Jonatan Ottino-González1, Anne Uhlmann2, Sage Hahn3, Zhipeng Cao3, Renata B Cupertino3, Nathan Schwab3, Nicholas Allgaier3, Nelly Alia-Klein4, Hamed Ekhtiari5, Jean-Paul Fouche6, Rita Z Goldstein4, Chiang-Shan R Li7, Christine Lochner8, Edythe D London9, Maartje Luijten10, Sadegh Masjoodi11, Reza Momenan12, Mohammad Ali Oghabian13, Annerine Roos14, Dan J Stein15, Elliot A Stein16, Dick J Veltman17, Antonio Verdejo-García18, Sheng Zhang7, Min Zhao19, Na Zhong19, Neda Jahanshad20, Paul M Thompson20, Patricia Conrod21, Scott Mackey3, Hugh Garavan3. 1. Department of Psychiatry, University of Vermont College of Medicine, Burlington, Vermont, United States. Electronic address: jottinog@uvm.edu. 2. Department of Child & Adolescent Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany. 3. Department of Psychiatry, University of Vermont College of Medicine, Burlington, Vermont, United States. 4. Department of Psychiatry & Neuroscience, Icahn School of Medicine at Mount Sinai, New York City, New York, United States. 5. Institute for Cognitive Sciences Studies, University of Tehran, Tehran, Iran; Iranian National Center for Addiction Studies, Tehran University of Medical Sciences, Tehran, Iran. 6. SA MRC Genomics and Brain Disorders Unit, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa. 7. Department of Psychiatry, Yale University, New Haven, Connecticut, United States. 8. SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa. 9. Department of Psychiatry and Biobehavioural Sciences, University of California, Los Angeles, California, United States. 10. Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands. 11. Shiraz Neuroscience Research Center, Shiraz University of Medical Sciences, Shiraz, Iran. 12. Clinical Neuroimaging Research Core, National Institutes on Alcohol Abuse & Alcoholism, National Institutes of Health, Bethesda, Maryland, United States. 13. Neuroimaging & Analysis Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran. 14. SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa; SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry & Neuroscience Institute, University of Cape Town, Cape Town, South Africa. 15. SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry & Neuroscience Institute, University of Cape Town, Cape Town, South Africa. 16. Neuroimaging Research Branch, Intramural Research Program, National Institute of Drug Abuse, Baltimore, Maryland, United States. 17. Department of Psychiatry, Amsterdam UMC - location VUMC, Amsterdam, the Netherlands. 18. School of Psychological Sciences & Turner Institute for Brain & Mental Health, Monash University, Melbourne, Australia. 19. Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China. 20. Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, San Diego, California, United States. 21. Department of Psychiatry, Université de Montreal, Montreal, Quebec, Canada.
Abstract
BACKGROUND: Nicotine and illicit stimulants are very addictive substances. Although associations between grey matter and dependence on stimulants have been frequently reported, white matter correlates have received less attention. METHODS: Eleven international sites ascribed to the ENIGMA-Addiction consortium contributed data from individuals with dependence on cocaine (n = 147), methamphetamine (n = 132) and nicotine (n = 189), as well as non-dependent controls (n = 333). We compared the fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) of 20 bilateral tracts. Also, we compared the performance of various machine learning algorithms in deriving brain-based classifications on stimulant dependence. RESULTS: The cocaine and methamphetamine groups had lower regional FA and higher RD in several association, commissural, and projection white matter tracts. The methamphetamine dependent group additionally showed lower regional AD. The nicotine group had lower FA and higher RD limited to the anterior limb of the internal capsule. The best performing machine learning algorithm was the support vector machine (SVM). The SVM successfully classified individuals with dependence on cocaine (AUC = 0.70, p < 0.001) and methamphetamine (AUC = 0.71, p < 0.001) relative to non-dependent controls. Classifications related to nicotine dependence proved modest (AUC = 0.62, p = 0.014). CONCLUSIONS: Stimulant dependence was related to FA disturbances within tracts consistent with a role in addiction. The multivariate pattern of white matter differences proved sufficient to identify individuals with stimulant dependence, particularly for cocaine and methamphetamine.
BACKGROUND: Nicotine and illicit stimulants are very addictive substances. Although associations between grey matter and dependence on stimulants have been frequently reported, white matter correlates have received less attention. METHODS: Eleven international sites ascribed to the ENIGMA-Addiction consortium contributed data from individuals with dependence on cocaine (n = 147), methamphetamine (n = 132) and nicotine (n = 189), as well as non-dependent controls (n = 333). We compared the fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) of 20 bilateral tracts. Also, we compared the performance of various machine learning algorithms in deriving brain-based classifications on stimulant dependence. RESULTS: The cocaine and methamphetamine groups had lower regional FA and higher RD in several association, commissural, and projection white matter tracts. The methamphetamine dependent group additionally showed lower regional AD. The nicotine group had lower FA and higher RD limited to the anterior limb of the internal capsule. The best performing machine learning algorithm was the support vector machine (SVM). The SVM successfully classified individuals with dependence on cocaine (AUC = 0.70, p < 0.001) and methamphetamine (AUC = 0.71, p < 0.001) relative to non-dependent controls. Classifications related to nicotine dependence proved modest (AUC = 0.62, p = 0.014). CONCLUSIONS: Stimulant dependence was related to FA disturbances within tracts consistent with a role in addiction. The multivariate pattern of white matter differences proved sufficient to identify individuals with stimulant dependence, particularly for cocaine and methamphetamine.
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