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. Author manuscript; available in PMC: 2023 Jul 1.
Published in final edited form as: Appl Neuropsychol Adult. 2020 May 28;29(4):462–468. doi: 10.1080/23279095.2020.1769099

Cognitive profiles in persons with opioid use disorder enrolled in methadone treatment

Victoria Sanborn a, John Gunstad a, Roman Shrestha b,c, Colleen B Mistler c,d, Michael M Copenhaver c,d
PMCID: PMC7704535  NIHMSID: NIHMS1617136  PMID: 32463730

Abstract

Cognitive impairment is common in persons with opioid use disorder and associated with poor treatment outcomes, including elevated risk for relapse. Much less is known about the underlying structure of these deficits and the possible presence of cognitive phenotypes. A total of 177 adults (average 42.2 years of age, 52.0% male, 65.5% Caucasian) enrolled in a methadone maintenance treatment program completed the NIH Toolbox as part of a larger project. Cluster analyses revealed a 2-cluster solution–persons with intact cognitive function in all domains (n = 93; Intact) and those with impairments on tests of attention and executive function (n = 83; Impaired). Follow-up analyses revealed that the Impaired group was slightly older, more likely to self-identify as a racial/ethnic minority, and less likely to report consuming alcohol four or more times per week. These findings suggest the existence of distinct cognitive profiles in persons with opioid use disorder and encourage further examination, particularly studies to examine the possible benefits of routine screening for cognitive impairment as part of substance use treatment.

Keywords: Cognitive impairment, drug treatment, methadone maintenance, neurocognitive dysfunction, opioid dependence

Introduction

A dramatic increase in the use of heroin or fentanyl and misuse of prescription opioids has led to an epidemic of overdose deaths around the world, specifically in North America (National Institute on Drug Abuse, 2019; World Health Organization, 2018). The recommended treatment of choice for opioid use disorder (OUD) involves opioid agonist therapy (OAT; e.g., methadone or buprenorphine) that is tapered over time (McCarty et al., 2018). However, a majority of patients relapse (Chalana et al., 2015; Strain, 2002) or are unable to accrue the expected benefits from treatment (Copenhaver et al., 2015), and there is an urgent need to identify novel risk factors for poor treatment outcomes.

There is reason to believe that cognitive impairment is a key risk factor for relapse and other poor treatment outcomes in persons with OUD. Long-term opioid use has been associated with reduced cortical thickness in the fron-tocingulate, insula (Li et al., 2014; Yuan et al., 2009), and ventral striatum (Stewart et al., 2019; Tolomeo et al., 2016). Consistent with these findings, reduced performance tests of attention and executive function are prevalent in persons enrolled in MMT (Ersche & Sahakian, 2007). In turn, these cognitive deficits are associated with poorer treatment retention and medication adherence (Altice et al., 2010; Kamarulzaman & Altice, 2015), as well as motivation and behavioral skills necessary for success (Anand et al., 2010; Bates et al., 2006; Blume et al., 1999; Huedo-Medina et al., 2016; Morgenstern & Bates, 1999; Nakagami et al., 2010). Performance on tests of learning and memory are also often impaired in persons with OUD (Curran et al., 2001; Darke et al., 2000; Rapeli et al., 2009) and have been associated with increased risk for relapse (Ross & Peselow, 2009). Such findings raise the possibiliy that early identification of cognitive dysfunction in persons with OUD may lead to enhanced treatment strategies designed to accommodate specific cognitive deficits.

