Copyright © 2019 Frontiers Production Office. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Improvements in Attention Following Cognitive Training With the Novel "Decoder" Game on an iPad

George Savulich1 , Emily Thorp1 , Thomas Piercy 1 , Katie A. Peterson2 , John D. Pickard2 and Barbara J. Sahakian1 \*

1Department of Psychiatry and Behavioural and Clinical Neuroscience Institute, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom, 2Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom

Work and study increasingly rely on the use of technologies requiring individuals to switch attention rapidly between emails, texts and tasks. This has led to healthy people having problems of attention and concentration and difficulties getting into the "flow," which impedes goal attainment and task completion. Possibly related to this, there is an increasing diagnosis of attention deficit hyperactivity disorder (ADHD) and prescriptions of drugs such as methylphenidate. In addition to ADHD, attention is impaired in other neuropsychiatric disorders, such as schizophrenia and in traumatic brain injury (TBI). Based on neuropsychological and neuroimaging evidence, we developed "Decoder," a novel game for targeted cognitive training of visual sustained attention on an iPad. We aimed to investigate the effects of cognitive training in 75 healthy young adults randomly assigned to a Cognitive Training (8 h of playing Decoder over 4 weeks; n = 25), Active Control (8 h of playing Bingo over 4 weeks; n = 25) or Passive Control (continuation of activities of daily living; n = 25) group. Results indicated that cognitive training with Decoder was superior to both control groups in terms of increased target sensitivity (A') on the Cambridge Neuropsychological Test Automated Battery Rapid Visual Information processing (CANTAB RVP) test, indicating significantly improved sustained visual attention. Individuals playing Decoder also showed significantly better performance on the Trail Making Test (TMT) compared with those playing Bingo. Significant differences in visual analogue scales were also found between the two gaming groups, such that Decoder received higher ratings of enjoyment, task-related motivation and alertness across all hours of game play. These data suggest that cognitive training with Decoder is an effective non-pharmacological method for enhancing attention in healthy young adults, which could be extended to clinical populations in which attentional problems persist.

Keywords: enhancement of attention, problems of attention/concentration, maintaining flow, cognitive training, Decoder game

#### Edited by:

Margarita Moreno, University of Almería, Spain

#### Reviewed by: Unai Diaz-Orueta, Maynooth University, Ireland Olga M. Bazanova, State Scientific-Research Institute of

Physiology & Basic Medicine, Russia \*Correspondence:

> Barbara J. Sahakian<EMAIL_ADDRESS>
Received: 28 September 2018 Accepted: 07 January 2019 Published: 21 January 2019

#### Citation:

Savulich G, Thorp E, Piercy T, Peterson KA, Pickard JD and Sahakian BJ (2019) Improvements in Attention Following Cognitive Training With the Novel "Decoder" Game on an iPad. Front. Behav. Neurosci. 13:2. doi: 10.3389/fnbeh.2019.00002

### INTRODUCTION

Neuropsychiatric disorders are disorders of cognition, motivation and their interaction (Sahakian, 2014). The most common disorder of neurodevelopmental origin is attention deficit hyperactivity disorder (ADHD), which affects up to 1 in 20 children in the United States (Faraone et al., 2003). In 2011, 11% of children aged 4–17 had received a diagnosis of ADHD (i.e., 6.4 million children; Visser et al., 2014). In approximately 80% of children with ADHD, symptoms persist into adolescence, with 60% still having ADHD as an adult (Kessler et al., 2005b). The most prominent cognitive deficit in ADHD is in attention or concentration, which was recognized as a ''subtype'' of the disorder in DSM-IV (American Psychiatric Association, 1994; Epstein and Loren, 2013), but has changed to a ''presentation'' in DSM-5 (American Psychiatric Association, 2013). Other problems in ADHD include deficits in working memory and increased impulsivity (Chamberlain et al., 2011, 2017). Although the degree to which core attentional impairments contribute to deficits in other cognitive domains is not well understood, all involve right frontal cortical dysfunction (Clark et al., 2007).

