Discussion: Scholarly article review guideline

Psychological Medicine

cambridge.org/psm

Original Article

Cite this article: Baldacchino A, Tolomeo S, Balfour DJ, Matthews K (2019). Profiles of visuospatial memory dysfunction in opioid- exposed and dependent populations. Psychological Medicine 49, 1174–1184. https:// doi.org/10.1017/S0033291718003318

Received: 19 April 2018 Revised: 7 October 2018 Accepted: 12 October 2018 First published online: 20 November 2018

Key words: Codeine; cognition; heroin; methadone; opioids; tramadol; visuospatial memory

Author for correspondence: A. Baldacchino, E-mail: [email protected]

© Cambridge University Press 2018

Profiles of visuospatial memory dysfunction in opioid-exposed and dependent populations

A. Baldacchino1, S. Tolomeo2, D.J. Balfour2 and K Matthews2

1Division of Population and Behavioural Science, School of Medicine, St Andrews University, St Andrews, Fife, UK and 2School of Medicine (Neuroscience), Ninewells Hospital & Medical School, University of Dundee, Dundee, Tayside, UK

Abstract

Background. Chronic opioid exposure is common world-wide, but behavioural performance remains under-investigated. This study aimed to investigate visuospatial memory performance in opioid-exposed and dependent clinical populations and its associations with measures of intelligence and cognitive impulsivity. Methods. We recruited 109 participants: (i) patients with a history of opioid dependence due to chronic heroin use (n = 24), (ii) heroin users stabilised on methadone maintenance treat- ment (n = 29), (iii) participants with a history of chronic pain and prescribed tramadol and codeine (n = 28) and (iv) healthy controls (n = 28). The neuropsychological tasks from the Cambridge Neuropsychological Test Automated Battery included the Delayed Matching to Sample (DMS), Pattern Recognition Memory, Spatial Recognition Memory, Paired Associate Learning, Spatial Span Task, Spatial Working Memory and Cambridge Gambling Task. Pre-morbid general intelligence was assessed using the National Adult Reading Test. Results. As hypothesised, this study identified the differential effects of chronic heroin and methadone exposures on neuropsychological measures of visuospatial memory ( p < 0.01) that were independent of injecting behaviour and dependence status. The study also identified an improvement in DMS performance (specifically at longer delays) when the methadone group was compared with the heroin group and also when the heroin group was stabilised onto methadone. Results identified differential effects of chronic heroin and methadone expo- sures on various neuropsychological measures of visuospatial memory independently from addiction severity measures, such as injecting behaviour and dependence status.

Introduction

Working memory (WM) is a limited capacity cognitive system that functions to hold informa- tion in an active manner to facilitate the performance of complex cognitive tasks (Miyake and Shah, 1999). Such tasks include, for example, language comprehension, learning, abstract thinking (Twamley et al., 2006), problem-solving (Westen, 2006), understanding the meaning of complex texts and planning verbal communications (Zihl and von Cramon, 1979). WM is limited in both capacity and duration and is often used synonymously, but inaccurately, with the term ‘short-term memory’ (Westen, 2006). Baddeley and Hitch (1974) expanded upon this WM concept and proposed a tripartite WM model that includes a central executive and two ‘slave systems’; the phonological loop and the visuospatial store. The visuospatial store is fur- ther broken down into (1) visual memory information that includes dimensions such as colour and shape and (2) spatial memory information that includes the capacity to understand, rea- son and to remember the spatial relations among objects or space. (Baddeley and Logie, 1999; Mammarella et al., 2008). There is accruing evidence that the two components of visuospatial memory are selectively engaged and/or processed by distinct brain regions and neuropsycho- logical functions (Della Sala et al., 1999; Passolunghi and Mammarella, 2010; Bormann et al., 2015; Eriksson et al., 2015).

There are a few brain imaging studies on visuospatial memory impairments among drug users. Kubler and colleagues reported that cocaine-dependent individuals were impaired in visuospatial WM. These were associated with prefrontal, cingulate and striatal regions (Kubler et al., 2005). In another study, opiate-dependent individuals were impaired in WM-related brain areas (Bach et al., 2012).

Hyman and colleagues have conceptualised the behavioural phenomena typically described as ‘addiction’ to a ‘pathological usurpation of the neural mechanisms of learning and memory that under normal circumstances serve to shape survival behaviours related to the pursuit of rewards and the cues that predict them’ (Hyman, 2005; Hyman et al., 2006).

In support of the potential centrality of learning and memory changes within drug addiction, two recent meta-analyses of observational studies suggested that chronic opioid exposure is asso- ciated with deficits across a range of different neuropsychological domains including attentional

set-shifting, spatial planning and (Baldacchino et al., 2012, 2017; Tolomeo et al., 2016, 2018). However, these meta-analyses also sug- gested that opioid-exposed groups with apparent WM impairments are a highly heterogenous group with mixed ages, educational attain- ment, gender and socio-economic status (Baldacchino et al., 2012, 2017). Additionally, visuospatial memory impairments in opioid-exposed groups are confounded by, for example, comorbid personality disorders (Prosser et al., 2008), anxiety and/or depres- sion (Henry et al., 2012), past and present medical conditions, neurological disorders and history of head trauma and non -fatal overdose (anoxic) episodes (Rounsaville et al., 1982; Specka et al., 2000; Prosser et al., 2008; Shmygalev et al., 2011). Cognitive function may also be influenced by the global sedative effects of opioid drugs, sub-acute responses to the drugs or the presence of untreated with- drawal states (Baldacchino et al., 2017) at time of testing. Table 1 summarises studies that have recorded significant impairments in visuospatial memory in chronic opioid using populations.

The present study aimed to extend our understanding of neuro- cognitive performance in dependent and non-dependent opioid users, focusing on visuospatial memory function. Employing an ambispective cohort design, we tested representative samples of male participants exposed to illicit and therapeutic opioid drugs and matched, non-substance using, healthy controls. Specifically, the study aimed to determine if performance on tasks measuring visuospatial memory, especially delayed memory performance, which is very sensitive for the varying of ‘executive demands’, was affected by (1) the type of the opioid exposure (methadone v. heroin) at different stages of treatment seeking, (2) the context (opioids pre- scribed for pain control compared with illicit opioids) and (3) the presence or absence of syndromal opioid dependence (opioid dependent compared with non–opioid users) and (4) administration route – injection status (opioid dependent and injecting compared with dependent and non-injecting participants). We have previously identified and reported differential effects of heroin, methadone and prescribed analgesic medication on neurocognitive measures of impulsivity (Baldacchino et al., 2014) from the same study cohort.

Method

Participants

Ethical permission for the conduct of this study was provided by the East of Scotland Research Ethics Service (REC reference num- ber: 06/S1401/32). A full description of the participants can be found in Baldacchino et al. (2014). Male only participants were recruited from UK NHS substance misuse and pain management services. A control group of healthy participants was also recruited. All participants were screened to exclude lifetime or current histories of psychosis, PTSD, neurological and neurodeve- lopmental disorders, borderline or psychopathic personality dis- orders, head injuries; individuals with a lifetime history of non-fatal overdose episodes requiring medical attention (e.g. ambulance call out, CPR), co-occurring benzodiazepine, psychos- timulant and alcohol dependence. All participants were screened by an experienced clinician (AB) for acute opioid and opioid withdrawal symptoms prior to the neuropsychological testing.