A better understanding of cognitive profiles in persons with OUD is needed in order to inform new treatment strategies. In addition to well-known patterns for neurological disorders, distinct cognitive profiles have been identified in healthy aging samples (Gunstad et al., 2006), as well as persons with a wide range of medical and psychiatric conditions (e.g., cardiovascular disease, bipolar disorder; (Hawkins et al., 2015; Lima et al., 2019; Miller et al., 2012), though not for persons with OUD. A better understanding of cognitive profiles in persons enrolled in drug treatment for OUD may provide important new insights. The goal of the current study was to identify potential cognitive profiles in persons with OUD undergoing drug treatment using objective cognitive testing. Based on the above findings, we hypothesized that distinct cognitive profiles reflecting respective impairments in attention, executive function, and/ or memory would emerge that could inform a range of future treatment strategies targeting persons with OUD. Specifically, behavioral interventions commonly used in OUD treatment (e.g., cognitive-behavioral therapy, contingency management) (Bickel et al., 2007; Gallagher et al., 2019; Ramsay, 2010) have been adapted for other neurologically-impaired populations (e.g., traumatic brain injury, early stages of dementia) (Tomaszewski Farias et al., 2018; White et al., 1997) and it is possible that a similar approach could be used for OUD treatment settings to improve outcomes.

Methods

Participants

A total of 177 individuals participated in the current project. Study inclusion/exclusion criteria included: 18 years or older, HIV -uninfected or status unknown (self-reported), reported drug- (i.e., sharing of injection equipment) or sex-related risk behavior (past 6 months), met DSM-5 criteria for OUD, and able to understand, speak, and read English. All participants were stabilized on methadone to treat opioid dependence during completion of study activities.

Instrumentation

NIH toolbox

The NIH Toolbox for the Assessment of Neurological and Behavioral Function–Cognition was developed to assess cognitive performance across the lifespan (Heaton et al., 2015). For the current study, memory was assessed using Picture Sequence Memory Test Forms A-C, executive function (EF) included Flanker Inhibitory Control and Attention Test, List Sorting Working Memory Test, Dimensional Change Card Sort Test (DCCS), processing speed/attention tasks included Pattern Comparison Processing Speed Test, and language tasks included Picture Vocabulary Test and Oral Reading Recognition Test. All individual test scores reflected fully corrected t-scores (i.e., controlling for age, race, education, mother’s education, handedness, and gender). Cognitive domain scores (i.e., memory, EF, attention and language) were calculated for the present study by averaging scores of individual tests reflecting respective domain abilities (e.g., averaged Picture Vocabulary and Reading Recognition scores to create language domain score).

Demographic and psychosocial characteristics

Participants completed a brief questionnaire (self-report) to gather information about age, sex, sexual orientation, ethnicity, marital status, educational attainment, and substance use history.

Depressive symptoms

Depressive symptoms were assessed using the 20-item Center for Epidemiological Studies Depression Scale (CES-D), with ≥16 indicative of moderate to severe depression (Radloff, 1977). The overall internal consistency (Cronbach’s alpha) for the scale was 0.90.

Procedures

Study methods were approved by the Institutional Review Board at the University of Connecticut and received board approval from the APT Foundation, Inc. methadone maintenance program in New Haven, CT. Participants were recruited using advertisements, flyers, word-of-mouth, and direct referral from local counselors. Study activities were conducted in a private room by trained research assistants. After providing written informed consent, participants completed a survey including socio-demographic characteristics and depressive symptoms, followed by administration of the NIH toolbox via iPad. Participants were reimbursed for the time required for their participation.

Data analysis

All data management and analyses were performed with IBM SPSS 25. Agglomerative hierarchical clustering was conducted on the average t-scores for each cognitive domain from NIH Toolbox (i.e., Attention, Executive Function, Language, and Memory) using Ward’s minimum-distance method and squared Euclidean distances for similarity. The dendogram was examined to determine the number of underlying clusters of cognitive test performance within the sample. The K-means algorithm was then used to generate the final solution. Clusters were examined for possible differences in demographic and psychosocial variables using t-tests or chi-square analyses, as appropriate.

Results

Demographic and psychosocial characteristics

Participants averaged 42.19 ± 10.22 years of age, with 52% male and 65% Caucasian. Notably, CES-D scores were elevated in the total sample and within both cognitive groups (M = 26.30 ± SD = 12.99). This composition reflects the patients presenting to our clinical service (Table 1).

Table 1.

Comparison of patient characteristics and cognitive scores using t test and chi square analyses.

