Skip to main content

Main menu

  • Home
  • Articles
    • Current
    • Special Feature Articles - Most Recent
    • Special Features
    • Colloquia
    • Collected Articles
    • PNAS Classics
    • List of Issues
    • PNAS Nexus
  • Front Matter
    • Front Matter Portal
    • Journal Club
  • News
    • For the Press
    • This Week In PNAS
    • PNAS in the News
  • Podcasts
  • Authors
    • Information for Authors
    • Editorial and Journal Policies
    • Submission Procedures
    • Fees and Licenses
  • Submit
  • Submit
  • About
    • Editorial Board
    • PNAS Staff
    • FAQ
    • Accessibility Statement
    • Rights and Permissions
    • Site Map
  • Contact
  • Journal Club
  • Subscribe
    • Subscription Rates
    • Subscriptions FAQ
    • Open Access
    • Recommend PNAS to Your Librarian

User menu

  • Log in
  • My Cart

Search

  • Advanced search
Home
Home
  • Log in
  • My Cart

Advanced Search

  • Home
  • Articles
    • Current
    • Special Feature Articles - Most Recent
    • Special Features
    • Colloquia
    • Collected Articles
    • PNAS Classics
    • List of Issues
    • PNAS Nexus
  • Front Matter
    • Front Matter Portal
    • Journal Club
  • News
    • For the Press
    • This Week In PNAS
    • PNAS in the News
  • Podcasts
  • Authors
    • Information for Authors
    • Editorial and Journal Policies
    • Submission Procedures
    • Fees and Licenses
  • Submit
Research Article

Brain monoamine oxidase A inhibition in cigarette smokers

Joanna S. Fowler, Nora D. Volkow, Gene-Jack Wang, Naomi Pappas, Jean Logan, Colleen Shea, David Alexoff, Robert R. MacGregor, David J. Schlyer, Ivana Zezulkova, and Alfred P. Wolf

    See allHide authors and affiliations

    PNAS November 26, 1996 93 (24) 14065-14069; https://doi.org/10.1073/pnas.93.24.14065
    Joanna S. Fowler
    • Find this author on Google Scholar
    • Find this author on PubMed
    • Search for this author on this site
    Nora D. Volkow
    • Find this author on Google Scholar
    • Find this author on PubMed
    • Search for this author on this site
    Gene-Jack Wang
    • Find this author on Google Scholar
    • Find this author on PubMed
    • Search for this author on this site
    Naomi Pappas
    • Find this author on Google Scholar
    • Find this author on PubMed
    • Search for this author on this site
    Jean Logan
    • Find this author on Google Scholar
    • Find this author on PubMed
    • Search for this author on this site
    Colleen Shea
    • Find this author on Google Scholar
    • Find this author on PubMed
    • Search for this author on this site
    David Alexoff
    • Find this author on Google Scholar
    • Find this author on PubMed
    • Search for this author on this site
    Robert R. MacGregor
    • Find this author on Google Scholar
    • Find this author on PubMed
    • Search for this author on this site
    David J. Schlyer
    • Find this author on Google Scholar
    • Find this author on PubMed
    • Search for this author on this site
    Ivana Zezulkova
    • Find this author on Google Scholar
    • Find this author on PubMed
    • Search for this author on this site
    Alfred P. Wolf
    • Find this author on Google Scholar
    • Find this author on PubMed
    • Search for this author on this site
    • Article
    • Figures & SI
    • Info & Metrics
    • PDF
    Loading

    Several studies have documented a strong association between smoking and depression. Because cigarette smoke has been reported to inhibit monoamine oxidase (MAO) A in vitro and in animals and because MAO A inhibitors are effective antidepressants, we tested the hypothesis that MAO A would be reduced in the brain of cigarette smokers. We compared brain MAO A in 15 nonsmokers and 16 current smokers with [11C]clorgyline and positron emission tomography (PET). Four of the nonsmokers were also treated with the antidepressant MAO inhibitor drug, tranylcypromine (10 mg/day for 3 days) after the baseline PET scan and then rescanned to assess the sensitivity of [11C]clorgyline binding to MAO inhibition. MAO A levels were quantified by using the model term λk3which is a function of brain MAO A concentration. Smokers had significantly lower brain MAO A than nonsmokers in all brain regions examined (average reduction, 28%). The mean λk3 values for the whole brain were 0.18 ± 0.04 and 0.13 ± 0.03 ccbrain (mlplasma)−1 min−1 for nonsmokers and smokers, respectively; P < 0.0003). Tranylcypromine treatment reduced λk3 by an average of 58% for the different brain regions. Our results show that tobacco smoke exposure is associated with a marked reduction in brain MAO A, and this reduction is about half of that produced by a brief treatment with tranylcypromine. This suggests that MAO A inhibition needs to be considered as a potential contributing variable in the high rate of smoking in depression and in the development of more effective strategies for smoking cessation.