The Heroin group (H) (N = 24) were ‘first-time’ referrals to a structured Methadone Maintenance Treatment (MMT) pro- gramme. The Methadone group (M) (N = 29) were established and stable participants in a MMT programme with objective con- firmation of the absence of illicit drug use for more than 6 months. Eighteen of the 29 MMT group participants who showed Ta

b le

1 . S u m m a ry

o f p re vi o u s re se a rc h ex p lo ri n g vi su o sp at ia l m em

o ry

p ro fi le s in

o p io id -d ep

en d en

t in d iv id u a ls

V is u o sp at ia l m em

o ry

(s ke tc h p a d )

Il lic it h er o in

u se

a n d

ex h ib it in g o p io id

d ep

en d en

ce C h ro n ic

m et h a d o n e u se rs

in o p io id -d ep

en d en

t in d iv id u a ls

C h ro n ic

b u p re n o rp h in e u se rs

in o p io id -d ep

en d en

t in d iv id u a ls

A b st in en

t b u t p re vi o u sl y

o p io id -d ep

en d en

t u se rs

P o ly d ru g m is u se

b u t

p re d o m in a n tl y o p io id

d ep

en d en

t

V is u a l m em

o ry

(v is u a l

ca ch e)

e. g.

co lo u r a n d

sh a p es

S te ve n s et

a l. (2 00 7) ↓

d = 0. 39

(D M S )

P ro ss er

et a l. (2 00 6) ↓ d = 0. 97

(B V R T )

P ir a st u et

a l. (2 00 6) ↓ d = 8. 26

(B V R T )

Li n et

a l. (2 01 2)

↓ d = 0. 60

(W A IS -I II )

a n d d = 3. 29

(B V R T )

M cD

o n a ld

et a l. (2 01 2)

↓ d = 0. 87

(R C FT

) W a n g et

a l. (2 01 4)

↓ d = 0. 31

(W A IS -I II )

a n d d = 0. 64

(s p a n o f vi su a l m em

o ry )

Ya te s et

a l. (2 00 9)

↓ d = 1. 08

(W A IS -I II )

P ir a st u et

a l. (2 00 6) ↓

d = 1. 16

(B V R T )

1 P ro ss er

et a l. (2 00 6)

↑ d = 0. 57

(B V R T )

1 M cD

o n a ld

et a l. (2 01 2)

↑ d

= 0. 32

(P A L)

Ya n et

a l. (2 01 4)

↓ d = 0. 92

(S O P T )

E rs ch e et

a l. (2 00 5,

20 06 )

↓ d = 0. 62

(P R M ) ; ↓ d = 0. 88

(P A L)

O rn st ei n et

a l. (2 00 0)

↓ d = 0. 80

(P R M );

↓ d = 0. 89

(P A L)

B ac h et

a l. (2 01 2)

↔ d = 0. 27

(S W M T )

S p at ia l m em

o ry

(i n n er

sc ri b e)

e. g.

m o ve m en

t se q u en

ce s

D a rk e et

a l. (2 00 0) ↓ d = 0. 85

(D ig it

S ym

b o l in W A IS

II I)

M cD

o n a ld

et a l. (2 01 2)

↓ d = 0. 75

(W A IS -I II )

O rn st ei n et

a l. (2 00 0)

↓ d = 0. 87

(S R M )

↓ d = 0. 53

(S W M )

* = O p io id

gr o u p co m p a re d w it h h ea lt h y co n tr o ls

u n le ss

o th er w is e st at ed

; 1 = A b st in en

t gr o u p co m p a re d w it h m et h a d o n e a n d /o r b u p re n o rp h in e gr o u p a n d N O T h ea lt h y co n tr o ls .

p < 0. 05 ; ↔

= n o d if fe re n ce

in n eu

ro p sy ch o lo gi ca l p er fo rm

a n ce ; ↓ = n eu

ro p sy ch o lo gi ca l im

p a ir m en

t p re se n t; ↑ = im

p ro ve m en

t in

n eu

ro p sy ch o lo gi ca l p er fo rm

a n ce

w h en

co m p a re d w it h h ea lt h y co n tr o ls , d = C o h en

’s ef fe ct

si ze

d ef in ed

a s th e d if fe re n ce

b et w ee n tw

o m ea n s d iv id ed

b y a st a n d a rd

d ev ia ti o n fo r th e d at a . S ta n d a rd is ed

ef fe ct

si ze s a re

re p o rt ed

re ga

rd le ss

o f th e st at is ti ca l si gn

if ic a n ce

(p va lu e)

o f th e re su lt s re p o rt ed

in th e o ri gi n al

st u d ie s.

B V R T,

B en

to n V is u a l R et en

ti o n Te st ; R C FT , R ey

C o m p le x Fi gu

re Te st ; S O P T,

S el f- O rd er ed

P o in ti n g Te st ; W A IS -I II , W es ch le r A d u lt In te lli ge n ce

S ca le - 3r d E d it io n ; W M S R , W es ch le r M em

o ry

S ca le

R ev is ed

; C A N TA

B :D M S , D el ay ed

M at ch in g to

S a m p le ; P A L,

P a ir ed

A ss o ci at e Le a rn in g Ta

sk , P R M , P at te rn

R ec o gn

it io n M em

o ry ; S S P , S p at ia l S p a n .

Psychological Medicine 1175

objective continuing clinical stability were retested 6 months after baseline testing. All recruits making up the H and MMT cohorts presented initially with opioid dependence and reported a history of more than 3 years of continuous and daily illicit opioid use.

Heroin participants (H) performed repeated neuropsycho- logical testing during a single blinded procedure that permitted the objective observation of participants (a) 3–5 hours after their last illicit heroin administration to minimise the confound- ing cognitive effects of acute intoxication; (b) 10–15 hours after the last heroin dose in a state of controlled opioid withdrawal and subsequently (c) following more than 2 weeks on a stable dose of MMT. Clinically this is known as tolerance testing which is a single-blinded procedure that permitted the objective observation of individuals during stages of acute intoxication, withdrawal and subsequent stabilisation on a fixed dose metha- done within a period of 7–14 days (Baldacchino, 2011).

The two opioid dependent groups (H and M groups) were matched for lifetime drug use history, morphine equivalent dosages and other drug use (including tobacco smoking) history 30 days prior to baseline testing. The CANTAB neuropsycho- logical tests presented here refer to the standard tests selected from the batteries used at baseline testing. Where available, paral- lel versions of the tasks were used with the same participant to minimise practice effects.

This approach offered the opportunity to test whether any visuospatial memory measures that differed from those of control participants represented a stable phenomenon, or could be modi- fied by differential opioid exposure and switch to an alternate opi- oid (MMT). A cohort of non-dependent participants prescribed opioids for chronic pain for more than 3 years (P) (N = 28) with no history of ‘illicit’ opioid use, or methadone treatment was also recruited. This group was prescribed tramadol, codeine, or both, for moderate chronic pain. Both P and HC groups were tested only once (Table 2).

Instruments

Clinical All subjects were screened using the MINI Plus v. 5.0 (Sheehan et al., 1998), Maudsley Addiction Profile (Marsden et al., 1998), and Fagerström Test for Nicotine Dependence (Fagerstrom and Schneider, 1989). Urine samples were collected from all partici- pants to confirm their history of recent opioid intake and to con- firm the absence of any other illicit drugs throughout the study period. The Clinical Opiate Withdrawal Scale (COWS) (Wesson & Ling, 2003), quantified the level of opioid withdrawal in the heroin group. A senior research nurse and an experienced clin- ician conducted the assessments. Both were clinically trained. No participants had HIV or AIDS or other medical comorbidities that could affect cognitive functions.

