Advertisement

Changes of frontal cortical subregion volumes in alcohol dependent individuals during early abstinence: associations with treatment outcome

  • Timothy C. DurazzoEmail author
  • Dieter J. Meyerhoff
ORIGINAL RESEARCH
  • 17 Downloads

Abstract

We previously reported that at 1-and-4 weeks of sobriety, those who relapsed after treatment demonstrated significantly smaller total frontal cortical volume than individuals who maintained abstinence for at least 12 months post treatment. The segmentation method employed did not permit examination of frontal subregions that serve as nodes of the executive, salience and emotional regulation networks; structural abnormalities in these circuits are associated with relapse in those seeking treatment for alcohol use disorders (AUD). The primary goal of this study was to determine if frontal cortical subregion volume recovery during early abstinence is associated with long-term abstinence from alcohol. We compared bilateral components of the dorsal prefrontal cortex, orbitofrontal cortex, anterior cingulate cortex and insula volumes, at 1 and 4 weeks of abstinence, between individuals who resumed drinking within 12 months of treatment (Relapsers) those who showed sustained abstinence over 12 months following treatment (Abstainers) and healthy Controls. At 1 and 4 weeks of sobriety, Relapsers demonstrated significantly smaller volumes than Controls in 15 of 20 regions of interest, while Abstainers only had smaller volumes than Controls in 5 of 20 regions. In Relapsers, increasing volumes over 1 month in multiple frontal subregions and the insula were associated with longer duration of abstinence after treatment. The persistent bilateral frontal and insula volume deficits in Relapsers over 4 weeks from last alcohol use may have implications for neurostimulation methods targeting anterior frontal/insula regions, and represent an endophenotype that differentiates those who respond more favorably to available psychosocial and pharmacological interventions.

Keywords

Alcohol use disorder Brain volumes Relapse Magnetic resonance imaging Neurostimulation 

Notes

Acknowledgments

This study was supported by National Institutes of Health (AA10788 to DJM and DA24136 to TCD) and Department of Veterans Affairs (RX002303 to TCD) with resources and use of facilities at the San Francisco VA Medical Center and the VA Palo Alto Health Care System. We thank Dr. Ellen Herbst, Ricky Chen and colleagues of the San Francisco Veterans Administration Substance Abuse Day Hospital and Dr. David Pating and colleagues at the Kaiser Permanente Chemical Dependency Recovery Program in San Francisco for their valuable assistance in participant recruitment. We thank Drs. Stefan Gazdzinski and Anderson Mon for MRI data acquisition, Dr. Xiaowei Zou for longitudinal FreeSurfer data processing, and Mr. Thomas Schmidt for assistance with psychiatric assessments and cohort maintenance. We also extend our gratitude to our participants, who made this research possible.

Funding

National Institutes of Health (AA10788 to Dieter J. Meyerhoff and DA24136 to Timothy C. Durazzo) and Department of Veterans Affairs (RX002303 to Timothy C. Durazzo) with resources and use of facilities at the San Francisco VA Medical Center and the VA Palo Alto Health Care System.

Compliance with ethical standards

Conflict of interest

The Authors received funding from the National Institutes and Department of Veterans Affairs. The Authors have no conflicts of interest to report.

Ethical approval

All procedures performed in this study involving human participants were in accordance with the ethical standards of institutional review boards of the University of California San Francisco and the San Francisco VA Medical Center and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Written informed consent was obtained from all participants included in the study prior to involvement in any research-related procedures.