    There are approximately 1 billion cigarette smokers in the world today and about 3 million die each year from smoking-associated illnesses (1). This places a sense of urgency on understanding the neuropharmacological properties of tobacco smoke and their relationship to smoking behavior and epidemiology. For example, it is not understood why smoking is more prevalent in depression and why smoking cessation is less successful in depressed patients (2, 3). Though it is unlikely that any one factor accounts for the strong association between smoking and depression, it is possible that tobacco smoke may have antidepressant properties. One of the molecular targets proposed to link smoking and depression is monoamine oxidase (MAO) (4, 5), an enzyme which was first associated with mood over 40 years ago when it was discovered that MAO inhibitors had antidepressant properties (6, 7).

    MAO exists in two subtypes (MAO A and B) that are different gene products (8, 9). In the brain, MAO A oxidizes serotonin and norepinephrine and is found primarily in catecholaminergic neurons, whereas MAO B oxidizes benzylamine and phenethylamine and is localized in serotonergic neurons and in glial cells (10). Both forms oxidize dopamine (11). The antidepressant effects of the nonselective MAO inhibitors are generally attributed to the inhibition of MAO A (12).

    We recently reported that smokers have reduced brain MAO B relative to nonsmokers and former smokers (13). Others have found that both MAO A and MAO B are reduced in animals exposed to tobacco smoke (14) and in vitro (15, 16), and that heavy smokers have reduced peripheral measures of both MAO A and B (5). It has also been demonstrated that nicotine is not responsible for MAO A inhibition (14, 16). To test the hypothesis that tobacco smoke exposure inhibits MAO A in the human brain, we compared a group of nonsmokers and a group of smokers using positron-emission tomography (PET) and [11C]clorgyline, a radiotracer which binds irreversibly to brain MAO A (17, 18). We also rescanned four of the nonsmokers after they had received a low dose (10 mg per day) of the MAO inhibitor drug tranylcypromine for 3 days to assess the sensitivity of [11C]clorgyline to MAO A inhibition.

    SUBJECTS AND METHODS

    Participants.

    These studies followed the guidelines of the Human Subjects Research Committee at Brookhaven National Laboratory, and informed consent was obtained from each subject after the procedures had been explained. Subjects were recruited by newspaper advertisements. Subjects were screened for a lack of history of current or past psychiatric or neurological disease, as well as for lack of history of drug or alcohol abuse (excluding caffeine for all subjects and nicotine for the smokers). Exclusion criteria were (i) current or past psychiatric disease other than nicotine dependence, (ii) neurological signs and/or history of neurological disease, (iii) history of head trauma, (iv) history of cardiovascular or endocrinological disease, (v) current medical illness, and (vi) dependence on any substance other than nicotine (for the smokers) or caffeine. Inclusion criteria for nonsmokers was that they had never smoked. Inclusion criteria for smokers was to be a current smoker and to have smoked at least 10 cigarettes per day for the preceding 1 year. Except for three of the female subjects who were on hormone replacement therapy, none of the subjects were taking medications at the time of the study, and any previous medications had been discontinued 8 days before the PET scan. Screens for drug use were performed prior to each PET scan. Smokers refrained from smoking during the entire PET study and were scanned 1.5–9.5 h (average time interval, 2.7 h) after the last cigarette. Three of the nonsmokers received a second PET scan on the same day to assess reproducibility of repeated PET measures. Four of the nonsmokers were scanned a second time after receiving tranylcypromine (30 mg total; 10 mg per day for 2 days prior and 10 mg on the day of the second PET scan) to assess the effect of MAO inhibition on [11C]clorgyline binding. The time interval between the baseline scan and the tranylcypromine scan varied from 2 weeks to 6 months.

    PET Imaging.

    PET scans were run on a whole-body, high-resolution positron emission tomograph [6.5 × 6.5 × 6.5 mm, full-width half maximum, 15 slices; Computer Technologies (Knoxville, TN; model CTI 931] which provided 15 slices of the brain. Subjects were prepared for scanning as described (19). Brain MAO A was measured using [11C]clorgyline, which was prepared as described (20). Each subject received [11C]clorgyline (4–16 mCi; 6–50 μg; 1 Ci = 37 GBq). Time-activity data for all brain regions was accumulated for 75 min following this sequence: ten 1 min frames, four 5 min frames, and six 7.5 min frames. An arterial plasma input function for [11C]clorgyline was measured. Arterial blood samples were withdrawn every 2.5 sec for the first 2 min [Ole Dich (Hvidovre, Denmark) automatic blood sampler], then every minute from 2–6 min, then at 8, 10, 15, 20, 30, 45, 60, and 75 min. Each arterial blood sample was centrifuged and plasma pipetted and counted. Plasma samples at 1, 5, 10, 20, 30, 45, 60, and 75 min were analyzed for [11C]clorgyline using the same solid phase extraction procedure described previously for [11C]l-deprenyl-D2 (19). An arterial input function was calculated from the total carbon-11 in plasma corrected for the fraction present as [11C]clorgyline.

