Association Between Smoking and Cholinergic Basal Forebrain Volume in Healthy Aging and Prodromal and Dementia Stages of Alzheimer’s Disease

Abstract

Background: Smoking has been found associated with decreased cerebral volumes in healthy adults and in various neuropsychiatric disorders.

Objective: We aimed to determine whether chronic nicotine exposure through smoking is associated with reduced volume of cortically projecting cholinergic basal forebrain nuclei in healthy aging, mild cognitive impairment (MCI), and dementia stages of Alzheimer’s disease (AD).

Methods: We retrieved cross-sectional data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database including 179 cognitively normal elderly subjects, 270 subjects with early stage MCI, 136 subjects in later, more advanced, stage of MCI, and 86 subjects in dementia stages of AD. We determined the association between past or current smoking versus lifetime non-smoker status on the volumes of the basal forebrain determined from volumetric MRI scans. Hippocampus volume was used as a control region. Significant effects were controlled for mediating or moderating effects of respiratory and cardiovascular morbidity.

Results: In cognitively healthy individuals and early MCI, past or current smoking was significantly associated with smaller basal forebrain volume. This effect was independent from age, sex, or cardiovascular or respiratory morbidity. Hippocampus volume was not associated with smoking. In late MCI and AD dementia, smoking was not associated with basal forebrain or hippocampus volumes.

Conclusions: Our findings suggest that chronic nicotine exposure through smoking may lead to atrophy of cholinergic input areas of the basal forebrain. This effect may account for an increased risk of AD dementia onset with smoking by exhausting the cholinergic system reserve capacity.

Keywords

Aging cholinergic system hippocampus magnetic resonance imaging nicotine

INTRODUCTION

Previous studies have shown a detrimental effect of smoking on brain volume in healthy adults as well as in a range of neuropsychiatric diseases such as multiple sclerosis, schizophrenia, alcoholism, or HIV [1 –8]. Functional studies suggest that short- and long-term nicotine exposure modulates cortical nicotine receptor expression in animal models (for review, see [9]) as well as in molecular imaging studies in humans (for review, see [10]). Cholinergic input to muscarinergic and nicotinergic acetylcholine receptors in the cerebral cortex of the human brain almost exclusively arises from the cholinergic nuclei of the basal forebrain (BF) [11, 12], a complex cell cluster extending about 20 mm in anterior posterior direction below the anterior commissure [13]. Using high resolution magnetic resonance imaging (MRI) in combination with high dimensional image warping and postmortem histology based labeling of the BF nuclei in MRI standard space [14 –16], previous studies have shown atrophy of the BF area over the adult age range as well as in prodromal and dementia stages of Alzheimer’s disease (AD) and Lewy-body dementia compared to healthy age-matched controls [17 –19].

Here, we studied whether a history of smoking was associated with the volume of the cholinergic BF in a large cohort of older people retrieved from the ADNI data base, spanning the range from healthy aging to prodromal and dementia stages of AD. Our analysis was based on the hypothesis that long-term nicotinergic stimulation may have detrimental effects on the integrity of the cholinergic projecting areas in the BF. To assess the specificity of these effects, we used the hippocampus formation as a control region and determined a potentially mediating effect of cardiovascular and respiratory morbidity.

METHODS

Data source

Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu/). The ADNI was launched in 2003 by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, the Food and Drug Administration, private pharmaceutical companies and non-profit organizations, with the primary goal of testing whether neuroimaging, neuropsychologic, and other biologic measurements can be used as reliable in-vivo markers of AD pathogenesis. A fuller description of ADNI and up-to-date information is available at http://www.adni-info.org.

Study participants

Structural MRI scans were retrieved from the ADNI-GO and ADNI-2 extensions of the ADNI project and included imaging data of 179 cognitively normal elderly subjects (CN), 270 subjects with early stage MCI (EMCI), 136 subjects in later, more advanced, stage of MCI (LMCI), and 86 subjects in dementia stages of AD. Participants who reported they never smoked cigarettes during lifetime were assigned to the non-smoker group (n = 415), and those who reported any history of cigarette smoking during lifetime were designated as smokers (n = 256). Only a subgroup of 18 cases from the 256 lifetime smokers indicated that they were currently smoking. The other lifetime smokers reported to have quit smoking on average since 31.5 (SD 14.5) years.

