Resting-state functional connectivity and nicotine addiction: Prospects for biomarker development

Annals of the New York Academy of Sciences

Volumn 1349, Issue 1, 2015, Pages 64-82

  Fedota, John R ^a   Stein, Elliot A ^a  

^a NATIONAL INSTITUTE ON DRUG ABUSE   (United States)

Author keywords

Addiction; Biomarkers; Nicotine; Resting state functional connectivity

Indexed keywords

BIOLOGICAL MARKER; NICOTINE;

ARTICLE; BOLD SIGNAL; CONNECTOME; EXECUTIVE FUNCTION; LIMBIC CORTEX; NEUROIMAGING; RESTING STATE NETWORK; SATIETY; TOBACCO DEPENDENCE; BRAIN MAPPING; COGNITION; DRUG EFFECTS; HUMAN; LIMBIC SYSTEM; METABOLISM; NERVE TRACT; PATHOPHYSIOLOGY; PHYSIOLOGY; PSYCHOLOGY; REST; WITHDRAWAL SYNDROME;

BIOMARKERS; BRAIN MAPPING; COGNITION; HUMANS; LIMBIC SYSTEM; NEURAL PATHWAYS; NICOTINE; REST; SUBSTANCE WITHDRAWAL SYNDROME; TOBACCO USE DISORDER;

EID: 84941051414     PISSN: 00778923     EISSN: 17496632     Source Type: Book Series    
DOI: 10.1111/nyas.12882     Document Type: Article

References (148)