Cognitive The neuropsychological tasks from the Cambridge Neuropsychological Test Automated Battery (CANTAB) (Robbins et al., 1994) were selected on the basis of their known sensitivity to detect impairments in (a) visual [Delayed Matching to Sample (DMS), Pattern Recognition Memory (PRM), Spatial Recognition Memory (SRM) and Paired Associate Learning (PAL)] and (b) spa- tial [Spatial Span Task (SSP) and Spatial WM (SWM) memory per- formance]. Pre-morbid general intelligence was assessed using the National Adult Reading Test (NART) (Nelson, 1982) (online Supplementary Table S1).

Data analysis

Data meeting assumptions of normality and homogeneity of vari- ance were analysed using ANOVA (Winer et al., 1991). All other data were compared using appropriate non-parametric tests (e.g. Kruskal–Wallis and Mann–Whitney tests). Preliminary analysis of all the experimental and control groups separately indicated that the samples did not come from normally distributed popula- tions with the same standard deviation. A planned (a priori) con- trasts analysis was, therefore, run to test for significant differences between the four independent study groups. Mann Whitney U tests established that NART, age, morphine equivalent dosage and previous alcohol use all needed to be used as covariates for further analyses.

Mann Whitney U tests were also performed to examine: (a) sociodemographic characteristics for participants in the H group, comparing those who experienced the lowest (n = 8) and highest (n = 8) scores on the COWS. Similarly, the same test was used to determine if there were differences between the H group of par- ticipants who were tested at baseline and those who were followed up and tested in withdrawal and, subsequently, on methadone. (b) sociodemographic characteristics for participants in the MMT group, comparing those tested at baseline (n = 29) and those fol- lowed up after 6 months (n = 18). (c) sociodemographic character- istics for participants in the H and M groups comparing those with a lifetime subjective history of injecting illicit opioids (n = 41) and those with no history of injecting (n = 11). A high COWS score was defined as a score between 18 and 25; a low COWS score was defined as a score 8–14.

The data were first analysed using an omnibus test to deter- mine if significant differences existed between the groups. If the test revealed significance, appropriate pair-wise comparisons were performed. In order to control for family-wise error, post hoc Bonferroni corrected pairwise comparisons was used (Field, 2009). P values <0.01 were considered significant. This minimised the effects of multiple comparisons, subgroup analyses and/or repeated measures as we were considering a family of statistical inferences simultaneously (Sainani, 2009). Those reported as

Table 2. Study procedures

Testing sessions Illicit or licit opioid use Opioid withdrawal 2–4 weeks on methadone 6 months on methadone

HEROIN [H] † † † —

CHRONIC PAIN [P] † — — —

METHADONE [M] † — — †

HEALTHY CONTROL [HC] † — — —

† = tested; — = not tested.

1176 A. Baldacchino et al.

between p < 0.05 and p > 0.01 are presented as non-significant trends when they are considered relevant to substantiate the inter- pretation of other significant results.

ANCOVA was used to test for group differences with respect to visuospatial memory performance measures. The PRM and SWM outcomes did not meet assumptions of normality and were square-root transformed prior to performing the ANCOVA. However, PAL outcomes were log10 transformed prior to per- forming the ANCOVA. For incremental levels of difficulty within the testing sessions, the within‐subject factor DIFFICULTY was introduced, [e.g. SWM (between/within search errors], SSP (span length between 1–9), DMS (0, 4 and 12 s delays) and PAL (1, 2, 3, 6, or 8 shapes)). Homogeneity of variance was assessed using the Mauchly Sphericity Test. Where data sets sig- nificantly ( p < 0.05) violated this requirement, the Greenhouse Geisser Epsilon (^ε) correction parameter for degrees of freedom was used to calculate a more conservative p value for each F ratio.

Further, a priori subgroup analyses were conducted using (1) a two-group factor reflecting DEPENDENCE status (H and M groups v. P and HC groups) and (2) a two-group factor reflecting INJECTING status (H and M injecting v. H and M never injecting groups) separately as between-subject factors. Importantly, we had specific a priori hypotheses about the impact of dependence on the H and M groups, however, we could not draw any particular conclusion about the exposure of the opiate use. In addition, we used DEPENDENCE as a proxy clinical measure of severity with- out any biological basis.

Similarly, repeated measures ANCOVA was used to evaluate all neuropsychological performance measures between the H group at baseline, in controlled opioid withdrawal and subsequently when stabilised on methadone with presumed opioid receptor occupancy state as a within-subjects factor. Similarly, repeated measures ANCOVA was performed for the M group at baseline and at 6 months follow up with duration as a within-subjects factor.

All analyses were conducted using SPSS for Windows (v.18, SPSS Inc. Chicago, Ill.).

Results

Demographic characteristics

A description of demographics, drug use and smoking variables for the four groups is presented in Table 3. The H and M groups differed from the P and HC groups with respect to several clinical characteristics. Opioid-dependent participants started to drink alcohol approximately 2 years earlier than the other groups. The mean morphine equivalent daily dose for the P group was significantly lower (59.1 mg) than the H and M groups (165.9 mg) ( p < 0.001).

When comparing high against low scores for COWS in the H group, there were no differences between age ( p = 0.88), SIMD score ( p = 0.75), years in education ( p = 0.38), years when starting using alcohol ( p = 0.07), alcohol amount used in last month ( p = 0.87) or current level of nicotine dependence (Fagerström scores) ( p = 0.96).

Similarly, there were no group differences identified on these measures when comparing H group tested at baseline and those retested either through the tolerance testing protocol 6 months later when taking methadone. There were no significant differ- ences with demographic and drug use characteristics between injecting participants (n = 43) and non-injecting participants

(n = 10). However, NART scores were significantly higher ( p < 0.01) in the injecting group.

Visual memory

Performance on DMS There was a significant effect on the percentage of correct responses [GROUP F (3100) = 10.3, p < 0.001]. There were no sig- nificant performance differences between groups with respect to the simultaneous matching condition. Post hoc Bonferroni com- parisons, however, showed participants from the H group made significantly more errors than (a) the HC group at the 0 ( p < 0.005), 4 ( p < 0.001) and 12 s ( p < 0.001) delay stages, (b) the P group for the 4 ( p < 0.01) and 12 s ( p < 0.005) delay stages and (c) the M group for 0 ( p < 0.005), 4 ( p < 0.005) and 12 s ( p < 0.001) delay stages (Fig. 1). In summary, the H group exhibited significant delay-dependent memory impairment when compared with the comparison and control groups.

Performance on PRM, SRM and PAL There were no significant GROUP effects on the number of cor- rect trials [F < 1] and mean response latencies [F < 1] on the PAL and PRM tests. There was a non-significant GROUP trend on the total number of correct trials [F (3102) = 3.6, p = 0.02] on the SRM only.

Spatial memory

Performance on SSP There was a significant GROUP [F (3102) = 16.8, p < 0.001] effect for total errors. Post hoc Bonferroni comparisons showed that the participants from the H group significantly made more errors compared with the M ( p < 0.001, d = 1.25) and HC ( p < 0.005, d = 1.14) groups (Fig. 2). The total error score for the P group lay between those of the H, M and HC groups and did not differ significantly from any of the other three groups ( p = 1.0).