95% CI
Sample M/% ± SD Impaired M/% ± SD Intact M/% ± SD Test p Lower Upper
Demographic/Psychosocial
 Age 42.19 ± 10.22 44.82 ± 10.57 39.82 ± 9.32 3.35 0.001 2.05 7.96
 Male 52.0% 52.3% 51.6% 0.01 0.92
 Heterosexual 76.8% 76.2% 77.4% 0.04 0.85
 Caucasian 65.5% 56.0% 74.2% 6.50 0.01
 Less than High School Education 28.2% 27.4 29.0% 0.37 0.83
 Married/Partnered 21.4% 16.7% 28.9% 3.35 0.19
 Alcohol 4+ days per week   9.6%   4.8% 14.0% 4.32 0.04
 CES-D 26.30 ± 12.99 26.71 ± 12.13 25.92 ± 13.77 0.40 0.69 −3.08 4.66
NIH Toolbox
 Total Composite Score 43.62 ± 11.88 35.75 ± 8.70 50.72 ± 9.70 10.76 <0.001 12.25 17.72
 Picture Sequence Memory Test 45.94 ± 8.10 42.94 ± 6.56 48.64 ± 8.43 4.97 <0.001 3.43 7.95
 Flanker Inhibitory Control & Attention 36.94 ± 11.27 32.69 ± 9.15 40.78 ± 11.67 5.10 <0.001 4.96 11.23
 List Sorting Working Memory 42.11 ± 10.56 38.26 ± 10.45 45.59 ± 9.44 4.90 <0.001 4.38 10.28
 Picture Vocabulary 46.39 ± 9.37 44.01 ± 9.32 48.54 ± 8.92 3.30 0.001 1.81 7.24
 Oral Reading Recognition 55.51 ± 12.05 51.92 ± 11.00 58.75 ± 12.10 3.92 <0.001 3.39 10.28
 Dimensional Change Card Sort 44.63 ± 13.91 37.12 ± 10.20 51.41 ± 13.36 7.93 <0.001 10.73 17.85
 Pattern Comparison Processing Speed 42.89 ± 15.68 29.35 ± 8.23 55.13 ± 8.23 19.20 <0.001 23.13 28.43
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Note. NIH Toolbox subtests reflect fully-corrected t scores; Picture Sequence Memory Test = average score of Forms A, B, and C.

Cognitive profiles in methadone maintenance treatment (MMT) patients

Examination of the dendogram from agglomerative hierarchical clustering suggested 2 clusters. K-means cluster analysis specifying two clusters found distinct subgroups of patients enrolled in MMT, specifically: Cluster 1 (n = 84; Impaired) who exhibited deficits in tests of attention and executive function; and Cluster 2 (n = 93; Intact) that displayed normal performance across all cognitive domains (Table 2 and Figure 1). Of the 177 cases, 165 were classified in the same cluster across the two analyses (93.2%).

Table 2.

T scores from K-means Cluster.

Domain Impaired Intact
Attention 29.35 55.13
Executive function 36.02 45.93
Language 47.96 53.65
Memory 42.94 48.64
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Figure 1.

Figure 1.

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NIH Toolbox t-scores of Impaired and Intact cognitive subtype groups.

Predictors of cognitive profiles in MMT

ANOVA and chi-square analyses were utilized to help identify characteristics that differed between Intact and Impaired subgroups. The Impaired subgroup was older [44.82 ± 10.57 vs. 39.82 ± 9.32; t(1, 175) = 3.35, p = 0.001], more likely to self-identify as a racial/ethnic minority [44.1% vs. 25.8%; λ2 (1) = 6.50, p = 0.01], and less likely to report consuming alcohol 4 or more times per week [4.8% vs. 14.0%; λ2 (1) = 4.32, p = 0.04] (Table 1).

Discussion

The current study discovered two distinct cognitive subtypes among our sample of individuals in treatment for OUD, one group with significant impairments on tests largely mediated by frontal brain regions (i.e., attention, executive function) and those with intact test performance across domains. Notably, a cognitive subtype reflecting poor memory did not emerge. The Impaired group was significantly older, more likely to self-identify as a racial/ethnic minority, and reported less alcohol use. No between-group differences emerged in current depressive symptoms, though both groups did exhibit clinically elevated levels of depression.