    Image Analysis.

    Regions of interest were drawn directly on PET scans. For the purpose of region identification, we added the images obtained from 30 to 75 min after tracer injection. A template that used as a reference the brain atlas of Matsui and Hirano (21) was projected into the “averaged” PET image and manually fitted for appropriate neuroanatomical location. The regions were then projected to the dynamic scans to obtain time-activity data. Regions of interest for the following brain areas were obtained: frontal cortex, parietal cortex, occipital cortex, temporal cortex, cingulate gyrus, thalamus, basal ganglia, and cerebellum. Regions were identified in at least two contiguous slices, and the weighted average was obtained for each region. For the thalamus, basal ganglia, frontal, parietal, and temporal cortices, the right and left regions were averaged. For the global region eight central planes were averaged.

    Data Analysis.

    For each subject, PET time-activity data from different brain regions along with time-activity data for [11C]clorgyline in arterial plasma were used to calculate the model term Ki, a kinetic constant which determines the rate of trapping of [11C]clorgyline (which is a function of both the concentration of MAO A and blood flow), and K1 the blood to tissue transport constant. An approximate blood volume correction (4%) was subtracted from the PET data prior to parameter optimization. K1 is related to blood flow (F) through the following equation (see ref. 22): Math where PS is the permeability-surface area product. Assuming a three-compartment model which allows for both the transport and trapping of ligand, Ki can be written as Math where k2 is the tissue to plasma efflux constant and k3 is proportional to the concentration of MAO A. Ki is also written in terms of the product λk3, which is independent of blood flow (λ = K1/k2) (23) but is a more robust parameter than k3 (24). λk3 can be calculated from Eq. 2 if Ki and K1 are known.

    Ki was obtained graphically from a transformation of plasma and tissue time-activity data as described by Patlak et al. (25). Ki was taken as the average of slopes from 6 to 45 min and from 6 to 55 min. The initial time was taken as the time from which linearity was observed. K1 was calculated by using a rearrangement of the method of Blomqvist (26). In this method the tissue radioactivity, region of interest (ROI) and plasma radioactivity (Cp) are related to model parameters as given by MathMath Using Ki from Eq. 2, this can be rearranged to give MathMath This is a bilinear regression with coefficients K1 and (k2 + k3) computed using standard methods (27).

    All data is presented as the mean ± standard deviation of the mean. Model terms K1 and λk3 were compared for nonsmokers and smokers using an unpaired t test (two tail) and for nonsmokers at baseline and after treatment with tranylcypromine using a paired t test. To assess whether K1 and λk3 were affected by gender, a two-factor (smoking status and gender) ANOVA was performed.

    RESULTS

    Subject data is presented in Table 1. The model term λk3 was reduced for smokers relative to nonsmokers (Table 2). An ANOVA analysis showed that the effect of smoking on λk3 was significant (F = 16; P < 0.0003 for the global value), but that there was not a significant effect of gender (F = 3; P = 0.1) and no smoking versus gender interaction (F = 0.3; P = 0.6). The region with highest difference between groups was the occipital cortex (reduced by 38%), and the average reduction for all cortical and subcortical structures was 28 ± 4%. Values for λk3 for the different brain regions are shown in Table 2. Individual data for the thalamus are shown in Fig. 1, and brain images are shown in Fig. 2. We found no relationship between number of cigarettes smoked per day and values for λk3 in this initial study. However, because the variability in smoking behavior in addition to dose would contribute to degree of MAO inhibition, this issue requires further investigation.

    View this table:
    • View inline
    • View popup

    Characteristics of subjects

    View this table:
    • View inline
    • View popup

    Comparison of model terms K1 and λk3 for nonsmokers and smokers for different brain regions

    Figure 1
    • Download figure
    • Open in new tab
    • Download powerpoint

    Comparison of MAO A levels in the thalamus (as expressed by the model term λk3) for nonsmokers (n = 15), smokers (n = 16), and nonsmokers who were treated with tranylcypromine (n = 4).