There was also a significant GROUP [F (3101) = 3.7, p < 0.01] effect for span length with post hoc Bonferroni comparisons show- ing the M group was significantly less able to recall successfully the longest sequence compared with HC group ( p < 0.01, d-1.17). The span length for the H ( p = .41) and the P ( p = .21) groups lay between those of the M and HC groups and did not differ significantly from any of the other groups (Fig. 2).

Performance on SWM There was a non-significant GROUP trend for total mean errors [F(3102) = 3.2, p = 0.03] and strategy score [F(3102) = 2.9, p = 0.04] (Tables 4 and 5).

Chronic opioid dependence or injecting status and visuospatial memory performance There were no significant effects for either of the factors DEPENDENCE or INJECTING STATUS on any of the DMS, PRM, SRM, and PAL outcome measures.

However, there were significant DEPENDENCE effects for total errors [F (3104) = 6.5, p < 0.01] on the SWM, but with no significant DEPENDENCE effects on the strategy score [F(1104) = 4.8,p = 0.03]. There was a significant DEPENDENCE status and task difficulty interaction on the SWM test for total errors [F (3133.75) = 6.2, p < 0.01]. Analysis using INJECTING status failed to reveal any significant effects or interactions on any SWM outcomes.

Psychological Medicine 1177

Table 3. Comparative demographic, clinical and substance use data for experimental and control groups

Demographic and clinical data HEROIN (H) METHADONE (M) PAIN (P) HEALTHY CONTROLS

(HC) Sig.¹

N 24 29 28 28 n/a

Age (yrs)* 26.30 (3.45) 27.30 (2.34) 33.97 (4.35) 24.12 (3.56) H > P = p < 0.001 M > P and M > HC = p < 0.01

SIMD* 3.60 (1.9) 3.41 (1.4) 4.60 (2.0) 5.90 (2.5) H > HC and M > HC = p < 0.001 M > P = p < 0.01

Unemployed (%) 87.50 86.2 50 0 p < 0.001

Stable accommodation (%)2 87 93 100 92.80 M > P = p < 0.005

Education (yrs) * 10.80(1.5) 10.60 (2.3) 11.18 (1.22) 15.40 (2.1) H > HC, P > HC and M > HC = p < 0.001

NART * 106.10 (12.2) 108.90 (7.6) 115.90 (4.9) 118.30 (5.1) P > H and HC>H = p < 0.001 HC>M = p < 0.001; P > M = p < 0.01

Drug, nicotine and alcohol histories (self-report) HEROIN METHADONE PAIN HEALTHY CONTROLS Sig.¹ H/M v. P/C

Percentage of nicotine smokers 91.67 89.65 39.29 3.57 H > P,H > HC, M > P, M > HC = p < 0.001

Days of alcohol use (last 30 days) * 2.20 (6.1) (n = 10) 4.0 (4.9) (n = 15) 5.10 (8.3) (n = 17) 4.00 (6.3) (n = 17) ns

Type of opioids and number of participants Heroin n = 24 Methadone n = 29 Tramadol n = 18 Codeine n = 13*2 n/a n/a

Daily intake expressed as morphine equivalence (mg)⁰ * 184.50(82.1) (n = 24) 147.40 (59.3) (n = 29) 59.10(46.8) (n = 28) n/a H > P and M > P = p < 0.001

Age first used heroin (yrs) * 19.40 (4.1) (n = 24) 17.90 (2.6) (n = 29) n/a n/a ns

Age opioid dependent (yrs) * 20.90 (3.9) (n = 24) 19.90 (2.8) (n = 29) n/a n/a ns

Age injecting opioids (yrs) * 20.50 (4.0) (n = 17) 19.10 (6.0) (n = 29) n/a n/a ns

Years of opioid use* 6.10 (2.9) (n = 24) 8.80 (2.8) (n = 29) 5.00(2.3) (n = 28) n/a M > H and M > P = p < 0.001

Stable methadone use (yrs) * n/a 1.30 (0.5) (n = 29) n/a n/a n/a

Days of heroin use (last 30 days) * 29.50(2.7)(n = 24) n/a n/a n/a ns

Sig¹ = significance at p < 0.01 two-tailed, 2Stable accommodation = own house + rented accommodation + living with parents (excluded hostel, student and homeless). * = mean total scores (+/- standard deviation), *1 = mean, *2 = Some participants prescribed Tramadol were also prescribed Codeine hence total number (31) higher than number recruited (n = 28). n/a, no data is relevant as the Pain and HC groups did not present with illicit heroin use and/or dependence history; yrs, years; SIMD, Scottish Index of Multiple Deprivation; NART, National Adult Reading Test; %, percentage; ns, not significant; N, Total number in group; yrs, years; n, number of individuals analysed; mg, milligrammes; ⁰Opioid equivalence: [Vieweg et al. (2005)].

1178 A . B a ld acch

in o et

a l.

There was a significant effect of DEPENDENCE status [F (1103) = 7.1, p < 0.01] for span length on the SSP test, but not for total errors [F (1104) = 1.1, p = 0.29]. There was no signifi- cant effect on INJECTING status on SSP outcomes.

Type of opioid exposure at different stages of treatment and visuospatial memory performance When the H group was tested during different states of opioid exposure (tolerance testing) there was a significant effect of on the DMS mean correct latency [F (2, 34.22) = 10.5, p < 0.001]. Post hoc Bonferroni comparisons showed a significant improve- ment at the 12 s delay stage ( p < 0.001) but not the 0 s and 4 s delay conditions. These improvements were noted in comparison with the stable MMT, the ‘withdrawal’ stage ( p < 0.005) and the

illicit heroin stage ( p < 0.001). There was no effect on PRM, SRM, PAL, SSP and SWM outcomes.

There was a trend ( p < 0.05) for the M group to improve on DMS and SWM outcomes in selecting the right stimulus follow- ing prolonged exposure to a stable dose of methadone. There were no significant additional effects on all PRM, SRM, PAL, and SSP outcomes in the M group following prolonged exposure to a stable dose of methadone.

Discussion

This study identified the differential effects of chronic heroin and methadone exposures on neuropsychological measures of visuo- spatial memory that were independent of estimates of addiction

Fig. 1. DMS-Percentage of correct responses at differ- ent delay conditions (Means and Standard Deviation). Post hoc Bonferroni comparisons identified participants from the HEROIN group significantly mak- ing more errors than did: the HEALTHY CONTROL group in the 0 (**p < 0.005), 4 (***p < 0.001) and 12 s (***p < 0.001) delay stages, the CHRONIC PAIN group for the 4 (*p < 0.01) and 12 s (**p < 0.005) delay stages and the METHADONE group for 0(**p < 0.005), 4 (**p < 0.005) and 12 s (***p < 0.001) delay stages. Sim = Simultaneous condition, S.D. = Standard Deviation.

Fig. 2. A. Total errors in Spatial Span (SSP) task (Means and Standard Deviation). Overall participants significantly made more errors [F (3102) = 16.8, p < 0.001]. Post hoc Bonferroni comparisons identified the HEROIN group participants significantly making more errors compared to the METHADONE ( p < 0.001) and HEALTHY CONTROL ( p < 0.005) groups. The total errors for the CHRONIC PAIN participants lay between those of the HEROIN, METHADONE and HEALTHY CONTROL partici- pants and did not differ significantly from these three groups ( p = 1.0). B. Span length in the SSP task (Means and Standard Deviation). Overall participants were significantly unable to recall successfully the longest sequence [F(3101) = 3.7, p < 0.01] with Post hoc Bonferroni comparisons identifying the METHADONE group as the group that significantly was less able to recall successfully the longest sequence compared to the HEALTHY CONTROL group ( p < 0.01).

