## Abstract

Alcohol dependence, a chronic relapsing disorder, is characterized by an impaired ability to regulate compulsive urges to consume alcohol. Very few empirical studies have examined the presence of these executive deficits, how they relate to craving, and the enduring nature of these deficits during abstinence. As such, the current study aimed to characterize these cognitive deficits within a sample of 24 alcohol-dependent participants post-detoxification and 23 non-alcohol-dependent participants. Participants were administered the Sustained Attention to Response Task to measure response inhibition and sustained attention and the Random Number Generation Task to examine executive deficits. Correlations between cognitive performance and clinical measures of alcohol dependence were examined. As predicted, the alcohol-dependent group exhibited poorer performance across the domains of response inhibition, executive function, and attentional control. Cognitive performance was related to clinical measures of craving and years of alcohol consumption, whereas the duration of abstinence was not associated with improved cognitive performance. These findings highlight the need for therapeutic strategies to target these enduring neurocognitive deficits in improving the treatment of alcohol dependence.

## Introduction

Alcohol dependence is a chronic relapsing disorder characterized by impaired control (Hyman and Malenka, 2001) and continued use despite recurrent health, psychological, and/or social consequences (DSM-IV-TR, American Psychiatric Association, 2000). A diminished capacity to regulate alcohol consumption, in spite of aversive consequences, remains a critical challenge for the treatment of alcohol use dependence (AUD) (Noël, Bechara, Brevers, Verbanck, & Campanella, 2010). Despite a robust literature documenting the neurotoxic effects of alcohol and alcohol-related brain damage (Alfonso-Loeches and Guerri, 2011), the behavioral aspects of impaired control in AUD patients without major brain damage require further investigation. Preliminary studies suggest that impairments in executive function (Kamarajan et al., 2004; Li, Luo, Yan, Bergquist, & Sinha, 2009; Noël et al., 2007; Rubio et al., 2008), and related frontostriatal dysfunction (Chanraud et al., 2007; Kamarajan et al., 2005; Li et al., 2009; Noël, Paternot, et al., 2001), are associated with compromised ability to regulate alcohol-seeking behaviors (Stavro, Pelletier, & Potvin, 2012). Additionally, these frontally mediated cognitive deficits persist beyond abstinence (Cohen, Porjesz, Begleiter, & Wang, 1997; Li et al., 2009; Noël et al., 2007), and relate to increased levels of craving (Anton, 2000) and treatment outcomes (Bates, Bowden, & Barry, 2002; Durazzo, Gazdzinski, Yeh, & Meyerhoff, 2008; Evren, Durkaya, Evren, Dalbudak, & Cetin, 2012; Noël et al., 2002), including increased vulnerability to relapse (Bowden-Jones, McPhillips, Rogers, Hutton, & Joyce, 2005; Sorg et al., 2012). Thus, the persistence of such executive deficits may be inextricably linked to a compromised ability to recover from AUD (Feil et al., 2010; Oscar-Berman and Marinković, 2007).

Growing evidence across various addicted populations, including cocaine (Li et al., 2008; Li, Milivojevic, Kemp, Hong, & Sinha, 2006), methamphetamine (Baicy & London, 2007; Monterosso, Aron, Cordova, Xu, & London, 2005), cannabis (Tapert et al., 2007), and opiate users (Verdejo-García et al., 2012; Verdejo-Garcia, Perales, & Perez-Garcia, 2007; Yücel et al., 2007), has demonstrated that substance-dependent individuals exhibit diminished executive skills (Feil et al., 2010; Verdejo-García, Lawrence, & Clark, 2008). Similarly for AUD, there is a small group of studies demonstrating impairments in executive function following detoxification (Li et al., 2009; Noël et al., 2007). These executive deficits, such as significant errors in response inhibition (Kamarajan et al., 2004; Li et al., 2009; Noël et al., 2007) and attentional control (Noël et al., 2007), are related to frontostriatal dysfunction (Kamarajan et al., 2004; Li et al., 2009) and are also implicated in the progression from heavy drinking to AUD (Rubio et al., 2008); indicating frontally mediated cognitive deficits within AUD populations. However, the scope of the literature remains somewhat limited: (i) only a small number of studies have addressed these executive deficits within an AUD population, (ii) cognitive deficits, such an impaired inhibition, are proposed to closely relate to levels of craving; however, there is currently minimal empirical support for this relationship, and (iii) although a number of these previous studies were conducted within AUD samples post-detoxification, there was no assessment of cognitive recovery following abstinence. As such, our main objectives were to expand upon findings of impaired executive function, and more specifically, deficits in inhibitory and attentional control in a sample of AUD participants, by further investigating whether these deficits relate to clinical measures of craving, and additionally, observe whether a relationship exists between improved neurocognitive function and duration of abstinence.

