. Lejuez, CW; Read, JP; Kahler, CW; Richards, JB; Ramsey, SE; Stuart, GL; Strong, DR; Brown, RA. Evaluation of a behavioral measure of risk taking: The Balloon Analogue Risk Task (BART). J. Exp. Psychol. Appl 2002, 8, 75–84. [Google Scholar]
  73. Razani, J; Boone, K; Lesser, I; Weiss, D. Effects of cigarette smoking history on cognitive functioning in healthy older adults. Am. J. Geriatr. Psychiatry 2004, 12, 404–411. [Google Scholar]
  74. Akiyama, H; Meyer, JS; Mortel, KF; Terayama, Y; Thornby, J; Konno, S. Normal human aging: Factors contributing to cerebral atrophy. J. Neurol. Sci 1997, 152, 39–49. [Google Scholar]
  75. Hayee, A; Haque, A; Anwarullah, A; Rabbani, M. Smoking enhances age related brain atrophy—A quantitative study with computed tomography. Bangladesh Med. Res. Counc. Bull 2003, 29, 118–124. [Google Scholar]
  76. Kubota, K; Matsuzawa, T; Fujiwara, T; Yamaguchi, T; Ito, K; Watanabe, H; Ono, S. Age-related brain atrophy enhanced by smoking: A quantitative study with computed tomography. Tohoku J. Exp. Med 1987, 153, 303–311. [Google Scholar]
  77. Swan, GE; DeCarli, C; Miller, BL; Reed, T; Wolf, PA; Carmelli, D. Biobehavioral characteristics of nondemented older adults with subclinical brain atrophy. Neurology 2000, 54, 2108–2114. [Google Scholar]
  78. Longstreth, WT, Jr; Arnold, AM; Manolio, TA; Burke, GL; Bryan, N; Jungreis, CA; O’Leary, D; Enright, PL; Fried, L. Clinical correlates of ventricular and sulcal size on cranial magnetic resonance imaging of 3,301 elderly people. The Cardiovascular Health Study. Collaborative Research Group. Neuroepidemiology 2000, 19, 30–42. [Google Scholar]
  79. Brody, AL; Mandelkern, MA; Jarvik, ME; Lee, GS; Smith, EC; Huang, JC; Bota, RG; Bartzokis, G; London, ED. Differences between smokers and nonsmokers in regional gray matter volumes and densities. Biol. Psychiatry 2004, 55, 77–84. [Google Scholar]
  80. Gallinat, J; Meisenzahl, E; Jacobsen, LK; Kalus, P; Bierbrauer, J; Kienast, T; Witthaus, H; Leopold, K; Seifert, F; Schubert, F; Staedtgen, M. Smoking and structural brain deficits: A volumetric MR investigation. Eur. J. Neurosci 2006, 24, 1744–1750. [Google Scholar]
  81. Almeida, OP; Garrido, GJ; Lautenschlager, NT; Hulse, GK; Jamrozik, K; Flicker, L. Smoking is associated with reduced cortical regional gray matter density in brain regions associated with incipient Alzheimer disease. Am. J. Geriatr. Psychiatry 2008, 16, 92–98. [Google Scholar]
  82. Paul, RH; Grieve, SM; Niaura, R; David, SP; Laidlaw, DH; Cohen, R; Sweet, L; Taylor, G; Clark, RC; Pogun, S; Gordon, E. Chronic cigarette smoking and the microstructural integrity of white matter in healthy adults: A diffusion tensor imaging study. Nicotine Tob. Res 2008, 10, 137–147. [Google Scholar]
  83. Lim, KO; Helpern, JA. Neuropsychiatric applications of DTI—A review. NMR Biomed 2002, 15, 587–593. [Google Scholar]
  84. Sundgren, PC; Dong, Q; Gomez-Hassan, D; Mukherji, SK; Maly, P; Welsh, R. Diffusion tensor imaging of the brain: Review of clinical applications. Neuroradiology 2004, 46, 339–350. [Google Scholar]
  85. Fukuda, H; Kitani, M. Cigarette smoking is correlated with the periventricular hyperintensity grade of brain magnetic resonance imaging. Stroke 1996, 27, 645–649. [Google Scholar]
  86. Jeerakathil, T; Wolf, PA; Beiser, A; Massaro, J; Seshadri, S; D’Agostino, RB; DeCarli, C. Stroke risk profile predicts white matter hyperintensity volume: the Framingham Study. Stroke 2004, 35, 1857–1861. [Google Scholar]
  87. Longstreth, WT, Jr; Arnold, AM; Beauchamp, NJ, Jr; Manolio, TA; Lefkowitz, D; Jungreis, C; Hirsch, CH; O’Leary, DH; Furberg, CD. Incidence, manifestations, and predictors of worsening white matter on serial cranial magnetic resonance imaging in the elderly: The Cardiovascular Health Study. Stroke 2005, 36, 56–61. [Google Scholar]
  88. Ding, J; Nieto, FJ; Beauchamp, NJ; Longstreth, WT, Jr; Manolio, TA; Hetmanski, JB; Fried, LP. A prospective analysis of risk factors for white matter disease in the brain stem: The Cardiovascular Health Study. Neuroepidemiology 2003, 22, 275–282. [Google Scholar]
  89. Longstreth, WT, Jr; Manolio, TA; Arnold, A; Burke, GL; Bryan, N; Jungreis, CA; Enright, PL; O’Leary, D; Fried, L. Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3,301 elderly people. The Cardiovascular Health Study. Stroke 1996, 27, 1274–1282. [Google Scholar]
  90. Marstrand, JR; Garde, E; Rostrup, E; Ring, P; Rosenbaum, S; Mortensen, EL; Larsson, HB. Cerebral perfusion and cerebrovascular reactivity are reduced in white matter hyperintensities. Stroke 2002, 33, 972–976. [Google Scholar]
  91. Bisschops, RH; van der Graaf, Y; Mali, WP; van der Grond, J. High total cerebral blood flow is associated with a decrease of white matter lesions. J. Neurol 2004, 251, 1481–1485. [Google Scholar]
  92. DeCarli, C; Murphy, DG; Tranh, M; Grady, CL; Haxby, JV; Gillette, JA; Salerno, JA; Gonzales-Aviles, A; Horwitz, B; Rapoport, SI; et al. The effect of white matter hyperintensity volume on brain structure, cognitive performance, and cerebral metabolism of glucose in 51 healthy adults. Neurology 1995, 45, 2077–2084. [Google Scholar]
  93. Gunning-Dixon, FM; Raz, N. The cognitive correlates of white matter abnormalities in normal aging: a quantitative review. Neuropsychology 2000, 14, 224–232. [Google Scholar]