References

  1. Bates, M. E., Buckman, J. F., & Nguyen, T. T. (2013). A role for cognitive rehabilitation in increasing the effectiveness of treatment for alcohol use disorders. Neuropsychology Review, 23(1), 27–47.  https://doi.org/10.1007/s11065-013-9228-3.CrossRefGoogle Scholar
  2. Beck, A., Wustenberg, T., Genauck, A., Wrase, J., Schlagenhauf, F., Smolka, M. N., . . . Heinz, A. (2012). Effect of brain structure, brain function, and brain connectivity on relapse in alcohol-dependent patients. Archives of General Psychiatry, 69(8), 842–852.  https://doi.org/10.1001/archgenpsychiatry.2011.2026.
  3. Bellamoli, E., Manganotti, P., Schwartz, R. P., Rimondo, C., Gomma, M., & Serpelloni, G. (2014). rTMS in the treatment of drug addiction: An update about human studies. Behavioural Neurology, 2014, 815215.  https://doi.org/10.1155/2014/815215.CrossRefGoogle Scholar
  4. Buhler, M., & Mann, K. (2011). Alcohol and the human brain: A systematic review of different neuroimaging methods. Alcoholism, Clinical and Experimental Research, 35(10), 1771–1793.  https://doi.org/10.1111/j.1530-0277.2011.01540.x.CrossRefGoogle Scholar
  5. Cardenas, V. A., Durazzo, T. C., Gazdzinski, S., Mon, A., Studholme, C., & Meyerhoff, D. J. (2011). Brain morphology at entry into treatment for alcohol dependence is related to relapse propensity. Biological Psychiatry, 70(6), 561–567.  https://doi.org/10.1016/j.biopsych.2011.04.003.CrossRefGoogle Scholar
  6. Cohen, J. (1977). Statistical power analysis for the behavioral sciences (rev. ed.). New York: Academic Press.Google Scholar
  7. Dunlop, K., Hanlon, C. A., & Downar, J. (2017). Noninvasive brain stimulation treatments for addiction and major depression. Annals of the New York Academy of Sciences, 1394(1), 31–54.  https://doi.org/10.1111/nyas.12985.CrossRefGoogle Scholar
  8. Durazzo, T. C., & Meyerhoff, D. J. (2017). Psychiatric, demographic, and brain morphological predictors of relapse after treatment for an alcohol use disorder. Alcoholism, Clinical and Experimental Research, 41(1), 107–116.  https://doi.org/10.1111/acer.13267.CrossRefGoogle Scholar
  9. Durazzo, T. C., Gazdzinski, S., Yeh, P. H., & Meyerhoff, D. J. (2008). Combined neuroimaging, neurocognitive and psychiatric factors to predict alcohol consumption following treatment for alcohol dependence. Alcohol and Alcoholism, 43(6), 683–691.  https://doi.org/10.1093/alcalc/agn078.CrossRefGoogle Scholar
  10. Durazzo, T. C., Tosun, D., Buckley, S., Gazdzinski, S., Mon, A., Fryer, S. L., & Meyerhoff, D. J. (2011). Cortical thickness, surface area, and volume of the brain reward system in alcohol dependence: Relationships to relapse and extended abstinence. Alcoholism, Clinical and Experimental Research, 35(6), 1187–1200.  https://doi.org/10.1111/j.1530-0277.2011.01452.x.CrossRefGoogle Scholar
  11. Durazzo, T. C., Mon, A., Pennington, D., Abe, C., Gazdzinski, S., & Meyerhoff, D. J. (2014). Interactive effects of chronic cigarette smoking and age on brain volumes in controls and alcohol-dependent individuals in early abstinence. Addiction Biology, 19(1), 132–143.  https://doi.org/10.1111/j.1369-1600.2012.00492.x.CrossRefGoogle Scholar
  12. Durazzo, T. C., Mon, A., Gazdzinski, S., Yeh, P. H., & Meyerhoff, D. J. (2015). Serial longitudinal magnetic resonance imaging data indicate non-linear regional gray matter volume recovery in abstinent alcohol-dependent individuals. Addiction Biology, 20(5), 956–967.  https://doi.org/10.1111/adb.12180.CrossRefGoogle Scholar
  13. Durazzo, T. C., Mon, A., Gazdzinski, S., & Meyerhoff, D. J. (2016). Regional brain volume changes in alcohol-dependent individuals during early abstinence: Associations with relapse following treatment. Addiction Biology, 22, 1416–1425.  https://doi.org/10.1111/adb.12420.CrossRefGoogle Scholar