1. Lerman, C. et al. 2015. Use of the nicotine metabolite ratio as a genetically informed biomarker of response to nicotine patch or varenicline for smoking cessation: a randomised, double-blind placebo-controlled trial. Lancet Respir. Med. 3: 131-138.
2. Mamoun, M., A.W. Bergen, J. Shieh, et al. 2015. Biomarkers of response to smoking cessation pharmacotherapies: progress to date. CNS Drugs 29: 359-369.
3. Koob, G.F. & N.D. Volkow . 2010. Neurocircuitry of addiction. Neuropsychopharmacology 35: 217-238.
4. Biswal, B., F. Zerrin Yetkin, V.M. Haughton & J.S. Hyde . 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34: 537-541.
5. Honey, C.J. et al. 2009. Predicting human resting-state functional connectivity from structural connectivity. Proc. Natl. Acad. Sci. U. S. A. 106: 2035-2040.
6. Bressler, S.L. & V. Menon . 2010. Large-scale brain networks in cognition: emerging methods and principles. Trends Cogn. Sci. 14: 277-290.
7. Van den Heuvel, M.P. & H.E. Hulshoff Pol . 2010. Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur. Neuropsychopharmacol. 20: 519-534.
8. Chen, S. et al. 2008. Group independent component analysis reveals consistent resting-state networks across multiple sessions. Brain Res. 1239: 141-151.
9. Damoiseaux, J.S. et al. 2006. Consistent resting-state networks across healthy subjects. Proc. Natl. Acad. Sci. U. S. A. 103: 13848-13853.
10. Zuo, X.-N. et al. 2010. Reliable intrinsic connectivity networks: test-retest evaluation using ICA and dual regression approach. Neuroimage 49: 2163-2177.
11. Guo, C.C. et al. 2012. One-year test-retest reliability of intrinsic connectivity network fMRI in older adults. Neuroimage 61: 1471-1483.
12. Kelly, A.M.C., L.Q. Uddin, B.B. Biswal, et al. 2008. Competition between functional brain networks mediates behavioral variability. Neuroimage 39: 527-537.
13. Baldassarre, A. et al. 2012. Individual variability in functional connectivity predicts performance of a perceptual task. Proc. Natl. Acad. Sci. U. S. A. 109: 3516-3521.
14. Zou, Q. et al. 2013. Intrinsic resting-state activity predicts working memory brain activation and behavioral performance. Hum. Brain Mapp. 34: 3204-3215.
15. Waites, A.B., A. Stanislavsky, D.F. Abbott & G.D. Jackson . 2005. Effect of prior cognitive state on resting state networks measured with functional connectivity. Hum. Brain Mapp. 24: 59-68.
16. Grigg, O. & C.L. Grady . 2010. Task-related effects on the temporal and spatial dynamics of resting-state functional connectivity in the default network. PLoS One 5: e13311.
17. Harrison, B.J. et al. 2008. Modulation of brain resting-state networks by sad mood induction. PLoS One 3: e1794.
18. Stevens, W.D., R.L. Buckner & D.L. Schacter . 2010. Correlated low-frequency BOLD fluctuations in the resting human brain are modulated by recent experience in category-preferential visual regions. Cereb. Cortex 20: 1997-2006.
19. De Gruttola, V.G. et al. 2001. Considerations in the evaluation of surrogate endpoints in clinical trials. Control. Clin. Trials 22: 485-502.
20. Van Dijk, K.R.A., M.R. Sabuncu & R.L. Buckner . 2012. The influence of head motion on intrinsic functional connectivity MRI. Neuroimage 59: 431-438.
21. Power, J.D., K.A. Barnes, A.Z. Snyder, et al. 2012. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59: 2142-2154.
22. Satterthwaite, T.D. et al. 2012. Impact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth. Neuroimage 60: 623-632.
23. Murphy, K., R.M. Birn, D.A. Handwerker, et al. 2009. The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? Neuroimage 44: 893-905.
24. Weissenbacher, A. et al. 2009. Correlations and anticorrelations in resting-state functional connectivity MRI: a quantitative comparison of preprocessing strategies. Neuroimage 47: 1408-1416.
25. Shirer, W.R., S. Ryali, E. Rykhlevskaia, et al. 2012. Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cereb. Cortex 22: 158-165.
26. Shmueli, K. et al. 2007. Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal. Neuroimage 38: 306-320.