  94. Ross, B; Bluml, S. Magnetic resonance spectroscopy of the human brain. Anat. Rec 2001, 265, 54–84. [Google Scholar]
  95. Moffett, JR; Ross, B; Arun, P; Madhavarao, CN; Namboodiri, AM. N-Acetylaspartate in the CNS: From neurodiagnostics to neurobiology. Prog. Neurobiol 2007, 81, 89–131. [Google Scholar]
  96. Miller, BL; Chang, L; Booth, R; Ernst, T; Cornford, M; Nikas, D; McBride, D; Jenden, DJ. In vivo 1H MRS choline: Correlation with in vitro chemistry/histology. Life Sci 1996, 58, 1929–1935. [Google Scholar]
  97. Gallinat, J; Lang, UE; Jacobsen, LK; Bajbouj, M; Kalus, P; von Haebler, D; Seifert, F; Schubert, F. Abnormal hippocampal neurochemistry in smokers: Evidence from proton magnetic resonance spectroscopy at 3 T. J. Clin. Psychopharmacol 2007, 27, 80–84. [Google Scholar]
  98. Gallinat, J; Schubert, F. Regional cerebral glutamate concentrations and chronic tobacco consumption. Pharmacopsychiatry 2007, 40, 64–67. [Google Scholar]
  99. Enoch, MA. Pharmacogenomics of alcohol response and addiction. Am. J. Pharmacogenomics 2003, 3, 217–232. [Google Scholar]
  100. George, TP; O’Malley, SS. Current pharmacological treatments for nicotine dependence. Trends Pharmacol. Sci 2004, 25, 42–48. [Google Scholar]
  101. Heinz, A; Schafer, M; Higley, JD; Krystal, JH; Goldman, D. Neurobiological correlates of the disposition and maintenance of alcoholism. Pharmacopsychiatry 2003, 36(Suppl 3), S255–258. [Google Scholar]
  102. Epperson, CN; O’Malley, S; Czarkowski, KA; Gueorguieva, R; Jatlow, P; Sanacora, G; Rothman, DL; Krystal, JH; Mason, GF. Sex, GABA, and nicotine: The impact of smoking on cortical GABA levels across the menstrual cycle as measured with proton magnetic resonance spectroscopy. Biol. Psychiatry 2005, 57, 44–48. [Google Scholar]
  103. Rogers, RL; Meyer, JS; Shaw, TG; Mortel, KF; Hardenberg, JP; Zaid, RR. Cigarette Smoking Decreases Cerebral Blood Flow Suggesting Increased Risk for Stroke. JAMA 1983, 250, 2796–2800. [Google Scholar]
  104. Yamashita, K; Kobayashi, S; Yamaguchi, S; Kitani, M; Tsunematsu, T. Effect of smoking on regional cerebral blood flow in the normal aged volunteers. Gerontology 1988, 34, 199–204. [Google Scholar]
  105. Rourke, SB; Dupont, RM; Grant, I; Lehr, PP; Lamoureux, G; Halpern, S; Yeung, DW. Reduction in cortical IMP-SPET tracer uptake with recent cigarette consumption in a young group of healthy males. San Diego HIV Neurobehavioral Research Center. Eur. J. Nucl. Med 1997, 24, 422–427. [Google Scholar]
  106. Meyer, JS; Rauch, G; Rauch, RA; Haque, A. Risk factors for cerebral hypoperfusion, mild cognitive impairment, and dementia. Neurobiol. Aging 2000, 21, 161–169. [Google Scholar]
  107. Akiyama, H; Meyer, JS; Mortel, KF; Terayama, Y; Thornby, JI; Konno, S. Normal human aging: Factors contributing to cerebral atrophy. J. Neurol. Sci 1997, 152, 39–49. [Google Scholar]
  108. Rezvani, AH; Levin, ED. Cognitive effects of nicotine. Biol. Psychiatry 2001, 49, 258–267. [Google Scholar]
  109. Mendrek, A; Monterosso, J; Simon, SL; Jarvik, M; Brody, A; Olmstead, R; Domier, CP; Cohen, MS; Ernst, M; London, ED. Working memory in cigarette smokers: Comparison to non-smokers and effects of abstinence. Addict. Behav 2006, 31, 833–844. [Google Scholar]
  110. Parrott, AC. Nicotine psychobiology: How chronic-dose prospective studies can illuminate some of the theoretical issues from acute-dose research. Psychopharmacology (Berl) 2006, 184, 567–576. [Google Scholar]
  111. Heishman, SJ; Kleykamp, BA; Singleton, EG. Meta-analysis of the acute effects of nicotine and smoking on human performance. Psychopharmacology (Berl) 2010, 210, 453–469. [Google Scholar]
  112. Xu, J; Mendrek, A; Cohen, MS; Monterosso, J; Rodriguez, P; Simon, SL; Brody, A; Jarvik, M; Domier, CP; Olmstead, R; Ernst, M; London, ED. Brain activity in cigarette smokers performing a working memory task: effect of smoking abstinence. Biol. Psychiatry 2005, 58, 143–150. [Google Scholar]
  113. Hukkanen, J; Jacob, P, 3rd; Benowitz, NL. Metabolism and disposition kinetics of nicotine. Pharmacol. Rev 2005, 57, 79–115. [Google Scholar]
  114. Domino, EF; Ni, L; Xu, Y; Koeppe, RA; Guthrie, S; Zubieta, JK. Regional cerebral blood flow and plasma nicotine after smoking tobacco cigarettes. Prog. Neuropsychopharmacol. Biol. Psychiatry 2004, 28, 319–327. [Google Scholar]
  115. Rose, JE; Behm, FM; Westman, EC; Mathew, RJ; London, ED; Hawk, TC; Turkington, TG; Coleman, RE. PET studies of the influences of nicotine on neural systems in cigarette smokers. Am. J. Psychiatry 2003, 160, 323–333. [Google Scholar]
  116. Domino, EF; Minoshima, S; Guthrie, SK; Ohl, L; Ni, L; Koeppe, RA; Cross, DJ; Zubieta, J. Effects of nicotine on regional cerebral glucose metabolism in awake resting tobacco smokers. Neuroscience 2000, 101, 277–282. [Google Scholar]
  117. Stapleton, JM; Gilson, SF; Wong, DF; Villemagne, VL; Dannals, RF; Grayson, RF; Henningfield, JE; London, ED. Intravenous nicotine reduces cerebral glucose metabolism: A preliminary study. Neuropsychopharmacology 2003, 28, 765–772. [Google Scholar]
  118. Bates, C; Jarvis, M; Connolly, G. Tobacco Additives: Cigarette Engineering and Nicotine Addiction; Massachusetts Tobacco Control Program: Boston, MA, USA, 1999; pp. 1–23. [Google Scholar]
