Neurocognitive decision-making processes of casual methamphetamine users.


Journal

NeuroImage. Clinical
ISSN: 2213-1582
Titre abrégé: Neuroimage Clin
Pays: Netherlands
ID NLM: 101597070

Informations de publication

Date de publication:
2019
Historique:
received: 25 08 2018
revised: 01 12 2018
accepted: 13 12 2018
pubmed: 8 1 2019
medline: 18 12 2019
entrez: 8 1 2019
Statut: ppublish

Résumé

Neuroadaptations caused by chronic methamphetamine (MA) use are likely major contributors to high relapse rate following treatment. Thus, focusing intervention efforts at pre-empting addiction in vulnerable populations, thereby preventing MA-use-induced neurological changes that make recovery so challenging, may prove more effective than targeting chronic users. One approach is studying casual/recreational users, not diagnosed with substance use disorder. This group may be at high risk for addiction due to their experience with MA. On the other hand, they may be resilient against addiction since they were able to maintain casual use over the years and not become addicted. Understanding their neuro-cognitive characteristics during decision-making and risk-taking would help solve this dilemma and, may help identify intervention strategies. Unfortunately, research on neuro-cognitive characteristics of casual MA users is currently lacking. In this work we begin to address this deficit. This study was part of a larger investigation of neural correlates of risky sexual decision-making in men who have sex with men. While undergoing functional magnetic resonance imaging, 31 casual MA users and 66 non-users performed the CUPS task, in which they decided to accept or refuse a series of mixed gambles. Convergent results from whole brain, region of interest and psychophysiological interaction (PPI) analyses are presented. Whole brain analysis identified an amygdala-striatal cluster with weaker activation in casual MA users compared to non-users during decision-making. Activity in that cluster inversely correlated with decisions to gamble: lower activation corresponded to higher risk taking. Using this cluster as a seed in PPI analyses, we identified a wide range of neural network differences between casual MA users and non-users. Parametric whole brain analyses identified clusters in the ventral striatum, posterior insula and precuneus where activations modulated by risk and reward were significantly weaker in casual MA users than in non-users. The striatal cluster identified in these analyses overlapped with the amygdala-striatal cluster. This work identified neural differences in casual MA users' reward processing and outcome learning systems which may underlie their increased real-world risk-taking. It suggests that while making decisions casual MA users focus primarily on potential gain unlike non-users who also take the riskiness of the choice into consideration.

Identifiants

pubmed: 30612937
pii: S2213-1582(18)30391-7
doi: 10.1016/j.nicl.2018.101643
pmc: PMC6411911
pii:
doi:

Substances chimiques

Central Nervous System Stimulants 0
Methamphetamine 44RAL3456C

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

101643

Subventions

Organisme : NIDA NIH HHS
ID : R01 DA031626
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM109996
Pays : United States

Informations de copyright

Copyright © 2018. Published by Elsevier Inc.

Auteurs

Vita Droutman (V)

University of Southern California, USA. Electronic address: vdroutman@gmail.com.

Feng Xue (F)

University of Southern California, USA; University of California San Diego, USA.

Emily Barkley-Levenson (E)

University of Southern California, USA; Hofstra University, USA.

Hei Yeung Lam (HY)

University of Southern California, USA.

Antoine Bechara (A)

University of Southern California, USA.

Benjamin Smith (B)

University of Southern California, USA.

Zhong-Lin Lu (ZL)

Ohio State University, USA.

Gue Xue (G)

Beijing Normal University, China.

Lynn C Miller (LC)

University of Southern California, USA.

Stephen J Read (SJ)

University of Southern California, USA.

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Classifications MeSH