Following this, we evaluated whether these neurocognitive deficits were related to heightened levels of craving, a multidimensional construct relating to a compromised ability to regulate drinking behaviors (Anton, 2000). A number of AUD studies have successfully utilized the obsessive-compulsive drinking scale (OCDS; Anton, Moak, & Latham, 1996), as a clinical measure of obsessive and compulsive craving (Anton, 2000). These studies have used the OCDS to demonstrate a relationship between craving, drinking behavior (Bottlender & Soyka, 2004; Gordon et al., 2006; Kranzler, Mulgrew, Modesto-Lowe, & Burleson, 1999; Roberts, Anton, Latham, & Moak, 1999; Schmidt, Helten, & Soyka, 2011), and an increased vulnerability to relapse (Bottlender & Soyka, 2004; Gordon et al., 2006; Kranzler et al., 1999; Schmidt et al., 2011). However, surprisingly, there are scarce empirical data regarding the relationship between neurocognitive deficits and heightened craving in AUD.

Finally, we assessed whether improved neurocognitive performance was associated with the duration of sobriety. Literature assessing this relationship remains limited (for an extensive review, seeStavro et al., 2012). A number of longitudinal studies suggest significant recovery in function across a wide range of cognitive domains, such as working memory, attention, short-term memory, and visuospatial ability (Brandt, Butters, Ryan, & Bayog, 1983; Sullivan, Rosenbloom, Lim, & Pfefferbaum, 2000), over the first few weeks to 1 year of abstinence (Crews et al., 2005; Stavro et al., 2012; Sullivan et al., 2000). However, enduring deficits across domains of memory (Brandt et al., 1983) and spatial processing (Fein, Torres, Price, & Di Sclafani, 2006) have also been reported following long-term abstinence. Although impairments in executive performance have been consistently observed across a number of studies assessing short-term abstinence (Chanraud et al., 2007; Davies et al., 2005; Manning et al., 2008; Zinn, Stein, & Swartzwelder, 2004), as noted by Starvo and colleagues (2012), there is a dearth of studies regarding long-term abstinence and inhibition/impulsivity in AUD.

As such, the current study had three major objectives: (i) Expand on previous AUD studies by further characterizing neurocognitive deficits within an AUD population via administration of the SART and RNG; (ii) explore whether the presence of these neurocognitive deficits is related to clinical measures of craving; and (iii) assess whether improved performance on the neurocognitive tasks is associated with the duration of abstinence.

## Methods

The study was approved by the Alfred Hospital Research and Ethics Unit review board committee and was conducted in collaboration with the Monash Alfred Psychiatry Research Centre and Turning Point Alcohol and Drug Centre. Active enrolment ran from October 2010 through September 2011. Consenting patients signed a detailed informed consent form prior to study enrolment; they were informed that participation was voluntary, and they could withdraw at any time without prejudice.

### Participants

Twenty-four AUD participants meeting the criteria for DSM-IV-TR Alcohol Dependence (DSM-IV-TR, American Psychiatric Association, 2000) were recruited within 2 years of successful completion of a detoxification program (Range = 5–668 days, Mean = 169 days, SD = 199 days, Median = 66.5). Screening procedure included a psychiatric history and medical phone interview conducted by a trained research assistant. Participants were recruited through treatment agencies by self or clinician referral. Posters and cards advertising the study were presented at participating centers and offered to detoxification clients who met the study entry criteria. Participants were paid $20 to participate in the study (covering travel costs and time taken to participate) and were aged between 18 and 60 years. Alcohol-dependent subjects were required to (a) have a current “Wechsler Test of Adult Reading (WTAR)” standardized score higher than 100, as an indication of no significant intellectual disability; (b) no self-reported drug or alcohol use since completing the detoxification program; and (c) have no current comorbid mental health disorder (including comorbid depression and polysubstance use). Individuals who had a history of significant head injury, intellectual disability, neurological disease, psychotic symptoms, or suicidal ideation were excluded from the study. Individuals engaging in pharmacotherapy programs were not excluded but type and levels of medication were reported (17% of participants were undergoing Acamprosate or Naltrexone treatment). To account for potential improvements associated with anti-craving pharmacotherapy, medication history was correlated with cognitive measurements; however, no significant relationship was observed. At screening, the AUD participants completed a general demographic questionnaire. #### Non-alcohol-dependent sample Twenty-three non-alcohol-dependent (non-AUD) participants, with a current “WTAR” standardized score higher than 100, no previous or current history of neurological disorder or psychiatric illness, or history of drug or alcohol abuse/dependence, were recruited through local advertisements and posters. Participants were aged between 18 and 60 years and paid$20 to participate (covering travel costs and time taken to participate). At screening, non-AUD were required to complete a general demographic questionnaire. Relevant demographic and participant characteristics for both AUD and non-AUD are summarized in Table 1.