  14. Fettes, P., Schulze, L., & Downar, J. (2017). Cortico-striatal-thalamic loop circuits of the orbitofrontal cortex: Promising therapeutic targets in psychiatric illness. Frontiers in Systems Neuroscience, 11, 25.  https://doi.org/10.3389/fnsys.2017.00025.
  15. Fischl, B., van der Kouwe, A., Destrieux, C., Halgren, E., Segonne, F., Salat, D.H., Busa, E., Seidman, L.J., Goldstein, J., Kennedy, D., Caviness, V., Makris, N., Rosen, B., Dale, A.M. (2004). Automatically parcellating the human cerebral cortex. Cerebral cortex, 14, 11–22Google Scholar
  16. George, M. S., Bohning, D. E., Lorberbaum, J. P., Nahas, Z., Anderson, B., Borckardt, J. J., Molnar, C., Kose, S., Ricci, R., & Rostogi, K. (2007). Overview of transcranial magnetic stimulation: History mechanisms physics and safety. In M. S. George & R. H. Belmaker (Eds.), Transcranial magnetic stimulation in clinical psychiatry (pp. 1–38). Washington, DC; London: American Psychiatric Publishing, Inc..Google Scholar
  17. Grant, B. F., Goldstein, R. B., Saha, T. D., Chou, S. P., Jung, J., Zhang, H., . . . Hasin, D. S. (2015). Epidemiology of DSM-5 alcohol use disorder: Results from the National Epidemiologic Survey on alcohol and related conditions III. JAMA Psychiatry, 72(8), 757–766.  https://doi.org/10.1001/jamapsychiatry.2015.0584.
  18. Mertens, J. R., Weisner, C., Ray, G. T., Fireman, B., & Walsh, K. (2005). Hazardous drinkers and drug users in HMO primary care: Prevalence, medical conditions, and costs. Alcoholism, Clinical and Experimental Research, 29(6), 989–998.CrossRefGoogle Scholar
  19. Meyerhoff, D. J., Durazzo, T. C., & Ende, G. (2013). Chronic alcohol consumption, abstinence and relapse: Brain proton magnetic resonance spectroscopy studies in animals and humans. Current Topics in Behavioral Neurosciences, 13, 511–540.  https://doi.org/10.1007/7854_2011_131.CrossRefGoogle Scholar
  20. Mon, A., Durazzo, T. C., Gazdzinski, S., Hutchison, K. E., Pennington, D., & Meyerhoff, D. J. (2013). Brain-derived neurotrophic factor (BDNF) genotype is associated with lobar gray and white matter volume recovery in abstinent alcohol dependent individuals. Genes, Brain, and Behavior, 12(1), 98–107.  https://doi.org/10.1111/j.1601-183X.2012.00854.x.CrossRefGoogle Scholar
  21. Moorman, D. E. (2018). The role of the orbitofrontal cortex in alcohol use, abuse, and dependence. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 87(Pt A, 85–107.  https://doi.org/10.1016/j.pnpbp.2018.01.010.CrossRefGoogle Scholar
  22. Paulus, M. P. (2007). Neural basis of reward and craving--a homeostatic point of view. Dialogues in Clinical Neuroscience, 9(4), 379–387.Google Scholar
  23. Pennington, D. L., Durazzo, T. C., Schmidt, T., Mon, A., Abe, C., & Meyerhoff, D. J. (2013). The effects of chronic cigarette smoking on cognitive recovery during early abstinence from alcohol. Alc Clin Exp Research, 37(7), 1220–1227.  https://doi.org/10.1111/acer.12089.CrossRefGoogle Scholar
  24. Rando, K., Hong, K. I., Bhagwagar, Z., Li, C. S., Bergquist, K., Guarnaccia, J., & Sinha, R. (2011). Association of frontal and posterior cortical gray matter volume with time to alcohol relapse: A prospective study. The American Journal of Psychiatry, 168(2), 183–192.  https://doi.org/10.1176/appi.ajp.2010.10020233.CrossRefGoogle Scholar
  25. Reuter, M., Schmansky, N. J., Rosas, H. D., & Fischl, B. (2012). Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage, 61(4), 1402–1418.  https://doi.org/10.1016/j.neuroimage.2012.02.084.CrossRefGoogle Scholar
  26. Rolls, E. T., & Grabenhorst, F. (2008). The orbitofrontal cortex and beyond: From affect to decision-making. Progress in Neurobiology, 86(3), 216–244.  https://doi.org/10.1016/j.pneurobio.2008.09.001.CrossRefGoogle Scholar
  27. Salling, M. C., & Martinez, D. (2016). Brain stimulation in addiction. Neuropsychopharmacology, 41(12), 2798–2809.  https://doi.org/10.1038/npp.2016.80.CrossRefGoogle Scholar
  28. Sankoh, A. J., Huque, M. F., & Dubey, S. D. (1997). Some comments on frequently used multiple endpoint adjustment methods in clinical trials. Statistics in Medicine, 16(22), 2529–2542.CrossRefGoogle Scholar
