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. Author manuscript; available in PMC: 2022 Oct 6.
Published in final edited form as: J Clin Exp Neuropsychol. 2021 Oct 6;43(6):599–610. doi: 10.1080/13803395.2021.1976734

Cognition during Active Methamphetamine Use versus Remission

Marilyn Huckans 1,2,3,4, Stephen Boyd 7, Grant Moncrief 1,2,5,8, Nathan Hantke 1,2,9, Bethany Winters 1,3,4, Kate Shirley 1, Emily Sano 1, Holly McCready 1,3,4,6, Laura Dennis 1,3,4,6, Milky Kohno 1,4,6, William Hoffman 1,2,3,4,6, Jennifer M Loftis 1,3,4,*
PMCID: PMC8979254  NIHMSID: NIHMS1739016  PMID: 34612792

Abstract

Objective:

To evaluate whether cognitive performance in adults with active methamphetamine use (MA-ACT) differs from cognitive performance in adults in remission from MA use disorder (MA-REM) and adults without a history of substance use disorder (CTLs).

Method:

MA-ACT (n = 36), MA-REM (n = 48), and CTLs (n = 62) completed the Neuropsychological Assessment Battery (NAB).

Results:

The MA-ACT group did not perform significantly worse than CTLs on any NAB Index. The MA-REM group performed significantly (p < 0.050) worse than CTLs on the NAB Memory Index. The MA-ACT group performed significantly better than CTLs and the MA-REM group on the Executive Functions Index.

Conclusions:

Some cognitive deficits are apparent during remission from MA use, but not during active use; this may result in clinical challenges for adults attempting to maintain recovery and continue with treatment.

Keywords: cognition, neuropsychological, addiction, dependence, substance use disorder, stimulant

Introduction

Methamphetamine (MA) use disorders cause significant distress to people with the addiction and to communities. Costs have been estimated at $23.4 billion annually in the United States (U.S.), with contributions related to crime, child endangerment, lost productivity, drug treatment, health care, MA production hazards, and premature death (Nicosia, Pacula, Kilmer, Lundberg, & Chiesa, 2009). While the worldwide impact of MA use is unknown, global trends indicate that MA use is spreading and, among amphetamines, MA use represents the greatest global health threat (UNODC, 2017). In the U.S., MA use is on the rise, and, in Oregon, almost twice as many individuals died from MA use as died from heroin use in 2016 (Robles, 2018). Currently, there are no Food and Drug Administration (FDA)-approved pharmacotherapies for MA addiction. Behavioral treatments continue to be the standard of care for all addictions, and, due to the chronic nature of addictions, relapse rates associated with current substance use treatment programs are very high (~40–60%) (McLellan, Lewis, O’Brien, & Kleber, 2000). Patients seeking treatment for MA addiction often experience challenging neuropsychiatric symptoms (i.e., cognitive complaints, anxiety, depression, fatigue, pain, sleep disturbances) (Basterfield, Hester, & Bowden, 2019; Darke, Kaye, McKetin, & Duflou, 2008; Fernandez-Serrano, Perez-Garcia, & Verdejo-Garcia, 2011; Glasner-Edwards et al., 2010; Hoffman et al., 2006; Huckans, Fuller, Chalker, Adams, & Loftis, 2015; Loftis, Choi, Hoffman, & Huckans, 2011; London et al., 2004; Potvin et al., 2018; Scott et al., 2007; Shoptaw, Peck, Reback, & Rotheram-Fuller, 2003; Zweben et al., 2004), and these symptoms are associated with poorer treatment outcomes including increased relapse rates, lower treatment retention rates, and reduced daily functioning (Aharonovich, Nunes, & Hasin, 2003; Fals-Stewart, 1993; Sadek, Vigil, Grant, & Heaton, 2007). For example, one study found that, compared to controls without addiction, adults in remission from MA addiction evidenced lower social adaptation and quality of life, and 83% evidenced psychiatric symptoms (Zhong et al., 2016). Another study found that, compared to controls, those with MA use disorders were more likely to over-estimate their memory abilities, and that these meta-memory deficits were associated with increased executive function impairment and increased depression (Casaletto et al., 2015). These studies highlight the importance of better understanding the nature and course of MA associated neuropsychiatric symptoms and developing helpful treatments (e.g., cognitive rehabilitation therapies or medications) to address them.

The present study evaluates cognitive performance in adults with current or recent MA use disorders. Previous neuropsychological studies have identified MA-associated impairments in a range of cognitive domains, including attention, memory, executive functions, language, and social cognition (Basterfield et al., 2019; Fernandez-Serrano et al., 2011; Hoffman et al., 2006; Huckans et al., 2015; Loftis et al., 2011; Potvin et al., 2018; Proebstl, Kamp, Koller, & Soyka, 2018; Scott et al., 2007). The specific type, severity, and longevity of MA-associated cognitive deficits have varied across studies, in part due to how study groups have been defined, choice of neuropsychological measures, and other design features.

Most neuropsychological studies have focused on adults in early remission from MA addiction versus adults with no history of addiction. In contrast, the present study uniquely compares adults with active versus past MA dependence. This is a follow-up to our previous study using a small battery of only one memory complaint questionnaire [Prospective and Retrospective Memory Questionnaire (PRMQ) (Smith, Della Sala, Logie, & Maylor, 2000)] and two cognitive subtests [Neuropsychological Assessment Battery (NAB) Digits Forward (Stern & White, 2003); Delis-Kaplan Executive Function System (DKEFS) Verbal Fluency (Delis, Kaplan, & Kramer, 2001)] where we found that cognitive challenges on those three measures were associated with remission from MA dependence but not active MA use (Huckans et al., 2015). This study lacked an objective measure of memory, and the battery did not include measures across a wide range of cognitive impairments previously found in adults in remission from MA dependence. Thus, we aim to confirm and further evaluate this finding in a new sample using the NAB’s (Stern & White, 2003) comprehensive battery of neuropsychological measures. The NAB is a well-validated, clinically relevant battery of neuropsychological subtests that have been co-normed using large samples representative of the U.S. population. Moreover, subtest scores are combined to yield useful and robust summary scores covering a comprehensive range of cognitive domains – Attention, Memory, Language, Visual-Spatial, Executive Functions, and Total Index scores.

Although not evaluated through our design, a number of potential mechanisms could contribute to cognitive deficits that appear during remission rather than active use. Stimulants are used to treat cognitive difficulties in adults with traumatic brain injuries (Johansson, Wentzel, Andrell, Mannheimer, & Ronnback, 2015; McAllister et al., 2016); thus, MA could be treating pre-existing deficits in active users. A large literature reveals that, within the central nervous system during remission from MA, there are persistent dopaminergic and other neurochemical changes (Proebstl et al., 2019; Scott et al., 2007) as well as inflammatory processes (Jan, Lin, Miles, Kydd, & Russell, 2012) and changes to grey and white matter, corticostriatal connectivity and activation patterns (Hoffman et al., 2020; Hoffman et al., 2008; Jan, Kydd, & Russell, 2012; Schwartz et al., 2010); some of these changes may be triggered by withdrawal from chronic MA dependence rather than active MA use. Other studies suggest that adaptive processes may occur during active use as a way to compensate for or protect against MA-induced neurotoxicity and cognitive decline (Jan, Lin, Miles, Kydd, & Russell, 2012).

In summary, the primary objective of the present paper was to determine whether adults with active MA use exhibit similar cognitive difficulties as those who are in remission from MA dependence. Given our previous findings (Huckans et al., 2015), we hypothesized that adults with active MA use would not perform significantly worse than controls with no history of substance dependence, while adults in remission from MA use would exhibit deficits compared to those actively using and controls. Our study, therefore, uniquely adds to the field by comparing neuropsychological outcomes in the context of active MA use versus remission, an issue that has been scarcely explored.