27. Shirer, W.R., H. Jiang, C.M. Price, et al. 2015. Optimization of rs-fMRI pre-processing for enhanced signal-noise separation, test-retest reliability, and group discrimination. Neuroimage 117: 67-79.
28. Power, J.D., B.L. Schlaggar & S.E. Petersen . 2014. Studying Brain Organization via Spontaneous fMRI Signal. Neuron 84: 681-696.
29. Rubinov, M. & O. Sporns . 2010. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52: 1059-1069.
30. Sutherland, M.T., X. Liang, Y. Yang & E.A. Stein . 2015. Beyond Functional Localization: Advancing the Understanding of Addiction-Related Processes by Examining Brain Connectivity. In The Wiley Handbook on the Cognitive Neuroscience of Addiction. S.J. Wilson, Ed.: 472-502. Chichester: Wiley-Blackwell.
31. Smith, S.M. et al. 2013. Functional connectomics from resting-state fMRI. Trends Cogn. Sci. 17: 666-682.
32. Sporns, O. 2011. Networks of the brain. Cambridge, MA: MIT Press.
33. Bullmore, E. & D.S. Bassett . 2011. Brain graphs: graphical models of the human brain connectome. Annu. Rev. Clin. Psychol. 7: 113-140.
34. Mckeown, M.J. et al. 1998. Analysis of fMRI data by blind separation into independent spatial components. Hum. Brain Mapp. 6: 160-188.
35. Smith, S.M. et al. 2009. Correspondence of the brain's functional architecture during activation and rest. Proc. Natl. Acad. Sci. U. S. A. 106: 13040-13045.
36. Raichle, M.E. et al. 2001. A default mode of brain function. Proc. Natl. Acad. Sci. U. S. A. 98: 676-682.
37. Greicius, M.D., B. Krasnow, A.L. Reiss & V. Menon . 2003. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc. Natl. Acad. Sci. U. S. A. 100: 253-258.
38. Greicius, M.D., G. Srivastava, A.L. Reiss & V. Menon . 2004. Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI. Proc. Natl. Acad. Sci. U. S. A. 101: 4637-4462.
39. Qin, P. & G. Northoff . 2011. How is our self related to midline regions and the default-mode network? Neuroimage 57: 1221-1233.
40. Fox, M.D. et al. 2005. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. U. S. A. 102: 9673-9678.
41. Honey, C.J., R. Kötter, M. Breakspear & O. Sporns . 2007. Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc. Natl. Acad. Sci. U. S. A. 104: 10240-10255.
42. Seeley, W.W. et al. 2007. Dissociable intrinsic connectivity networks for salience processing and executive control. J. Neurosci. 27: 2349-2356.
43. Sridharan, D., D.J. Levitin & V. Menon . 2008. A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proc. Natl. Acad. Sci. U. S. A. 105: 12569-12574.
44. Belcher, A.M. et al. 2013. Large-scale brain networks in the awake, truly resting marmoset monkey. J. Neurosci. 33: 16796-804.
45. Lu, H. et al. 2012. Rat brains also have a default mode network. Proc. Natl. Acad. Sci. U. S. A. 109: 3979-3984.
46. Vincent, J.L. et al. 2007. Intrinsic functional architecture in the anaesthetized monkey brain. Nature 447: 83-86.
47. Hamilton, J.P. et al. 2011. Default-mode and task-positive network activity in major depressive disorder: implications for adaptive and maladaptive rumination. Biol. Psychiatry 70: 327-333.
48. Castellanos, F.X. et al. 2008. Cingulate-precuneus interactions: a new locus of dysfunction in adult attention-deficit/hyperactivity disorder. Biol. Psychiatry 63: 332-337.
49. Prado, J. & D.H. Weissman . 2011. Heightened interactions between a key default-mode region and a key task-positive region are linked to suboptimal current performance but to enhanced future performance. Neuroimage 56: 2276-2282.
50. Menon, V. 2011. Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn. Sci. 15: 483-506.
51. Van den Heuvel, M.P. & O. Sporns . 2013. Network hubs in the human brain. Trends Cogn. Sci. 17: 683-696.
52. Sonuga-Barke, E.J.S. & F.X. Castellanos . 2007. Spontaneous attentional fluctuations in impaired states and pathological conditions: a neurobiological hypothesis. Neurosci. Biobehav. Rev. 31: 977-986.