Details on participants’ demographics are shown in Table 1. Detailed inclusion criteria for the diagnostic categories can be found at the ADNI web site (http://adni.loni.usc.edu/methods/). Briefly, CN subjects have Mini-Mental State Examination (MMSE) scores between 24–30 (inclusive), a Clinical Dementia Rating (CDR) = 0, are non-depressed, non-MCI, and non-demented. EMCI subjects have MMSE scores between 24–30 (inclusive), a subjective memory concern reported by subject, informant, or clinician, objective memory loss measured by education adjusted scores on delayed recall (one paragraph from Wechsler Memory Scale Logical Memory II; education adjusted scores:≥16 years: 9–11; 8–15 years: 5–9; 0–7 years: 3–6), a CDR = 0.5, absence of significant levels of impairment in other cognitive domains, essentially preserved activities of daily living, and an absence of dementia. Diagnosis of LMCI differs from that of EMCI only in a higher degree of objective memory impairment (education adjusted scores:≥16 years: ≤8; 8–15 years: #x2264;4; 0–7 years: ≤2). Subjects with AD dementia have initial MMSE scores between 20–26 (inclusive), a CDR = 0.5 or 1.0 and fulfill NINCDS-ADRDA criteria for clinically probable AD [20].

For additional analyses, participants were dichotomized into amyloid-low and amyloid-high groups based on amyloid-sensitive AV45-PET imaging. Cortex-to-whole cerebellum AV45 standardized uptake value ratios (SUVR) have been calculated and made available on the ADNI server by one of the ADNI PET core laboratories (Jagust Lab, UC Berkley; http://adni.loni.usc.edu/methods/pet-analysis; [21]). Amyloid status was determined from these values using a recommended threshold of SUVR≥1.17, which has been found to be indicative of pathological levels of amyloid associated with AD dementia in a clinicopathologic correlation study [22].

All procedures performed in the ADNI studies involving human participants were in accordance with the ethical standards of the institutional research committees and with the 1964 Helsinki declaration and its later amendments.

Written informed consent was obtained from all participants and/or authorized representatives and the study partners before any protocol-specific procedures were carried out in the ADNI study.

Imaging data acquisition

ADNI-GO/-2 MRI data were acquired on multiple 3-T MRI scanners using scanner-specific T1-weighted sagittal 3D MPRAGE sequences. In order to increase signal uniformity across the multicenter scanner platforms, original MPRAGE acquisitions in ADNI undergo standardized image pre-processing correction steps. Detailed information on the different imaging protocols employed across ADNI sites and standardized image pre-processing steps can be found on the ADNI website (http://adni.loni.usc.edu/methods/).

Imaging data processing

Imaging data were processed by using statistical parametric mapping (SPM8, Wellcome Trust Center for Neuroimaging) and the VBM8-toolbox (http://dbm.neuro.uni-jena.de/vbm/) implemented in MATLAB R2013b (MathWorks, Natick, MA) as described previously [23]. First, MRI scans were automatically segmented into grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) partitions of 1.5 mm isotropic voxel-size using the tissue prior free segmentation routine of the VBM8-toolbox. The resulting GM and WM partitions of each subject in native space were then high-dimensionally registered to an aging/AD-specific reference template from a previous study [24] using DARTEL [25]. Structural brain characteristics change considerably in advanced age and AD and spatial registration accuracy worsens with deviance from the template characteristics, rendering the MNI standard space template inappropriate for high-dimensional deformation-based morphometry studies of aged and demented populations. Therefore, the reference template in this study was derived by DARTEL-aligning 50 healthy elderly subjects and 50 subjects with very mild, mild, and moderate AD retrieved from an open access MRI database (http://www.oasis-brains.org), and thus reflects unbiased aging/AD-specific structural characteristics. Individual flow-fields resulting from the DARTEL registration to the reference template were used to warp the GM segments and voxel-values were modulated for volumetric changes introduced by the high-dimensional normalization, such that the total amount of GM volume present before warping was preserved.