Table 1.

Demographic and clinical data for AUD and non-AUD participants

AUD

Non-AUD

T-test/χ2 test
n Mean ± SD n Mean ± SD
Sustained Attention to Response Task
Age (years) 24 40 (11) 23 35 (9) p = .067
Gender ratio (M:F) 24 11:13 23 11:12 p = .891
BDI 24 16.83 (12.93) 23 2.83 (3.81) p < .005
Years of education 24 13 (2) 23 15 (2) p < .005
WTAR (standardized) 24 113 (5.28) 23 118 (3.74) p < .005
SADQ 24 30.96 (14.43) 23 0.83 (2.04) p < .005
OCDS 24 27.87 (7.47) 23 1.65 (3.23) p < .005
Random Number Generation Task
Age (years) 23 40 (11) 21 34 (9) p = .101
Gender ratio (M:F) 23 10:13 21 10:11 p = .783
BDI 23 16.83 (13.22) 21 3.10 (3.88) p < .005
Years of education 23 13 (2) 21 15 (2) p < .005
WTAR (standardized) 23 113 (5.23) 21 118 (3.88) p = .001
SADQ 23 31.45 (14.57) 21 0.90 (2.12) p < .005
OCDS 23 28.05 (7.61) 21 1.81 (3.34) p < .005
Clinical Data
Years of severe alcohol use 24 18 (11) 23 NA NA
Standard drinks per day prior to detox 24 15 (6) 23 1.0 (0.24) p < .005
Days since detox 24 169 (199) 23 NA NA
AUD

Non-AUD

T-test/χ2 test
n Mean ± SD n Mean ± SD
Sustained Attention to Response Task
Age (years) 24 40 (11) 23 35 (9) p = .067
Gender ratio (M:F) 24 11:13 23 11:12 p = .891
BDI 24 16.83 (12.93) 23 2.83 (3.81) p < .005
Years of education 24 13 (2) 23 15 (2) p < .005
WTAR (standardized) 24 113 (5.28) 23 118 (3.74) p < .005
SADQ 24 30.96 (14.43) 23 0.83 (2.04) p < .005
OCDS 24 27.87 (7.47) 23 1.65 (3.23) p < .005
Random Number Generation Task
Age (years) 23 40 (11) 21 34 (9) p = .101
Gender ratio (M:F) 23 10:13 21 10:11 p = .783
BDI 23 16.83 (13.22) 21 3.10 (3.88) p < .005
Years of education 23 13 (2) 21 15 (2) p < .005
WTAR (standardized) 23 113 (5.23) 21 118 (3.88) p = .001
SADQ 23 31.45 (14.57) 21 0.90 (2.12) p < .005
OCDS 23 28.05 (7.61) 21 1.81 (3.34) p < .005
Clinical Data
Years of severe alcohol use 24 18 (11) 23 NA NA
Standard drinks per day prior to detox 24 15 (6) 23 1.0 (0.24) p < .005
Days since detox 24 169 (199) 23 NA NA

Notes: BDI = Beck Depression Inventory; WTAR = Wechsler Test of Adult Reading;

SADQ = Severity of alcohol dependence questionnaire; OCDS = Obsessive Compulsive Drinking Scale; Year of Severe Alcohol Use = Levels of alcohol consumption according to the Australian standard of risky or hazardous levels as defined by the Alcohol User Disorders Identification Test (AUDIT);

NA = Not Applicable.

### Clinical Measures

#### General demographics questionnaire

Screening of participants included basic demographics: gender, age, marital status, country of origin, education, age of first treatment, and number of detoxifications.

##### Beck Depression Inventory

The Beck Depression Inventory (BDI), a “self-reported” inventory, measures the severity of depressive symptoms (Beck & Steer, 1987) and is widely used in clinical samples and addicted populations.

The WTAR uses a 50-item word list to measure a pre-morbid level of intellectual functioning (Wechsler, 2001).

### Substance Dependence, Alcohol Use, and Craving Measures

#### Severity of Alcohol Dependence Questionnaire

A 20-item scale that measures degree of dependence upon alcohol (Stockwell, Hodgson, & Edwards, 1979; Stockwell, Sitharthan, McGrath, & Lang, 1994).