Methylphenidate, a common treatment for ADHD, and other ''cognitive-enhancing'' drugs such as modafinil are now frequently used at University and in the workplace to stay alert, maintain concentration and increase task-related motivation (Maher, 2008; Sahakian et al., 2015; Brühl and Sahakian, 2016; d'Angelo et al., 2017). A recent publication by Maier et al. (2018) reported the increasing ''lifestyle'' use of these drugs by healthy people for improving cognitive performance when studying or at work. Sahakian et al. (2015) and Brühl and Sahakian (2016) further reviewed the reasons for this increasing lifestyle use, including competition in a global environment; stress and frequent travel leading to poor quality sleep and jet lag; work where even small mistakes can have major consequences; and getting ''into the flow'' of work. It has been emphasized that the emergence of new technologies requiring rapid responses to emails and texts and the working on multiple projects simultaneously, that young people including students are having more problems with sustaining attention and frequently become distracted (Gazzaley and Rosen, 2016). This may be why the Care Quality Commission (2016) reported an over 50% increase in prescriptions for methylphenidate over a 5-year period. They attributed this rise to two main factors: increasing prescriptions for diagnosed childhood and adult ADHD and to its potential for diversion and misuse. As attention/concentration is an important cognitive domain for healthy people to function at work, home or University successfully, and because it is a core deficit in ADHD and in other neuropsychiatric disorders such as schizophrenia and in people with traumatic brain injury (TBI), the current study focused on its enhancement through cognitive training using a game. Whereas pharmacological cognitive enhancement raises a number of safety and ethical concerns in healthy people, non-pharmacological strategies offers low-risk, non-invasive alternatives (Savulich et al., 2017a).

Systematic reviews and meta-analyses have shown that cognitive training is an effective behavioral intervention in healthy people and in those with psychiatric disorders, although the degree of generalization to domains other than the one being trained is not always clear (e.g., Wykes et al., 2011; Kelly et al., 2014). Cognitive training is designed to stimulate cognitive function over time, leading to neuroplastic changes and improved functioning of the underlying neural network (Keshavan et al., 2014). In healthy people, cognitive training of working memory has been shown to increase dopamine receptor density and D1 binding potential in prefrontal and parietal regions of the brain (McNab et al., 2009; Klingberg, 2010). In healthy older adults, training-related cognitive outcomes have shown associations with increased hippocampal activation (Kirchoff et al., 2012) and white matter integrity in the ventral attention network (Strenziok et al., 2014). Studies from our own laboratory have shown simultaneous effects on cognition and motivation following targeted cognitive training with evidencebased memory games (e.g., ''Wizard1 ''), including patient groups with schizophrenia (Sahakian et al., 2015) and amnestic mild cognitive impairment (aMCI; Savulich et al., 2017b). In these studies, gaming technology was used to maximize cognitive training by maintaining high levels of enjoyment and task-related motivation, with no participant dropping out. Higher duration of cognitive training with a game was also associated with better memory outcomes in professional rugby players at high-risk of concussion (Sahakian et al., 2018).

In this study, we present data from healthy people who played a novel game called ''Decoder'' on an iPad in order to cognitively train sustained attention. Original features are the extensive neuropsychological and neuroimaging evidence-base for this game (Coull et al., 1996; Aron et al., 2003; Hampshire et al., 2010; del Campo et al., 2013; Pironti et al., 2014). In addition, Decoder was developed as a game for cognitive training in collaboration with a games developer to ensure that it maintained motivation with continued play and that it was individually titrated for difficulty. Throughout gameplay, gangs and missions are procedurally generated: each player gets their own unique game world. Each player can unlock regions and progress through the game in their own way. Decoder was designed for play on an iPad with a view to transfer to mobile phones for greater accessibility and utilization in any environment (e.g., at work, home, bus or train). As control groups for the Decoder training group, we included a group that played ''Bingo'' on an iPad for the same length of time (active control group) and a group that was assessed at baseline and retested at the conclusion of the study (passive control group). We hypothesized that only the group who played the Decoder game would improve their performance following cognitive training as assessed by target sensitivity (A') on the Cambridge Neuropsychological Test Automated Battery Rapid Visual Information processing (CANTAB RVP) test, a reliable and objective measure of sustained attention. To determine whether cognitive training of concentration had any deleterious effects on rapid shifts of attention, a different form of attention, we also examined performance on the Trail Making Test (TMT; Reitan and Wolfson, 1985). In neuroimaging studies,

1www.peak.net/advanced-training

performance on this task has been shown to activate the left dorsolateral prefrontal cortex and areas involved in motor control (Zakzanis et al., 2005). We predicted that playing Decoder would have no effect on TMT performance.

### MATERIALS AND METHODS

#### Participants

Seventy-five participants were recruited from the local Cambridgeshire area. Inclusion criteria were fluency in English; age 18–30; not currently taking any psychiatric medication or receiving a psychological treatment; and not having a current or past psychiatric diagnosis. All participants were screened on these criteria using the Mini-International Neuropsychiatric Inventory (Lecrubier et al., 1997) and the Adult ADHD Self-Report Scale (Kessler et al., 2005a).

#### Neuropsychological Assessment

#### The National Adult Reading Test (Nelson, 1982; NART)

The National Adult Reading test (NART) is a 50-item estimate of premorbid intelligence. Participants are instructed to read aloud 50 words of atypical pronunciation. Higher scores (0–50) indicate more correct responses (i.e., a higher estimate of intelligence).