Extraction of imaging features from basal forebrain and hippocampus regions-of-interest

The cholinergic nuclei are not directly visible on current structural MRI contrasts and no comprehensive set of external landmarks has been identified that could be used for indirect manual delineation of the cholinergic BF on MRI scans. In the current study, we localized the cholinergic space of the BF based on a cytoarchitectonic map of BF cholinergic nuclei in MNI space, derived from combined histology and MRI of a postmortem brain [14]. We examined the BF region combining the areas of Ch1, Ch2, Ch3, and Ch4, according to Mesulam’s nomenclature [26]. Ch4 represents the Nucleus basalis Meynert, Ch1 the medial septal nuclei, Ch2 the vertical part of the diagonal band of Broca, and Ch3 the horizontal part of the diagonal band of Broca. The BF mask is shown in Fig. 1. All regions were combined into a single measure.

The hippocampal ROI mask was obtained by manual delineation of the hippocampus in the reference template using the interactive software package Display (McConnell Brain Imaging Centre at the Montreal Neurological Institute) and a previously described protocol for segmentation of the medial temporal lobe [27].

Individual GM volumes of the ROIs were extracted automatically from the warped GM segments by summing up the modulated GM voxel values within the respective ROI masks in the reference space. For further analyses, the extracted regional GM volumes were scaled by the total intracranial volume, calculated as the sum of total volumes of the GM, WM, and CSF partitions.

Statistics

All variables used for analysis, except the volume measures, were directly retrieved from the ADNI database. Between group effects for age and education were determined using Student’s t-test, for MMSE scores using Mann-Whitney U test, and for sex using χ ² statistics. Volume measurements were compared between diagnostic groups using ANCOVA models, controlling for age and sex, with pairwise follow-up tests using the post-hoc Scheff $\overset{´}{e}$ test. Main effects of smoking behavior, including smoking status (past or current smokers versus lifetime non-smokers), average number of packs per day during smoking periods, duration of smoking in years, and time since cessation of smoking in former smokers, on BF and hippocampus volumes were assessed in ANCOVA models controlling for age and sex within each diagnostic group. In case of a significant effect, an extended model assessed an interaction effect of smoking behavior by respiratory or cardiovascular morbidity (yes/no) to determine if these morbidities moderated the effect of smoking behavior on volumes. Statistical significance was assumed at a p-value<0.05. Calculations were performed with IBM SPSS Statistics version 22 (http://www-01.ibm.com/software/analytics/spss).

In a complementary voxel-wise analysis, we assessed the specificity of our effects. We determined the effect of past or current smoker status on GM changes in the bilateral hippocampus region using the previously described hippocampus mask. The analysis was controlled for sex and age, and in order to control the type 2 error we used a liberal threshold of p < 0.01, uncorrected.

RESULTS

Between groups effects

Between groups differences in demographic variables are shown in Table 1. In the ANCOVA models, all volumetric variables showed a significant overall effect across the four diagnostic groups (CN, EMCI, LMCI, AD dementia) at F(3, 664)>30.7, p < 0.001. In post hoc pairwise analyses, hippocampus volumes were significantly different between all groups with AD < LMCI<EMCI<CN for both hemispheres (p < 0.027, post hoc Scheff $\overset{´}{e}$ test). BF volume was significantly different between all groups at p < 0.001 in post hoc Scheff $\overset{´}{e}$ tests, except between EMCI cases and controls (p = 0.92, post hoc Scheff $\overset{´}{e}$ test), with an ensuing difference of AD < MCI<EMCI = CN.