    Figure 2
    • Download figure
    • Open in new tab
    • Download powerpoint

    Pixel by pixel images of the model term which is a function of MAO A activity for a nonsmoker (Top row), a smoker (Middle row), and the same nonsmoker after treatment with tranylcypromine (Bottom row). The same four planes of the brain are shown for each subject and correspond to brain sections at 5.8 cm (level of the occipital cortex and the lateral ventricles), 5.1 cm (level of the occipital cortex and the lateral ventricles), 4.5 cm (level of the thalamus), and 1 cm (level of the lower temporal poles and the cerebellum) above the canthomeatal line (proceeding from left to right). The color scale represents values of λk3 [scales from 0.4 (red) to 0 (black)]. The values of λk3 for the thalamus are 0.319, 0.212, and 0.128 ccbrain (mlplasma)−1 min−1 for the nonsmoker, the smoker and the nonsmoker treated with tranylcypromine, respectively. The corresponding K1 values are 0.434, 0.421, 0.404 ml/cc/min.

    In contrast to λk3, the plasma to brain transfer constant, K1, did not differ between nonsmokers and smokers for any brain region (Table 2). However, there was a significant main effect of gender on K1 for all brain regions with an average of 21% higher values for women (means 0.341 ± 0.066 versus 0.257 ± 0.016 for the global value for females and males respectively; ANOVA: F = 26, P < 0.0001; data for other brain regions not shown). There was not a smoking versus gender interaction effect on K1 (F = 0.2; P = 0.6).

    To assess the reproducibility of repeated measures of [11C]clorgyline binding, three of the nonsmokers received a second PET scan 2 h after the first. The average change in K1 and λk3 for the nine brain regions examined for the three subjects was 9.5 ± 4.0% and 10.9 ± 5.0%, respectively (data not shown).

    To assess the sensitivity of [11C]clorgyline binding to MAO inhibition, four of the nonsmokers were scanned at baseline and then treated with the nonselective MAO inhibitor drug, tranylcypromine (10 mg/day) for 3 days prior to a second scan. Tranylcypromine decreased λk3 by an average of 58% in all brain regions (Table 3; P < 0.0001, paired t test, two tail; see Fig. 1 for individual data on the thalamus and Fig. 2 for brain images) but did not change K1 (data not shown). Differences in λk3 were similar for all brain regions. The arterial plasma integral for labeled clorgyline after tranylcypromine was an average of 30 ± 3% greater than that at baseline (357 ± 64 and 465 ± 84 at baseline and after treatment; P < 0.0001, paired t test, two tail).

    View this table:
    • View inline
    • View popup

    Effect of treatment with tranylcypromine on λk3 (n = 4)

    DISCUSSION

    The main finding in this study is that smokers have reduced brain MAO A relative to nonsmokers as measured with [11C]clorgyline and PET. This result is consistent with the results of studies in animals exposed to tobacco smoke (14) and on peripheral measures of MAO A in heavy smokers (5). The reduction in brain MAO A was not accounted for by reduced brain blood flow since the plasma to brain transfer constant K1 did not differ between smokers and nonsmokers [though we did find a lower blood to brain transfer constant K1 in men relative to women which is likely to reflect the known lower cerebral blood flow in males than in females (28)].

    The ability of PET and labeled clorgyline to measure brain MAO A is supported by prior studies in mice (17), by the reduction in the binding of labeled clorgyline by treatment for 3 days with the nonselective MAO inhibitor tranylcypromine (10 mg per day; Table 3), and by the general similarity in the relative regional distribution of carbon-11 as determined by PET with the regional distribution of MAO A in human brain postmortem. MAO A has been reported to be relatively high in thalamus (29, 30) and in occipital cortex (31), with intermediate values in basal ganglia, frontal, and temporal cortices (29, 32) and a low concentration in cerebellum (31), though this comparison is limited by the lack of a single postmortem study measuring the same regions sampled by PET.

    MAO A inhibition would be predicted to spare norepinephrine and serotonin, both of which are MAO A-specific substrates, in addition to dopamine, which is a substrate for both MAO subtypes. The monoamines norepinephrine and serotonin are linked to mood as is evidenced by the effectiveness of the tricyclic antidepressants and selective serotonin reuptake inhibitors in the treatment of depression (12). Since MAO A inhibitors are also effective antidepressants, the inhibition of human brain MAO A by smoke may contribute to the difficulty of smoking cessation in depression (2). More specifically, withdrawal from cigarettes would not only represent withdrawal from nicotine but also withdrawal from MAO A inhibitory substances in smoke.

    A question that remains is the extent to which the level of MAO A inhibition achieved by cigarette smoke is clinically significant. Estimates of the degree to which MAO A needs to be inhibited for clinical improvement vary from 20% to 80% (as assessed from peripheral measures of monoamine metabolites) (5, 33, 34). The level of MAO A inhibition which we measured in smokers (28%) is in the low range of these values (assuming that peripheral measures of monoamine metabolites reflect brain MAO A levels). Since the time interval between the last cigarette and the PET scan averaged 2.7 h, which is longer than the typical recycle time of 1 h or less for the smoker, it is possible that the degree of inhibition was underestimated in our study. However, future studies are required to determine if the level of MAO A inhibition achieved with chronic exposure to cigarette smoke is associated with antidepressant effects.