Materials and Methods

Subjects

Participants (n = 147) were recruited from Portland, Oregon area addiction treatment centers and the community through word of mouth and via study advertisements posted in clinics, websites, and newspapers. Adults (≥ 21 years old) were enrolled into one of three study groups based on the following inclusion criteria:

  • Active methamphetamine (MA) dependence (MA-ACT) (n = 36). Based on the Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition (DSM-IV) (APA, 2000) with confirmation by the Mini International Neuropsychiatric Interview questionnaire (MINI) (Sheehan et al., 1998), meets criteria for dependence on MA but no other substances (except nicotine or caffeine), AND MA use was > 2 days per week for > 1 year (past year), AND tests positive for MA but no other substances (including alcohol or marijuana) on urine drug analysis.

  • Early remission from MA dependence (MA-REM) (n = 48): Based on the DSM-IV (APA, 2000) and MINI (Sheehan et al., 1998), meets criteria for dependence on MA but no other substances (except nicotine or caffeine), AND MA use was > 2 days per week for > 1 year (during the year prior to remission), AND has maintained remission for > 1 month and < 6 months, AND does not test positive for MA or other substances (including alcohol or marijuana) on urine drug analysis.

  • Non-dependent controls (CTLs) (n = 62): Based on the DSM-IV (APA, 2000) and MINI (Sheehan et al., 1998), has never met criteria for past substance dependence (except nicotine or caffeine), AND does not test positive for MA or other substances (including alcohol or marijuana) on urine drug analysis.

Participants were excluded from participation based on the following exclusion criteria:

  • Visible intoxication or impaired capacity to understand study risks and benefits or otherwise provide informed consent.

  • History of major medical illness or current use of medications that are likely to be associated with serious neurological or immune dysfunction (e.g., stroke, neurodegenerative disease, traumatic brain injury, multiple sclerosis, human immunodeficiency virus (HIV) infection, immunosuppressants, antivirals, benzodiazepines, opiates, stimulants, antipsychotics, anticholinergics, antiparkinson agents).

  • Based on the DSM-IV (APA, 2000) and MINI (Sheehan et al., 1998), meets criteria for past or current manic episode, schizophrenia, schizoaffective disorder, or other psychotic disorder (other than brief substance induced psychosis for the MA-ACT and MA-REM groups).

  • Heavy alcohol use as defined by the National Institute on Alcohol Abuse and Alcoholism [women: average alcohol use ≥ 7 standard drinks weekly for ≥ 1 year; men: average alcohol use ≥ 14 standard drinks weekly for ≥ 1 year, (NIAAA, 2019)].

  • Use of marijuana > 2 times per month (marijuana is currently legal for recreational and medicinal uses in Oregon).

  • Use of illicit substances (other than MA for the MA-ACT and MA- REM groups).

1226 potential participants were screened, 788 of whom were found ineligible (99 for medical exclusions, 79 for medications, 39 for psychiatric exclusions, 411 because of exclusionary substance use characteristics, and 160 due to other or unknown reasons). The remaining 438 eligible participants were either scheduled for enrollment, declined to participate, or were lost to follow up.

Procedures

The protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki (6th revision, 2008) and was approved by the joint Institutional Review Board (IRB) of the VA Portland Health Care System (VAPORHCS) and Oregon Health & Science University (OHSU) (IRB#8702). Research participants gave written informed consent after the procedures of the study were explained in full.

Participants were compensated with grocery store vouchers ($125) for completing the following procedures: clinical interview, including the MINI (Sheehan et al., 1998) to assess for psychiatric and substance use disorders; urine drug analysis (Rapid Exams, Inc., Van Buren, AR); hepatitis C virus (HCV) and HIV antibody screening (OraSure Technologies, Inc., Bethlehem, PA); and a battery of neuropsychological tests.

The NAB (Grohman & Fals-Stewart, 2004; Stern & White, 2003) was used as the neuropsychological assessment battery. This well-validated, copyrighted and commercially available battery of subtests assesses a variety of cognitive domains. We administered the entire battery, including modules on attention, memory, language, spatial, and executive functions. Using published procedures and norms, raw subtest scores were converted into standard scores that were demographically corrected for age, gender, and education. Individual subtest scores were also combined to create summary Index Scores for each of the Attention, Memory, Language, Spatial, and Executive Functions Modules, as well as a Total Index Score for the entire battery.

Neuropsychological tests were administered and scored according to standard published procedures by one of three psychometrists (one with a PhD in psychology, one with an MA in counseling, and one with a BS in psychology) who were trained and supervised by a licensed psychologist and neuropsychologist (MH). All tests were scored and then re-scored by separate psychometrists. All data were entered into a database that was then double-checked by a separate psychometrist prior to analysis.

Statistical Analyses

Chi-square tests of independence and analyses of variance (ANOVA) procedures were conducted to examine potential differences between the groups on key demographic, clinical and substance use variables. No significant covariates were indicated based on these analyses. We did not include age, gender, or education as covariates, because these variables were corrected for using demographically corrected NAB scores. Multivariate analysis of variance (MANOVA) tests were used to examine group differences on neurocognitive assessments. Significant group differences on NAB scores from the MANOVA analyses were probed further with univariate analyses of variance (ANOVA) using a Bonferroni correction to control for alpha inflation. Prior to conducting follow-up univariate ANOVAs, the homogeneity of variance assumption was tested for all five NAB index scores and the total score. Based on a series of Levene’s F tests, the homogeneity of variance assumption was considered satisfied, even though the Levene’s F tests for all but the Executive Function Index were statistically significant (p > 0.05). Specifically, although the Levene’s F test suggested that the variances associated with the subscales were not homogenous, an examination of the standard deviations revealed that none of the largest standard deviations were more than four times the size of the corresponding smallest, suggesting that the ANOVA would be robust in this case (Howell, 2009).

Results

Demographic, Clinical and Substance Use Characteristics

As described in Table 1, groups significantly (p < 0.050) differed in terms of several demographic and clinical characteristics. The MA-REM group had more males than the MA-ACT or CTL groups. The CTLs had significantly lower rates of psychiatric diagnoses and tobacco use than the MA-ACT and MA-REM groups, but rates were not significantly different across the MA groups. The MA-ACT group was older than the MA-REM and CTL groups. Average years of education ranged from 11.83 to 13.25 years (some high school to some college), and the MA-REM group had fewer years of education than the MA-ACT or CTL groups. Groups did not significantly differ in terms of race, body mass index (BMI), rates of current prescription drug use, or rates of current medical conditions.

Table 1.

Group Comparisons of Demographic and Clinical Characteristics

Total MA-ACT MA-REM CTL X2 p
N 147 36 48 62
Gender (% Male) 87 (60%) 17 (47%)a 40 (83%)b 30 (48%)a 16.755 <0.001
Race (% White) 127 (87%) 32 (89%)a 43 (90%)a 52 (84%)a 0.933 0.627
Rx Meds (% Current) 56 (38%) 13 (36%)a 21 (44%)a 22 (36%)a 0.884 0.643
Med Cond (% Current) 38 (26%) 11 (31%)a 16 (33%)a 11 (18%)a 3.925 0.141
Psyc Cond (% Current) 43 (30%) 19 (53%)a 20 (42%)a 4 (7%)b 28.659 <0.001
Tobacco (% Current) 84 (58%) 31 (86%)a 40 (83%)a 13 (22%)b 56.960 <0.001
Mean (SD) Mean (SD) Mean (SD) Mean (SD) F p
Age 37.74 (11.41) 43.36 (9.54)a 36.60 (10.89)b 35.35 (11.85)b 6.401 0.002
Education (years) 12.70 (1.56) 12.89 (1.43)a,b 11.83 (1.37)c 13.26 (1.49)b 13.679 <0.001
BMI 27.73 (6.02) 27.10 (6.01)a 27.62 (4.63)a 28.17 (6.95)a 0.362 0.697
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Note. For non-continuous variables, data are reported as number (%), and results reflect chi square tests. For continuous variables, data are reported as mean (SD), and results reflect analysis of variance (ANOVA) procedures followed by post hoc comparisons with Bonferroni corrections for multiple comparisons.

a,b,c

Study groups with the same superscript letter have proportions or means that do not differ significantly at the 0.05 level based on post hoc tests; study groups with different superscript letters have proportions or means that significantly differ at the 0.05 level based on post hoc tests.