Association with smoking behavior

Smoking status (past or current smokers versus lifetime non-smokers) was significantly associated with respiratory morbidity (χ ² = 9.6, 1 df, p < 0.002), but not with cardiovascular morbidity (χ ² = 0.4, 1 df, p = 0.53) across all cases. In the healthy controls and the EMCI cases, smoking status was significantly associated with BF volume (partial r = –0.17, 157 df, p < 0.03 for controls, and partial r = –0.15, 265 df, p < 0.02 for EMCI, respectively), but not with left or right hippocampus volumes (p > 0.1 for all comparisons), when controlling for age and sex. In MCI and AD dementia cases, there were no associations between smoking status and any volume (p > 0.19 for all comparisons). Packs per day and duration of smoking within the past or current smokers, and time since cessation of smoking in former smokers were not associated with any volume in any diagnostic group.

To control for presence or absence of AD type amyloid accumulation, we repeated the analyses in the amyloid positive and negative diagnostic subgroups. In 136 CN amyloid negative cases, the partial correlation between BF volume and past smoking, controlling for age and sex, was –0.21, p < 0.02, but was –0.02, p = 0.93, in the 43 amyloid positive CN cases. In contrast, 72 amyloid positive and 14 amyloid negative AD dementia cases showed no significant effect of smoking on BF volume, after controlling for age and sex (partial r = 0.06, p = 0.86, for amyloid negative AD, and r = –0.13, p = 0.28, for amyloid positive AD). When classifying according to amyloid status, there were no significant effects of past smoking on left and right hippocampus volumes in any diagnostic group (p > 0.18 for all comparisons).

Following up on the significant effects, there was no interaction effect for respiratory or cardiovascular morbidity by smoking status (past or current versus lifetime non-smokers) on the BF volume in controls and EMCI cases. There was no significant association between respiratory or cardiovascular morbidity and BF volume in the control and EMCI groups (r between –0.002 and –0.092, p > 0.22 for all comparisons), excluding the possibility of a mediation effect of smoking status on volume via respiratory or cardiovascular morbidity.

In a complementary voxel-wise analysis restricted to the bilateral hippocampus region, even at a very liberal threshold of 50 contiguous voxel passing an uncorrected p-value of 0.01, we could not find an association between past or current smoker status and regional GM volume within the left or right hippocampus in the healthy controls or EMCI cases.

DISCUSSION

We found a significant association between smoking and BF volumes in healthy older controls and in cases with EMCI, with smaller volumes in past or current smokers compared to lifetime non-smokers. This effect was specific for the BF, it did not occur in the hippocampus. It was independent from the presence of cardiovascular or respiratory morbidity, both of which were not related to brain volumes. Interestingly, it was the status past or current smoker versus lifetime non-smoker, not the intensity or duration of smoking that accounted for the association of smoking with BF volumes.

Previous observations in healthy people [1, 2] as well as in people with different neuropsychiatric diseases, including schizophrenia [3], alcoholism [8], or multiple sclerosis [4], suggested that smoking was associated with reduced brain volumes or MR spectroscopy markers of neuronal viability [28], particularly in prefrontal areas. Most previous studies investigated small samples of people stratified according to current smoker and non-smoker status [1–3 , 28]. In some of these studies, not only smoker status but also intensity and duration of smoking were associated with brain volumes [1 , 4]. One recent study in a population based sample of 964 people spanning the adult age range found current smoker status associated with reduced GM volume in prefrontal and anterior cingulate areas, but independently of intensity of smoking as assessed by pack years [29].

Here, we describe an association between smoking and the volume of the cholinergic BF region in healthy aging and very mild cognitive impairment. Our study was based on the notion that the cholinergic system is strongly associated with nicotine effects. Thus, upregulation of nicotinergic α ₄β₂ receptors is believed to mediate tolerance to nicotine [30], and extrinsic nicotine from smoking acts as a weak agonist on nicotinic receptors in the cerebral cortex [31]. The cholinergic input to cortical nicotinergic and muscarinergic acetylcholine receptors in the human brain almost exclusively arises from the cholinergic nuclei of the BF. According to Mesulam’s nomenclature [26], the cholinergic BF is composed of four groups of cholinergic cells which correspond to the medial septum (Ch1), the vertical and horizontal limb of the diagonal band of Broca (Ch2 and Ch3), and the Nucleus basalis Meynert (Ch4). The Nucleus basalis Meynert is the largest cholinergic nucleus of the BF and can be further subdivided into anterior medial and lateral (Ch4am and Ch4al), intermediate (Ch4i) and posterior regions (Ch4p). A further part of the cholinergic BF is the nucleus subputaminalis, which has only been described in the human and anthropoid monkey brain, and can be regarded as a lateral extension of rostral and anterointermediate parts of the Nucleus basalis Meynert [32].