    We recently reported that MAO B was inhibited by about 40% in smokers relative to nonsmokers and former smokers (13). This finding, coupled with the results of the present study, raises the question of whether chronic, partial reduction of both MAO A and B by smoke enhances neurotransmitter activity. Studies in animals have reported that the simultaneous inhibition of both MAO subtypes produces large increases in serotonin outflow (35). Since both MAO A and B break down dopamine, their simultaneous inhibition by smoke may combine to enhance brain dopamine which has been implicated in reward and reinforcement (36). This may enhance the behavioral and addictive properties of nicotine and other substances of abuse by preventing the breakdown of dopamine and other neurotransmitters which are released by abused substances (37). This may be relevant to the comorbidity between smoking and alcoholism and the addiction to other substances (38).

    Though MAO B inhibition by smoke has been discussed as a mechanism which may account for the decreased risk of Parkinson disease in smokers (39), MAO A may also play a role in the link between Parkinson disease and smoking because it is compartmentalized within dopaminergic neurons. This may place dopaminergic neurons at risk for damage from reactive oxygen species resulting from MAO A generated hydrogen peroxide (40).

    Though this study only measured the effects of cigarette smoke on brain MAO A, it is possible that smoke may also inhibit MAO A in peripheral organs. MAO A inhibition in the liver is of particular importance because of its role in breaking down vasoactive amines associated with hypertension. Future studies are needed to determine whether liver MAO A is inhibited by cigarette smoke and its potential contribution to toxicity associated with cigarette smoke.

    In conclusion, we report here the first direct observation in the human brain that tobacco smoke exposure is associated with a reduction in MAO A. Since MAO A breaks down monoamines linked to mood, this study suggests that MAO A inhibition needs to be considered as a link between tobacco smoke exposure and depression. It also provides support for a recent trial using MAO inhibitor therapy for smoking cessation (41) Even though this study suggests a mechanism by which smoking may provide relief in depressed individuals, the adverse effects of smoking are overwhelming (42). Thus the challenge remains to understand the neurochemical effects of smoke that contribute to smoking behavior and epidemiology and to use this knowledge to develop better therapies for smoking cessation, particular in that subgroup of individuals who consistently relapse.

    Acknowledgments

    We are grateful to Robert Carciello, Richard Ferrieri, Donald Warner, Payton King, Noelwah Netusil, and Carol Redvanly for their advice and assistance and to the subjects who volunteered for this study. This research was carried out at Brookhaven National Laboratory under Contract DE-AC02-76CH00016 with the U.S. Department of Energy and supported by its Office of Health and Environmental Research and also by the National Institutes of Health (National Institute of Neurological Diseases and Stroke, Grant NS 15380, and National Institute on Drug Abuse, Grant DA 06891).

    Footnotes

      • ↵ To whom reprint requests should be addressed. e-mail: fowler{at}simbrain.chm.bnl.gov.

      • Alfred P. Wolf

      • The publication costs of this article were defrayed in part by page charge payment. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. §1734 solely to indicate this fact.

      • Abbreviations: MAO A and B, monoamine oxidase A and B; PET, positron-emission tomography.

      • Accepted September 9, 1996.
      • Copyright © 1996, The National Academy of Sciences of the USA