Psychological Medicine 1179

severity (injecting behaviour, dependence status). The study also identified an improvement in DMS performance (specifically at longer delays) when the M group were compared with the H group and also when the H group was tolerance tested and then stabilised on methadone.

Interpretation

Although there are likely commonalities in the ways in which all opioids can affect cognitive performance, much can be learned from considering the distinctive features of each type of opioid and its effect on visuospatial memory. In this study, we have described significant differences in performance between the her- oin, methadone and chronic pain groups. The H but not the M group differentially showed impairment in visual memory whereas both the H and M groups showed impairment in spatial

memory. Importantly, the performance of the licit opioid-exposed group was broadly similar to that of the HC group. However, due to the significantly lower dose equivalence in the licit opioid group one needs to cautiously suggest that the impairments in visuospatial memory reported are evoked by chronic exposure to illicit opioids. Participants with potential confounders, such as impaired mood state (Jollant et al., 2007), non-fatal overdoses, co-morbid personality disorders (Vassileva et al., 2007) or a co-occurrence of polydrug dependence were excluded from the study. Thus, the impairments in visuospatial memory measures, seen in the participants who are opioid dependent, cannot be caused by these potential confounders.

Heroin users presented with significant delayed memory impairments when compared with either M or P groups using a cross-sectional comparison. These impairments diminished with a longer duration of stable methadone. Additionally, within-

Table 4. Summary of baseline neuropsychological findings for memory and learning (not adjusted for covariates)

HEROIN N = 24 METHADONE

N = 29 CHRONIC PAIN

N = 28 HEALTHY CONTROL

N = 28

Memory and Learning Mean (s.d) Mean (s.d) Mean (s.d) Mean (s.d) Sig. d

Delayed Matching to Sample (DMS)

Total Number of Correct Responses (all delays)

22.04 (3.59) 25.76 (2.87) 25.39 (3.04) 27.43 (1.89) P > H*** C > H***

1.00 1.87

Mean Correct Latency (all delays)

3630.61 (922.64) 4372.35 (1579.98) 3310.22 (1049.63) 3536.67 (745.99) M > P** 0.79

Paired Associate Learning (PAL) (log10)

Total Errors (Adjusted) 0.22 (0.45) 0.46 (0.64) 0.01 (0.06) 0.00 (0.00) NS

Mean Errors to Success 0.51 (0.26) 0.42 (0.19) 0.45 (0.27) 0.29 (0.19) C < H* 0.97

Mean Trials to Success 0.45 (0.09) 0.41 (0.06) 0.43 (0.09) 0.37 (0.05) C < H* 1.10

Memory Score 1.23 (0.11) 1.30 (0.06) 1.27 (0.08) 1.33 (0.06) C > H*** 1.13

Stages Completed 0.94 (0.03) 0.95 (0.02) 0.95 (0.02) 0.95 (0.00) NS

Stages complete 1st trial 0.79 (0.08) 0.84 (0.05) 0.82 (0.07) 0.87 (0.04) C > H*** 1.26

Pattern Recognition Memory (PRM) (SQRT)

Percentage Trials

Correct 9.18 (0.69) 9.27 (0.51) 9.26 (0.49) 9.64 (0.46) C > H* 0.78

Incorrect 3.37 (1.97) 3.46 (1.46) 3.43 (1.52) 1.97 (1.74) C < H* C < M*

0.75 0.92

Correct Response Latency

Correct 45.82 (5.36) 47.88 (5.98) 46.37 (6.74) 46.60 (5.56) NS

Incorrect 49.80 (11.99) 54.48 (11.74) 50.22 (10.76) 50.15 (8.96) NS

Spatial Recognition Memory (SRM)

Number of Trials

Correct 15.58 (2.21) 16.48 (1.94) 15.89 (1.77) 17.75 (1.51) C > H*** 1.15

Incorrect 4.42 (2.21) 3.52 (1.94) 4.11 (1.77) 2.25 (1.51) C < M***

1.15

Mean Latency

Correct 1979.04 (432.81) 2358.89 (810.68) 2035.71 (505.51) 2040.19 (469.3) NS

Incorrect 2172 (878.02) 2520.36 (966.13) 2199.65 (737.71) 2456.38 (122.41) NS

d = effect size, SQRT = square root transformation; log10 = logarithmic 10 transformation, Sig = significance. * = p < 0.01, ** = p < 0.005, *** = p < 0.001, NS = no significant impairment in neuropsychological outcomes with p < 0.01, H = HEROIN Group, P = CHRONIC PAIN Group, M = METHADONE Group, C = HEALTHY CONTROL Group.

1180 A. Baldacchino et al.

subject comparisons of participants who had used illicit heroin but had been transferred to a stable dose of methadone for only a few weeks also described a significant improvement in visuo- spatial impairments when stabilised on methadone. The poor per- formance of the H group compared with the M group supports previous findings of deficits in learning and memory that may be a function of damage from neurotoxicity to the hippocampal formation in the temporal lobe (Day et al., 2003) which is struc- turally altered by drug addictions (Robbins and Everitt, 2002) and possibly reversed through administration of opioid replacement therapy such as methadone.

However, since DMS outcome impairment did increase signifi- cantly as a function of delay in the heroin group, the results are also suggesting that the impairment might also lie in higher order cognitive executive processes rather than solely as impair- ment in the memory storage process.

Additionally, tasks such as Paired Associates Learning (PAL) are associated with hippocampal function and may be highly sen- sitive to identify those with memory impairments.

Even though this study did not investigate the cognitive impairments observed in response to different opioids using

molecular pharmacological techniques one still needs to be aware that heroin, methadone, codeine and tramadol interact with different μ opioid receptor subtypes exhibiting different acti- vation profiles. This results in subtle pharmacological differences in potency, effectiveness, tolerability and neurotoxicity of the drugs (Pasternak, 2012). These opioids also have variable agonist activity at both δ and κ opioid receptors (Pathan and Williams, 2012). Furthermore, the active metabolites of heroin and metha- done display multimodal subunit-dependent antagonism of 5-HT3 receptors (Deeb et al., 2009) and methadone, but not her- oin, displays N-methyl- D-aspartate (NMDA) receptor antagonist properties (Davis and Inturrisi, 1999). The licit opioid users were prescribed either tramadol, codeine or both in much lower mor- phine equivalent doses. Tramadol, like methadone, is an opioid receptor agonist that, in addition to its MOP effects, also have activity at other non-opioid sites through the modulation of sero- tonin and norepinephrine reuptake (Pathan and Williams, 2012). These cellular and molecular variations might determine different neuropsychological impairments (Baldacchino et al., 2014).