  119. Fowles, J; Bates, M; Noiton, D. The Chemical Constituents in Cigarettes and Cigarette Smoke: Priorities for Harm Reduction; Epidemiology and Toxicology Group: Porirua, New Zealand, 2000; pp. 1–65. [Google Scholar]
  120. Park, EM; Park, YM; Gwak, YS. Oxidative damage in tissues of rats exposed to cigarette smoke. Free Radic. Biol. Med 1998, 25, 79–86. [Google Scholar]
  121. Muscat, JE; Kleinman, W; Colosimo, S; Muir, A; Lazarus, P; Park, J; Richie, JP, Jr. Enhanced protein glutathiolation and oxidative stress in cigarette smokers. Free Radic. Biol. Med 2004, 36, 464–470. [Google Scholar]
  122. Yang, YM; Liu, GT. Injury of mouse brain mitochondria induced by cigarette smoke extract and effect of vitamin C on it in vitro. Biomed. Environ. Sci 2003, 16, 256–266. [Google Scholar]
  123. Deveci, S; Deveci, F; Acik, Y; Ozan, A. The measurement of exhaled carbon monoxide in healthy smokers and non-smokers. Resp. Med 2004, 98, 551–556. [Google Scholar]
  124. Macdonald, G; Kondor, N; Yousefi, V; Green, A; Wong, F; Aquino-Parsons, C. Reduction of carboxyhaemoglobin levels in the venous blood of cigarette smokers following the administration of carbogen. Radiother. Oncol 2004, 73, 367–371. [Google Scholar]
  125. Alonso, JR; Cardellach, F; Casademont, J; Miro, O. Reversible inhibition of mitochondrial complex IV activity in PBMC following acute smoking. Eur. Respir. J 2004, 23, 214–218. [Google Scholar]
  126. Moriarty, SE; Shah, JH; Lynn, M; Jiang, S; Openo, K; Jones, DP; Sternberg, P. Oxidation of glutathione and cysteine in human plasma associated with smoking. Free Radic. Biol. Med 2003, 35, 1582–1588. [Google Scholar]
  127. Panda, K; Chattopadhyay, R; Chattopadhyay, DJ; Chatterjee, IB. Vitamin C prevents cigarette smoke-induced oxidative damage in vivo. Free Radic. Biol. Med 2000, 29, 115–124. [Google Scholar]
  128. Anbarasi, K; Vani, G; Balakrishna, K; Devi, CS. Effect of bacoside A on membrane-bound ATPases in the brain of rats exposed to cigarette smoke. J. Biochem. Mol. Toxicol 2005, 19, 59–65. [Google Scholar]
  129. Xiao, AY; Wei, L; Xia, S; Rothman, S; Yu, SP. Ionic mechanism of ouabain-induced concurrent apoptosis and necrosis in individual cultured cortical neurons. J. Neurosci 2002, 22, 1350–1362. [Google Scholar]
  130. Abreu-Villaca, Y; Seidler, FJ; Tate, CA; Slotkin, TA. Nicotine is a neurotoxin in the adolescent brain: critical periods, patterns of exposure, regional selectivity, and dose thresholds for macromolecular alterations. Brain Res 2003, 979, 114–128. [Google Scholar]
  131. Slotkin, TA. Nicotine and the adolescent brain: insights from an animal model. Neurotoxicol. Teratol 2002, 24, 369–384. [Google Scholar]
  132. Mendez-Alvarez, E; Soto-Otero, R; Sanchez-Sellero, I; Lopez-Rivadulla Lamas, M. In vitro inhibition of catalase activity by cigarette smoke: relevance for oxidative stress. J. Appl. Toxicol 1998, 18, 443–448. [Google Scholar]
  133. Anbarasi, K; Vani, G; Balakrishna, K; Devi, CS. Effect of bacoside A on brain antioxidant status in cigarette smoke exposed rats. Life Sci 2006, 78, 1378–1384. [Google Scholar]
  134. Sachdev, PS; Anstey, KJ; Parslow, RA; Wen, W; Maller, J; Kumar, R; Christensen, H; Jorm, AF. Pulmonary function, cognitive impairment and brain atrophy in a middle-aged community sample. Dement. Geriatr. Cogn. Disord 2006, 21, 300–308. [Google Scholar]
  135. Hawkins, BT; Brown, RC; Davis, TP. Smoking and ischemic stroke: A role for nicotine? Trends Pharmacol. Sci 2002, 23, 78–82. [Google Scholar]
  136. Gerzanich, V; Zhang, F; West, GA; Simard, JM. Chronic nicotine alters NO signaling of Ca(2+) channels in cerebral arterioles. Circ. Res 2001, 88, 359–365. [Google Scholar]
  137. Terborg, C; Bramer, S; Weiller, C; Rother, J. Short-term effect of cigarette smoking on CO(2)-induced vasomotor reactivity in man: A study with near-infrared spectroscopy and tanscranial Doppler sonography. J. Neurol. Sci 2002, 205, 15–20. [Google Scholar]
  138. Michael Pittilo, R. Cigarette smoking, endothelial injury and cardiovascular disease. Int. J. Exp. Pathol 2000, 81, 219–230. [Google Scholar]
  139. Zubieta, J; Lombardi, U; Minoshima, S; Guthrie, S; Ni, L; Ohl, LE; Koeppe, RA; Domino, EF. Regional cerebral blood flow effects of nicotine in overnight abstinent smokers. Biol. Psychiatry 2001, 49, 906–913. [Google Scholar]
  140. Jeerakathil, T; Wolf, PA; Beiser, A; Hald, JK; Au, R; Kase, CS; Massaro, JM; DeCarli, C. Cerebral microbleeds: Prevalence and associations with cardiovascular risk factors in the Framingham Study. Stroke 2004, 35, 1831–1835. [Google Scholar]
  141. Liao, D; Cooper, L; Cai, J; Toole, J; Bryan, N; Burke, G; Shahar, E; Nieto, J; Mosley, T; Heiss, G. The prevalence and severity of white matter lesions, their relationship with age, ethnicity, gender, and cardiovascular disease risk factors: the ARIC Study. Neuroepidemiology 1997, 16, 149–162. [Google Scholar]
  142. Chalela, JA; Wolf, RL; Maldjian, JA; Kasner, SE. MRI identification of early white matter injury in anoxic-ischemic encephalopathy. Neurology 2001, 56, 481–485. [Google Scholar]
  143. Bartzokis, G. Age-related myelin breakdown: A developmental model of cognitive decline and Alzheimer’s disease. Neurobiol Aging 2004, 25, 5–18, author reply 49–62.. [Google Scholar]