BMI = Body mass index; CTL = Adults without any history of substance use disorder; MA = Methamphetamine; MA-ACT = Adults with MA use disorders who are actively using; MA-REM = Adults who are in remission from MA use disorder; Med Cond = Currently diagnosed with a mild, stable medical condition; Psych Cond = Currently diagnosed with a mild, stable psychiatric disorder. RX Meds = Currently taking prescription medications; SD = Standard deviation; Tobacco = Currently using tobacco products regularly.

As described in Table 2, the substance use groups significantly (p < 0.050) differed in terms of several MA use characteristics. As expected, the MA-ACT group used MA more recently than the MA-REM group. The MA-ACT group used fewer grams per day of MA over the past year than the MA-REM group had during the year prior to remission. The MA-ACT group used MA more frequently during the past year than the MA-REM group had used during the year prior to remission. The MA-ACT group reported more days of MA use across their lifetime than the MA-REM group. Groups did not significantly differ in terms of age of first MA use.

Table 2.

Group Comparisons of Methamphetamine Use Characteristics

Total MA-ACT MA-REM F p
Age of First Use, years 20.43 (7.86) 19.83 (6.35) 20.38 (8.69) 0.100 0.753
Days Since Last Use 73.49 (59.34) 0.67 (1.23) 104.27 (54.58) 125.64 <0.001
Frequency, 12 months1 1.64(0.92) 1.92(1.08) 1.50 (0.83) 4.026 0.048
Quantity, 12 months2 1.23 (1.96) 0.49(0.45) 1.61 (2.16) 9.402 0.003
Lifetime Duration, days 4,508 (2,828) 5,676 (3019) 4,043 (2,494) 6.308 0.014
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Note. Data are reported as mean (SD), and results reflect analysis of variance (ANOVA) procedures. MA = Methamphetamine; MA-ACT = Adults with MA use disorders who are actively using; MA-REM = Adults who are in remission from MA use disorder.

1

Frequency of use during the past 12 months for the MA-ACT group, and frequency of use during the 12 months prior to remission for the MA-REM group. Frequency was rated on a 6-point scale as follows: 1 = every day; 2 = 5–6 days per week; 3 = 3–4 days per week; 4 = 1 or 2 days per week; 5 = 1–3 days per month; 6 = less than once per month.

2

Quantity in average grams per day used during the past 12 months for the MA-ACT group, and average grams per day used in the 12 months prior to remission for the MA-REM group.

In order to identify appropriate covariates for subsequent analyses, correlation analyses were run between NAB scores and any continuous variables from Tables 1 and 2 that were found to be significantly different across groups (data not shown); ANOVA was used for any significant categorical variables from Table 1 (data not shown). None of the variables with significant group differences in Table 1 significantly correlated with NAB outcomes. Groups with and without tobacco use, any psychiatric diagnosis, depression, or PTSD also did not significantly differ on any of the NAB index scores (data not shown). Thus, Table 1 variables were not used as covariates in subsequent analyses. Regarding Table 2 variables, among the MA-ACT and MA-REM groups, days since last use was significantly correlated with the NAB Executive Functions Index (r = −0.340, p = 0.002). Average quantity of MA used during the past 12 months of active use was significantly correlated with the NAB Attention Index (r = −0.248, p = 0.023) and Spatial Index (r = −0.226, p = 0.040). Average frequency of MA used during the past 12 months of active use was significantly correlated with the NAB Attention Index (r = 0.340, p = 0.002), Memory Index (r = 0.366, p = 0.001), and Executive Functions Index (r = 0.228, p = 0.037). Lifetime duration of MA use was significantly correlated with the Executive Functions Index (r = 0.251, p = 0.23). These results indicate that group membership determined the relationship between the MA use variables and NAB outcomes rather than the MA use variables influencing the outcomes in a predictable manner. For example, a higher quantity of MA use and more days since last MA use (both characteristic of the MA-REM group) were correlated with worse NAB scores, while more frequent MA use and a longer lifetime duration of MA use (both characteristic of the MA-ACT group) was correlated with better NAB scores. When group membership is determined nonrandomly, it is generally considered inappropriate to use ANCOVA to control for group differences that may be systematic or based on group membership (Miller & Chapman, 2001). Thus, Table 2 variables were not used as covariates in subsequent analyses.

Group Differences Using Demographically (Age, Gender, and Education) Corrected NAB Scores

Group differences in demographically corrected NAB scores were assessed with a series of multivariate and univariate analysis of variance procedures. Demographically corrected NAB scores are adjusted for age, gender, and years of education so these were not used as covariates.

Equivalence tests were conducted to test the hypothesis that the active use group does not differ significantly from participants in the control group on neuropsychological test performance. Equivalence bounds were computed using the smallest effect size of interest, which was determined as Cohen’s d = 0.12. The results of the equivalence tests were non-significant for all subscales except executive functioning. The equivalence test for the executive functioning subscale was non-significant, t(56.75) = 1.855, p = 0.966, given equivalence bounds of −1.398 and 1.398 (on a raw scale) and an alpha of 0.05. The null hypothesis test was significant, t(56.75) = 2.405, p = 0.0194, given an alpha of 0.05. Based on the equivalence test and the null-hypothesis test combined, we can conclude that the observed effect is statistically different from zero and statistically not equivalent to zero. For all other subtests we can conclude that the observed effect is statistically not different from zero.

A multivariate test of significance indicated significant differences across the six NAB Index Scores (Attention, Memory, Language, Spatial, Executive Functions, and Total) between the three groups (Wilks’ Λ = 0.754, F(12, 272) = 3.438, p < 0.001, ηp2 = 0.132). As described in Table 3, univariate tests of the individual cognitive domains revealed that significant (p < 0.050) group differences were evident for the Memory Index and Executive Functions Index scores. Bonferroni corrected pairwise group comparisons (criterion alpha = 0.017 for three comparisons for each NAB index) indicated the MA-ACT group never performed significantly worse than CTLs on any NAB Index. The MA-REM group performed significantly (p < 0.050) worse than CTLs on the NAB Memory Index. The MA-ACT group performed significantly better than CTLs and the MA-REM group on the Executive Functions Index. We found similar results when using Tukey HSD to correct for multiple comparisons.

Table 3.

Univariate Tests of Group Differences on Demographically (age, gender, education) Corrected NAB Index Scores

95% Confidence Interval
NAB Index Score df df error F p ηp2 Group Means Lower Bound Upper Bound
Attention 2 141 2.179 .117 .030 CTLa 94.98 91.73 98.23
MA-ACTa 99.72 95.49 103.95
MA-REMa 94.17 90.47 97.87
Language 2 141 0.302 .740 .004 CTLa 98.51 95.45 101.57
MA-ACTa 96.75 92.76 100.74
MA-REMa 98.62 95.13 102.11
Memory 2 141 7.985 < .001 .102 CTLa 97.63 94.28 100.97
MA-ACTa,b 94.11 89.76 98.46
MA-REMb 87.42 83.62 91.23
Spatial 2 141 0.765 .467 .011 CTLa 106.13 102.29 109.97
MA-ACTa 105.72 100.72 110.72
MA-REMa 102.66 98.28 107.04
Executive Functions 2 141 6.662 .002 .086 CTLa 102.26 99.47 105.06
MA-ACTb 108.44 104.81 112.08
MA-REMa 99.66 96.48 102.84
Total Score 2 141 2.905 .058 .040 CTLa 99.57 96.48 102.66
MA-ACTa 101.08 97.06 105.11
MA-REMa 95.06 91.54 98.58
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Note. Results reflect analysis of variance (ANOVA) procedures, followed by post hoc comparisons with Bonferroni corrections for multiple comparisons.

a,b,c

Study groups with the same superscript letter have means that do not differ significantly at the 0.05 level based on post hoc tests; study groups with different superscript letters have means that significantly differ at the 0.05 level based on post hoc tests.