The different neuropsychological impairments observed in the heroin and methadone cohorts might be linked to other factors

Table 5. Summary of results from analysis of visuospatial test outcomes*. Unless specified comparison is with HEALTHY CONTROL and/or PAIN participants1

HEROIN v. METHADONE v. CHRONIC PAIN v. HEALTHY CONTROL

Opioid DEPENDENCE (OD) v. Non- Opioid DEPENDENCE (Non- OD)

INJECTING (INJ) v. non- injecting

(a) Visual Memory

Delayed Matching to Sample (DMS)

Total Correct Responses (0,4,12 s delay stages)

↓HEROIN ↔ ↔

Pattern Recognition Memory (PRM)

Total Number of Correct Trials*

↔ ↔ ↔

Spatial Recognition Memory (SRM)

Total Number of Correct Trials*

↓HEROIN ↔ ↔

Paired Associate Learning (PAL)

Mean Total Number of Errors* ↔ ↔ ↔

Memory Score* ↓HEROIN ↔ ↔

(b)Spatial Memory

Spatial Span (SSP)

Span Length* ↓METHADONE ↓OD > Non- OD ↔

Total Errors* ↓HEROIN ↔ ↔

Spatial Working Memory (SWM)

Total Search Errors* ↓HEROIN,↓METHADONE ↓OD > Non- OD ↔

Between Search Errors* ↓HEROIN,↓ METHADONE ↓OD > Non- OD ↔

Within Search Errors* ↔ ↔ ↔

Double Search Errors* ↔ ↔ ↔

* = ANCOVA ‘between subject factor’ of GROUP, DEPENDENCE and INJECTING analysed separately; 1 = significant effects with p < 0.01, ↓ = significant neuropsychological impairments present, ↔ = no significant neuropsychological impairments present.

Psychological Medicine 1181

that could selectively influence visuospatial processing. Human studies found impaired vigilance and slower reaction times in patients receiving high doses of methadone (Hepner et al., 2002). This suggests that there might be a trade-off between the intended effects of opioid agonists and the promotion of cognitive abilities. Current results suggest that SWM capacity is intact in opiate-dependent patients when treated with a moderate opioid dose. However, there may be individual patients (e.g. those treated with high opioid doses, using illicit heroin or using non-opioid drugs frequently) that show deficits in SWM. The strict method- ology of our study attempted to minimise such effects.

Limitations

This study recruited treatment-seeking males and, thus, results may not generalise to non-treatment seeking, or female, popula- tions (Ardila et al., 2011; McGivern et al., 2012). It is important to appreciate the potential impact social deprivation and ageing may have on the neuropsychological performance in opioid dependence (Hackman et al., 2010). Studies indicate that negative and stressful events during the early life period can persistently affect brain development and cognitive function such as learning and memory (Hanson et al., 2015; Krugers et al., 2017). Drug use and risk factor histories of participants were, by necessity, based upon self-report, and no blood, hair or saliva samples taken to validate the accuracy of the information. Neuropsychological research has shown that consumption of alcohol, benzodiazepines and psychostimulants are potentially important confounding variables (Koob and Volkow, 2010). The present study used strin- gent criteria to exclude regular and dependent users of most psy- choactive substances. The exception to this was lack of nicotine use in the healthy controls. We could not control for the effects of this psychostimulant and this may have influenced our results due to its known effects on visuospatial memory (Richards et al., 2003). This study also conducted urine drug screen analysis to confirm the absence of recent amphetamine, opioids, benzodi- azepine and cocaine use prior to every session.

Opioid-dependent participants had a mean daily dose of 165 mg morphine equivalent. The P group, however, had a sig- nificant lower mean daily dose of 59.1 mg morphine equivalent. Opioids can cause measurable cognitive impairment even at low doses and equi-analgesic doses of different opioids may have non- linear and non-equivalent adverse cognitive effects (Gagnon and Bruera, 1999). Opioid drug dose is often the only drug treatment variable that is included in the analyses of correlates of perform- ance in visuospatial memory. Grevert et al. (1977) reported a stat- istically significant correlation (0.37) between methadone dose and trials needed for correct visual recognition. However, when more rigorous statistical methods have been used (such as covari- ance or regression analyses), the relationships between methadone (Specka et al., 2000; Prosser et al., 2008; Soyka et al., 2008; Yin et al., 2012) or buprenorphine (Lintzeris et al., 2006; Loeber et al., 2008; Shmygalev et al., 2011) doses and cognitive perform- ance have turned out to be very low and statistically non- significant. In this study, we could not repeat cognitive testing in the healthy control and we could not recruit groups with simi- lar socioeconomic status. It would be warranted for future studies as this will give a further confirmation of the cognitive improve- ment found in this study

Finally, we want to highlight that there is no literature to com- pare if any, dose-related cognitive effects between prescribed methadone, tramadol, codeine and/or combinations.

Clinical interpretation

This study identified opioid specific visuospatial memory impair- ments that need to be considered within the recovery-oriented treatment programmes for opioid-dependent populations (Ekhtiari et al., 2017). The visuospatial memory impairments will have implications for the successful outcomes of current non- pharmacological approaches, such as relapse prevention techni- ques and motivational enhancement therapies since all these interventions demand intact sophisticated encoding and retrieval strategies, visual processing and inhibition of irrelevant informa- tion. These approaches are reported to improve outcomes in indi- viduals with opioid dependence when they are used to complement traditional therapeutic interventions (Ruiz-Sánchez de León et al., 2011; Rezapour et al., 2015). The aims of these novel clinical interventions are to improve the general cognitive functioning, in particular, executive and memory functioning, which the results of this study suggest may be compromised in opioid-dependent treatment-seeking populations.

Supplementary material. The supplementary material for this article can be found at https://doi.org/10.1017/S0033291718003318

Acknowledgements. The authors thank NHS Fife Research and Development Department for assistance with the purchase of the CANTAB system. The authors also acknowledge the following services that helped to identify eligible participants. Fife NHS Addiction Services, Tayside Pain Services, Tayside Arrest Referral Team (NCH), Fife Drug Treatment and Testing Order Team, Frontline Fife, Fife Intensive Rehabilitation Support Team, Fife NHS Clinical Psychology Service and a multitude of General Practitioner Practices and NHS Primary Care Health Centres in the Fife and Tayside areas. Finally, to all individuals who gave up so much of their valuable time to participate in this study with the sole aim of helping in the better understanding of the ‘brain’ in addiction.

Declaration of interest. A.B. has received unrestricted educational grants from Schering Plough, Merck Serono, Lundbeck and Indivior. D.J.B. has received research support from Vifor Pharma and a BBSRC Case award in col- laboration with MSD and an honorarium from the Society for Research on Nicotine & Tobacco as Editor-in-Chief the Society’s research journal, Nicotine & Tobacco Research. K.M. has chaired advisory boards for studies of deep brain stimulation for obsessive-compulsive disorder sponsored by Medtronic. He has received educational grants from Cyberonics Inc. and Schering Plough, and he has received research project funding from Merck Serono, Lundbeck and Indivior and also from St Jude Medical for a multicentre clinical trial of deep brain stimulation for depression. He has received travel and accommodation support to attend meetings from Medtronic, St Jude Medical, the Focused Ultrasound Foundation and The Leksell Gamma Knife Society.

References

Ardila M, Rosselli E, Matute O and Inozemtseva O (2011) Gender differ- ences in cognitive development. Developmental Psychology 47, 984–990.

Awh E, Vogel EK and Oh SH (2006) Interactions between attention and working memory. Neuroscience 139, 201–208.

Bach P, Vollstädt-Klein S, Frischknecht U, Hoerst M, Kiefer F, Mann K, Ende G and Hermann D (2012) Diminished brain Functional Magnetic Resonance imaging activation in patients on opiate maintenance despite nor- mal spatial working memory task performance. Clinical Neuropharmacology 35, 153–160.

Baddeley AD and Hitch GJ (1974) Working memory. In Bower GA (ed.), Recent Advances in Learning and Motivation. New York: Academic Press, pp. 47–90.

Baddeley AD and Logie RH (1999) Working memory: the multiple compo- nent model. In Miyake A and Shah P (eds), Models of Working Memory. New York: Cambridge University Press, pp. 29–61.

