White matter microstructure differences in individuals with dependence on cocaine, methamphetamine, and nicotine: Findings from the ENIGMA-Addiction working group

Jonatan Ottino-González, Anne Uhlmann, Sage Hahn, Zhipeng Cao, Renata B. Cupertino, Nathan Schwab, Nicholas Allgaier, Nelly Alia-Klein, Hamed Ekhtiari, Jean Paul Fouche, Rita Z. Goldstein, Chiang Shan R. Li, Christine Lochner, Edythe D. London, Maartje Luijten, Sadegh Masjoodi, Reza Momenan, Mohammad Ali Oghabian, Annerine Roos, Dan J. SteinElliot A. Stein, Dick J. Veltman, Antonio Verdejo-García, Sheng Zhang, Min Zhao, Na Zhong, Neda Jahanshad, Paul M. Thompson, Patricia Conrod, Scott Mackey, Hugh Garavan

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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.

Original languageEnglish
Article number109185
JournalDrug and Alcohol Dependence
Volume230
DOIs
StatePublished - 1 Jan 2022

Keywords

  • Addiction
  • DTI
  • FA
  • Machine learning
  • Myelin

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