CTL = Adults without any history of substance use disorder; df = Degrees of freedom; MA = Methamphetamine; MA-ACT = Adults with MA use disorders who are actively using; MA-REM = Adults who are in remission from MA use disorder; NAB = Neuropsychological Assessment Battery.

Discussion

Consistent with previous studies finding memory impairments in adults with MA dependence during remission (Huckans et al., 2015; Iudicello et al., 2010; Simon, Dean, Cordova, Monterosso, & London, 2010), we found that the MA-REM group performed significantly (p < 0.050) worse than CTLs on the NAB Memory Index (Table 3). Although previous studies have found MA-associated deficits in other cognitive domains such as attention, executive functioning, working memory, and global cognitive functioning during early remission (Basterfield et al., 2019; Boileau et al., 2008; Ellis et al., 2016; Fernandez-Serrano et al., 2011; Hoffman et al., 2006; Loftis et al., 2011; Paulus et al., 2002; Scott et al., 2007; Simon et al., 2010), we did not find significant differences between the MA-REM and CTL groups on other NAB Indexes. This would be consistent with other studies that have failed to find cognitive deficits associated with MA remission (Chou et al., 2007; Jaffe et al., 2005; Wang et al., 2004). Studies have markedly varied in terms of their inclusion criteria, sampling techniques, and cognitive measures, perhaps contributing to the mixed findings in the literature. For example, length of abstinence varies considerably across studies and may be of importance. One longitudinal study tested adults with MA dependence 4–9 days into abstinence and then again one month into abstinence (Simon et al., 2010). At baseline, adults with MA dependence performed significantly worse than controls (without dependence) on a Stroop test and on a cognitive battery as a whole; change scores did not differ across groups, suggesting that the participants with MA dependence did not experience significant improvement across the first month of abstinence. In contrast, a cross-sectional study found that adults recently abstinent from MA (> 3 weeks < 6 months of remission) performed significantly worse on a Stroop task than controls without a history of MA dependence and adults who had been abstinent for more than one year, suggesting improvements in aspects of cognition may be seen with longer periods of remission (Salo et al., 2009). Similarly, another longitudinal study tested adults recently abstinent from MA (> 10 days < 90 days of abstinence) at baseline and more than one year later and compared groups who did and did not remain abstinent to controls without dependence (Iudicello et al., 2010). Both the abstinent and non-abstinent groups performed worse than controls in terms of global neuropsychological functioning and a range of cognitive domains at baseline. At follow up, the non-abstinent group continued to perform worse than controls in terms of global cognition and all cognitive domains; however, the abstinent and control groups performed similarly in terms of global neuropsychological functioning and all cognitive domains except working memory and executive functioning. Overall, these studies suggest that adults with MA dependence experience cognitive impairments during early remission from MA dependence, with partial cognitive recovery after a year of remission. Our results indicate that, at the group level, when using the NAB Indexes with adults attending community-based addiction treatment centers during early remission (> 1 month < 6 months of remission) from MA dependence, MA-associated deficits appear to be most prominent in the memory domain. Notably, the NAB is a comprehensive neuropsychological battery that is co-normed across subtests, domains, and indexes, whereas most previous studies used subtests from a variety of sources and normative samples.

Consistent with our hypothesis that active MA use would not be associated with cognitive deficits, our results indicate that the MA-ACT group did not perform significantly worse than CTLs on any NAB Index. In fact, the MA-ACT group performed better than both CTLs and the MA-REM group on the NAB Executive Functions Index (Table 3). These findings are consistent with our previous study (Huckans et al., 2015) using different measures but comparing groups of MA-ACT, MA-REM, and CTL subjects. In this latter study, we found that both the MA-ACT and MA-REM groups reported significantly more symptoms of depression and anxiety than CTLs. However, while the MA-REM group reported significantly more memory complaints and performed significantly worse on Digits Forward and Verbal Fluency than CTLs, the MA-ACT group did not report more cognitive complaints or display more cognitive deficits relative to CTLs. Our results are also consistent with a longitudinal study that tested individuals recently abstinent from MA (< 7 days of abstinence) monthly for up to six months and then compared three groups - those who relapsed (one positive drug screen but not more than two across the study period), those who demonstrated continuous abstinence (no positive drug screens), and those who demonstrated continuous use (all positive drug screens) (Simon, Dacey, Glynn, Rawson, & Ling, 2004). On the Repeated Memory Test, a word and picture learning test, the continuous use group performed significantly better than both other groups, and the continuous abstinence group performed better than the relapse group. On the Selective Reminding Test, a word list learning test, the continuous use group performed better than the continuous abstinence group, with those who relapsed performing in the middle but not significantly different than the other groups. In contrast to our study, another study found that individuals actively using MA in the community who tested positive on a urine drug screen performed worse than controls on the Repeated Memory Test, Digit Symbol, Trails B, the Shipley-Hartford Abstract Thinking Task, and a Stroop test, but not on the Wisconsin Card Sorting Test, FAS Fluency, or Backward Digit Span (Simon et al., 2000). This study did not exclude for major or unstable medical illnesses, mania, psychotic disorder, heavy alcohol use, or medications known to impact cognition like our study did, and they did not report on rates of these conditions; thus, it is unclear whether this may have accounted for the difference in findings across our studies (e.g., if their active MA group had higher rates of these conditions than their controls). Although a few other studies have found cognitive impairments associated with recent MA use [reviewed in (Guerin et al., 2019)], these studies have generally tested in an inpatient setting while individuals were detoxing from MA (rather than actively using MA at their preferred frequency and quantity in the community as in our study) (Dean et al., 2011; Su et al., 2015), or the studies included individuals who were abstinent from MA for a longer duration (e.g., they no longer tested positive for MA on a urine drug screen, or they reported last using MA > 4 days ago) than what might be considered “active use” as defined in our study (i.e., all our participants tested positive for MA on a urine drug screen on the day of testing and reported that their last use was < 5 days ago and typically more recently) (Andres et al., 2016; Kalechstein, Newton, & Green, 2003; Nestor, Ghahremani, Monterosso, & London, 2011).

Taken together, our two studies confirm that while cognitive deficits are often present during early abstinence and remission from MA use, they may not be apparent during active use for adults without major, severe, or unstable medical conditions, psychosis, mania, or other substance use disorders except nicotine or tobacco use disorders. Our study design cannot verify the mechanism for this finding, but several hypotheses are worthy of discussion and further investigation. First, as a stimulant, MA may be treating pre-existing cognitive deficits or causing improvements in cognition that are not sustained following remission; this would be consistent with the literature on the efficacy of stimulant treatments for conditions such as attention deficit/hyperactivity disorder (Coghill et al., 2014), narcolepsy (Harsh et al., 2006), and mild traumatic brain injuries (Johansson, Wentzel, Andrell, Mannheimer, & Ronnback, 2015; McAllister et al., 2016). Second, it is possible that pathological processes that occur following withdrawal and remission from MA dependence result in cognitive deficits being more prominent or first appearing during remission. This would be consistent with the literature showing neurochemical, functional, and structural brain changes following withdrawal and remission from MA. MA targets dopaminergic systems in the brain; chronic use of MA, as well as high doses, has been associated with a depletion of dopamine, a reduction in dopamine transporter levels, damage to dopamine nerve terminals, and long-term reduction in other aspects of terminal integrity (Proebstl et al., 2019; Scott et al., 2007). Neuroimaging studies have shown that remission from MA use disorder is associated with changes in grey and white brain matter volume and/or density, corticostriatal connectivity, and cortical activation patterns during resting state and cognitive tasks (Hoffman et al., 2020; Hoffman et al., 2008; Jan, Kydd, & Russell, 2012; Schwartz et al., 2010). Additional studies have shown that inflammatory mechanisms may contribute to the persisting or emerging cognitive deficits following withdrawal and remission from MA use disorders (Huckans et al., 2015; Kohno et al., 2019; Kohno et al., 2018; Loftis et al., 2011). Consistent with the present study, another group found that, compared to controls without dependence, participants actively using MA (tested positive on urine drug screen) had an increase in striatal volume as well as fewer errors on the Go/No-Go task (Jan, Lin, Miles, Kydd, & Russell, 2012); the authors suggest that the volumetric increase during active use may be adaptive to compensate for MA-induced dopaminergic neurotoxicity in order to preserve cognitive function. Similarly, another group found that levels of brain-derived neurotrophic factor (BDNF) were higher in adults in early withdrawal from MA dependence (1–7 of abstinence in a detox facility) compared to controls, but that these levels decreased and were no different from controls one month later (Ren et al., 2016); these authors speculated that increases in BDNF during active use may be a compensatory or protective response to MA-induced neurotoxicity.