Our findings may be interpreted in respect to the effects of long-term stimulation of nicotinic receptors. In adolescent rats even short term nicotine exposure resulted in a decrease of choline acetyltransferase activity, a constitutive marker for cholinergic nerve terminals, in the midbrain and an increase of choline acetyltransferase activity in the hippocampus [33], suggesting that nicotine exposure not only alters postsynaptic nicotine receptor expression but also presynaptic input of cholinergic nerve terminals. These effects may eventually lead to a decline in the size of the projecting areas in the BF. One could speculate that such an effect may at least partly account for the association of smoking during mid-life with an increased risk for the clinical manifestation of AD dementia [34], since a structurally impaired BF may provide a decreased reserve of the cholinergic system against progressive AD related lesions. The specificity of our findings is supported by the absence of effects of smoking on hippocampus volume, which was further confirmed in a voxel-based analysis within the hippocampus that did not find effects of smoking on hippocampus either despite a very liberal level of significance. Hippocampus volume was found decreased in currently smoking healthy middle-aged people [2], but had not been studied before in relation to past smoking and in higher age. The hippocampus is a primary target of AD related lesions [35] and receives strong cholinergic input from the BF [11]. However, its integrity appeared to be less affected by effects of smoking than the cholinergic BF itself in our study. The specificity of the smoking effect is further underscored by the absence of a mediating or moderating effect of cardiovascular or respiratory morbidity. Respiratory morbidity was related to smoking but not to BF or hippocampus volumes in our analysis.

Effects were detectable only in cognitively healthy controls and EMCI cases. The absence of effects of past or current smoking on BF volumes in MCI and AD dementia cases suggests that with the presence of major AD related neuropathological lesions affecting the BF, the effect of other risk factors, such as smoking, becomes no more detectable. This notion is supported by the finding that in amyloid positive healthy control cases, harboring preclinical AD pathology, the effect of smoking on BF volume was lost. The EMCI cases represent a heterogeneous group in respect to the presence of AD pathology. In some features they resemble more the healthy controls than the LMCI cases, including amyloid load [23], and rate of conversion to dementia [36]. So the assumption that EMCI in general represents a transition stage between healthy controls and LMCI is not yet sufficiently proven.

We need to consider alternative explanations of our findings. Reversing the causality, a smaller volume of the cholinergic BF may represent a trait marker for an increased risk for smoking behavior. Reduced striatal dopamine receptor availability has been suggested as a trait marker for impulsivity and dependence on noradrenergic stimulating drugs [37]. Nicotine dependence has been shown to be associated with reduced dopaminergic receptor availability as well [38], but also with the functionality of postsynaptic nicotinergic receptors as evidenced by polymorphisms in nicotinergic receptor genes that associate with a higher vulnerability for tobacco dependence [39]. However, to our knowledge there are no studies on the association of risk of smoking with the integrity of the presynaptic cholinergic system.

Another explanation would be that smoking behavior serves as a proxy for other lifestyle or health factors that underlie the association between smoking and BF volume. The observation that it was the status past or current smoker versus non-smoker rather than the duration or intensity of smoking that carried the effect supports this explanation. Still, the majority of cases had quit smoking already decades before current assessment so that effects of smoking related lifestyle or health on brain volumes appear unlikely. In addition, we could rule out respiratory or cardiovascular morbidity as potential co-factors, although other smoking-related health factors need to be tested in subsequent studies.