53. Cole, D.M. et al. 2010. Nicotine replacement in abstinent smokers improves cognitive withdrawal symptoms with modulation of resting brain network dynamics. Neuroimage 52: 590-599.
54. Picard, F. et al. 2013. High density of nicotinic receptors in the cingulo-insular network. Neuroimage 79: 42-51.
55. Baker, T.B. et al. 2007. Time to first cigarette in the morning as an index of ability to quit smoking: implications for nicotine dependence. Nicotine Tob. Res. 9(Suppl 4): S555-S570.
56. Japuntich, S.J. et al. 2011. Smoker characteristics and smoking-cessation milestones. Am. J. Prev. Med. 40: 286-294.
57. Fidler, J.A., L. Shahab & R. West . 2011. Strength of urges to smoke as a measure of severity of cigarette dependence: comparison with the fagerström test for nicotine dependence and its components. Addiction 106: 631-638.
58. Brody, A.L. et al. 2014. Brain nicotinic acetylcholine receptor availability and response to smoking cessation treatment: a randomized trial. JAMA Psychiatry 71: 797-805.
59. Garrison, K.A. & M.N. Potenza . 2014. Neuroimaging and biomarkers in addiction treatment. Curr. Psychiatry Rep. 16: 513.
60. Volkow, N.D. & R.D. Baler . 2013. Brain imaging biomarkers to predict relapse in alcohol addiction. JAMA Psychiatry 70: 661-663.
61. Volkow, N.D., G. Koob & R. Baler . 2015. Biomarkers in substance use disorders. ACS Chem. Neurosci. 6: 522-525.
62. Reske, M. & M.P. Paulus . 2011. The Diagnostic and Therapeutic Potential of Neuroimaging in Addiction Medicine. In Neuroimaging in Addiction. B. Adinoff & E.A. Stein, Eds.: 321-343. Chichester: John Wiley & Sons.
63. Swan, G.E. & C.N. Lessov-Schlaggar . 2007. The effects of tobacco smoke and nicotine on cognition and the brain. Neuropsychol. Rev. 17: 259-273.
64. Goldstein, R.Z. & N.D. Volkow . 2002. Drug addiction and its underlying neurobiological basis: neuroimaging evidence for the involvement of the frontal cortex. Am. J. Psychiatry 159: 1642-1652.
65. Goldstein, R.Z. & N.D. Volkow . 2011. Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications. Nat. Rev. Neurosci. 12: 652-669.
66. Rose, E.J. et al. 2013. Acute nicotine differentially impacts anticipatory valence- and magnitude-related striatal activity. Biol. Psychiatry 73: 280-288.
67. Jasinska, A.J., T. Zorick, A.L. Brody & E.A. Stein . 2014. Dual role of nicotine in addiction and cognition: a review of neuroimaging studies in humans. Neuropharmacology 84C: 111-122.
68. Sutherland, M.T. et al. 2015. Neurobiological impact of nicotinic acetylcholine receptor agonists: an ALE meta-analysis of pharmacological neuroimaging studies. Biol. Psychiatry. doi:10.1016/j.biopsych.2014.12.021.
69. Hong, L.E. et al. 2009. Association of nicotine addiction and nicotine's actions with separate cingulate cortex functional circuits. Arch. Gen. Psychiatry 66: 431-441.
70. Sutherland, M.T., A.J. Carroll, B.J. Salmeron, et al. 2013. Insula's functional connectivity with ventromedial prefrontal cortex mediates the impact of trait alexithymia on state tobacco craving. Psychopharmacology (Berl) 228: 143-155.
71. Haller, S. et al. 2013. Dynamic reconfiguration of human brain functional networks through neurofeedback. Neuroimage 81: 243-252.
72. Moran, L.V., H. Sampath, P. Kochunov & L.E. Hong . 2013. Brain circuits that link schizophrenia to high risk of cigarette smoking. Schizophr. Bull. 39: 1373-1381.
73. Hong, L.E. et al. 2011. A CHRNA5 allele related to nicotine addiction and schizophrenia. Genes Brain Behav. 10: 530-535.
74. Moran, L.V., H. Sampath, E.A. Stein & L.E. Hong . 2012. Insular and anterior cingulate circuits in smokers with schizophrenia. Schizophr. Res. 142: 223-229.
75. Green, M.F. 1996. What are the functional consequences of neurocognitive deficits in schizophrenia? Am. J. Psychiatry 153: 321-330.
76. Kumari, V. & P. Postma . 2005. Nicotine use in schizophrenia: the self medication hypotheses. Neurosci. Biobehav. Rev. 29: 1021-1034.
77. Hahn, B. et al. 2013. A test of the cognitive self-medication hypothesis of tobacco smoking in schizophrenia. Biol. Psychiatry 74: 436-443.