Relevant to clinicians, if patients are noticing that new cognitive problems are appearing during remission from MA addiction, or if these patients were successfully self-treating pre-existing cognitive deficits during active MA use, this may be a significant disincentive and barrier to remaining in treatment and recovery. Clinicians may need to provide additional guidance and support to adults recovering from addictions in order to address this concern. For example, our group has found Compensatory Cognitive Training (CCT), a manualized, group-based cognitive rehabilitation therapy focusing on skills to address problems with attention, memory, prospective memory, and executive function, to be efficacious for Veterans with a history of traumatic brain injury and mild cognitive impairments (Huckans et al., 2010; Storzbach et al., 2017), and we are conducting funded randomized controlled trials and non-funded pilot studies to evaluate its efficacy for adults with addictions and other conditions (e.g., PTSD, older adults with mild cognitive impairment). Future studies could evaluate whether CCT or other cognitive rehabilitation therapies might be useful for adults with MA and other substance use disorders, as a supplement to their substance use treatment program and as a way to reduce relapse rates and improve treatment adherence.

Limitations

The present study includes several limitations. Group sample sizes may have limited our statistical power to some extent. A cross-sectional study design does not allow for definitive conclusions on causality. For example, genetic variability has been shown to contribute to differences in cognition and craving among individuals with a history of MA use (Cherner et al., 2010; Loftis et al., 2019); if there were non-uniform differences in relevant genetic alleles (or other moderators we did not asses for) across groups due to sampling error, this may have confounded our results to some extent. Some research suggests that cognitive impairments may predate MA use and addiction at least in some individuals, but our study design does not allow us to confirm or disconfirm this possibility (Dean, Morales, Hellemann, & London, 2018). Our sample was largely middle aged, male, and Caucasian, with all adults residing in one Northwest metropolitan area (the greater Portland, Oregon area), so results may not be generalizable to more diverse populations. Although we controlled for gender using demographically corrected NAB scores, it is unclear to what extent the lower percentage of females in the remission group may have impacted results. Females more frequently report negative side effects of both prescribed and non-prescribed stimulants (Smith, Martel & DeSantis, 2017), so it is possible, for example, that the over-representation of males in the remission group masked the group differences to some extent. In order to make the sample generalizable to typical populations with MA dependence, we included individuals with common comorbidities such as stable medical conditions, mild and currently stable psychiatric diagnoses, and tobacco use disorders. These variables were either not significantly different across groups or they did not correlate with outcomes, so we did not control for them as covariates in statistical analyses. However, it is possible group differences on these or other variables influenced results in an unknown manner. Given that the present study was focused on evaluating cognitive performance in adults with current or recent MA use disorders, adults with dependence on other substances (other than nicotine or caffeine) were excluded, which may make the findings less generalizable to individuals with polysubstance use disorders.

Study visits were conducted either at the VAPORHCS or at community addiction treatment centers for adults in remission from MA dependence. Active users were seen primarily at the VAPORHCS, so it is possible our MA-ACT sample may be less generalizable to adults who are not able or willing to come to our facility (e.g., our MA-ACT sample may have been higher functioning or had less or more severe use patterns). Our MA-REM sample may also be less generalizable to adults in remission who are not engaged in community outpatient or inpatient treatment programs (e.g., those using private treatment centers or who are self-treated). It is unclear why our MA-REM group reported greater quantity of use while the MA-ACT group reported higher frequency of use, or how this may have impacted our results. Perhaps this was sampling error and due to chance. Or, perhaps those who use greater amounts in lower frequency are more likely to experience the negative effects of MA use and enter into treatment. In contrast, perhaps those who use smaller amounts more frequently are more likely to experience the positive effects of MA use and continue to use. Future studies could explore these possibilities as well as how different patterns of use impact cognition during both active use and remission, but this is beyond the scope of the present paper due to study design and sample size.

Because this study was initiated prior to the Diagnostic and Statistical Manual of Mental Disorders 5th Edition (DSM5) (APA, 2013) release date in 2013, substance dependence was established using the MINI (Sheehan et al., 1998), which is based on the DSM-IV (APA, 2000) diagnostic criteria and classifications for substance use disorders; it is unclear how results may have varied if we had used lengthier structured clinical interviews and the DSM5 diagnostic criteria [e.g., Structured Clinical Interview for DSM-5, (First, Williams, Karg, & Spitzer, 2015)]. Of note, we did not assess for number of relapses or attempts at sobriety, so it is unclear how this may have impacted outcomes. This paper was limited in scope to NAB Index scores and does not include analyses based on subtest scores, in part to improve statistical power and focus; however, future papers or studies could expand analyses to address subtest specific hypotheses and questions. Lastly, our analyses were limited to group comparisons. Some research suggests group comparisons may not reliably detect and reflect clinical presentations that vary from individual to individual but are due to similar diagnoses or processes (Gallistel, 2012; Morgan, 2009; Whitfield, Allaire, Belue, & Edwards, 2008); therefore, future studies could include analyses that better address individual variability in MA associated cognitive symptoms.

Conclusions

Some cognitive deficits are apparent during remission from MA use disorders, but not during active use. Clinical implications may include a disincentive for adults to stay in recovery and the importance of developing appropriate cognitive rehabilitation therapies or medications to support adults during remission.

Acknowledgments

The authors would like to thank the study participants and staff at each of the recruitment sites, including Central City Concern, CODA, Inc., De Paul Treatment Centers, Native American Rehabilitation Association of the Northwest, Outside In, Volunteers of America Treatment Centers, Oregon Health & Science University, and the Veterans Affairs Portland Health Care System Mental Health Division and Substance Abuse Treatment Program. The authors are also grateful to Alissa Bazinet, Ph.D. and Matthew Arbuckle, B.S. for their many contributions to this project as Research Assistants and Psychometrists.