      References

      1. ↵
        1. Wald N J,
        2. Hackshaw A K
        (1996) Br Med Bull 52:3–11, pmid:8746292.
        OpenUrlAbstract/FREE Full Text
      2. ↵
        1. Glassman A H,
        2. Helzer J E,
        3. Covey L S,
        4. Cottler L B,
        5. Stetner F,
        6. Tipp J E,
        7. Johnson J
        (1990) J Am Med Assoc 264:1546–1549, pmid:2395194.
        OpenUrlAbstract/FREE Full Text
      3. ↵
        1. Breslau N,
        2. Kilbey M,
        3. Andreski P
        (1993) Arch Gen Psychiatry 50:31–35, pmid:8422219.
        OpenUrlAbstract/FREE Full Text
      4. ↵
        1. Boulton A A,
        2. Yu P H,
        3. Tipton K
        (1988) Lancet i:114–115.
        OpenUrl
      5. ↵
        1. Berlin I,
        2. Said S,
        3. Spreus-Varoquaux O,
        4. Olivares R,
        5. Launay J-M,
        6. Puech A J
        (1995) Biol Psychiatry 38:756–761, pmid:8580230.
        OpenUrlCrossRefPubMed
      6. ↵
        1. Selikoff I J,
        2. Robitzek E H,
        3. Ornstein G G
        (1952) J Am Med Assoc 150:973–980.
        OpenUrlAbstract/FREE Full Text
      7. ↵
        1. Zeller E A,
        2. Barsky J,
        3. Berman E R
        (1955) J Biol Chem 214:267–274, pmid:14367385.
        OpenUrlFREE Full Text
      8. ↵
        1. Berry M D,
        2. Juorio A V,
        3. Paterson I A
        (1994) Prog Neurobiol 42:375–391, pmid:8058968.
        OpenUrlCrossRefPubMed
      9. ↵
        1. Bach A W J,
        2. Lin N C,
        3. Johnson D L,
        4. Abell C W,
        5. Bembenek M E,
        6. Kwan S-W,
        7. Seeburg P H,
        8. Shih J C
        (1988) Proc Natl Acad Sci USA 85:4934–4938, pmid:3387449.
        OpenUrlAbstract/FREE Full Text
      10. ↵
        1. Riederer P,
        2. Konradi C,
        3. Schay V,
        4. Kienzl E,
        5. Birkmayer G,
        6. Danielczyk W,
        7. Sofic E,
        8. Youdim M B H
        (1986) Adv Neurol 45:111–118.
        OpenUrl
      11. ↵
        1. Glover V,
        2. Elsworth J D,
        3. Sandler M
        (1980) J Neural Transm Suppl 16:163–172, pmid:6776235.
        OpenUrlPubMed
      12. ↵
        1. Caldecott-Hazard S,
        2. Schneider L S
        (1992) Synapse 10:141–168, pmid:1585257.
        OpenUrlCrossRefPubMed
      13. ↵
        1. Fowler J S,
        2. Wang G-J,
        3. Volkow N D,
        4. Pappas N,
        5. Logan J,
        6. MacGregor R R,
        7. Alexoff D,
        8. Wolf A P,
        9. Warner D,
        10. Cilento R,
        11. Zezulkova I
        (1996) Nature (London) 379:733–736, pmid:8602220.
        OpenUrlCrossRefPubMed
      14. ↵
        1. Pavlin R,
        2. Sket D
        (1993) Farm Vestn 44:185–192.
        OpenUrl
      15. ↵
        1. Yu P H,
        2. Boulton A A
        (1987) Life Sci 41:675–682, pmid:3613836.
        OpenUrlCrossRefPubMed
      16. ↵
        1. Carr L A,
        2. Basham J K
        (1991) Life Sci 48:1173–1177, pmid:2002748.
        OpenUrlCrossRefPubMed
      17. ↵
        1. MacGregor R R,
        2. Halldin C,
        3. Fowler J S,
        4. Wolf A P,
        5. Arnett C D,
        6. Langström B,
        7. Alexoff D
        (1985) Biochem Pharmacol 34:3207–3210, pmid:3929788.
        OpenUrlCrossRefPubMed
      18. ↵
        1. Fowler J S,
        2. MacGregor R R,
        3. Wolf A P,
        4. Arnett C D,
        5. Dewey S L,
        6. Schlyer D,
        7. Christman D,
        8. Logan J,
        9. Smith M,
        10. Sachs H,
        11. Aquilonius S M,
        12. Bjurling P,
        13. Halldin C,
        14. Hartwig P,
        15. Leenders K L,
        16. Lundquist H,
        17. Oreland L,
        18. Stalnacke C-G,
        19. Langström B
        (1987) Science 235:481–485, pmid:3099392.
        OpenUrlAbstract/FREE Full Text
      19. ↵
        1. Fowler J S,
        2. Wang G-J,
        3. Logan J,
        4. Xie S,
        5. Volkow N D,
        6. MacGregor R R,
        7. Schlyer D J,
        8. Pappas N,
        9. Alexoff D L,
        10. Patlak C,
        11. Wolf A P
        (1995) J Nucl Med 36:1255–1262, pmid:7790952.
        OpenUrlAbstract/FREE Full Text
      20. ↵
        1. MacGregor R R,
        2. Fowler J S,
        3. Wolf A P,
        4. Langström B,
        5. Halldin C
        (1988) J Labelled Compd Radiopharm 25:1–9.
        OpenUrl
      21. ↵
        1. Matsui T,