  144. Bartzokis, G. Quadratic trajectories of brain myelin content: Unifying construct for neuropsychiatric disorders. Neurobiol. Aging 2004, 25, 49–62. [Google Scholar]
  145. Chiolero, A; Faeh, D; Paccaud, F; Cornuz, J. Consequences of smoking for body weight, body fat distribution, and insulin resistance. Am. J. Clin. Nutr 2008, 87, 801–809. [Google Scholar]
  146. Convit, A. Links between cognitive impairment in insulin resistance: An explanatory model. Neurobiol Aging 2005, 26(Suppl 1), 31–35. [Google Scholar]
  147. Ward, MA; Carlsson, CM; Trivedi, MA; Sager, MA; Johnson, SC. The effect of body mass index on global brain volume in middle-aged adults: A cross sectional study. BMC Neurol 2005, 5, 23. [Google Scholar]
  148. Haltia, LT; Viljanen, A; Parkkola, R; Kemppainen, N; Rinne, JO; Nuutila, P; Kaasinen, V. Brain white matter expansion in human obesity and the recovering effect of dieting. J. Clin. Endocrinol. Metab 2007, 92, 3278–3284. [Google Scholar]
  149. Gazdzinski, S; Kornak, J; Weiner, MW; Meyerhoff, DJ. Body mass index and magnetic resonance markers of brain integrity in adults. Ann. Neurol 2008, 63, 652–657. [Google Scholar]
  150. Jeong, SK; Nam, HS; Son, MH; Son, EJ; Cho, KH. Interactive effect of obesity indexes on cognition. Dement. Geriatr. Cogn. Disord 2005, 19, 91–96. [Google Scholar]
  151. Dani, JA; De Biasi, M. Cellular mechanisms of nicotine addiction. Pharmacol. Biochem. Behav 2001, 70, 439–446. [Google Scholar]
  152. Mansvelder, HD; De Rover, M; McGehee, DS; Brussaard, AB. Cholinergic modulation of dopaminergic reward areas: Upstream and downstream targets of nicotine addiction. Eur. J. Pharmacol 2003, 480, 117–123. [Google Scholar]
  153. Vallejo, YF; Buisson, B; Bertrand, D; Green, WN. Chronic nicotine exposure upregulates nicotinic receptors by a novel mechanism. J. Neurosci 2005, 25, 5563–5572. [Google Scholar]
  154. Volkow, ND; Fowler, JS; Ding, YS; Wang, GJ; Gatley, SJ. Imaging the neurochemistry of nicotine actions: Studies with positron emission tomography. Nicotine Tob Res 1999, 1(Suppl 2), S127–132, discussion S139–140.. [Google Scholar]
  155. Haustein, KO. Smoking tobacco, microcirculatory changes and the role of nicotine. Int. J. Clin. Pharmacol. Ther 1999, 37, 76–85. [Google Scholar]
  156. Breslau, N; Novak, SP; Kessler, RC. Psychiatric disorders and stages of smoking. Biol. Psychiatry 2004, 55, 69–76. [Google Scholar]
  157. Rohde, P; Kahler, CW; Lewinsohn, PM; Brown, RA. Psychiatric disorders, familial factors, and cigarette smoking: II. Associations with progression to daily smoking. Nicotine Tob. Res 2004, 6, 119–132. [Google Scholar]
  158. Ohlmeier, MD; Peters, K; Te Wildt, BT; Zedler, M; Ziegenbein, M; Wiese, B; Emrich, HM; Schneider, U. Comorbidity of alcohol and substance dependence with attention-deficit/hyperactivity disorder (ADHD). Alcohol Alcoholism 2008, 43, 300–304. [Google Scholar]
  159. Schumann, A; Hapke, U; Meyer, C; Rumpf, HJ; John, U. Prevalence, characteristics, associated mental disorders and predictors of DSM-IV nicotine dependence. Eur. Addict. Res 2004, 10, 29–34. [Google Scholar]
  160. Paperwalla, KN; Levin, TT; Weiner, J; Saravay, SM. Smoking and depression. Med Clin North Am 2004, 88. [Google Scholar]
  161. Fergusson, DM; Goodwin, RD; Horwood, LJ. Major depression and cigarette smoking: Results of a 21-year longitudinal study. Psychol. Med 2003, 33, 1357–1367. [Google Scholar]
  162. Smith, RC; Singh, A; Infante, M; Khandat, A; Kloos, A. Effects of cigarette smoking and nicotine nasal spray on psychiatric symptoms and cognition in schizophrenia. Neuropsychopharmacology 2002, 27, 479–497. [Google Scholar]
  163. Dolan, SL; Sacco, KA; Termine, A; Seyal, AA; Dudas, MM; Vessicchio, JC; Wexler, BE; George, TP. Neuropsychological deficits are associated with smoking cessation treatment failure in patients with schizophrenia. Schizophr. Res 2004, 70, 263–275. [Google Scholar]
  164. Pignatti, R; Bertella, L; Albani, G; Mauro, A; Molinari, E; Semenza, C. Decision-making in obesity: A study using the Gambling Task. Eat. Weight Disord 2006, 11, 126–132. [Google Scholar]
  165. Cotman, CW; Berchtold, NC. Exercise: A behavioral intervention to enhance brain health and plasticity. Trends Neurosci 2002, 25, 295–301. [Google Scholar]
  166. Dishman, RK; Berthoud, HR; Booth, FW; Cotman, CW; Edgerton, VR; Fleshner, MR; Gandevia, SC; Gomez-Pinilla, F; Greenwood, BN; Hillman, CH; Kramer, AF; Levin, BE; Moran, TH; Russo-Neustadt, AA; Salamone, JD; Van Hoomissen, JD; Wade, CE; York, DA; Zigmond, MJ. Neurobiology of exercise. Obesity (Silver Spring) 2006, 14, 345–356. [Google Scholar]
  167. Hillman, CH; Erickson, KI; Kramer, AF. Be smart, exercise your heart: Exercise effects on brain and cognition. Nat. Rev. Neurosci 2008, 9, 58–65. [Google Scholar]
  168. Duman, RS. Neurotrophic factors and regulation of mood: role of exercise, diet and metabolism. Neurobiol Aging 2005, 26(Suppl 1), 88–93. [Google Scholar]
  169. Parrott, MD; Greenwood, CE. Dietary influences on cognitive function with aging: From high-fat diets to healthful eating. Ann. N. Y. Acad. Sci 2007, 1114, 389–397. [Google Scholar]