#### Timeline Follow Back

A 4-week calendar which asks the participant to retrospectively estimate their alcohol consumption over the previous month. In the current study, the Timeline Follow Back is a secondary screening procedure to confirm detoxification and to quantify the amount of drug or alcohol consumption in the month leading up to the study.

#### Obsessive Compulsive Drinking Scale

A pencil and paper self-rating questionnaire that assesses overall craving, as well as compulsivity related to craving and drinking behavior. The OCDS is a good tool for monitoring the severity of substance use and has predictive validity for relapse to drinking (Anton et al., 1996).

### Neuropsychological Assessment

#### Sustained Attention to Response Task

Participants were asked to respond quickly and accurately to the presentation of single digits (1–9) with a button press, with the exception of the number “3,” the target stimulus (Robertson et al., 1997). The stimuli appeared in black in the centre of a white background, presented in a random order in a block of 297 trials, with 33 possible No/Go (number 3) responses. Each stimulus was presented for 150 ms, with varying inter-stimulus interval (ISI) durations (1,000, 1,500, and 1,250 ms) randomly distributed throughout the session (Bonnefond, Doignon-Camus, Touzalin-Chretien, & Dufour, 2010; Dockree, Kelly, Robertson, Reilly, & Foxe, 2005). The variable ISI was used to minimize speed/accuracy trade-offs. Prior to recording, participants were administered an 18-trial demonstration sequence, with two possible No/Go trials presented randomly. Participants were informed that speed of response and accuracy were of equal importance. Reaction time (RT), commission errors, performance variability, and omission errors were recorded.

Participants were asked to generate a random sequence of digits. To describe the concept of randomness, the “hat” analogy was used (Baddeley, 1966; Horne et al., 1982). Participants are instructed to imagine that the numbers 0–9 are written on pieces of paper, these numbers are placed in a hat, one number is taken out of the hat, participants are required to call out that number and then return the number to the hat. By repeating this process, they will be generating a list of random numbers. All instructions were computerized and participants were instructed to synchronize their verbal response with a pacing black “X” stimulus displayed on the computer screen at a rate of 1 “X” stimulus per second, for a sequence of 20 numbers. The first sequence was conducted as a demonstration trial. Following the demonstration, the experimental trial began and participants were required to generate 5 trials of 20 numbers, thus generating a 100-digit trial. Throughout the task, participants wore a headset with a microphone and verbal responses were recorded through the computer. Executive processes, such as inhibition and deviations from randomness, were calculated according to the indices of random factors (Jahanshahi et al., 2006), stratified according to the RNG factors relating to repetition, seriation, and randomness (Table 2).

Table 2.

Definition of indices within the RNG Task

Factor Index name Definition
Repetition: Measures the number of times an individual repeats the same digit in a successive order Total Repetition (TR) The sum of number of double repetitions (e.g. 4, 4), triple repetitions (e.g. 4,4,4), double digrams (e.g. 1,5, … 1,5), and triple digrams (e.g. 1,5,4 … 1,5,4)
Seriation: Measures the number of consecutive digrams. All count/series scores are also calculated according to length of series Total Series (TS) Number of pairs of consecutive digrams (e.g. 4, 5)
Total Count Score (TCS) Total count score sums the sequence length of counting in ascending or descending series in steps of 1 (e.g. 1, 2, 3 or 8, 7, 6, 5) and counting in ascending or descending series in steps of 2 (e.g. 2, 4, 6, 8 or 7, 5, 3, 1). In calculating the count scores, the sequence length is squared and then summed together (Jahanshahi et al., 2006
Randomness Random Number Index (RNI) A measure which reflects the difference between the observed and expected probability of all possible number pairs. The higher the reported RNI, the less random the series
Unique Triplets The number of triplets that are unique are counted. There are 98 (N-2) triplets in a series of 100 responses. The fewer number of unique triplets, the increased tendency to repeat certain runs of digits
Factor Index name Definition
Repetition: Measures the number of times an individual repeats the same digit in a successive order Total Repetition (TR) The sum of number of double repetitions (e.g. 4, 4), triple repetitions (e.g. 4,4,4), double digrams (e.g. 1,5, … 1,5), and triple digrams (e.g. 1,5,4 … 1,5,4)
Seriation: Measures the number of consecutive digrams. All count/series scores are also calculated according to length of series Total Series (TS) Number of pairs of consecutive digrams (e.g. 4, 5)
Total Count Score (TCS) Total count score sums the sequence length of counting in ascending or descending series in steps of 1 (e.g. 1, 2, 3 or 8, 7, 6, 5) and counting in ascending or descending series in steps of 2 (e.g. 2, 4, 6, 8 or 7, 5, 3, 1). In calculating the count scores, the sequence length is squared and then summed together (Jahanshahi et al., 2006
Randomness Random Number Index (RNI) A measure which reflects the difference between the observed and expected probability of all possible number pairs. The higher the reported RNI, the less random the series
Unique Triplets The number of triplets that are unique are counted. There are 98 (N-2) triplets in a series of 100 responses. The fewer number of unique triplets, the increased tendency to repeat certain runs of digits

