White matter microstructure differences in individuals with dependence on cocaine, methamphetamine, and nicotine: Findings from the ENIGMA-Addiction working group. (1st January 2022)

• Record Type: Journal Article
• Authors: Ottino-González, Jonatan; Uhlmann, Anne; Hahn, Sage; Cao, Zhipeng; Cupertino, Renata B.; Schwab, Nathan; Allgaier, Nicholas; Alia-Klein, Nelly; Ekhtiari, Hamed; Fouche, Jean-Paul; Goldstein, Rita Z.; Li, Chiang-Shan R.; Lochner, Christine; London, Edythe D.; Luijten, Maartje; Masjoodi, Sadegh; Momenan, Reza; Oghabian, Mohammad Ali; Roos, Annerine; Stein, Dan J.; Stein, Elliot A.; Veltman, Dick J.; Verdejo-García, Antonio; Zhang, Sheng; Zhao, Min; Zhong, Na; Jahanshad, Neda; Thompson, Paul M.; Conrod, Patricia; Mackey, Scott; Garavan, Hugh;
• Is Part Of: Drug and alcohol dependence. Volume 230(2022)
• Publication Date: 2022-01-01
• Subjects: Addiction -- DTI -- FA -- Myelin -- Machine learning; Drug abuse -- Periodicals; Alcoholism -- Periodicals; 616.86
• DOI: 10.1016/j.drugalcdep.2021.109185
• Languages: English
• ISSNs: 0376-8716

Abstract

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. Highlights: Evidence of white matter differences in stimulant dependence is inconsistent. Most studies are often underpowered and limited to a few a priori selected tracts. We provide robust evidence of white matter differences in stimulant dependence. Machine learning methods can classify stimulant dependence using DTI-derived data.