  170. Wadley, VG; McClure, LA; Howard, VJ; Unverzagt, FW; Go, RC; Moy, CS; Crowther, MR; Gomez, CR; Howard, G. Cognitive status, stroke symptom reports, and modifiable risk factors among individuals with no diagnosis of stroke or transient ischemic attack in the REasons for Geographic and Racial Differences in Stroke (REGARDS) Study. Stroke 2007, 38, 1143–1147. [Google Scholar]
  171. Gustafson, D; Lissner, L; Bengtsson, C; Bjorkelund, C; Skoog, I. A 24-year follow-up of body mass index and cerebral atrophy. Neurology 2004, 63, 1876–1881. [Google Scholar]
  172. Ng, TP; Feng, L; Niti, M; Yap, KB. Albumin, haemoglobin, BMI and cognitive performance in older adults. Age Ageing 2008, 37, 423–429. [Google Scholar]
  173. Atkinson, HH; Cesari, M; Kritchevsky, SB; Penninx, BW; Fried, LP; Guralnik, JM; Williamson, JD. Predictors of combined cognitive and physical decline. J. Am. Geriatr. Soc 2005, 53, 1197–1202. [Google Scholar]
  174. Bretsky, P; Guralnik, JM; Launer, L; Albert, M; Seeman, TE. The role of APOE-epsilon4 in longitudinal cognitive decline: MacArthur Studies of Successful Aging. Neurology 2003, 60, 1077–1081. [Google Scholar]
  175. Cabeza, R. Cognitive neuroscience of aging: Contributions of functional neuroimaging. Scand. J. Psychol 2001, 42, 277–286. [Google Scholar]
  176. Catani, M; Cherubini, A; Howard, R; Tarducci, R; Pelliccioli, GP; Piccirilli, M; Gobbi, G; Senin, U; Mecocci, P. (1)H-MR spectroscopy differentiates mild cognitive impairment from normal brain aging. Neuroreport 2001, 12, 2315–2317. [Google Scholar]
  177. Charlton, RA; McIntyre, DJ; Howe, FA; Morris, RG; Markus, HS. The relationship between white matter brain metabolites and cognition in normal aging: The GENIE study. Brain Res 2007, 1164, 108–116. [Google Scholar]
  178. Elias, MF; Elias, PK; Sullivan, LM; Wolf, PA; D’Agostino, RB. Obesity, diabetes and cognitive deficit: The Framingham Heart Study. Neurobiol Aging 2005, 26(Suppl 1), 11–16. [Google Scholar]
  179. Finkel, D; Reynolds, CA; McArdle, JJ; Pedersen, NL. Age changes in processing speed as a leading indicator of cognitive aging. Psychol. Aging 2007, 22, 558–568. [Google Scholar]
  180. Meyerhoff, DJ; Durazzo, TC. Proton magnetic resonance spectroscopy in alcohol use disorders: A potential new endophenotype? Alcohol Clin. Exp. Res 2008, 32, 1146–1158. [Google Scholar]
  181. Rigbi, A; Kanyas, K; Yakir, A; Greenbaum, L; Pollak, Y; Ben-Asher, E; Lancet, D; Kertzman, S; Lerer, B. Why do young women smoke? V. Role of direct and interactive effects of nicotinic cholinergic receptor gene variation on neurocognitive function. Genes Brain Behav 2008, 7, 164–172. [Google Scholar]
  182. Durazzo, TC; Gazdzinski, S; Banys, P; Meyerhoff, DJ. Brain metabolite concentrations and neurocognition during short-term recovery from alcohol dependence: Preliminary evidence of the effects of concurrent chronic cigarette smoking. Alcohol Clin. Exp. Res 2006, 30, 539–551. [Google Scholar]
  183. Gazdzinski, S; Durazzo, TC; Studholme, C; Song, E; Banys, P; Meyerhoff, DJ. Quantitative brain MRI in alcohol dependence: preliminary evidence for effects of concurrent chronic cigarette smoking on regional brain volumes. Alcohol Clin. Exp. Res 2005, 29, 1484–1495. [Google Scholar]
  184. Cummings, JL. Frontal-subcortical circuits and human behavior. J. Psychosom Res 1998, 44, 627–628. [Google Scholar]
  185. Saint-Cyr, JA. Frontal-striatal circuit functions: context, sequence, and consequence. J. Int. Neuropsychol. Soc 2003, 9, 103–127. [Google Scholar]
  186. Kalivas, PW; Volkow, ND. The neural basis of addiction: a pathology of motivation and choice. Am. J. Psychiatry 2005, 162, 1403–1413. [Google Scholar]
  187. Baler, RD; Volkow, ND. Drug addiction: the neurobiology of disrupted self-control. Trends Mol. Med 2006, 12, 559–566. [Google Scholar]
  188. Kalivas, PW; O’Brien, C. Drug addiction as a pathology of staged neuroplasticity. Neuropsychopharmacology 2008, 33, 166–180. [Google Scholar]
  189. Makris, N; Oscar-Berman, M; Jaffin, SK; Hodge, SM; Kennedy, DN; Caviness, VS; Marinkovic, K; Breiter, HC; Gasic, GP; Harris, GJ. Decreased volume of the brain reward system in alcoholism. Biol. Psychiatry 2008, 64, 192–202. [Google Scholar]
  190. Paulus, MP. Neural basis of reward and craving—A homeostatic point of view. Dialogues Clin Neurosci 2007, 9, 379–387. [Google Scholar]
  191. Volkow, ND; Wang, GJ; Fowler, JS; Telang, F. Overlapping neuronal circuits in addiction and obesity: Evidence of systems pathology. Philos. Trans. R Soc. Lond. B Biol. Sci 2008, 363, 3191–3200. [Google Scholar]
  192. Koob, GF. Alcoholism: allostasis and beyond. Alcohol Clin. Exp. Res 2003, 27, 232–243. [Google Scholar]
  193. Lubman, DI; Yucel, M; Pantelis, C. Addiction, a condition of compulsive behaviour? Neuroimaging and neuropsychological evidence of inhibitory dysregulation. Addiction 2004, 99, 1491–1502. [Google Scholar]
  194. Wang, Z; Faith, M; Patterson, F; Tang, K; Kerrin, K; Wileyto, EP; Detre, JA; Lerman, C. Neural substrates of abstinence-induced cigarette cravings in chronic smokers. J. Neurosci 2007, 27, 14035–14040. [Google Scholar]
  195. Mitchell, SH. Measuring impulsivity and modeling its association with cigarette smoking. Behav. Cogn. Neurosci. Rev 2004, 3, 261–275. [Google Scholar]