Fewer participants completed the RNG task relative to the SART, since the RNG algorithms can only process data from participants who complete a minimum of 90% of the RNG required responses. Thus, data from participants who completed less than 90% of the RNG required responses were excluded. To address the possible influence of the exclusion of these data on the results, we ran an additional statistical check of whether these participants (less than 90% of the RNG complete) also performed more poorly on the SART but no significant correlation was found.

### Procedure

Following baseline screening and completion of demographic questionnaires, cognitive performance of both AUD subjects and non-AUD was evaluated via two computerized cognitive tasks using E-prime V1 technology (Psychology Software Tools): The SART (Robertson et al., 1997) and the RNG (Baddeley, 1966). Participants were seated in a quiet, well-lit room, 30 cm from a 17-inch computer screen. Computerized task instructions were presented in English. Each task began with a short demonstration task to ensure that participants understood the task requirements.

### Data Analysis

Comparability of AUD and non-AUD participants was assessed using χ²-tests for categorical and t-tests for continuous variables (Table 1). Stem-plots located extreme outliers (>±2.5 SD), and outliers were brought to within 2.5 SD of the mean. For all data meeting assumptions of normality, tests were run at an alpha level of 0.05. There were no significant violations of homogeneity of regression. To address cases of violations of unequal variance (i.e. Levene's statistic found to be significant), statistics were run at a more conservative alpha level of 0.025 (Keppel & Wickens, 2004).

For the SART, an analysis of covariance (ANCOVA) was administered to measure group differences across variables (RT, performance variability, and omission errors). For the SART, an ANCOVA was administered to measure group differences across variables (RT, performance variability, and omission errors). Across all SART and RNG variables, the significant differences in BDI, WTAR, years of education, and age (near significance) were controlled for as covariates. Performance variability was calculated according to the standard deviation of the RTs on correct response trials (Go trials) divided by the mean RT of each subject. For the number of commission errors, SART RT was also controlled for as a potentially confounding variable (Helton et al., 2009; Manly, Robertson, Galloway, & Hawkins, 1999). For the RNG analyses, an ANCOVA was also used to evaluate group differences across the RNG indices, with both BDI and WTAR being controlled for as covariates. Pearson's correlation also examined potential associations between cognitive variables (SART and RNG), general demographics, drinking and alcohol questionnaires, as well as BDI scores within groups. All data analyses were performed using SPSS for Windows, version 15.

## Results

### SART Performance

The AUD group exhibited impairments in cognitive inhibition, attention (commission errors), and psychomotor control (SART RT).

The AUD group (M = 12.75, SD = 6.29) made significantly more commission errors (failure to stop on a No/Go trial) than non-AUD (M = 7.7, SD = 3.17), F(1, 40) = 9.120, p = .004. A significant difference in SART RT was also revealed between AUD (M = 373 ms, SD = 63 ms) and non-AUD (M = 359 ms, SD = 54 ms), F(1, 41) = 4.714, p = .036. There were no observable differences in performance variability or number of omission errors between the two groups (refer to Table 3).

Table 3.

Performance means and standard deviations on the SART variables for AUD and non-AUD participants

SART AUD (mean ± SDNon-AUD (mean ± SDSignificance p-value (ANCOVA) Effect size (Cohen's d
Commission errors 12.75 (6.29) 7.7 (3.17) .004 1.00
Reaction time (ms) 373 (63) 359 (54) .036 0.24
Performance variability 0.28 (0.11) 0.22 (0.05) .24 0.69
Omission errors 2.75 (2.86) 0.78 (1.13) .13 0.90
SART AUD (mean ± SDNon-AUD (mean ± SDSignificance p-value (ANCOVA) Effect size (Cohen's d
Commission errors 12.75 (6.29) 7.7 (3.17) .004 1.00
Reaction time (ms) 373 (63) 359 (54) .036 0.24
Performance variability 0.28 (0.11) 0.22 (0.05) .24 0.69
Omission errors 2.75 (2.86) 0.78 (1.13) .13 0.90

Notes: RNG = Random Number Generation; AUD = Alcohol use dependence; ANCOVA = analysis of covariance.

