Methamphetamine use and cognitive function: A systematic review of neuroimaging research
Introduction
Amphetamines, a group of stimulants that includes amphetamine and methamphetamine (MA), are the second most abused worldwide after cannabis (United Nations Office on Drugs and Crime, 2017a). Global prevalence for amphetamines in 2015 was estimated to be 0.8%. In some parts of the world, the numbers are higher; prevalence in North America was 2.0% and in Oceania was 1.9%. The global market for amphetamines was dominated by MA, which accounted for 72% of the global seizures of amphetamines (United Nations Office on Drugs and Crime, 2017b).
Chronic MA abuse has been associated with an impaired cognitive function in several domains: attention, memory, and executive function (Kalechstein et al., 2003; Rendell et al., 2009; Simon et al., 2000). However, as the studies in MA individuals were cross-sectional, it is not possible to conclude that the cognitive deficits were MA-related. Animal studies have provided some evidence for the cognitive decline as the result of repeated MA administration; memory deficits have been observed in rodents (Kamei et al., 2006; Mizoguchi et al., 2011; Nagai et al., 2007; Noda et al., 2010) and impaired inhibitory control has been observed in monkeys (Groman et al., 2012). Study in twins also reported that amphetamine users had poorer performance on attention and psychomotor functions than their non-using twins (Toomey et al., 2003). Interestingly, one study has reported that, despite the higher rates of impairment in the MA group relative to controls in learning, psychomotor, and attention domains, impaired and normal MA subjects were comparable in all MA use parameters, such as age of first use, years of use, amount of use, and length of abstinence, suggesting the potential contribution of other factors, e.g., genetics (Cherner et al., 2010; Dean et al., 2013). Taken together, these findings support the notion that MA abuse causes cognitive decline, at least in some individuals (Dean et al., 2013).
Neuroimaging techniques have been used to study brain structure, metabolism, and function in MA users. Some regions have been reported to show significant differences between MA and control groups: cortical gray matter (GM), such as the prefrontal cortex (PFC) and the anterior cingulate cortex (ACC); subcortical GM, such as the striatum; and the frontal white matter (WM). In early studies using Positron Emission Tomography (PET), lower dopamine transporters (McCann et al., 1998; Sekine et al., 2001; Volkow et al., 2001c) and dopamine receptor availability (Lee et al., 2009; Volkow et al., 2001a), as well as lower serotonin transporter density (Sekine et al., 2006) have been observed in the striatal region in groups of MA subjects. Studies using Magnetic Resonance Imaging (MRI) have reported larger striatal volume (Jernigan et al., 2005) and smaller cortical volume in MA groups (Morales et al., 2012; Nakama et al., 2011). Meanwhile, studies using Magnetic Resonance Spectroscopy (MRS) in MA individuals have observed lower N-acetylaspartate (NAA) level, a proposed marker for neuronal integrity (Moffett et al., 2007; Sullivan et al., 2001), in the frontal WM and the ACC (Ernst et al., 2000; Nordahl et al., 2002; Sailasuta et al., 2010). Diffusion Tensor Imaging (DTI) studies have reported lower restricted diffusion or fractional anisotropy (FA) in frontal WM of MA groups (Alicata et al., 2009; Tobias et al., 2010). Lower restricted diffusion or higher apparent diffusion may indicate impaired WM integrity. Lastly, studies using Functional Magnetic Resonance Imaging (fMRI) have reported lower activation in the PFC and the ACC in groups of MA users during attention (Nestor et al., 2011) and decision-making tasks (Paulus et al., 2003; Stewart et al., 2014). Attenuated activation may reflect reduced resources to process information and may result in performance deficits.
