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5 - Human Neurobiological Approaches to Hedonically Motivated Behaviors

from Part II - Clinical and Research Methods in the Addictions

Published online by Cambridge University Press:  13 July 2020

By
Kyle S. Burger ,
Grace E. Shearrer  and
Jennifer R. Sadler
Edited by
Steve Sussman
Steve Sussman
Affiliation:
University of Southern California
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Summary

Neuroimaging techniques have rapidly expanded our understanding of how the brain responds to addiction in humans. This chapter will discuss methods used to assess brain response, how the data is analyzed, and how it can be used to better understand addiction. Foundational to inferences drawn from these methods is study design. Common designs employed in human neuroimaging research are discussed, including cross-sectional designs, longitudinal/cohort designs, and experimental designs. A description of various neuroimaging methods and their strengths and weaknesses is included: functional magnetic resonance imaging (fMRI), positron-emission tomography, electroencephalogram, magnetoencephalography, structural MRI, and resting state fMRI. Given its popularity in research, discussion of MRI includes details on paradigm design and data analysis of functional and structural MRI, as well as some common oversights in data processing and interpretation of results.

Keywords

neuroimaging designs and techniquesfMRIsMRIPETEEGMEG
Type
Chapter
Information
The Cambridge Handbook of Substance and Behavioral Addictions , pp. 53 - 61
Publisher: Cambridge University Press
Print publication year: 2020