Sources of Funding/Declaration of Interests Statement

This material is the result of work supported, in part, by the National Institute on Drug Abuse [Methamphetamine Research Center (MARC), WFH, MH, JML under grant #P50 DA018165], [MK under grant #T32 DA007262], [JML, MH under grant #U18 DA052351-01]; the National Institute on Alcohol Abuse and Alcoholism [MK under grant #T32 AA007468]; United States Department of Veterans Affairs Biomedical Laboratory Research and Development Merit Review Program [JML under grant #I01 BX002061]; United States Department of Veterans Affairs Clinical Sciences Research and Development Merit Review Program [WFH under grant #I0CX001558]; the United States Department of Veterans Affairs Clinical Sciences Research and Development Career Development Program [MK under grant #CX17008-CDA2]; the United States Department of Justice [WFH under grant #2010-DD-BX-0517]; and Oregon Health & Science University Center for Women’s Health – Circle of Giving [MK under grant #APSYC0287]. The work was conducted using facilities at the Veterans Affairs Portland Health Care System (VAPORHCS) and Oregon Health & Science University, Portland, OR, USA. Several of the authors are or were Veterans Affairs employees (Marilyn Huckans, PhD, LP, Director of Training for Psychology, Staff Psychologist and Neuropsychologist, VAPORHCS, Portland, OR; Stephen Boyd, PhD, LP, Psychology Postdoctoral Fellow, VAPORHCS, Portland, OR; Grant Moncrief, PsyD, Psychology Intern, VAPORHCS, Portland, OR; Nathan Hantke, PhD, LP, Staff Psychologist, VAPORHCS, Portland, OR; Bethany Winters, MA, Research Assistant, VAPORHCS, Portland, OR; Kate Shirley, MA, Research Assistant, VAPORHCS, Portland, OR; Emily Sano, MA, Research Assistant, VAPORHCS, Portland, OR; Holly McCready, Research Assistant, VAPORHCS, Portland, OR; Laura Dennis, Research Assistant, VAPORHCS, Portland, OR; Milky Kohno, PhD, Research Health Science Specialist, VAPORHCS, Portland, OR; William Hoffman, PhD, MD, Staff Psychiatrist, VAPORHCS, Portland, OR; Jennifer M. Loftis, PhD, Research Scientist, VAPORHCS, Portland, OR). The contents do not represent the views of the United States Department of Veterans Affairs or the United States Government. The authors do not have any additional disclosures or conflicts of interest to report.

Data Availability Statement

The data that support the findings of this study are available through the Translational Service Core Biorepository of the Methamphetamine Research Center (MARC), Portland, OR, USA. The Biorepository’s protocol was approved by the Veterans Affairs Portland Health Care System (VAPORHC)’s Institutional Review Board (IRB). Interested investigators may apply for release of original data through the VAPORHCS IRB and the Biorepository Director (JML). Application instructions are available from the corresponding author (JML), upon reasonable written request.