78. Hong, L.E. et al. 2011. Nicotine enhances but does not normalize visual sustained attention and the associated brain network in schizophrenia. Schizophr. Bull. 37: 416-425.
79. Lyons, M.J. et al. 2002. Nicotine and familial vulnerability to schizophrenia: a discordant twin study. J. Abnorm. Psychol. 111: 687-693.
80. Weiland, B.J., A. Sabbineni, V.D. Calhoun, et al. 2014. Reduced executive and default network functional connectivity in cigarette smokers. Hum. Brain Mapp. 36: 872-882.
81. Lin, F., G. Wu, L. Zhu & H. Lei . 2014. Altered brain functional networks in heavy smokers. Addict. Biol. 36: 872-882.
82. Janes, A.C., L.D. Nickerson, B.D.B. Frederick & M.J. Kaufman . 2012. Prefrontal and limbic resting state brain network functional connectivity differs between nicotine-dependent smokers and non-smoking controls. Drug Alcohol Depend. 125: 252-259.
83. Janes, A.C., S. Farmer, A.L. Peechatka, et al. 2015. Insula-dorsal anterior cingulate cortex coupling is associated with enhanced brain reactivity to smoking cues. Neuropsychopharmacology 40: 1561-1568.
84. McClernon, F.J., R.V. Kozink, A.M. Lutz & J.E. Rose . 2009. 24-h smoking abstinence potentiates fMRI-BOLD activation to smoking cues in cerebral cortex and dorsal striatum. Psychopharmacology (Berl.) 204: 25-35.
85. Zang, Y., T. Jiang, Y. Lu, et al. 2004. Regional homogeneity approach to fMRI data analysis. Neuroimage 22: 394-400.
86. Wu, G., S. Yang, L. Zhu & F. Lin . 2015. Altered spontaneous brain activity in heavy smokers revealed by regional homogeneity. Psychopharmacology (Berl.) 232: 2481-2489.
87. Hahn, B. et al. 2007. Nicotine enhances visuospatial attention by deactivating areas of the resting brain default network. J. Neurosci. 27: 3477-3489.
88. Hahn, B. et al. 2009. Performance effects of nicotine during selective attention, divided attention, and simple stimulus detection: an fMRI study. Cereb. Cortex 19: 1990-2000.
89. Luijten, M. et al. 2014. Systematic review of ERP and fMRI studies investigating inhibitory control and error processing in people with substance dependence and behavioural addictions. J. Psychiatry Neurosci. 39: 149-169.
90. Tang, J. et al. 2012. Altered spontaneous activity in young chronic cigarette smokers revealed by regional homogeneity. Behav. Brain Funct. 8: 44.
91. Yu, R. et al. 2013. Regional homogeneity changes in heavy male smokers: a resting-state functional magnetic resonance imaging study. Addict. Biol. 18: 729-731.
92. Floyd, T.F., S.J. Ratcliffe, J. Wang, et al. 2003. Precision of the CASL-perfusion MRI technique for the measurement of cerebral blood flow in whole brain and vascular territories. J. Magn. Reson. Imaging 18: 649-655.
93. Wetherill, R.R., K. Jagannathan, J. Shin & T.R. Franklin . 2014. Sex differences in resting state neural networks of nicotine-dependent cigarette smokers. Addict. Behav. 39: 789-792.
94. Franklin, T.R. et al. 2014. The effects of chronic cigarette smoking on gray matter volume: influence of sex. PLoS One 9: e104102.
95. McClernon, F.J., R.V. Kozink & J.E. Rose . 2008. Individual differences in nicotine dependence, withdrawal symptoms, and sex predict transient fMRI-BOLD responses to smoking cues. Neuropsychopharmacology 33: 2148-2157.
96. Field, M. & T. Duka . 2004. Cue reactivity in smokers: the effects of perceived cigarette availability and gender. Pharmacol. Biochem. Behav. 78: 647-652.
97. Wetter, D.W. et al. 1999. Gender differences in smoking cessation. J. Consult. Clin. Psychol. 67: 555-562.
98. Perkins, K.A. 2001. Smoking cessation in women. CNS Drugs 15: 391-411.
99. Heishman, S.J., B.A. Kleykamp & E.G. Singleton . 2010. Meta-analysis of the acute effects of nicotine and smoking on human performance. Psychopharmacology (Berl.) 210: 453-469.
100. Sutherland, M.T., M.J. McHugh, V. Pariyadath & E.A. Stein . 2012. Resting state functional connectivity in addiction: lessons learned and a road ahead. Neuroimage 62: 1-15.
101. Tanabe, J. et al. 2011. Nicotine effects on default mode network during resting state. Psychopharmacology (Berl.) 216: 287-295.