  29. Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H., Kenna, H., . . . Greicius, M. D. (2007). Dissociable intrinsic connectivity networks for salience processing and executive control. The Journal of Neuroscience, 27(9), 2349–2356.  https://doi.org/10.1523/jneurosci.5587-06.2007.
  30. Seo, D., & Sinha, R. (2014). The neurobiology of alcohol craving and relapse. Handbook of Clinical Neurology, 125, 355–368.  https://doi.org/10.1016/b978-0-444-62619-6.00021-5.CrossRefGoogle Scholar
  31. Seo, D., & Sinha, R. (2015). Neuroplasticity and predictors of alcohol recovery. Alcohol Research: Current Reviews, 37(1), 143–152.Google Scholar
  32. Seo, S., Mohr, J., Beck, A., Wustenberg, T., Heinz, A., & Obermayer, K. (2015). Predicting the future relapse of alcohol-dependent patients from structural and functional brain images. Addiction Biology, 20(6), 1042–1055.  https://doi.org/10.1111/adb.12302.CrossRefGoogle Scholar
  33. Sobell, L. C., Sobell, M. B., Maisto, S. A., & Cooper, A. M. (1985). Time-line follow-back assessment method. NIAAA treatment handbook series (Vol. 2, pp. 85–1380).Google Scholar
  34. Sobell, L. C., Sobell, M. B., Riley, D. M., Schuller, R., Pavan, D. S., Cancilla, A., . . . Leo, G. I. (1988). The reliability of alcohol abusers' self-reports of drinking and life events that occurred in the distant past. Journal of Studies on Alcohol, 49(3), 225–232.Google Scholar
  35. Stokes, M. G., Chambers, C. D., Gould, I. C., Henderson, T. R., Janko, N. E., Allen, N. B., & Mattingley, J. B. (2005). Simple metric for scaling motor threshold based on scalp-cortex distance: Application to studies using transcranial magnetic stimulation. Journal of Neurophysiology, 94(6), 4520–4527.  https://doi.org/10.1152/jn.00067.2005.CrossRefGoogle Scholar
  36. Stokes, M. G., Chambers, C. D., Gould, I. C., English, T., McNaught, E., McDonald, O., & Mattingley, J. B. (2007). Distance-adjusted motor threshold for transcranial magnetic stimulation. Clinical Neurophysiology, 118(7), 1617–1625.  https://doi.org/10.1016/j.clinph.2007.04.004.CrossRefGoogle Scholar
  37. Tessner, K. D., & Hill, S. Y. (2010). Neural circuitry associated with risk for alcohol use disorders. Neuropsychology Review, 20(1), 1–20.  https://doi.org/10.1007/s11065-009-9111-4.CrossRefGoogle Scholar
  38. Volkow, N. D., & Baler, R. D. (2014). Addiction science: Uncovering neurobiological complexity. Neuropharmacology, 76 Pt B, 235–249.  https://doi.org/10.1016/j.neuropharm.2013.05.007.
  39. Volkow, N. D., Wang, G. J., Tomasi, D., & Baler, R. D. (2013). Unbalanced neuronal circuits in addiction. Current Opinion in Neurobiology, 23(4), 639–648.  https://doi.org/10.1016/j.conb.2013.01.002.CrossRefGoogle Scholar
  40. Williams, L. M. (2016). Precision psychiatry: A neural circuit taxonomy for depression and anxiety. Lancet Psychiatry, 3(5), 472–480.  https://doi.org/10.1016/s2215-0366(15)00579-9.CrossRefGoogle Scholar
  41. Witkiewitz, K. (2011). Predictors of heavy drinking during and following treatment. Psychology of Addictive Behaviors, 25(3), 426–438.  https://doi.org/10.1037/a0022889.CrossRefGoogle Scholar
  42. Witkiewitz, K., & Marlatt, G. A. (2007). Modeling the complexity of post-treatment drinking: it's a rocky road to relapse. Clinical Psychology Review, 27(6), 724–738.  https://doi.org/10.1016/j.cpr.2007.01.002.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Psychiatry and Behavioral SciencesStanford University School of MedicineStanfordUSA
  2. 2.Mental Illness Research and Education Clinical Centers (151Y) and Sierra-Pacific War Related Illness and Injury Study Centers, VA Palo Alto Health Care SystemPalo AltoUSA
  3. 3.Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoUSA
  4. 4.Center for Imaging of Neurodegenerative DiseasesSan Francisco VA Medical CenterSan FranciscoUSA

Personalised recommendations