### Correlational Data for the SART

There were significant negative correlations between commission errors and SART RT across both AUD (r = −.655, n = 24, p = .001, two-tailed) and non-AUD (r = −.460, n = 23, p = .027, two-tailed) groups. Commission errors also negatively correlated with age (r = −.433, n = 24, p = .035) in the AUD group only. In addition, SART RT positively correlated with age (r = .417, n = 24, p = .043) and the OCDS scale (r = .424, n = 24, p = .039) in the AUD group. Also, the increased number of years of alcohol use was positively related to age of participants (r = .756, n = 24, p < .005). No further significant correlations were identified.

### RNG Task Performance

The AUD group presented with a greater number of repetitions and increased departures from randomness.

In terms of repetition, the AUD group (M = 46.87, SD = 4.65) presented with a significantly greater number of total repetitions relative to the non-AUD group (M = 44.29, SD = 4.99), F(1,38) = 5.603, p = .023. With regard to departures from randomness, the AUD group (M = 0.35, SD = 0.05) had a significantly increased response bias index (i.e. a greater RNG ratio), compared with the non-AUD group (M = 0.30, SD = 0.04), F(1,38) = 7.124, p = .011 (Table 4).

Table 4.

Performance means and standard deviations for RT (ms) for the RNG Task indices for AUD and non-AUD participants

RNG AUD (mean ± SDNon-AUD (mean ± SDSignificance p-value (ANCOVA) Effect size (Cohen's d
Total Repetition 46.87 (4.65) 44.29 (4.99) .023 0.51
Total Series 33.52 (7.63) 28.76 (6.95) .054 0.62
Total Count Score 71.17 (25.23) 57.62 (17.61) .53 0.60
Random Number Index 0.35 (0.05) 0.30 (0.04) .011 1.05
Unique Triplets 68.74 (7.53) 72.86 (6.15) .47 0.58
RNG AUD (mean ± SDNon-AUD (mean ± SDSignificance p-value (ANCOVA) Effect size (Cohen's d
Total Repetition 46.87 (4.65) 44.29 (4.99) .023 0.51
Total Series 33.52 (7.63) 28.76 (6.95) .054 0.62
Total Count Score 71.17 (25.23) 57.62 (17.61) .53 0.60
Random Number Index 0.35 (0.05) 0.30 (0.04) .011 1.05
Unique Triplets 68.74 (7.53) 72.86 (6.15) .47 0.58

Notes: RNG = Random Number Generation; AUD = Alcohol use dependence; ANCOVA = analysis of covariance.

#### Correlational data for the RNG

Pearson's correlation revealed a significant positive relationship between the Random Number Index ratio and years of alcohol use (r = .460, n = 23, p = .027) in the AUD group only. No further significant correlations were identified.

## Discussion

The current study revealed regulatory deficits in cognitive inhibition and attentional control in the AUD group relative to the non-AUD group. For the SART, the AUD group demonstrated a compromised ability to inhibit prepotent responses to inappropriate stimuli (i.e. commission errors) and slower RTs. In addition, increased age was related to both slower RT, and a reduced number of commission errors, in the AUD group only. Slower SART response times were related to increased clinical symptoms of craving in the AUD group. For the RNG, the AUD group exhibited impaired output inhibition and were unable to successfully suppress previous representations (excessive repetition), as well as demonstrating difficulty inhibiting automatic responses to previously learned schemata (deviations from randomness). Moreover, a relationship between increased deviations from randomness and increased number of years of alcohol consumption (prior to detoxification) was observed. Finally, there was no significant relationship between improved performance on cognitive tasks and duration of abstinence.

The inability to suppress an automatic response to the presentation of inappropriate stimuli on the SART (Braet et al., 2009; Chambers et al., 2006) has been suggested to rely on frontal circuitry (Manly et al., 2003; Molenberghs et al., 2009; Rubia et al., 2003). In the current study, the AUD group demonstrated a reduced capacity to discriminate between conflicting response possibilities, and a diminished ability to inhibit their prepotent responses to the “No/Go” target, resulting in commission errors. These findings are consistent with previous studies that found AUD individuals made more errors in response inhibition (Kamarajan et al., 2004; Li et al., 2009; Noël et al., 2007; Rubio et al., 2008) and that these impairments were associated with frontal dysfunction (Chanraud et al., 2007; Kamarajan et al., 2005; Li et al., 2009; Noël, Paternot, et al., 2001). Thus, the current study provides further neuropsychological evidence of frontally mediated impairments of response inhibition in AUD participants post-detoxification. Furthermore, it appears that administration of the SART, which is considered a sensitive measure of frontally mediated regulatory control, may provide clinically relevant screening information regarding the presence of these regulatory deficits within a sample of detoxified patients, which may help identify those patients who are more at risk of relapse.