  196. VanderVeen, JW; Cohen, LM; Cukrowicz, KC; Trotter, DR. The role of impulsivity on smoking maintenance. Nicotine Tob. Res 2008, 10, 1397–1404. [Google Scholar]
  197. Hodgkinson, CA; Yuan, Q; Xu, K; Shen, PH; Heinz, E; Lobos, EA; Binder, EB; Cubells, J; Ehlers, CL; Gelernter, J; Mann, J; Riley, B; Roy, A; Tabakoff, B; Todd, RD; Zhou, Z; Goldman, D. Addictions biology: Haplotype-based analysis for 130 candidate genes on a single array. Alcohol Alcoholism 2008, 43, 505–515. [Google Scholar]
  198. Huang, W; Payne, TJ; Ma, JZ; Li, MD. A functional polymorphism, rs6280, in DRD3 is significantly associated with nicotine dependence in European-American smokers. Am. J. Med. Genet. Part B 2008, 147B, 1109–115. [Google Scholar]
  199. Batra, V; Patkar, AA; Berrettini, WH; Weinstein, SP; Leone, FT. The genetic determinants of smoking. Chest 2003, 123, 1730–1739. [Google Scholar]
  200. Hoft, NR; Corley, RP; McQueen, MB; Schlaepfer, IR; Huizinga, D; Ehringer, MA. Genetic Association of the CHRNA6 and CHRNB3 Genes with Tobacco Dependence in a Nationally Representative Sample. Neuropsychopharmacology 2009, 34, 698–706. [Google Scholar]
Table 1. Neurocognitive studies of chronic smoking in adults (sorted by age group, then year of publication).
Table 1. Neurocognitive studies of chronic smoking in adults (sorted by age group, then year of publication).
Authors [reference number]Smokers (n)NSC (n)Age group/Mean age or (range)Neurocognitive measures or domains assessedMajor findings (all reported findings are statistically significant unless otherwise indicated)
Jacobsen et al. (2005) [23]41 current32Adolescents & Young adults/16.8 ± 1.2Hopkins Verbal Learning Test-Revised n-back task (measure of working memory, Connors Continuous Performance Task, auditory and visual selective attention, verbal and visuospatial divided attentionSmokers demonstrated poorer working memory than NSC. Earlier age of smoking onset was related to poorer working memory. Male smokers were inferior to female smokers on measures of selective and divided attention.
Spilich et al. (1992) [71]45 current45Young adults/19.2 ± 1.2Visual search speed/accuracy, sustained visual attention, working, memory, information processing speedSmokers performed worse than NSC on all measures of sustained attention and information processing speed.
Elwan et al. (1997) [70]60 current69Young adults through older adults/49.9 ± 3.8 (20–76)Paced Auditory Serial Attention Test, Trail Making Test A and BNo significant differences observed between smokers and NSC on any measure.
Lejuez et al. (2002) [72]26 current34Young adults/20.1 ± 2.8Balloon Analogue Risk Task (BART), Iowa Gambling Task (IGT)Smokers demonstrated increased risk-taking levels on the BART compared to NSC. Smokers and NSC showed no differences on the IGT.
Fried et al. (2006) [25]27 current
11 former
64Young adults/(17–21)WAIS-III, Wechsler Memory Scale-III, Peabody Picture Vocabulary, Test of Variables of AttentionOverall, current smokers performed worse than NSC on measures of receptive and expressive language, oral arithmetic and auditory-verbal memory.
Yakir et al. (2007) [24]91 current
40 former
151Young adults (all female)/23.9 ± 2.2CogScan (v4.0): a comprehensive battery assessing information processing speed, sustained attention, fine motor skills, auditory-verbal and visuospatial memory, reasoning and impulsivity.Current smokers showed poorer sustained attention, impulse control and planning/reasoning than NSC. Former smokers had poorer impulse control and planning/reasoning than NSC. Current and former smokers were not significantly different on any measure.
Weiser et al. (2009) [27]5762 current
695 former
13,764Young adults (all males)/(18–21)Measures of verbal comprehension, verbal and non-verbal abstraction, and mathematical knowledge, Individual measures combined to form composite score of general IQ.Current and former smokers performed worse than NSC, although the difference between former smokers and NSC was trivial with respect to effect size after adjustment for socioeconomic status. Current smokers who smoked more than 11 cigarettes per day showed the poorest performance relative to NSC.
Ernst et al. (2001) [35]14 current
15 former
9Young and Middle aged adults/(21–45)Domains assessed were verbal reasoning and working memory.Current smokers and former smokers showed poorer working memory than NSC. Current smokers had poorer working memory than former smokers.
Sakurai and Kanazawa (2002) [62]20 current20Young and Middle aged Adults/(23–41)Measures of auditory-verbal learning and memory, mental arithmetic, and verbal fluencyNo differences between smokers and NSC on any task.
Paul et al. (2006) [33]62 current62Young and middle aged adults/28.1 ± 7.2, 55.2 ± 7.3Domains assessed included executive function, finger tapping speed, learning and memory, sustained attention, word fluency, working memorySmokers performed more poorly than NSC on one measure of executive function. Middle aged smokers showed poorer auditory-verbal memory than aged NSC and young adult smokers.
George et al. (2002) [36]29 current16Middle aged adults/41.5 ± 10.3Domains assessed were visuospatial working memory, cognitive flexibilitySmokers exhibited worse visuospatial working memory.