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References

Babbs, R. K., Sun, X., Felsted, J., et al. (2013). Decreased caudate response to milkshake is associated with higher body mass index and greater impulsivity. Physiology & Behavior, 121, 103–111.CrossRefGoogle ScholarPubMed
Benedict, C., Brooks, S. J., O’Daly, O. G., et al. (2012). Acute sleep deprivation enhances the brain’s response to hedonic food stimuli: an fMRI study. Journal of Clinical Endocrinology and Metabolism, 97(3), E443–447. doi: 10.1210/jc.2011-2759CrossRefGoogle ScholarPubMed
Berkman, E. T. & Falk, E. B. (2013). Beyond brain mapping using neural measures to predict real-world outcomes. Current Directions in Psychological Science, 22(1), 45–50.CrossRefGoogle ScholarPubMed
Berridge, K. C. (2012). From prediction error to incentive salience: mesolimbic computation of reward motivation. European Journal of Neuroscience, 35(7), 1124–1143. doi: 10.1111/j.1460-9568.2012.07990.xCrossRefGoogle ScholarPubMed
Blum, K., Cull, J. G., Braverman, E. R. & Comings, D. E. (1996). Reward deficiency syndrome. American Scientist, 84(2), 132–145.Google Scholar
Boswell, R. G., Sun, W., Suzuki, S. & Kober, H. (2018). Training in cognitive strategies reduces eating and improves food choice. Proceedings of the National Academy of Sciences, 115(48), E11238–E11247.Google Scholar
Buhle, J. T., Silvers, J. A., Wager, T. D., et al. (2014). Cognitive reappraisal of emotion: a meta-analysis of human neuroimaging studies. Cerebral Cortex, 24(11), 2981–2990.Google Scholar
Burger, K. S. (2017). Frontostriatal and behavioral adaptations to daily sugar-sweetened beverage intake: a randomized controlled trial. The American Journal of Clinical Nutrition, 105(3), 555–563. doi: 10.3945/ajcn.116.140145Google Scholar
Chen, G., Saad, Z. S., Britton, J. C., Pine, D. S. & Cox, R. W. (2013). Linear mixed-effects modeling approach to fMRI group analysis. Neuroimage, 73, 176–190.CrossRefGoogle ScholarPubMed
Crockford, D. N., Goodyear, B., Edwards, J., et al. (2005). Cue-induced brain activity in pathological gamblers. Biological Psychiatry, 58(10), 787–795.CrossRefGoogle ScholarPubMed
Dawe, S. & Loxton, N. J. (2004). The role of impulsivity in the development of substance use and eating disorders. Neuroscience and Biobehavioral Reviews, 28(3), 343–351. doi: 10.1016/j.neubiorev.2004.03.007CrossRefGoogle ScholarPubMed
Fowler, J. S., Volkow, N. D., Ding, Y. S., et al. (1998). PET and the study of drug action in the human brain. Pharmaceutical News, 5, 11–16.Google Scholar
Frank, G. K. W., Reynolds, J. R., Shott, M. E., et al. (2012). Anorexia nervosa and obesity are associated with opposite brain reward response. Neuropsychopharmacology, 37(9), 2031–2046.CrossRefGoogle ScholarPubMed
Goldstein, R. Z. & Volkow, N. D. (2011). Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications. Nature Reviews Neuroscience, 12(11), 652.CrossRefGoogle ScholarPubMed
Goldstein, R. Z., Tomasi, D., Alia-Klein, N., et al. (2009). Dopaminergic response to drug words in cocaine addiction. Journal of Neuroscience, 29(18), 6001–6006.CrossRefGoogle ScholarPubMed
Goudriaan, A. E., et al. (2010). Brain activation patterns associated with cue reactivity and craving in abstinent problem gamblers, heavy smokers and healthy controls: an fMRI study. Addiction Biology, 15(4), 491–503.Google Scholar
Haller, S. & Bartsch, A. J. (2009). Pitfalls in fMRI. European Radiology, 19(11), 2689–2706.CrossRefGoogle ScholarPubMed
Koehler, S., Hasselmann, E., Wüstenberg, T., et al. (2015). Higher volume of ventral striatum and right prefrontal cortex in pathological gambling. Brain Structure and Function, 220(1), 469–477. doi: 10.1007/s00429-013-0668-6CrossRefGoogle ScholarPubMed
Koob, G. F. & Volkow, N. D. (2010). Neurocircuitry of addiction. Neuropsychopharmacology, 35(1), 217–238. doi: http://dx.doi.org/10.1038/npp.2009.110CrossRefGoogle ScholarPubMed
Lieberman, M. D. & Cunningham, W. A. (2009). Type I and Type II error concerns in fMRI research: re-balancing the scale. Social Cognitive and Affective Neuroscience, 4(4), 423–428.CrossRefGoogle ScholarPubMed
Liu, J., Liang, J., Qin, W., et al. (2009). Dysfunctional connectivity patterns in chronic heroin users: an fMRI study. Neuroscience Letters, 460(1), 72–77. doi: 10.1016/j.neulet.2009.05.038CrossRefGoogle ScholarPubMed
Martinez, D., Slifstein, M., Narendran, R., et al. (2009). Dopamine D1 receptors in cocaine dependence measured with PET and the choice to self-administer cocaine. Neuropsychopharmacology, 34(7), 1774.CrossRefGoogle ScholarPubMed
Morgan, V. L., Dawant, B. M., Li, Y. & Pickens, D. R. (2007). Comparison of fMRI statistical software packages and strategies for analysis of images containing random and stimulus-correlated motion. Computerized Medical Imaging and Graphics, 31(6), 436–446.Google Scholar
Poldrack, R. A., Fletcher, P. C., Henson, R. N., et al. (2008). Guidelines for reporting an fMRI study. Neuroimage, 40(2), 409–414.Google Scholar
Potenza, M. N., Steinberg, M. A., Skudlarski, P., et al. (2003). Gambling urges in pathological gambling: a functional magnetic resonance imaging study. Archives of General Psychiatry, 60(8), 828–836. doi: 10.1001/archpsyc.60.8.828CrossRefGoogle ScholarPubMed
Volkow, N., Chang, L., Wang, G. J., et al. (2001). Low level of brain dopamine D2 receptors in methamphetamine abusers: association with metabolism in the orbitofrontal cortex. American Journal of Psychiatry, 158(12), 2015–2021.Google Scholar
Volkow, N. D., Fowler, J. S., Wang, G. J., et al. (2009). Imaging dopamine’s role in drug abuse and addiction. Neuropharmacology, 56, 3–8.Google Scholar
Vul, E. & Pashler, H. (2012). Voodoo and circularity errors. Neuroimage, 62(2), 945–948.CrossRefGoogle ScholarPubMed
Wang, G.-J., Volkow, N. D., Thanos, P. K. & Fowler, J. S. (2004). Similarity between obesity and drug addiction as assessed by neurofunctional imaging: a concept review. Journal of Addictive Diseases, 23(3), 39–53.CrossRefGoogle ScholarPubMed
Wilcox, C. E., Braskie, M. N., Kluth, J. T. & Jagust, W. J. (2010). Overeating behavior and striatal Dopamine with 6-[1 8 F]-Fluoro-L-m-Tyrosine PET. Journal of Obesity, 2010, 909348.CrossRefGoogle Scholar
Woo, C. W., Krishnan, A. & Wager, T. D. (2014). Cluster-extent based thresholding in fMRI analyses: pitfalls and recommendations. Neuroimage, 91, 412–419.CrossRefGoogle ScholarPubMed
Yarkoni, T. (2009). Big correlations in little studies: Inflated fMRI correlations reflect low statistical power – Commentary on Vul et al. (2009). Perspectives on Psychological Science, 4(3), 294–298.Google Scholar
Zhao, L. Y., Tian, J., Wang, W., et al. (2012). The role of dorsal anterior cingulate cortex in the regulation of craving by reappraisal in smokers. PLoS ONE, 7(8) e43598.Google ScholarPubMed

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