A limitation of this study, despite the large number of cases, is the characteristics of the sample with a very small number of people who were currently smoking (18 of 256 people who reported to have ever smoked during their lifetime), and a long period of on average three decades since when smoking had been quit in the former smokers. Only 2.7% of the 671 people in the cohort reported to be current smokers which is below the population average of 8.8% current smokers in the US population 65 years and older reported by the Center for Disease Control in 2013 (http://www.cdc.gov/tobacco/campaign/tips/resources/data/cigarette-smoking-in-united-states.html). This suggests that the ADNI cohort represents a positive selection from the population in respect to health and lifestyle, which is quite understandable given the complex study protocol and the recruitment at large university centers. It also agrees with the high overall level of education in the ADNI cohort compared to the US population. Thus, findings on health risks in the ADNI cohort may not generalize well to the general population. This is of minor relevance in our study where we looked mechanistically on the association between smoking and cholinergic system integrity, not dealing with smoking as a health risk per se. Still, one might assume that the effect size of smoking on brain structure was diluted through the long period of smoking cessation, which would also account for the only moderate effect sizes of the associations between smoking and respiratory disease. Thus, although it is a very positive event for the ADNI participants that their large majority had never smoked or quit smoking already a long time ago, it limits the potential effect size of smoking on brain structure.

In summary, we found a significant and selective association of smoking with the volume of the cholinergic BF in healthy aging and very mild cognitive impairment that may represent a long-term effect of external stimulation of cortical nicotine receptors on presynaptic cholinergic input from the BF. Alternative explanations need to be ruled out in future clinical experiments, including similar studies in young adults that would help to exclude that a smaller BF is a risk marker for smoking behavior rather than the result of smoking. If these effects can be ruled out, our data would suggest a potential mechanism by which smoking may increase the risk of AD dementia onset through its negative effects on the structural integrity of the cholinergic system, i.e., by exhausting the cholinergic system reserve capacity.

Footnotes

ACKNOWLEDGMENTS

Authors’ disclosures available online ().

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). The ADNI was launched in 2003 by the National Institute on Aging (NIA), the National Institute of Biomedical Imaging and Bioengineering (NIBIB), the Food and Drug Administration (FDA), private pharmaceutical companies and non-profit organizations, as a $60 million, 5-year public-private partnership. ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuroimaging at the University of Southern California.

References

Gallinat

, Meisenzahl

, Jacobsen

, Kalus

, Bierbrauer

, Kienast

, Witthaus

, Leopold

, Seifert

, Schubert

, Staedtgen

(2006) Smoking and structural brain deficits: A volumetric MR investigation. Eur J Neurosci 24, 1744–1750.

Durazzo

, Meyerhoff

, Nixon

(2013) Interactive effects of chronic cigarette smoking and age on hippocampal volumes. Drug Alcohol Depend 133, 704–711.

Schneider

, White

, Hass

, Geisler

, Wallace

, Roessner

, Holt

, Calhoun

, Gollub

, Ehrlich

(2014) Smoking status as a potential confounder in the study of brain structure in schizophrenia. J Psychiatr Res 50, 84–91.

Durhan

, Diker

, Has

, Karakaya

, Kurne

, Karabudak

, Oguz

(2015) Assessment of the effect of cigarette smoking on regional brain volumes and lesion load in patients with clinically isolated syndrome. Int J Neurosci. doi: 10.3109/00207454.2015.1073727

Van Haren

, Koolschijn

, Cahn

, Schnack

, Hulshoff Pol

, Kahn

(2010) Cigarette smoking and progressive brain volume loss in schizophrenia. Eur Neuropsychopharmacol 20, 454–458.

Tregellas

, Shatti

, Tanabe

, Martin

, Gibson

, Wylie

, Rojas

(2007) Gray matter volume differences and the effects of smoking on gray matter in schizophrenia. Schizophr Res 97, 242–249.

Durazzo

, Rothlind

, Cardenas

, Studholme

, Weiner

, Meyerhoff

(2007) Chronic cigarette smoking and heavy drinking in human immunodeficiency virus: Consequences for neurocognition and brain morphology. Alcohol 41, 489–501.

Durazzo

, Cardenas

, Studholme

, Weiner

, Meyerhoff

(2007) Non-treatment-seeking heavy drinkers: Effects of chronic cigarette smoking on brain structure. Drug Alcohol Depend 87, 76–82.