Schinka et al. (2002) [67]174 current
80 former
204Middle aged adults/38.4 ± 2.3CVLT, WAIS-R Block Design, Rey-Osterreith Complex Figure, Wisconsin Card Sorting Test, Paced Auditory Serial Attention Test, Grooved Pegboard, semantic fluency, global cognitive functionHigher pack years of smoking was related to lower global cognitive functioning.
Kalmijn et al. (2002) [32]529 current
715 former
619Middle aged adults/56.4 ± 7.1Domains assessed were auditory-verbal and visuospatial learning and memory, cognitive flexibility, processing speed, global cognitive functionCurrent smokers showed poorer cognitive flexibility and processing speed than NSC. No differences between former smokers and NSC on any measure.
Sabia et al. (2008) [43]815 current
2,030 former
2,543Middle age adults/56 ± 6Mill Hill Vocabulary Test, measures of verbal and semantic fluency, verbal and mathematical reasoning, auditory verbal learningIn cross-sectional analyses, smoking history was associated with increased risk of poor memory. Over 4–7 years, current smokers and recent former smokers showed significantly greater declines in reasoning than never smokers. No significant declines in cognitive function were observed in former smokers.
Cerhan et al. (1998) [56]13,913 total participants, numbers of current, former smokers and NSC not providedNAMiddle age and older adults/(45–69)WAIS-R Digit Symbol Test, measures of auditory-verbal memory and verbal fluencyCurrent smokers demonstrated poorer performance on Digit Symbol and auditory-verbal memory. For smokers, greater lifetime number of cigarettes was related to poorer Digit Symbol and auditory-verbal memory performance.
Hill et al. (2003) [30]164 current438Middle aged & older adults/NAWAIS-R Block Design and measures of auditory-verbal learning and memory, general knowledge, word comprehensionSmokers, irrespective of age performed worse than NSC on Block Design and on measures of auditory-verbal memory.
Razani et al. (2004) [73]125 former or never smokers. Groups were retro-spectively divided into non/light, moderate heavy and heavy smokers based on pack years. Only 2 of 127 subjects were active smokers.NAMiddle aged & older adults 65.9 ± 8.3WAIS-R Digit Symbol and Digit Span, WMS-R Logical Memory and Visual Reproduction, Rey-Osterrieth Complex Figure—Immediate Recall, Stroop Word and color trials, WCSTHeavy smokers performed worse than moderate smokers and non/light smokers on the WCST.
Hill (1989) [42]11 current
12 former
53Older adults/71.6 ± 4.9WAIS-R Block Design, Digit Symbol, and Digit Span; WMS Logical Memory and Associative Memory, Bender Gestalt, Cross Off Task, word fluency and Digit Symbol.At the baseline assessment current smokers performed worse than former smokers and NSC on the Cross Off task. At reassessment (15 months after baseline), current smokers performed worse than former and current smokers on the Cross Off Task and Digit Symbol.
Hebert et al. (1993) [69]current
Older adults/(65 to ≥ 80)Measures of auditory-verbal memory, working memory and orientationCurrent and former smokers showed no significant decline over a 3-year period on any measure relative to NSC after control for age, sex, education and income.
Launer et al. (1996) [53]110 current
288 former
91Older adults/75 ± 4.5MMSESmokers performed worse on the MMSE than NSC after correction for age, education and alcohol consumption. Over a 3-year period, current smokers with cardiovascular disease and/or diabetes showed the greatest decline in MMSE scores.
Ford et al. (1996) [68]259 current and former smokers combined369Older adults/>75Pfeiffer Short Portable Mental Status Questionnaire (PSPMSQ)Baseline and change over 4 years on the PSPMSQ was not associated with smoking status.
Galanis et al. (1997) [46]921 current
1,334 former
1,174Older adults/77.4 ± 4.6Cognitive Abilities Screening Test (CASI): includes task of attention, concentration abstraction, judgment, verbal and verbal fluency. A composite CASI score was formed from the individual components.After adjustment for age, education and Japanese acculturation, current and former smokers had lower CASI score than never smokers. Higher risk of impaired performance on CASI scores was associated with current and former smoking.
Edelstein et al. (1998) [34]114 current407Older adults/72.0 ± 9.2MMSE, Trail Making Test Part–B, Buschke Selective Reminding Test, Modified Version of WMS Visual Reproduction, semantic fluency and auditory-verbal memory.No differences between male smokers and male NSC. Female smokers demonstrated poorer performance than female NSC on the MMSE.
Cervilla et al. (2000) [51]80 current
204 former
134Older adults/(65–95)Organic Brain Syndrome Scale (OBS) (measure of orientation to person time, place and context)After controlling for sex, age, alcohol consumption, education, depression and baseline cognitive function, current smokers had a 3.7 fold risk of impaired performance on the OBS after one year.
Schinka et al. (2002) [64]334 participants with various smoking and alcohol use histories.61Older adults/(60–84)MMSE, Hopkins Verbal Learning Test, Stroop Color Word test, Trail Making Test Part–BNo significant effects were found for alcohol or cigarette consumption on any measure.
Chen et al. (2003) [66]195 current
51 former
68Older adults/72 ± 6Cognitive Abilities Screening Instrument (measure of global cognitive function)No significant group differences on the Cognitive Abilities Screening Instrument.
Deary et al. (2003) [39]126 current
278 former
387Older adults/75.6 ± 5.4Moray House Test (MHT). Included measures of verbal, numerical, and verbal reasoning. Global MHT score formed from the individual components.After adjusting for MHT score at age 11 years of age, education and sex, current smokers had lower MHT scores than NSC and former smokers. NSC and former smokers were not different.
Schinka et al. (2003) [65]30 current86Older adults/(60–84)MMSE, Hopkins Verbal Learning Test, Stroop Color-Word TestPack years significantly predicted MMSE and auditory-verbal memory scores, but only accounted for 1.8% of variance in auditory-verbal memory.
Huadong et al. (2003) [44]720 current
276 former
1,976Older adults/>60MMSECurrent smokers had 2.3 greater risk of impaired MMSE score (i.e., <17) relative to NSC after adjustment for age, sex, education, occupation and alcohol use.
Ott et al. (2004) [50]2,037 current
3,372 former
3,800Older adults/>65MMSECurrent smokers relative to never smokers showed a greater rate of decrease in MMSE scores over approximately 2 years controlled for age, sex, education, baseline MMSE, history of myocardial infarction, and cerebrovascular accident. Higher pack years was associated with higher rate of decline in MMSE.