Several articles have reviewed brain alterations and cognitive function in MA users. Salo and Fassbender (2012) reviewed neuroimaging studies (PET, MRI, MRS, DTI, and fMRI) conducted in long-term MA users and discussed relevant cognitive findings from MA and control groups. Another review by Jan et al. (2012a) focused on structural and functional imaging studies (PET, MRI, and fMRI) in MA abusers and the effect of abstinence on the brain measures. Hart et al. (2012) reviewed the findings from studies that assessed acute and long-term effects of MA use on cognitive functions and discussed relevant neuroimaging data (PET, MRI, DTI, and fMRI). While these reviews discussed neuroimaging and/or cognitive findings from MA subjects and controls, currently no review has been published on the relationship between cognitive function and brain measures in MA subjects. This review aims to fill the gap and systematically clarify the link between cognitive performance and brain measures, such as regional volume, glucose metabolism, NAA levels, FA values, and activation, in individuals with a history of MA or amphetamine abuse. In this review, we identify relevant studies using different imaging modalities such as PET, MRI, DTI, MRS, and fMRI. We believe that investigating the brain-behavior correlation will assist in understanding the neural mechanisms underlying cognitive impairment in MA individuals, providing data were available from different stages of MA abuse (e.g., recreational use, dependent use, early abstinence, and protracted abstinence). In addition, understanding the relationship between brain measures and cognitive function in MA users may help in identifying potential targets for MA abuse prevention and treatment. As the affected regions in drug addiction overlap with the regions involved in cognitive function, such as memory, cognitive control, and decision-making (Koob and Volkow, 2016; Schoenbaum et al., 2006), targeting these neurobiological circuits may help preventing transition from occasional to compulsive drug use, and may have clinical implications for preventing relapse (Hester et al., 2010; Suckling and Nestor, 2017; Verdejo-García et al., 2006).
Section snippets
Data source
Relevant articles were collected from three databases: Ovid, Scopus, and Web of Science between 1980 and January 2017. The search strategy was designed to retrieve as many studies as possible that were conducted using neuroimaging techniques and evaluated cognitive function in participants who were users or past users of MA. The search was performed using three groups of keywords: (1) terms related to cognitive function; (2) methamphetamine or amphetamine; and (3) terms related to neuroimaging (
Results
Twenty-nine studies met the inclusion criteria: 28 studies of MA users and 1 study of polydrug users (Koester et al., 2013). Two studies were performed in active-users (Jan et al., 2012b; Kim et al., 2016), while the rest were carried out with abstinent subjects (n = 27). The neuroimaging techniques used in these studies were PET (n = 6), MRI (n = 6), perfusion MRI (n = 1), DTI (n = 3), MRS (n = 3), and fMRI (n = 10). The included studies were assessed and grouped by cognitive domain:
Discussion
The main finding of this review is that there is a strong indication of the relationship between brain imaging measures and cognitive performance in MA users. In all cognitive domains, MA subjects showed poorer performance than controls. Qualitative comparisons across different imaging modalities showed that in the majority of cases, poorer performance was associated with deficits in the brain measures, such as lower metabolism, GM density, FA, NAA, and activation. In this discussion, deficits
Conclusions
Recent studies have provided some evidence for altered cognitive function in MA users in several domains, notably in cognitive control and decision-making. Consistent correlations have also been observed between performance on these functions and brain measures in some regions, particularly in the ACC, PFC, and striatum. Future research that investigates the relationship between the brain-behavior data and MA use parameters is essential to identify regions that are more vulnerable to the
Role of funding source
No funding was directly attained that supported this research. Sabrini Sabrini was supported by the Doctoral Scholarship, provided by LPSDM of Aceh Government, Republic of Indonesia.
Contributors
SS: Performed the systematic search, article retrieval, screening and analysis of the review. Also, was the lead author to write the manuscript. Approved the final version of the manuscript.
GYW: Was involved in drafting the objectives of the review, analysis, interpretation of results and writing of the manuscript. Approved the final version of the manuscript.
JCL: Was involved in the analysis, interpretation of results and writing of the manuscript. Approved the final version of the manuscript.
Conflict of interest
No conflict declared
Acknowledgements
No acknowledgements
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