Goriounova

, Mansvelder

(2012) Short- and long-term consequences of nicotine exposure during adolescence for prefrontal cortex neuronal network function. Cold Spring Harb Perspect Med 2, a012120.

10.

Cosgrove

, Esterlis

, Sandiego

, Petrulli

, Morris

(2015) Imaging Tobacco Smoking with PET and SPECT. Curr Top Behav Neurosci 24, 1–17.

11.

Mesulam

(1990) Human brain cholinergic pathways. Prog Brain Res 84, 231–241.

12.

Mesulam

, Geula

(1988) Nucleus basalis (Ch4) and cortical cholinergic innervation in the human brain: Observations based on the distribution of acetylcholinesterase and choline acetyltransferase. J Comp Neurol 275, 216–240.

13.

Heinsen

, Hampel

, Teipel

(2006) Computer-assisted 3D reconstruction of the Nucleus basalis complex, including the Nucleus subputaminalis, Brain 129, E43.

14.

Teipel

, Flatz

, Heinsen

, Bokde

, Schoenberg

, Stockel

, Dietrich

, Reiser

, Moller

, Hampel

(2005) Measurement of basal forebrain atrophy in Alzheimer’s disease using MRI. Brain 128, 2626–2644.

15.

Zaborszky

, Hoemke

, Mohlberg

, Schleicher

, Amunts

, Zilles

(2008) Stereotaxic probabilistic maps of the magnocellular cell groups in human basal forebrain. Neuroimage 42, 1127–1141.

16.

Kilimann

, Grothe

, Heinsen

, Alho

, Grinberg

, Amaro

Jr , Dos Santos

, da Silva

, Mitchell

, Frisoni

, Bokde

, Fellgiebel

, Filippi

, Hampel

, Kloppel

, Teipel

(2014) Subregional Basal forebrain atrophy in Alzheimer’s disease: A multicenter study. J Alzheimers Dis 40, 687–700.

17.

Grothe

, Ewers

, Krause

, Heinsen

, Teipel

, Alzheimer’s Disease Neuroimaging Initiative (2014) Basal forebrain atrophy and cortical amyloid deposition in nondemented elderly subjects. Alzheimers Dement 10, S344–S353.

18.

Grothe

, Schuster

, Bauer

, Heinsen

, Prudlo

, Teipel

(2014) Atrophy of the cholinergic basal forebrain in dementia with Lewy bodies and Alzheimer’s disease dementia. J Neurol 261, 1939–1948.

19.

Muth

, Schonmeyer

, Matura

, Haenschel

, Schroder

, Pantel

(2010) Mild cognitive impairment in the elderly is associated with volume loss of the cholinergic basal forebrain region. Biol Psychiatry 67, 588–591.

20.

McKhann

, Drachman

, Folstein

, Katzman

, Price

, Stadlan

(1984) Clinical diagnosis of Alzheimer’s disease: Report of the NINCDS-ADRDA work group under the auspices of the department of health and human services task force on Alzheimer’s disease. Neurology 34, 939–944.

21.

Landau

, Mintun

, Joshi

, Koeppe

, Petersen

, Aisen

, Weiner

, Jagust

(2012) Amyloid deposition, hypometabolism, and longitudinal cognitive decline. Ann Neurol 72, 578–586.

22.

Fleisher

, Chen

, Liu

, Roontiva

, Thiyyagura

, Ayutyanont

, Joshi

, Clark

, Mintun

, Pontecorvo

, Doraiswamy

, Johnson

, Skovronsky

, Reiman

(2011) Using positron emission tomography and florbetapir F18 to image cortical amyloid in patients with mild cognitive impairment or dementia due to Alzheimer disease. Arch Neurol 68, 1404–1411.

23.

Teipel

, Heinsen

, Amaro

Jr , Grinberg

, Krause

, Grothe

, Alzheimer’s Disease Neuroimaging Initiative (2014) Cholinergic basal forebrain atrophy predicts amyloid burden in Alzheimer’s disease. Neurobiol Aging 35, 482–491.

24.