Finding two broad cognitive subtypes (i.e., 47% impaired vs 53% intact cognitive function) is consistent with the high prevalence of impairment on frontal tasks seen in persons with substance use disorder (SUD) (Ersche et al., 2012; Verdejo-Garcia et al., 2006), as well as the rate of impairment previously identified via self-report among methadone patients (Copenhaver et al., 2015). Interestingly, this association may also suggest the presence of a bidirectional relationship that leads to deterioration over time. Persons with frontal systems dysfunction are known to develop SUDs at a higher rate, as poor executive function in childhood is linked to diagnosis of these disorders later in life (Aytaclar et al., 1999). Similarly, individual variables including current or recent substance use, longer duration of use, and history of overdose is associated with poorer cognitive function (Dassanayake et al., 2012; Elkana et al., 2019; Schiltenwolf et al., 2014), and OUD is associated with cortical atrophy and reduced functional connectivity on neuroimaging (Ivers et al., 2018; Kivisaari et al., 2010; Lin et al., 2012; Qiu et al., 2015).

Though memory is also often impaired in persons enrolled in MMT (Curran et al., 2001; Darke et al., 2000), it did not emerge as a key feature in the cognitive profiles of the current sample. The exact reason for this finding is not clear, but may involve differences in this sample’s prior drug use, current methadone dosing, or neurologic history (e.g., traumatic brain injury)–as each are known to impact memory function based on past work (Lin et al., 2012; Prosser et al., 2006). Future work is needed to better understand this pattern of findings.

The current study also found patient characteristics linked to cognitive subtypes in OUD patients enrolled in MMT. Though age was controlled for as part of primary analyses, persons in the Impaired group were slightly older (average of 44.8 vs 29.8 years) than those with intact functionong. This pattern is likely attributable to a longer duration of opioid use, as chronic opioid use has been associated with greater impairment in executive functions (Gruber et al., 2007). However, it is also possible that these cognitive deficits reflect a differential metabolism of opioids with age and sustained use (Warner-Smith et al., 2001).

Participants with impaired frontal systems function were also more likely to self-identify as a racial/ethnic minority. This aligns with previous findings showing that Black and Hispanic adults perform poorly vs. White peers on neuropsychological testing (Diaz-Venegas et al., 2016; Early et al., 2013), and have increased risk for developing neurodegenerative disorders like Alzheimer’s disease (Mehta & Yeo, 2017; W euve et al., 2018). These discrepancies are often attributed to differences in educational and early experiences (Early et al., 2013), and may reflect elevated premorbid risk. Additional work is needed to clarify this possibility.

Persons in the Impaired group also reported less frequent consumption of alcohol, though the mechanism underlying this association is unclear. We initially posited that those participants with frontal systems dysfunction may use less alcohol due to greater use of other illicit substances. However, exploratory analyses in a subset of participants with additional data showed that the Intact cognitive function group was also more likely to report using heroin (t = −3.10, df = 122, p < 0.01) and/or cocaine (t = −2.11, df = 122, p = 0.04) in the past month. An alternative explanation is that patients with intact cognitive function were more willing to report substance use than participants with impaired test performance. If replicated, this counterintuitive finding should be explored further in future research endeavors.

Though not a primary focus of the current study, results showed that participants in the Intact and Impaired groups reported comparable and elevated levels of depressive symptoms. Depression can adversely impact neuropsychological test performance (Boone et al., 1995; Weiland-Fiedler et al., 2004), but less is known about its impact on NIH Toolbox test performance. Studies in older adults show inconsistent findings (Zahodne et al., 2014a, 2014b), though a recent study found an independent effect of depression in persons post-mild TBI (Terry et al., 2019). Depression is prevalent in persons with OUD and linked to poorer outcomes (Martins et al., 2012; Roos et al., 2020), and future work is needed to clarify its specific contribution to cognitive deficits in persons with OUD.