        2. Hirano A
        (1978) An Atlas of the Human Brain for Computerized Tomography (Gustav Fischer, Stuttgart, Germany).
      22. ↵
        1. Koeppe R A,
        2. Holthoff A,
        3. Frey K A,
        4. Kilbourn M R,
        5. Kuhl D A
        (1991) J Cerebr Blood Flow Metab 111:735–744.
        OpenUrl
      23. ↵
        1. Logan J,
        2. Dewey S L,
        3. Wolf A P,
        4. Fowler J S,
        5. Brodie J D,
        6. Angrist B,
        7. Volkow N D,
        8. Gatley S J
        (1991) Synapse 9:195–207, pmid:1685599.
        OpenUrlCrossRefPubMed
      24. ↵
        1. Fowler J S,
        2. Volkow N D,
        3. Logan J,
        4. Schlyer D J,
        5. MacGregor R R,
        6. Wang G-J,
        7. Wolf A P,
        8. Pappas N,
        9. Alexoff D,
        10. Shea C,
        11. Gatley S J,
        12. Dorflinger E,
        13. Yoo K,
        14. Morawsky L,
        15. Fazzini E
        (1993) Neurology 43:1984–1992, pmid:8413955.
        OpenUrlAbstract/FREE Full Text
      25. ↵
        1. Patlak C,
        2. Fenstermacher J D,
        3. Blasberg R G
        (1983) J Cerebr Blood Flow Metab 3:1–7, pmid:6822610.
        OpenUrlPubMed
      26. ↵
        1. Blomqvist G
        (1984) J Cerebr Blood Flow Metab 4:629–632, pmid:6334095.
        OpenUrlPubMed
      27. ↵
        1. Walpole R E,
        2. Myers R H
        (1978) Probability and Statistics for Engineers and Scientists (Macmillan, New York), 2nd Ed. pp 256–259.
      28. ↵
        1. Esposito G,
        2. Van Horn J D,
        3. Weinberger D R,
        4. Berman K F
        (1996) J Nucl Med 37:559–564, pmid:8691239.
        OpenUrlAbstract/FREE Full Text
      29. ↵
        1. Oreland L,
        2. Arai Y,
        3. Stenstrom A,
        4. Fowler C J
        (1983) Mod Probl Pharmacopsychiatry 19:246–254, pmid:6408409.
        OpenUrlPubMed
      30. ↵
        1. Rao V L R,
        2. Giguere J-F,
        3. Layrargues G P,
        4. Butterworth R F
        (1993) Brain Res 621:349–352, pmid:8242348.
        OpenUrlCrossRefPubMed
      31. ↵
        1. Glover V,
        2. Elsworth J D,
        3. Sandler M
        (1980) J Neural Transm Suppl 16:163–172, pmid:6776235.
        OpenUrlPubMed
      32. ↵
        1. Saura J,
        2. Bleuel Z,
        3. Ulrich J,
        4. Mendelowitsch A,
        5. Chen K,
        6. Shih J C,
        7. Malherbe P,
        8. Da Prada M,
        9. Richards J G
        (1996) Neuroscience 70:755–774, pmid:9045087.
        OpenUrlCrossRefPubMed
      33. ↵
        1. McDaniel K
        (1986) Clin Neuropharmacol 9:207–234, pmid:3521845.
        OpenUrlPubMed
      34. ↵
        1. Berlin I,
        2. Zimmer R,
        3. Thiede H-M,
        4. Payan C,
        5. Hergueta T,
        6. Robin L,
        7. Puech A J
        (1990) Br J Clin Pharmacol 30:805–816, pmid:1705137.
        OpenUrlPubMed
      35. ↵
        1. Celada P,
        2. Bel N,
        3. Artigas F
        (1994) J Neural Transm Suppl 41:357–363, pmid:7931251.
        OpenUrlPubMed
      36. ↵
        1. Di Chiara G,
        2. Imperato A
        (1988) Proc Natl Acad Sci USA 85:5274–5278, pmid:2899326.
        OpenUrlAbstract/FREE Full Text
      37. ↵
        1. Pontieri F E,
        2. Tanda G,
        3. Orzi F,
        4. Di Chiara G
        (1996) Nature (London) 382:255–257, pmid:8717040.
        OpenUrlCrossRefPubMed
      38. ↵
        1. Henningfield J E,
        2. Clayton R,
        3. Pollen W
        (1990) Br J Addict 85:279–292, pmid:2180511.
        OpenUrlCrossRefPubMed
      39. ↵
        1. Morens D M,
        2. Grandinetti A,
        3. Reed M D,
        4. White L R,
        5. Ross G W
        (1995) Neurology 45:1041–1051, pmid:7783862.
        OpenUrlAbstract/FREE Full Text
      40. ↵
        1. Olanow C W
        (1993) Trends Neurosci 16:439–444, pmid:7507613.
        OpenUrlCrossRefPubMed
      41. ↵
        1. Berlin I,
        2. Said S,
        3. Spreus-Varquaux A
        (1995) Clin Pharmacol Ther 58:444–452, pmid:7586937.
        OpenUrlCrossRefPubMed
      42. ↵
        1. Bloom F E,
        2. Kupfer D J
        1. Henningfield J E,
        2. Schuh L M,
        3. Jarvik M E
        (1995) in Psychopharmacology, The Fourth Generation of Progress, eds Bloom F E, Kupfer D J(Raven, New York), pp 1715–1729.
      PreviousNext
      Back to top
      Article Alerts
      Email Article