Grothe

, Heinsen

, Teipel

(2012) Atrophy of the cholinergic basal forebrain over the adult age range and in early stages of Alzheimer’s disease. Biol Psychiatry 71, 805–813.

25.

Ashburner

(2007) A fast diffeomorphic image registration algorithm. Neuroimage 38, 95–113.

26.

Mesulam

, Mufson

, Levey

, Wainer

(1983) Cholinergic innervation of cortex by the basal forebrain: Cytochemistry and cortical connections of the setal area, diagonal band nuclei, nucleus basalis (substantia innominata), and hypothalamus in the rhesus monkey. J Comp Neurol 214, 170–197.

27.

Pruessner

, Li

, Serles

, Pruessner

, Collins

, Kabani

, Lupien

, Evans

(2000) Volumetry of hippocampus and amygdala with high-resolution MRI and three-dimensional analysis software: Minimizing the discrepancies between laboratories. Cereb Cortex 10, 433–442.

28.

Durazzo

, Meyerhoff

, Mon

, Abe

, Gazdzinski

, Murray

(2016) Chronic cigarette smoking in healthy middle-aged individuals is associated with decreased regional brain N-acetylaspartate and glutamate levels. Biol Psychiatry 79, 481–488.

29.

Fritz

, Wittfeld

, Schmidt

, Domin

, Grabe

, Hegenscheid

, Hosten

, Lotze

(2014) Current smoking and reduced gray matter volume-a voxel-based morphometry study. Neuropsychopharmacology 39, 2594–2600.

30.

Collins

, Bhat

, Pauly

, Marks

(1990) Modulation of nicotine receptors by chronic exposure to nicotinic agonists and antagonists. , 68-82; discussion, Ciba Found Symp 152, 82–66.

31.

Benowitz

(2009) Pharmacology of nicotine: Addiction, smoking-induced disease, and therapeutics. Annu Rev Pharmacol Toxicol 49, 57–71.

32.

Boban

, Kostovic

, Simic

(2006) Nucleus subputaminalis: Neglected part of the basal nucleus of Meynert, Brain 129, E4243; author reply E43.

33.

Abreu-Villaca

, Seidler

, Qiao

, Tate

, Cousins

, Thillai

, Slotkin

(2003) Short-term adolescent nicotine exposure has immediate and persistent effects on cholinergic systems: Critical periods, patterns of exposure, dose thresholds. Neuropsychopharmacology 28, 1935–1949.

34.

Rusanen

, Kivipelto

, Quesenberry

Jr , Zhou

, Whitmer

(2011) Heavy smoking in midlife and long-term risk of Alzheimer disease and vascular dementia. Arch Intern Med 171, 333–339.

35.

Price

, Morris

(1999) Tangles and plaques in nondemented aging and “preclinical” Alzheimer’s disease. Ann Neurol 45, 358–368.

36.

Kljajevic

, Grothe

, Ewers

, Teipel

, Alzheimer’s Disease Neuroimaging Initiative (2014) Distinct pattern of hypometabolism and atrophy in preclinical and predementia Alzheimer’s disease. Neurobiol Aging 35, 1973–1981.

37.

Lee

, London

, Poldrack

, Farahi

, Nacca

, Monterosso

, Mumford

, Bokarius

, Dahlbom

, Mukherjee

, Bilder

, Brody

, Mandelkern

(2009) Striatal dopamine d2/d3 receptor availability is reduced in methamphetamine dependence and is linked to impulsivity. J Neurosci 29, 14734–14740.

38.

Fehr

, Yakushev

, Hohmann

, Buchholz

, Landvogt

, Deckers

, Eberhardt

, Klager

, Smolka

, Scheurich

, Dielentheis

, Schmidt

, Rosch

, Bartenstein

, Grunder

, Schreckenberger

(2008) Association of low striatal dopamine d2 receptor availability with nicotine dependence similar to that seen with other drugs of abuse. Am J Psychiatry 165, 507–514.

39.

Brunzell

, Stafford

, Dixon

(2015) Nicotinic receptor contributions to smoking: Insights from human studies and animal models. Curr Addict Rep 2, 33–46.