102. Wylie, K.P., D.C. Rojas, J. Tanabe, et al. 2012. Nicotine increases brain functional network efficiency. Neuroimage 63: 73-80.
103. Viswanath, H. et al. 2015. Alterations in interhemispheric functional and anatomical connectivity are associated with tobacco smoking in humans. Front. Hum. Neurosci. 9: 116.
104. MacDonald, A.W., J.D. Cohen, V.A. Stenger & C.S. Carter . 2000. Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science 288: 1835-1838.
105. Giessing, C., C.M. Thiel, A.F. Alexander-Bloch, et al. 2013. Human brain functional network changes associated with enhanced and impaired attentional task performance. J. Neurosci. 33: 5903-5914.
106. Lim, J. et al. 2010. Imaging brain fatigue from sustained mental workload: an ASL perfusion study of the time-on-task effect. Neuroimage 49: 3426-3435.
107. Hughes, J.R. 2007. Effects of abstinence from tobacco: etiology, animal models, epidemiology, and significance: a subjective review. Nicotine Tob. Res. 9: 329-339.
108. Janes, A.C., S. Farmer, B. deB Frederick, et al. 2014. An increase in tobacco craving is associated with enhanced medial prefrontal cortex network coupling. PLoS One 9: e88228.
109. Breckel, T.P.K., C.M. Thiel & C. Giessing . 2013. The efficiency of functional brain networks does not differ between smokers and non-smokers. Psychiatry Res. 214: 349-356.
110. Friston, K.J. et al. 1997. Psychophysiological and modulatory interactions in neuroimaging. Neuroimage 6: 218-229.
111. O'Reilly, J.X., M.W. Woolrich, T.E.J. Behrens, et al. 2012. Tools of the trade: psychophysiological interactions and functional connectivity. Soc. Cogn. Affect. Neurosci. 7: 604-609.
112. Moran-Santa Maria, M.M. et al. 2015. Right anterior insula connectivity is important for cue-induced craving in nicotine-dependent smokers. Addict. Biol. 20: 407-414.
113. Claus, E.D., S.K. Blaine, F.M. Filbey, et al. 2013. Association between nicotine dependence severity, BOLD response to smoking cues, and functional connectivity. Neuropsychopharmacology 38: 2363-2372.
114. Wang, K. et al. 2014. The neural mechanisms underlying the acute effect of cigarette smoking on chronic smokers. PLoS One 9: e102828.
115. Sutherland, M.T. et al. 2013. Down-regulation of amygdala and insula functional circuits by varenicline and nicotine in abstinent cigarette smokers. Biol. Psychiatry 74: 538-546.
116. Lerman, C. et al. 2014. Large-scale brain network coupling predicts acute nicotine abstinence effects on craving and cognitive function. JAMA Psychiatry 71: 523-530.
117. Ding, X. & S.-W. Lee . 2013. Changes of functional and effective connectivity in smoking replenishment on deprived heavy smokers: a resting-state FMRI study. PLoS One 8: e59331.
118. Craig, A.D.B. 2009. How do you feel-now? The anterior insula and human awareness. Nat. Rev. Neurosci. 10: 59-70.
119. Smith, S.M. et al. 2011. Network modelling methods for FMRI. Neuroimage 54: 875-891.
120. Oncken, C. et al. 2006. Efficacy and safety of the novel selective nicotinic acetylcholine receptor partial agonist, varenicline, for smoking cessation. Arch. Intern. Med. 166: 1571-1577.
121. Hu, Y., B.J. Salmeron, H. Gu, et al. 2015. Impaired functional connectivity within and between frontostriatal circuits and its association with compulsive drug use and trait impulsivity in cocaine addiction. JAMA Psychiatry 72: 584-592.
122. Gu, H. et al. 2010. Mesocorticolimbic circuits are impaired in chronic cocaine users as demonstrated by resting-state functional connectivity. Neuroimage 53: 593-601.
123. Contreras-Rodríguez, O. et al. 2015. Increased corticolimbic connectivity in cocaine dependence versus pathological gambling is associated with drug severity and emotion-related impulsivity. Addict. Biol. doi:10.1111/adb.12242.
124. Pariyadath, V., J.L. Gowin & E.A. Stein . 2016. Mesocorticolimbic circuitry in addiction: insights from resting state functional connectivity. In Progress in Brain Research: Neuroscience for Addiction Medicine: From Prevention to Rehabilitation. H. Ekhtiari & M. Paulus, Eds. Amsterdam: Elsevier.