1182 A. Baldacchino et al.

Baldacchino A (2011) Procedure for Tolerance Testing (A1), NHS Fife Addiction Services. Fife Area and Drug Therapeutic Committee (ADTC), Fife. https://www.fifeadtc.scot.nhs.uk/media/7032/a1-procedure-for-tolerance- testing-final-april-2011.pdf. Accessed 23rd September 2018.

Baldacchino A, Balfour D, Passetti F, Humphris G and Matthews K (2012) The neuropsychological consequences of chronic opioid use: a quantitative review and meta-analysis. Neuroscience and Biobehavioral Review 36, 2056– 2068.

Baldacchino A, Balfour DJ and Matthews K (2014) Impulsivity and opioid drugs: differential effects of heroin, methadone and prescribed analgesic medication. Psychological Medicine 8, 1–13.

Baldacchino A, Armanyous M, Balfour D, Humphris G and Matthews K (2017) The neuropsychological consequences of chronic methadone use: a quantitative review and meta-analysis. Neuroscience and Biobehavioural Review 73, 23–38.

Bormann T, Seyboth M, Umarova R and Weiller C (2015) ‘I know your name, but not your number’–patients with verbal short-term memory defi- cits are impaired in learning sequences of digits. Neuropsychologia 72, 80–86.

Darke S, Sims J, McDonald S and Wickes W (2000) Cognitive impairment among methadone maintenance patients. Addiction 95, 687–695.

Davis AM and Inturrisi CE (1999) D-Methadone blocks morphine tolerance and N-methyl-D-aspartate-induced hyperalgesia. Journal of Pharmacology and Experimental Therapeutics 289, 1048–1053.

Day M, Langston R and Morris RGM (2003) Glutamate-receptor-mediated encoding and retrieval of paired-associate learning. Nature 424, 205–209.

Deeb TZ, Sharp D and Hales TG (2009) Direct subunit-dependent multi- modal 5-hydroxytryptamine receptor antagonism by methadone. Molecular Pharmacology 75, 908–917.

Della Sala S, Gray C, Baddeley A, Allamano N and Wilson L (1999) Pattern span: a tool for unwelding visuo-spatial memory. Neuropsychologia 37, 1189–1199.

Ekhtiari H, Rezapour T, Robin RL, Aupperle L and Paulus MP (2017) Neuroscience-informed psychoeducation for addiction medicine: A neuro- cognitive perspective. Progress in Brain Research ISSN 0079-6123 https:// doi.org/10.1016/bs.pbr.2017.08.013. Accessed 20th January 2018.

Eriksson J, Vogel EK, Lansner A, Bergstrom F and Nyberg L (2015) Neurocognitive architecture of working memory. Neuron 88, 33–46.

Ersche KD, Fletcher PC, Lewis SJG, Clark L, Stocks-Gee G, London M, Deakin JB, Robbins TW and Sahakian BJ (2005) Abnormal frontal activa- tions related to decision-making in current and former amphetamine and opiate dependent individuals. Psychopharmacology 180, 612–623.

Ersche KD, Clark L, London M, Robbins TW and Sahakian BJ (2006) Profile of executive and memory function associated with amphetamine and opiate dependence. Neuropsychopharmacology 31, 1036–1047.

Fagerstrom KO and Schneider NG (1989) Measuring nicotine dependence: a review of the Fagerstrom Tolerance Questionnaire. Journal of Behavioral Medicine 12, 159–182.

Fernandez-Serrano MJ, Perez-Garcia M and Verdejo-Garcia A (2011) What are the specific v. generalised effects of drugs of abuse on neuropsycho- logical performance? Neuroscience and Biobehavioural Reviews 35, 377–406.

Field A (2009) Discovering Statistics Using SPSS, 3rd Edn. London: SAGE Publications.

Gagnon B and Bruera E (1999) Differences in the ratios of morphine to methadone in patients with neuropathic pain versus non-neuropathic pain. Journal of Pain Symptom Management 18, 120–125.

Grevert P, Masover B and Goldstein A (1977) Failure of methadone and levo- methadyl acetate (levo-alpha-acetylmethadol, LAAM) maintenance to effect memory. Archives of General Psychiatry 34, 849–853.

Hackman DA, Farah MJ and Meaney MJ (2010) Socioeconomic status and the brain: mechanistic insights from human and animal research. Nature Reviews Neuroscience 11, 651–659.

Hanson JL, Nacewicz BM, Sutterer MJ, Cayo AA, Schaefer SM, Rudolph KD, Shirtcliff EA, Pollak SD and Davidson RJ (2015) Behavioral problems after early life stress, contributions of the hippocampus and amygdala. Biological Psychiatry 77, 314–323.

Henry PK, Umbricht A, Kleykamp BA, Vandrey R, Strain EC, Bigelow GE and Mintzer MZ (2012) Comparison of cognitive performance in metha- done maintenance patients with and without current cocaine dependence. Drug and Alcohol Dependence 124, 167–171.

Hepner IJ, Homewood J and Taylor AJ (2002) Methadone disrupts perform- ance on the working memory version of the morris water task. Physiology and Behavior 76, 41–49.

Hyman SE (2005) Addiction: a disease of learning and memory. American Journal of Psychiatry 162, 1414–1422.

Hyman SE, Malenka RC and Nestler EJ (2006) Neural mechanisms of addic- tion: the role of reward-related learning and memory. Annual Review of Neuroscience 29, 565–598.

Jollant F, Guillaume S, Jaussent I, Castelnau D, Malafosse A and Courtet P (2007) Impaired decision-making in suicide attempters may increase the risk of problems in affective relationships. Journal of Affective Disorders 99, 59–62.

Koob GF and Volkow ND (2010) Neurocircuitry of addiction. Neuropsychopharmacology 35, 217–238.

Krugers HJ, Arpa JM, Xiong H, Kanatsou S, Lesuis SL, Korosi A, Joels M and Lucassen PJ (2017) Early life adversity: lasting consequences for emo- tional learning. Neurobiology of Stress 6, 14–21.

Kübler A, Murphy K and Garavan H (2005) Cocaine dependence and atten- tion switching within and between verbal and visuospatial working mem- ory. European Journal of Neuroscience 21, 1984–1992.

Lin W-C, Chou K-H, Chen H-L, Huang C-C, Lu C-H, Li S-H, Wang Y-L, Cheng Y-F, Lin C-P and Chen C-C (2012) Structural deficits in the emo- tion circuit and cerebellum are associated with depression, anxiety and cog- nitive dysfunction in methadone maintenance patients: a voxel-based morphometric study. Psychiatry Research 201, 89–97.

Lintzeris N, Mitchell TB, Bond A, Nestor L and Strang J (2006) Interactions on mixing diazepam with methadone or buprenorphine in maintenance patients. Journal of Clinical Psychopharmacology 26, 274–283.

Loeber S, Kniest A, Diehl A, Mann K and Croissant B (2008) Neuropsychological functioning of opiate-dependent patients: a nonrando- mized comparison of patients preferring either buprenorphine or metha- done maintenance treatment. American Journal of Drug and Alcohol Abuse 34, 584–593.

Mammarella IC, Pazzaglia F and Cornoldi C (2008) Evidence for different components in children’s visuospatial working memory. British Journal of Developmental Psychology 26, 337–335.

Marsden J, Gossop G, Stewart D, Best D, Farrell M, Lehmann P, Edwards C and Strang J (1998) The maudsley addiction profile (MAP): a brief instru- ment for assessing treatment outcome. Addiction 93, 1857–1867.