With regard to attentional control, the monotonous nature of the SART lulls participants into a disengaged task-driven response mode (Fassbender et al., 2004; Robertson et al., 1997). Thus, the AUD group exhibited poor attentional capacity, and an automaticity of response to the infrequent No/Go targets, which contributed to the increase in commission errors. This inability to maintain goal-directed focus may underlie difficulties engaging in non-alcohol seeking behaviors (Weinstein & Cox, 2006) and continued alcohol consumption (Cox, Hogan, Kristian, & Race, 2002).

Consistent with previous cognitive studies, we also revealed slower RT in the AUD group (Cohen et al., 1997; Lawrence, Luty, Bogdan, Sahakian, & Clark, 2009; Li et al., 2009; Vivian, Goldstein, & Shelly, 1973). RT is a complex measure of information processing, which includes stimulus identification, response selection, and the resulting motor response (Cohen et al., 1997). A recent exploratory study identified that chronic AUD participants present with alterations of functional connectivity in the frontal premotor-cerebellar circuitry (Rogers, Parks, Nickel, Katwal, & Martin, 2012), the same cognitive-motor circuitry required for coordinating higher order motor function (Middleton & Strick, 2000; Tekin & Cummings, 2002). Thus, increased RT in the AUD group may reflect an interaction between deficits in general cognitive impairment and psychomotor slowing. In addition, these slower RTs were associated with clinical measures of craving (i.e. OCDS total) in the AUD group. Previous studies have reported that craving levels, as identified by the OCDS, were sensitive to alcoholism severity (Anton et al., 1996), predictive of short-term resumption of drinking outcomes (Kranzler et al., 1999; Roberts et al., 1999), and an increased vulnerability to relapse (Anton et al., 1996; Bottlender & Soyka, 2004; Gordon et al., 2006; Schmidt et al., 2011). Therefore, slower RT may characterize a cognitive-psychomotor cortical impairment specific to AUD and is consistent with previous studies that identified psychomotor slowing is related to severity of AUD (Lawrence et al., 2009) and chronic alcohol exposure (De Wilde, Dom, Hulstijn, & Sabbe, 2007; Lawrence et al., 2009).

The negative relationship between SART RT and commission errors, across both groups, indicates a speed/accuracy trade-off (Helton et al., 2009). Participants had to respond as quickly and as accurately as possible; thus, strategic planning was involved in regulating the speed of response against the perceived utility of successfully suppressing an inappropriate response (Manly et al., 1999). However, the AUD group, despite exhibiting a significantly slower RT relative to non-AUD group, still made more errors on tasks of response inhibition and sustained attention, thereby compounding the severity of these cognitive impairments.

Increased age was related to both a reduced number of commission errors, and a slower RT, in the AUD group only. These results support findings by Carriere, Cheyne, Solman, and Smilek (2010), whereby performance on the SART declines as a function of age. As individuals age, their strategic responses (i.e. speed-accuracy trade-off) to the SART improve (Carriere et al., 2010). However, in the present study, as expected, age also correlated with years of alcohol use, suggesting that, even with less exposure to alcohol, the younger AUD participants presented with greater cognitive impairment. Consequently, and in contrast, there was evidence of compromised strategic planning and cognitive ability in younger AUD participants. Recent studies suggest that the younger the age of onset of AUD, the greater the likely neurological damage, and impaired cognitive functioning (Chanraud et al., 2007; Hermens et al., 2013). Thus, developmental factors may be involved in the impaired neurocognitive factors associated with AUD. Further studies, which assess the developmental impact of alcohol consumption on cognitive function and response inhibition, are warranted.

The RNG Task also characterized frontally mediated cognitive inhibitory deficits in AUD participants post-detoxification. Successful performance on the RNG Task relies on the recruitment of complex executive processes, such as generation of novel responses, the ability to inhibit automatic responses to previously learned schemata, and the ability to maintain attentional control. The AUD group presented with cognitive impairments across two of the major RNG factors: Repetition and randomness.