125. Cho, Y.T. et al. 2013. Nucleus accumbens, thalamus and insula connectivity during incentive anticipation in typical adults and adolescents. Neuroimage 66: 508-521.
126. Ducci, F. & D. Goldman . 2012. The genetic basis of addictive disorders. Psychiatr. Clin. North Am. 35: 495-519.
127. Wassenaar, C.A. et al. 2011. Relationship between CYP2A6 and CHRNA5-CHRNA3-CHRNB4 variation and smoking behaviors and lung cancer risk. J. Natl. Cancer Inst. 103: 1342-1346.
128. Chen, L.-S. et al. 2012. Interplay of genetic risk factors (CHRNA5-CHRNA3-CHRNB4) and cessation treatments in smoking cessation success. Am. J. Psychiatry 169: 735-742.
129. Hong, L.E. et al. 2010. A genetically modulated, intrinsic cingulate circuit supports human nicotine addiction. Proc. Natl. Acad. Sci. U. S. A. 107: 13509-13514.
130. Hanlon, C.A. et al. 2013. Reduction of cue-induced craving through realtime neurofeedback in nicotine users: the role of region of interest selection and multiple visits. Psychiatry Res. 213: 79-81.
131. Kim, D.-Y., S.-S. Yoo, M. Tegethoff, et al. 2015. The inclusion of functional connectivity information into fMRI-based neurofeedback improves its efficacy in the reduction of cigarette cravings. J. Cogn. Neurosci. 27: 1552-1572.
132. Paulus, M.P., J.L. Stewart & L. Haase . 2013. Treatment approaches for interoceptive dysfunctions in drug addiction. Front. Psychiatry 4: 137.
133. Gorelick, D.A., A. Zangen & M.S. George . 2014. Transcranial magnetic stimulation in the treatment of substance addiction. Ann. N. Y. Acad. Sci. 1327: 79-93.
134. Bough, K.J. et al. 2013. Biomarkers for smoking cessation. Clin. Pharmacol. Ther. 93: 526-538.
135. Greicius, M. 2008. Resting-state functional connectivity in neuropsychiatric disorders. Curr. Opin. Neurol. 24: 424-430.
136. Addicott, M.A., M.M. Sweitzer, B. Froeliger, et al. 2015. Increased functional connectivity in an insula-based network is associated with improved smoking cessation outcomes. Neuropsychopharmacology. doi:10.1038/npp.2015.114.
137. McHugh, M.J. et al. 2013. Striatal-insula circuits in cocaine addiction: implications for impulsivity and relapse risk. Am. J. Drug Alcohol Abuse 39: 424-432.
138. Camchong, J., A. Stenger & G. Fein . 2013. Resting-state synchrony during early alcohol abstinence can predict subsequent relapse. Cereb. Cortex 23: 2086-2099.
139. Buckner, R.L. et al. 2009. Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease. J. Neurosci. 29: 1860-1873.
140. Bullmore, E. & O. Sporns . 2009. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10: 186-198.
141. Cole, M.W. et al. 2013. Multi-task connectivity reveals flexible hubs for adaptive task control. Nat. Neurosci. 16: 1348-1355.
142. Pariyadath, V., E.A. Stein & T.J. Ross . 2014. Machine learning classification of resting state functional connectivity predicts smoking status. Front. Hum. Neurosci. 8: 425.
143. Jarbo, K. & T.D. Verstynen . 2015. Converging structural and functional connectivity of orbitofrontal, dorsolateral prefrontal, and posterior parietal cortex in the human striatum. J. Neurosci. 35: 3865-3878.
144. Choi, E.Y., B.T.T. Yeo & R.L. Buckner . 2012. The organization of the human striatum estimated by intrinsic functional connectivity. J. Neurophysiol. 108: 2242-2263.
145. Gabrieli, J.D.E., S.S. Ghosh & S. Whitfield-Gabrieli . 2015. Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience. Neuron 85: 11-26.
146. Whelan, R. & H. Garavan . 2014. When optimism hurts: inflated predictions in psychiatric neuroimaging. Biol. Psychiatry 75: 746-748.
147. Pereira, F., T. Mitchell & M. Botvinick . 2009. Machine learning classifiers and fMRI: a tutorial overview. Neuroimage 45: S199-S209.
148. Deshpande, G. et al. 2010. Recursive cluster elimination based support vector machine for disease state prediction using resting state functional and effective brain connectivity. PLoS One 5: e14277.