McDonald S, Darke S, Kaye S and Torok M (2012) Deficits in social percep- tion in opioid maintenance patients, abstinent opioid users and non-opioid users. Addiction 108, 566–574.

McGivern RF, Adams A, Handa RJ and Pineda JA (2012) Men and women exhibit a differential bias for processing movement versus objects. PLoS ONE 7, e32238 http://dx.doi.org/10.1371/journal.pone.003223. Accessed 20th August 2018.

Miyake A and Shah P (1999) Models of Working Memory. Mechanisms of Active Maintenance and Executive Control. Cambridge: Cambridge University Press.

Nelson HE (1982) National Adult Reading Test (NART): Test Manual. Windsor, UK: NFER. Nelson.

Ornstein TJ, Iddon JL, Baldacchino AM, Sahakian BJ, London M, Everitt BJ and Robbins TW (2000) Profiles of cognitive dysfunction in chronic amphet- amine and heroin abusers. Neuropsychopharmacology 23, 113–126.

Passolunghi MC and Mammarella IC (2010) Spatial and visual working memory ability in children with difficulties in arithmetic word problem solving. European Journal of Cognitive Psychology 22, 944–963.

Pasternak GW (2012) Preclinical pharmacology and opioid combinations. Pain Medicine 13, S4–S11.

Pathan H and Williams J (2012) Basic opioid pharmacology: an update. British Journal of Pain 6, 11–16.

Pirastu M, Fais R, Messina M, Bini V, Spiga S, Falconieri D and Diana M (2006) Impaired decision-making in opiate-dependent subjects: effect of pharmacological therapies. Drug and Alcohol Dependence 83, 163–168.

Prosser J, Cohen LJ, Steinfeld M, Eisenberg D, London ED and Galynker II (2006) Neuropsychological functioning in opiate dependent subjects receiv- ing and following methadone maintenance treatment. Drug and Alcohol Dependence 84, 240–247.

Psychological Medicine 1183

Prosser J, Eisenberg D, Davey E, Steinfeld M, Cohen L, London E and Galynker I (2008) Character pathology and neuropsychological test per- formance in remitted opiate dependence. Substance Abuse Treatment, Prevention, and Policy 3, 23.

Rezapour T, Hatami J, Farhoudian A, Sofuoglu M, Noroozi A, Daneshmand R, Samiei A and Ekhtiari H (2015) NEuro COgnitive REhabilitation for disease of addiction (NECOREDA) program: from devel- opment to trial. Basic and Clinical Neuroscience 6, 291–298.

Richards M, Jarvis MJ, Thompson N and Wadsworth ME (2003) Cigarette smoking and cognitive decline in midlife: evidence from a prospective birth cohort study. American Journal in Public Health 93, 994–998.

Robbins TW and Everitt BJ (2002) Limbic-striatal memory systems and drug addiction. Neurobiology of Learning and Memory 78, 625–636.

Robbins TW, James M, Owen AM, Sahakian BJ, McInnes L and Rabbitt P (1994) Cambridge Neuropsychological Test Automated Battery (CANTAB): a factor analytic study of a large sample of normal elderly volunteers. Dementia (basel, Switzerland) 5, 266–281.

Rounsaville JB, Jones C, Novelly RA and Kleber A (1982) Neuropsychological functioning in opiate addicts. The Journal of Nervous and Mental Disease 170, 209–216.

Ruiz-Sánchez de León JM, Pedrero-Pérez EJ, Rojo-Mota G, Llanero- Luque M and Puerta-García C (2011) A proposal for a protocol of neuro- psychological assessment for use in addictions. Revista di Neurologia 53, 483–493.

Sainani KL (2009) The problem of multiple testing. PM&R 1, 1098–1103. Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E,

Hergueta T, Baker R and 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. Journal of Clinical Psychiatry 59, 22–33.

Shmygalev S, Damm M, Weckbecker K, Berghaus G, Petzke F and Sabatowski R (2011) The impact of long-term maintenance treatment with buprenorphine on complex psychomotor and cognitive function. Drug and Alcohol Dependence 117, 190–197.

Soyka M, Lieb M, Kagerer S, Zingg C, Koller G, Lehnert P, Limmer P, Kuefner H and Henning-Fast K (2008) Cognitive functioning during methadone and buprenorphine treatment. Journal of Clinical Psychopharmacology 28, 699–703.

Specka M, Finkbeiner T, Lodemann E, Leifert K, Kluwig J and Gastpar M (2000) Cognitive-motor performance of methadone maintained patients. European Addiction Research 6, 8–19.

Stevens A, Peschk I and Schwarz J (2007) Implicit learning, executive func- tion and hedonic activity in chronic polydrug abusers, currently abstinent polydrug abusers and controls. Addiction 102, 937–946.

Tolomeo S, Gray S, Matthews K, Steele JD and Baldacchino A (2016) Multifaceted impairments in impulsivity and brain structural abnormalities in opioid dependence and abstinence. Psychological Medicine 46, 2841–2853.

Tolomeo S, Matthews K, Steele JD and Baldacchino A (2018) Compulsivity in opioid dependence. Progress in Neuro-Psychopharmacology and Biological Psychiatry 81, 333–339.

Twamley EW, Palmer BW, Jeste PV, Taylor MJ and Heaton RK (2006) Transient and executive function working memory in schizophrenia. Schizophrenia Research 87, 185–190.

Wang GY, Wouldes TA, Kydd R, Jensen M and Russell B (2014) Neuropsychological performance of methadone-maintained opiate users. Journal of Psychopharmacology 28, 789–799.

Wesson DR and Ling W (2003) The clinical opiate withdrawal scale (COWS). Journal of Psychoactive Drugs 35, 253–259.

Westen D (2006) Implications of research in cognitive neuroscience for psy- chodynamic psychotherapy. Focus 4, 215–222.

Winer BJ, Brown DR and Michels KM (1991) Statistical Principles in Experimental Design, 3rd Edn. New York: McGraw Hill.

Vassileva J, Petkova P, Georgiev S, Martin EM, Tersiyski R, Raycheva M, Velinov V and Marinov P (2007) Impaired decision-making in psycho- pathic heroin addicts. Drug and Alcohol Dependence 8, 287–289.

Vieweg VR, Carlyle-Lipps WF and Fernandez A (2005) Opioids and metha- done equivalents for clinicians. Primary care companion. Journal of Clinical Psychiatry 7, 86–88.

Yan W-S., Li Y-H, Xiao L, Zhua N, Bechara A and Sui N (2014) Working memory and affective decision-making in addiction: A neurocognitive com- parison between heroin addicts, pathological gamblers and healthy controls. Drug and Alcohol Dependence 134, 194–200.

Yates ST (2009) An evaluation of the cognitive functioning of individuals on Methadone Maintenance Treatment and its relation to treatment adherence. A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy in Psychology, University of Waikato, New Zealand.

Yin LS, Li ZZ, Pang LJ, Zhu CY, Wang SM, Zhang L, Tang WC and Dai J (2012) Effects of methadone maintenance treatment on working memory in male heroin dependent patients. Zhonghua Yi Xue Za Zhi Journal 92, 464–467.

Zihl J and von Cramon D (1979) The contribution of the “second” visual sys- tem to directed visual attention in man. Brain 102, 853–856.

1184 A. Baldacchino et al.

Copyright © Cambridge University Press 2018