References

  1. Aharonovich E, Nunes E, & Hasin D (2003). Cognitive impairment, retention and abstinence among cocaine abusers in cognitive-behavioral treatment. Drug Alcohol Depend, 71(2), 207–211. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Andres T, Ernst T, Oishi K, Greenstein D, Nakama H, & Chang L (2016). Brain Microstructure and Impulsivity Differ between Current and Past Methamphetamine Users. J Neuroimmune Pharmacol, 11(3), 531–541. doi: 10.1007/s11481-016-9675-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. APA. (2000). Diagnostic and statistical manual for mental disorders (Fourth Edition, Text Revision ed.). Washington, DC: American Psychiatric Association. [Google Scholar]
  4. APA. (2013). Diagnostic and Statistical Manual of Mental Disorders (5th ed.). Washington, D.C.: American Psychiatric Association. [Google Scholar]
  5. Basterfield C, Hester R, & Bowden SC (2019). A meta-analysis of the relationship between abstinence and neuropsychological functioning in methamphetamine use disorder. Neuropsychology, 33(5), 739–753. doi: 10.1037/neu0000552 [DOI] [PubMed] [Google Scholar]
  6. Boileau I, Rusjan P, Houle S, Wilkins D, Tong J, Selby P, . . . Kish SJ (2008). Increased vesicular monoamine transporter binding during early abstinence in human methamphetamine users: Is VMAT2 a stable dopamine neuron biomarker? J Neurosci, 28(39), 9850–9856. doi: 10.1523/JNEUROSCI.3008-08.2008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Casaletto KB, Obermeit L, Morgan EE, Weber E, Franklin DR, Grant I, . . . Translational Methamphetamine Aids Research Center Group. (2015). Depression and executive dysfunction contribute to a metamemory deficit among individuals with methamphetamine use disorders. Addict Behav, 40, 45–50. doi: 10.1016/j.addbeh.2014.08.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Cherner M, Bousman C, Everall I, Barron D, Letendre S, Vaida F, et al. Cytochrome P450–2D6 extensive metabolizers are more vulnerable to methamphetamine-associated neurocognitive impairment: preliminary findings. J Int Neuropsychol Soc, 16, 890–901. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Chou YH, Huang WS, Su TP, Lu RB, Wan FJ, & Fu YK (2007). Dopamine transporters and cognitive function in methamphetamine abuser after a short abstinence: A SPECT study. Eur Neuropsychopharmacol, 17(1), 46–52. doi: 10.1016/j.euroneuro.2006.05.002 [DOI] [PubMed] [Google Scholar]
  10. Coghill DR, Seth S, Pedroso S, Usala T, Currie J, & Gagliano A (2014). Effects of methylphenidate on cognitive functions in children and adolescents with attention-deficit/hyperactivity disorder: evidence from a systematic review and a meta-analysis. Biol Psychiatry, 76(8), 603–615. doi: 10.1016/j.biopsych.2013.10.005 [DOI] [PubMed] [Google Scholar]
  11. Darke S, Kaye S, McKetin R, & Duflou J (2008). Major physical and psychological harms of methamphetamine use. Drug Alcohol Rev, 27(3), 253–262. doi: 10.1080/09595230801923702 [DOI] [PubMed] [Google Scholar]
  12. Dean AC, Morales AM, Hellemann G, & London ED (2018). Cognitive deficit in methamphetamine users relative to childhood academic performance: link to cortical thickness. Neuropsychopharmacology, 43(8), 1745–1752. doi: 10.1038/s41386-018-0065-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Dean AC, Sevak RJ, Monterosso JR, Hellemann G, Sugar CA, & London ED (2011). Acute modafinil effects on attention and inhibitory control in methamphetamine-dependent humans. J Stud Alcohol Drugs, 72(6), 943–953. doi: 10.15288/jsad.2011.72.943 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Delis D, Kaplan E, & Kramer J (2001). Delis-Kaplan Executive Functioning System. New York, NY: Harcourt Assessment Company. [Google Scholar]
  15. Ellis C, Hoffman W, Jaehnert S, Plagge J, Loftis JM, Schwartz D, & Huckans M (2016). Everyday problems with executive dysfunction and impulsivity in adults recovering from methamphetamine addiction. Addict Disord Their Treat, 15(1), 1–5. doi: 10.1097/ADT.0000000000000059 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Fals-Stewart W (1993). Neurocognitive defects and their impact on substance abuse treatment. Journal of Addictions and Offender Counseling, 13, 46–57. [Google Scholar]
  17. Fernandez-Serrano MJ, Perez-Garcia M, & Verdejo-Garcia A (2011). What are the specific vs. generalized effects of drugs of abuse on neuropsychological performance? Neurosci Biobehav Rev, 35(3), 377–406. doi: 10.1016/j.neubiorev.2010.04.008 [DOI] [PubMed] [Google Scholar]
  18. First M, Williams J, Karg R, & Spitzer R (2015). Structured clinical interview for DSM-5-Research Version (SCID-5 for DSM-5, Research Version; SCID-5-RV). Arlington, VA: American Psychiatric Association. [Google Scholar]
  19. Gallistel CR (2012). On the evils of group averaging: commentary on Nevin’s “Resistance to extinction and behavioral momentum”. Behav Processes, 90(1), 98–99; discussion 100. doi: 10.1016/j.beproc.2012.02.013 [DOI] [PubMed] [Google Scholar]
  20. Glasner-Edwards S, Mooney LJ, Marinelli-Casey P, Hillhouse M, Ang A, Rawson RA, & Methamphetamine Treatment Project Corporate. (2010). Psychopathology in methamphetamine-dependent adults 3 years after treatment. Drug Alcohol Rev, 29(1), 12–20. doi: 10.1111/j.1465-3362.2009.00081.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Grohman K, & Fals-Stewart W (2004). The detection of cognitive impairment among substance-abusing patients: the accuracy of the neuropsychological assessment battery-screening module. Exp Clin Psychopharmacol, 12(3), 200–207. doi: 10.1037/1064-1297.12.3.200 [DOI] [PubMed] [Google Scholar]
  22. Guerin AA, Bonomo Y, Lawrence AJ, Baune BT, Nestler EJ, Rossell SL, & Kim JH (2019). Cognition and Related Neural Findings on Methamphetamine Use Disorder: Insights and Treatment Implications From Schizophrenia Research. Front Psychiatry, 10, 880. doi: 10.3389/fpsyt.2019.00880 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Harsh JR, Hayduk R, Rosenberg R, Wesnes KA, Walsh JK, Arora S, . . . Roth T (2006). The efficacy and safety of armodafinil as treatment for adults with excessive sleepiness associated with narcolepsy. Curr Med Res Opin, 22(4), 761–774. doi: 10.1185/030079906X100050 [DOI] [PubMed] [Google Scholar]
  24. Hoffman WF, Jacobs MB, Dennis LE, McCready HD, Hickok AW, Smith SB, & Kohno M (2020). Psychopathy and Corticostriatal Connectivity: The Link to Criminal Behavior in Methamphetamine Dependence. Front Psychiatry, 11, 90. doi: 10.3389/fpsyt.2020.00090 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Hoffman WF, Moore M, Templin R, McFarland B, Hitzemann RJ, & Mitchell SH (2006). Neuropsychological function and delay discounting in methamphetamine-dependent individuals. Psychopharmacology (Berl), 188(2), 162–170. doi: 10.1007/s00213-006-0494-0 [DOI] [PubMed] [Google Scholar]
  26. Hoffman WF, Schwartz DL, Huckans MS, McFarland BH, Meiri G, Stevens AA, & Mitchell SH (2008). Cortical activation during delay discounting in abstinent methamphetamine dependent individuals. Psychopharmacology (Berl), 201(2), 183–193. doi: 10.1007/s00213-008-1261-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Howell DC (2009). Statistical methods for psychology. Scarborough, ON: Nelson Education. [Google Scholar]
  28. Huckans M, Fuller BE, Chalker AL, Adams M, & Loftis JM (2015). Plasma Inflammatory Factors Are Associated with Anxiety, Depression, and Cognitive Problems in Adults with and without Methamphetamine Dependence: An Exploratory Protein Array Study. Front Psychiatry, 6, 178. doi: 10.3389/fpsyt.2015.00178 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Huckans M, Pavawalla S, Demadura T, Kolessar M, Seelye A, Roost N, . . . Storzbach D (2010). A pilot study examining effects of group-based Cognitive Strategy Training treatment on self-reported cognitive problems, psychiatric symptoms, functioning, and compensatory strategy use in OIF/OEF combat veterans with persistent mild cognitive disorder and history of traumatic brain injury. J Rehabil Res Dev, 47(1), 43–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Iudicello JE, Woods SP, Vigil O, Scott JC, Cherner M, Heaton RK, . . . HIV Neurobehavioral Research Center Group. (2010). Longer term improvement in neurocognitive functioning and affective distress among methamphetamine users who achieve stable abstinence. J Clin Exp Neuropsychol, 32(7), 704–718. doi: 10.1080/13803390903512637 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Jaffe C, Bush KR, Straits-Troster K, Meredith C, Romwall L, Rosenbaum G, . . . Saxon AJ (2005). A comparison of methamphetamine-dependent inpatients childhood attention deficit hyperactivity disorder symptomatology. J Addict Dis, 24(3), 133–152. doi: 10.1300/J069v24n03_11 [DOI] [PubMed] [Google Scholar]
  32. Jan RK, Kydd RR, & Russell BR (2012). Functional and structural brain changes associated with methamphetamine abuse. Brain Sci, 2(4), 434–482. doi: 10.3390/brainsci2040434 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Jan RK, Lin JC, Miles SW, Kydd RR, & Russell BR (2012). Striatal volume increases in active methamphetamine-dependent individuals and correlation with cognitive performance. Brain Sci, 2(4), 553–572. doi: 10.3390/brainsci2040553 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Johansson B, Wentzel AP, Andrell P, Mannheimer C, & Ronnback L (2015). Methylphenidate reduces mental fatigue and improves processing speed in persons suffered a traumatic brain injury. Brain Inj, 29(6), 758–765. doi: 10.3109/02699052.2015.1004747 [DOI] [PubMed] [Google Scholar]
  35. Kalechstein AD, Newton TF, & Green M (2003). Methamphetamine dependence is associated with neurocognitive impairment in the initial phases of abstinence. J Neuropsychiatry Clin Neurosci, 15(2), 215–220. doi: 10.1176/jnp.15.2.215 [DOI] [PubMed] [Google Scholar]