Excessive repetition reflects general deficits in the inhibition of a previous response or representation (Williams et al., 2002). Thus, impairments in inhibiting a previous response, and the inability to generate a novel response, may reflect impaired cognitive flexibility (i.e. the ability to adjust behavioral responses according to goal-related objectives) within the AUD group. Previous studies have also found that AUD is characterized by disrupted cognitive flexibility (Fernández-Serrano, Pérez-García, & Verdejo-García, 2011) and related deficits in frontal lobe functioning (Ratti, Bo, Giardini, & Soragna, 2002) that persist following detoxification (Noël, Van Der Linden, et al., 2001).

Departures from randomness, however, relate to an inability to produce a unique sequence of numbers, while inhibiting previously learned schemata or strategies (Jahanshahi et al., 2006; Knoch et al., 2005). The AUD group exhibited a significantly impaired ability to generate a random series, and these deficits were associated with increased years of alcohol use. Thus, difficulties generating a novel response could relate to a compromised ability to develop strategies to avoid alcohol-seeking behavior, while inhibition of responses to learned schemata, could be associated with dysregulated control over previously established (possibly related to years of alcohol use) compulsive responses to alcohol-seeking behaviors.

Finally, we assessed the relationship between neurocognitive recovery and duration of sobriety across all the cognitive and demographic variables, but observed no significant associations. As such, our findings are consistent with previous studies that find executive impairments persist beyond early abstinence (Chanraud et al., 2007; Davies et al., 2005; Manning et al., 2008; Zinn et al., 2004), but they also suggest that these deficits are enduring and are evident across the first 2 years of abstinence. The persistence of these executive deficits, and the compromised ability to inhibit the urge to drink, are likely to be associated with reduced treatment success and an increased propensity to relapse (Ihara, Berrios, & London, 2000; Morrison, 2011).

When combined, our findings suggest that both the SART and RNG tasks are a valuable clinical tool for clinicians to examine enduring cognitive and attentional deficits which persist beyond detoxification. In addition, the enduring nature of these regulatory deficits and reduced cognitive-motor ability is associated with increased levels of craving, and we speculate, an increased vulnerability to relapse. Therefore, our findings highlight the importance of screening for these neurocognitive and attentional deficits, and moreover, designing interventions to target these specific deficits, within recently detoxified AUD participants.

### Limitations

There are a number of potential confounding variables that suggest the results must be interpreted with caution. To begin, our study sample was small and dependent upon a self-report measure of abstinence, which may have been biased to meet entry requirements of the study. Additionally, the correlation observed between age and years of alcohol use suggests that the association between either of these variables and neurocognitive measures should be interpreted with caution. Notably, there were also significant differences observed on measures of depressive symptoms and WTAR between the groups. Although we addressed this limitation statistically by controlling for this covariate, it is notable that the lower score was not low enough to represent intellectual disability, which suggests that the AUD group represents a well-functioning cohort of AUD patients who were able to successfully detoxify from alcohol dependence. At the same time, however, it is plausible that the differences in WTAR score may be interpreted as reflecting differences in premorbid intellectual functioning, which is consistent with the lower years of education. Given the cross-sectional nature of the study, it is difficult to ascertain whether the identified frontally mediated cognitive inhibitory deficits are directly related to chronic alcohol consumption, reflect a pre-existing vulnerability, or are a combination of both (Feil et al., 2010). Finally, although we provided cross-sectional evidence of enduring cognitive deficits, further longitudinal studies are required to confirm these findings. Despite these limitations, our findings provide new evidence regarding the neurocognitive profile of AUD participants post-detoxification.

## Conclusion

In summary, the present study addressed its three main objectives: (a) Provided further evidence of impairment of executive function across the constructs of cognitive inhibition and attentional control in an AUD population; (b) identified an association between clinical measures of craving and general cognitive impairment; and (c) demonstrated that cognitive performance does not improve with increasing duration of abstinence. Our findings add to the existing literature on cognitive dysfunction in addiction and highlight the need for interventions that specifically target these neurocognitive deficits, so as to improve treatment outcomes for patients with AUD.

## Funding

J.N.-F. is a recipient of the Graduate Women Victoria scholarship, which supported the development of this study. P.B.F. has received equipment for research from Medtronic Ltd, MagVenture A/S, and Brainsway Ltd. He has undertaken research with funding and equipment from Cervel Neurotech. He is supported by a NHMRC Practitioner Fellowship.

None declared.

## Acknowledgements

Sincere appreciation is expressed to Dr Simon Moss for his assistance with data analysis and statistical support, and Ben Carr for his development of the RNG algorithms.

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## Author notes

Present address: Department of Physics of Complex Systems, Weizmann Institute of Science, 76100 Rehovot, Israel.