Limitations and future directions

There are several limitations to the current study that should be considered. As the study was cross-sectional in design, participants likely completed cognitive testing at different points during their recovery. As cognitive function is known to improve with periods of sustained abstinence (Ieong & Yuan, 2017) and duration, intensity, and types of substances misused can impact cognitive function (Arias et al., 2016; Barahmand et al., 2016; Bracken et al., 2012), these factors may have influenced testing performance. Additionally, methadone itself can impact testing, as higher dosing and longer periods of methadone use have been associated with poorer performance on executive function tasks in similar samples (Barahmand et al., 2016; Rass et al., 2014). As such, cognitive testing likely reflected a combination of acute effects as well as potential cognitive decline due to a possible age by duration of substance use interaction. Future prospective studies should examine such factors to determine if the current findings generalize to other samples.

Another limitation of the current study is the lack of neuroimaging data and detailed neurological, medical, and other substance use history. Some developmental disorders (e.g., Attention Deficit Hyperactivity Disorder; ADHD); (Ameringer & Leventhal, 2013; Fiksdal Abel et al., 2017) and medical conditions (i.e., Hepatitis-C) (Hagan et al., 2011) that are known to impact cognitive function are common in OUD patients. Similarly, information was not available regarding the possible protective effect of cognitive reserve and/or the potential test impact of poorer quality education (independent of number of years) or acculturation. Such factors are associated with cognitive function in persons with substance use history and many of the participants in the current study were missing data related to other substance use which may also contribute to cognitive function, as factors such as duration of use is a predictor of cognitive dysfunction (Arias et al., 2016). As these and similar factors were not available through the current study, it is difficult to determine whether they may have impacted testing performance and/or whether the current sample is representative of the general population of persons in substance use treatment. Future research may also consider these and similar conditions to elucidate potential associations with test performance and subjective reporting.

The current findings raise the possibility that cognitive screening may have important clinical utility in MMT populations. As the NIH Toolbox, an easily administered measure, was able to identify cognitive subtypes, even shorter screening measures (e.g., Montreal Cognitive Assessment; MoCA) (Copersino et al., 2009) have the potential to provide considerable clinical value more efficiently. Alternatively, the predictive value of self-reported patient characteristics (i.e., age, race/ethnicity) for cognitive subtypes noted in the current study suggests self-report questionnaires (e.g., Brief Inventory of Neuro-cognitive Impairment (BINI) (Copenhaver et al., 2015) also have potential as a method for identifying persons at highest risk for cognitive impairment. After identification of impairment, a number of possible interventions could be utilized. Cognitive rehabilitation therapy and working memory training have been found to improve cognitive performance and treatment outcomes in persons with OUD engaged in MMT (Olga Rass et al., 2014; Rezapour et al., 2019); and it is possible their use in similar populations could lead to improved treatment outcomes. Similarly, accommodation strategies could be readily implemented within cognitive-behavioral, contingency management or psychoeducational approaches often used in persons with OUD (Bickel et al., 2007; Gallagher et al., 2019; Ramsay, 2010). Such adaptations have been successfully utilized in other patient populations with known cognitive impairment, including attention-deficit hyperactivity disorder, traumatic brain injury, and dementia (Canela et al., 2017; Kysow et al., 2017; Tomaszewski Farias et al., 2018; White et al., 1997). Based on this, it is plausible that even minor accommodations such as tailoring the frequency/duration of sessions, use of memory aids, and assessing comprehension of session-related information may lead to improved patient outcomes.

The current study identified two cognitive subtypes (i.e., impaired frontal systems function, intact cognitive function) in persons with OUD enrolled in MMT. To our knowledge, this is the first study to distinguish cognitive profiles among persons with OUD. Future work should examine the possible benefits of using routine screening for cognitive dysfunction in this high-risk population to inform treatment strategies and optimize patient outcomes.

Acknowledgments

The authors thank Brian Sibilio, Pramila Karki, and Tanya Adlerfor their contributions to this study.

Funding

This work was supported by grants from the National Institute on Drug Abuse for Research [K01DA051346 to RS, R01DA044867 to MMC], and from the National Institute of Mental Health [5T32MH074387-14] to CBM.

Footnotes

Disclosure statement

No potential conflict of interest was reported by the author(s).

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