Reitz et al. (2005) [54]90 current
135 former
184Older adults/75.6 ± 5.4MMSE, Boston Diagnostic Aphasia Evaluation: Boston Naming Test, Category Naming, Phrase Repetition, Complex Ideational Material, WAIS-R Similarities, Nonverbal Identities and Oddities from The DRS, Rosen Drawing Test, Buschke Selective Reminding Test, Benton Visual Retention Test.Over approximately five years, there was no association between current or former smoking status and change in cognition in those <75 years of age. For those >75 years of age, current smokers showed greater decline in memory than former smokers and NSC. The memory declines were greatest in current smokers who were not carriers of the ApoEɛ4 allele.
Whalley et al. (2005) [41]90 current
135 former
184Older adults/64Raven’s Standard Progressive Matrices, Rey Auditory Verbal Learning Test, WAIS-R Digit Symbol and Block Design, Uses of Common Objects Test and a composite measure of all tests.After adjusting for childhood IQ, age, education, occupation, lung function, any history of smoking was associated with lower scores on Digit Symbol.
Fischer et al. (2006) [47]262 current
75 former
NAOlder adults/75MMSELonger duration of smoking was associated with lower MMSE after adjusting for vascular risk factors and use of antihypertensive medication.
Stewart et al. (2006) [45]135 current
246 former
217Older adults/64.5 ± 6.5Raven’s Standard Progressive Matrices, Rey Auditory Verbal Learning Test, Trail Making Test, Digit Symbol Test, Mill Hill Vocabulary Scale (MHS), MMSE and a composite measure of all tests.In men, after adjusting for age, blood pressure and total cholesterol, higher pack years was associated lower scores on Auditory-verbal leaning, Digit Symbol Test, MHS in men. In women, higher pack years was associated with lower MMSE. In women, current smoking status was associated with poorer auditory-verbal learning.
Starr et al. (2006) [31]289 total participants. Number of smokers, NSC, not providedNAOlder adults/64 and 66Raven’s Standard Progressive Matrices, Rey Auditory Verbal Learning Test, WAIS-R Digit Symbol and Block Design, Uses of Common Objects TestCurrent smokers performed worse NSC and former smokers on auditory-verbal learning and information processing speed after adjusting for childhood IQ.
Note. DRS: Mattis Dementia Rating Scale; MMSE: Mini Mental Status Examination; NA: Not available; NSC: non-smoking (never-smoker) control; WAIS-III: Wechsler Adult Intelligence Scale-3rd Edition; WAIS-R: Wechsler Adult Intelligence-Revised; WMS-R: Wechsler Memory Scale-Revised.
Table 2. Computerized tomography and magnetic resonance neuroimaging studies of chronic smoking in adults (sorted by imaging modality, then age group).
Table 2. Computerized tomography and magnetic resonance neuroimaging studies of chronic smoking in adults (sorted by imaging modality, then age group).
Authors [reference number]Smokers (n)NSC (n)Age group/mean age or (range)Neuroimaging modalityMajor findings (all findings are statistically significant unless otherwise indicated)
Akiyama et al. (1997) [74]104 current and former smokers combined173Young to older adults/(22–89)CT (volumetric and cortical perfusion)A history of smoking (i.e., current or former smoking) was associated with lower global cerebral perfusion after control for hypertension, WM disease and age. Over approximately 3 years, smokers showed greater decrease in global perfusion and greater global atrophy than NSC.
Kubota et al. (1987) [76]159 current Non-smoking group contained 177 never and 17 “light” smokersNAMiddle aged and older adults/(40–69)CTCurrent smokers from 50 to 69 showed greater global atrophy than never/light smokers.
Hayee et al. (2003) [75]219 current, Non-smoking group contained 183 never smokers and 17 “light” smokersNAMiddle aged and older adults/(40–70)CTCurrent smokers between 50–70 years of age showed greater global atrophy than the non-smoking group.
Brody et al. (2004) [79]19 current17Young to older adults/(21–65)MRISmokers showed lower volumes and densities in the anterior frontal lobe GM, smaller volume of the left dorsal anterior cingulate gyrus, and lower GM density of the right cerebellum relative to NSC. Higher pack years was associated with lower anterior frontal GM.
Gallinat et al. (2006) [80]22 current23Adults/30.6 ± 7.7MRISmokers demonstrated smaller GM volumes and densities in frontal, temporal and occipital regions compared to NSC. Smokers also showed lower volume or density in the thalamus, cerebellum and other subcortical nuclei/regions. Higher pack years was related to lower frontal, temporal and cerebellar GM volume.
Paul et al. (2008) [82]10 current10Middle aged adults/38.5 ± 13.4MRI (diffusion tensor imaging)Smokers showed higher fractional anisotropy (FA) in the body and whole corpus callosum than NSC. Smokers with low Fagerstrom Test for Nicotine Dependence scores (mean = 1.6) showed higher FA in the whole corpus callosum than smokers with high scores (mean = 5.6).
Longstreth et al. (2000) [78]3,301 total participants, numbers of NSC, and smokers not providedNAOlder adults/>65MRIOver 5 years, higher pack years in men was related to increased ventricular volume in men and associated with increased sulcal volume in women, after control for age and vascular risk factors.
Almeida et al. (2008) [81]39 current39Older adults/75.4 ± 3.3MRISmokers showed decreased GM densities in the posterior cingulate gyrus and precuneus bilaterally, right thalamus and right precentral gyrus.
Epperson et al. (2005) [102]16 current20Adults/34 ± 11MRSGamma-aminobutyric acid (GABA) levels n the occipital GM were not different between NSC and male smokers. Female smokers showed a significant reduction in GABA levels during the follicular phase of the menstrual cycle. GABA levels showed no changes after 48 hours of smoking cessation in both males and females.
Gallinat et al. (2007) [97]13 current13Adults/36.6 ± 10.1MRSSmokers showed lower N-acetylaspartate levels than NSC in the left hippocampus. Higher pack years was related to higher choline-containing compounds in the anterior cingulate gyrus.
Gallinat and Schubert (2007) [98]13 current
9 Former
13Adults/36.1 ± 9.8MRSNo significant group differences were observed in glutamate levels of the anterior cingulate cortex and left hippocampus.
Note. CT: computed tomography; GM: Gray Matter; MRI: magnetic resonance imaging; MRS: magnetic resonance spectroscopy; NA: not available; NSC: non-smoking (never-smoker) control; WM: white matter.
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