  36. Kohno M, Link J, Dennis LE, McCready H, Huckans M, Hoffman WF, & Loftis JM (2019). Neuroinflammation in addiction: A review of neuroimaging studies and potential immunotherapies. Pharmacol Biochem Behav, 179, 34–42. doi: 10.1016/j.pbb.2019.01.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Kohno M, Loftis JM, Huckans M, Dennis LE, McCready H, & Hoffman WF (2018). The relationship between interleukin-6 and functional connectivity in methamphetamine users. Neurosci Lett, 677, 49–54. doi: 10.1016/j.neulet.2018.04.037 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Loftis JM, Choi D, Hoffman W, & Huckans MS (2011). Methamphetamine causes persistent immune dysregulation: a cross-species, translational report. Neurotox Res, 20(1), 59–68. doi: 10.1007/s12640-010-9223-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Loftis JM, Lasarev M, Shi X, Lapidus J, Janowsky A, Hoffman WF, Huckans M (2019). Trace amine-associated receptor gene polymorphism increases drug craving in individuals with methamphetamine dependence. PLoS One, 14(10), e0220270. doi: 10.1371/journal.pone.0220270 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. London ED, Simon SL, Berman SM, Mandelkern MA, Lichtman AM, Bramen J, . . . Ling W (2004). Mood disturbances and regional cerebral metabolic abnormalities in recently abstinent methamphetamine abusers. Arch Gen Psychiatry, 61(1), 73–84. doi: 10.1001/archpsyc.61.1.73 [DOI] [PubMed] [Google Scholar]
  41. McAllister TW, Zafonte R, Jain S, Flashman LA, George MS, Grant GA, . . . Stein MB (2016). Randomized Placebo-Controlled Trial of Methylphenidate or Galantamine for Persistent Emotional and Cognitive Symptoms Associated with PTSD and/or Traumatic Brain Injury. Neuropsychopharmacology, 41(5), 1191–1198. doi: 10.1038/npp.2015.282 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. McLellan AT, Lewis DC, O’Brien CP, & Kleber HD (2000). Drug dependence, a chronic medical illness: implications for treatment, insurance, and outcomes evaluation. Jama, 284(13), 1689–1695. [DOI] [PubMed] [Google Scholar]
  43. Miller GA, & Chapman JP (2001). Misunderstanding analysis of covariance. J Abnorm Psychol, 110(1), 40–48. doi: 10.1037//0021-843x.110.1.40 [DOI] [PubMed] [Google Scholar]
  44. Morgan DL, & Morgan RK (2009). SIngle-Case Research Methods for the Behavioral and Health Sciences. Thousand Oaks, CA: Sage Publications, Inc. [Google Scholar]
  45. Nestor LJ, Ghahremani DG, Monterosso J, & London ED (2011). Prefrontal hypoactivation during cognitive control in early abstinent methamphetamine-dependent subjects. Psychiatry Res, 194(3), 287–295. doi: 10.1016/j.pscychresns.2011.04.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. NIAAA. (2019). Drinking Levels Defined. Retrieved from https://www.niaaa.nih.gov/alcohol-health/overview-alcohol-consumption/moderate-binge-drinking
  47. Nicosia N, Pacula RL, Kilmer B, Lundberg R, & Chiesa J (2009). The economic cost of methamphetamine use in the United States, 2005, MG-829-MPF/NIDA. Retrieved from http://www.rand.org/pubs/monographs/MG829
  48. Paulus MP, Hozack NE, Zauscher BE, Frank L, Brown GG, Braff DL, & Schuckit MA (2002). Behavioral and functional neuroimaging evidence for prefrontal dysfunction in methamphetamine-dependent subjects. Neuropsychopharmacology, 26(1), 53–63. doi: 10.1016/S0893-133X(01)00334-7 [DOI] [PubMed] [Google Scholar]
  49. Potvin S, Pelletier J, Grot S, Hebert C, Barr AM, & Lecomte T (2018). Cognitive deficits in individuals with methamphetamine use disorder: A meta-analysis. Addict Behav, 80, 154–160. doi: 10.1016/j.addbeh.2018.01.021 [DOI] [PubMed] [Google Scholar]
  50. Proebstl L, Kamp F, Koller G, & Soyka M (2018). Cognitive Deficits in Methamphetamine Users: How Strong is The Evidence? Pharmacopsychiatry, 51(6), 243–250. doi: 10.1055/s-0043-123471 [DOI] [PubMed] [Google Scholar]
  51. Proebstl L, Kamp F, Manz K, Krause D, Adorjan K, Pogarell O, . . . Kambeitz J (2019). Effects of stimulant drug use on the dopaminergic system: A systematic review and meta-analysis of in vivo neuroimaging studies. Eur Psychiatry, 59, 15–24. doi: 10.1016/j.eurpsy.2019.03.003 [DOI] [PubMed] [Google Scholar]
  52. Ren W, Tao J, Wei Y, Su H, Zhang J, Xie Y, . . . He J (2016). Time-Dependent Serum Brain-Derived Neurotrophic Factor Decline During Methamphetamine Withdrawal. Medicine (Baltimore), 95(5), e2604. doi: 10.1097/MD.0000000000002604 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Robles F (2018, February 13, 2018). Meth, the Forgotten Killer, Is Back. And It’s Everywhere. The New York Times. Retrieved from https://www.nytimes.com/2018/02/13/us/meth-crystal-drug.html
  54. Sadek JR, Vigil O, Grant I, & Heaton RK (2007). The impact of neuropsychological functioning and depressed mood on functional complaints in HIV-1 infection and methamphetamine dependence. J Clin Exp Neuropsychol, 29(3), 266–276. [DOI] [PubMed] [Google Scholar]
  55. Salo R, Nordahl TE, Galloway GP, Moore CD, Waters C, & Leamon MH (2009). Drug abstinence and cognitive control in methamphetamine-dependent individuals. J Subst Abuse Treat, 37(3), 292–297. doi: 10.1016/j.jsat.2009.03.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Schwartz DL, Mitchell AD, Lahna DL, Luber HS, Huckans MS, Mitchell SH, & Hoffman WF (2010). Global and local morphometric differences in recently abstinent methamphetamine-dependent individuals. Neuroimage, 50(4), 1392–1401. doi: 10.1016/j.neuroimage.2010.01.056 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Scott JC, Woods SP, Matt GE, Meyer RA, Heaton RK, Atkinson JH, & Grant I (2007). Neurocognitive effects of methamphetamine: a critical review and meta-analysis. Neuropsychol Rev, 17(3), 275–297. doi: 10.1007/s11065-007-9031-0 [DOI] [PubMed] [Google Scholar]
  58. Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, . . . Dunbar GC (1998). The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry, 59 Suppl 20, 22–33;quiz 34–57. [PubMed] [Google Scholar]
  59. Shoptaw S, Peck J, Reback CJ, & Rotheram-Fuller E (2003). Psychiatric and substance dependence comorbidities, sexually transmitted diseases, and risk behaviors among methamphetamine-dependent gay and bisexual men seeking outpatient drug abuse treatment. J Psychoactive Drugs, 35 Suppl 1, 161–168. doi: 10.1080/02791072.2003.10400511 [DOI] [PubMed] [Google Scholar]
  60. Simon SL, Dacey J, Glynn S, Rawson R, & Ling W (2004). The effect of relapse on cognition in abstinent methamphetamine abusers. J Subst Abuse Treat, 27(1), 59–66. doi: 10.1016/j.jsat.2004.03.011 [DOI] [PubMed] [Google Scholar]
  61. Simon SL, Dean AC, Cordova X, Monterosso JR, & London ED (2010). Methamphetamine dependence and neuropsychological functioning: evaluating change during early abstinence. J Stud Alcohol Drugs, 71(3), 335–344. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Smith G, Della Sala S, Logie RH, & Maylor EA (2000). Prospective and retrospective memory in normal ageing and dementia: a questionnaire study. Memory, 8(5), 311–321. doi: 10.1080/09658210050117735 [DOI] [PubMed] [Google Scholar]
  63. Smith TE, Martel MM, & DeSantis AD (2017). Subjective report of side effects of prescribed and non-prescribed psychostimulant use in young adults. Subst Use Misuse, 52(4), 548–552. doi: 10.1080/10826084.2016.1240694 [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Stern R, & White T (2003). Neuropsychological assessment battery. Lutz, FL: Psychological Assessment Resources. [Google Scholar]
  65. Storzbach D, Twamley EW, Roost MS, Golshan S, Williams RM, O’Neil M, . . . Huckans M (2017). Compensatory Cognitive Training for Operation Enduring Freedom/Operation Iraqi Freedom/Operation New Dawn Veterans With Mild Traumatic Brain Injury. J Head Trauma Rehabil, 32(1), 16–24. doi: 10.1097/HTR.0000000000000228 [DOI] [PubMed] [Google Scholar]
  66. Su H, Tao J, Zhang J, Xie Y, Wang Y, Zhang Y, . . . He J (2015). The Effects of BDNF Val66Met Gene Polymorphism on Serum BDNF and Cognitive Function in Methamphetamine-Dependent Patients and Normal Controls: A Case-Control Study. J Clin Psychopharmacol, 35(5), 517–524. doi: 10.1097/JCP.0000000000000390 [DOI] [PubMed] [Google Scholar]
  67. UNODC. (2017). United Nations Office on Drugs and Crime, World Drug Report 2017 (ISBN: 978–92-1–148291-1, eISBN: 978–92-1–060623-3, United Nations publication, Sales No. E17.XI.6) Retrieved from Vienna, Austria: [Google Scholar]
  68. Wang GJ, Volkow ND, Chang L, Miller E, Sedler M, Hitzemann R, . . . Fowler JS (2004). Partial recovery of brain metabolism in methamphetamine abusers after protracted abstinence. Am J Psychiatry, 161(2), 242–248. doi: 10.1176/appi.ajp.161.2.242 [DOI] [PubMed] [Google Scholar]
  69. Whitfield KE, Allaire JC, Belue R, & Edwards CL (2008). Are comparisons the answer to understanding behavioral aspects of aging in racial and ethnic groups? J Gerontol B Psychol Sci Soc Sci, 63(5), P301–308. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Zhong N, Jiang H, Du J, Zhao Y, Sun H, Xu D, . . . Zhao M (2016). The cognitive impairments and psychological wellbeing of methamphetamine dependent patients compared with health controls. Prog Neuropsychopharmacol Biol Psychiatry, 69, 31–37. doi: 10.1016/j.pnpbp.2016.04.005 [DOI] [PubMed] [Google Scholar]
  71. Zweben JE, Cohen JB, Christian D, Galloway GP, Salinardi M, Parent D, . . . Methamphetamine Treatment Project. (2004). Psychiatric symptoms in methamphetamine users. Am J Addict, 13(2), 181–190. doi: 10.1080/10550490490436055 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available through the Translational Service Core Biorepository of the Methamphetamine Research Center (MARC), Portland, OR, USA. The Biorepository’s protocol was approved by the Veterans Affairs Portland Health Care System (VAPORHC)’s Institutional Review Board (IRB). Interested investigators may apply for release of original data through the VAPORHCS IRB and the Biorepository Director (JML). Application instructions are available from the corresponding author (JML), upon reasonable written request.

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