      Thank you for your interest in spreading the word on PNAS.

      NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

      Enter multiple addresses on separate lines or separate them with commas.
      Brain monoamine oxidase A inhibition in cigarette smokers
      (Your Name) has sent you a message from PNAS
      (Your Name) thought you would like to see the PNAS web site.
      CAPTCHA
      This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
      Citation Tools
      Brain monoamine oxidase A inhibition in cigarette smokers
      Joanna S. Fowler, Nora D. Volkow, Gene-Jack Wang, Naomi Pappas, Jean Logan, Colleen Shea, David Alexoff, Robert R. MacGregor, David J. Schlyer, Ivana Zezulkova, Alfred P. Wolf
      Proceedings of the National Academy of Sciences Nov 1996, 93 (24) 14065-14069; DOI: 10.1073/pnas.93.24.14065

      Citation Manager Formats

      • BibTeX
      • Bookends
      • EasyBib
      • EndNote (tagged)
      • EndNote 8 (xml)
      • Medlars
      • Mendeley
      • Papers
      • RefWorks Tagged
      • Ref Manager
      • RIS
      • Zotero
      Request Permissions
      Share
      Brain monoamine oxidase A inhibition in cigarette smokers
      Joanna S. Fowler, Nora D. Volkow, Gene-Jack Wang, Naomi Pappas, Jean Logan, Colleen Shea, David Alexoff, Robert R. MacGregor, David J. Schlyer, Ivana Zezulkova, Alfred P. Wolf
      Proceedings of the National Academy of Sciences Nov 1996, 93 (24) 14065-14069; DOI: 10.1073/pnas.93.24.14065
      del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
      • Tweet Widget
      • Facebook Like
      • Mendeley logo Mendeley
      Proceedings of the National Academy of Sciences: 93 (24)
      Table of Contents

      Submit

      Sign up for Article Alerts

      Jump to section

      • Article
        • SUBJECTS AND METHODS
        • RESULTS
        • DISCUSSION
        • Acknowledgments
        • Footnotes
        • References
      • Figures & SI
      • Info & Metrics
      • PDF

      You May Also be Interested in

      Landscape from near Ravenna, Nebraska.
      Food production and air quality
      A study examines how agriculture influences mortality due to poor air quality in the United States.
      Image credit: Jason D. Hill.
      Red trinitite sample containing the quasicrystal.
      Quasicrystal from first nuclear detonation
      Researchers report a unique quasicrystal discovered in the remnants of the first nuclear bomb detonation.
      Image credit: Luca Bindi and Paul J. Steinhardt.
      House sparrow.
      Global abundance of birds
      A study estimates that there are 50 billion birds in the world, with the majority in palearctic and nearctic realms.
      Image credit: Corey T. Callaghan.
      A colorful male and a drab female cichlid swim through freshwater plants.
      Inner Workings: Reeling in answers to the “freshwater fish paradox”
      Saltwater is far more abundant on Earth, yet about half of the known fish species live in freshwater. The longstanding question is why.
      Image credit: Florian Moser (photographer).
      A refinery sends polluting smoke into the air under hazy skies as the sun sets.
      Opinion: The power and promise of improved climate data infrastructure
      To effectively track and measure emissions reductions, we need a Greenhouse Gas Information System.
      Image credit: Shutterstock/Tatiana Grozetskaya.

      Similar Articles

      Site Logo
      Powered by HighWire
      • Submit Manuscript
      • Twitter
      • Youtube
      • Facebook
      • RSS Feeds
      • Email Alerts

      Articles

      • Current Issue
      • Special Feature Articles – Most Recent
      • List of Issues

      PNAS Portals

      • Anthropology
      • Chemistry
      • Classics
      • Front Matter
      • Physics
      • Sustainability Science
      • Teaching Resources

      Information

      • Authors
      • Editorial Board
      • Reviewers
      • Subscribers
      • Librarians
      • Press
      • Cozzarelli Prize
      • Site Map
      • PNAS Updates
      • FAQs
      • Accessibility Statement
      • Rights & Permissions
      • About
      • Contact

      Feedback    Privacy/Legal

      Copyright © 2021 National Academy of Sciences. Online ISSN 1091-6490. PNAS is a partner of CHORUS, COPE, CrossRef, ORCID, and Research4Life.