#### Cognitive Measures of Attention

#### CANTAB Rapid Visual Information Processing Test2 ; (RVP)

The RVP test is a measure of visual sustained attention. Participants are asked to detect sequences of digits (e.g., 2-4-6, 3- 5-7, 4-6-8). A white box appears in the middle of screen, of which digits from two to nine appear in a pseudo-random order, at a rate of 100 digits per minute. Participants are instructed to press a button every time they detect a sequence. The main outcome measure is A', a signal detection measure of sensitivity to the target, regardless of response tendency. The duration of the test is approximately 5 min.

#### Trail Making Test (Reitan and Wolfson, 1985; TMT)

The TMT is a measure of attention, visual search, scanning, processing speed and task switching. Participants are asked to connect a set of 25 targets as quickly but as accurately as possible in a sequential order. In Part A, the targets are numbers (e.g., 1, 2, 3, etc.) and in Part B, the targets alternate between numbers and letters (e.g., 1, A, 2, B, 3, C, etc.). Errors are corrected by the experimenter during the task. The main outcome measure is the time to completion (seconds).

#### Personality Trait Measures

All participants completed a battery of baseline personality trait measures including the Barratt Impulsiveness Scale (BIS; Barratt, 1994), the Impulsive Behavior Scale (UPPS-P; Whiteside and Lynam, 2001), the Sensation Seeking Scale (SSS; Zuckerman et al., 1964), the Apathy Evaluation Scale (AES; Glenn, 2005), the Beck Depression Inventory (BDI; Beck et al., 1996) and the Behavioral Inhibition/Avoidance Scale (BIS/BAS; Carver and White, 1994).

### Decoder

Decoder was developed in collaboration between the research team including a professional games developer and healthy young adults, with later engagement of patients with acquired brain injuries to ensure that it was fun, motivational, easy to understand and in line with a concept suitable for both healthy samples and clinical samples in line with public and patient involvement in order to enhance uptake. Our target cognitive process for enhancement was sustained attention (i.e., concentration). The object of the game is to assume the role of a Signal Intelligence officer tasked with breaking up global criminal gangs. To do this, the player must decode their communication by engaging in a visual sustained attention task using different combinations of number strings among distractors, in which the success of each mission is rewarded by exposing letters of the next criminal location (with higher scores revealing more letters). Each location generates a maximum of three missions, which are titrated for individual performance in real-time. High levels of enjoyment and motivation were prioritized during development of the game. Immediate engagement is achieved through the use of visual feedback and music to give the game a sense of flow, responsiveness and excitement. To keep the game motivating across several plays, a large number of variants, including short- (e.g., unlocking new regions) and long-term (e.g., earning all ranks) goals, are used. Personalization is achieved through selection of a character and backstory.

#### Procedure

This study received full ethical approval from the University of Cambridge Psychology Research ethics committee (reference Pre.2015.092). All participants provided written informed consent. This study comprised a three-group, randomized controlled design, including: the Cognitive Training Group (''Decoder''), the Active Control Group (''Bingo'') and the Passive Control Group (No Game). Potential participants were first screened for inclusion/exclusion criteria by telephone. Invited participants wishing to participant were then asked to attend a baseline testing session, in which they provided basic demographic information and completed the cognitive test battery, followed by all personality trait questionnaires. Participants were also asked about their technology use including the frequency of time spent using the Internet each week, frequency of playing computer games for enjoyment and prior regular use of cognitive training games or applications with the aim of improving cognition. Any participants reporting current use of any cognitive training games or applications were asked to stop throughout the duration of the study. Participants were then randomized to one of three groups.

Participants in the Cognitive Training Group were invited to attend eight, 1 h sessions of supervised cognitive training (i.e., play Decoder on an iPad, as described above). Similarly, participants in the Active Training Group were invited to attend

2www.cambridgecognition.com

eight, 1 h sessions of supervised Bingo on an iPad. Bingo was selected as the ''active control'' game because it uses the same type of stimuli as Decoder (i.e., number strings), but does not contain any element of cognitive training of sustained attention. In this version, participants were required to use a very low level of visual search, with reward only given when obtaining five numbers in a row. After each hour of gameplay, participants in the Cognitive Training and Active Control Groups were asked to rate their experience in terms of enjoyment, desire to continue, alertness and positive mood using 10-cm visual analogue scales. Participants in the Passive Control Group were not given access to either game and continued their daily lives as usual.

A maximum of 4 weeks after the baseline testing session, all participants then completed an outcome testing session, which was identical to the baseline testing session in terms of outcome measures.

#### Statistical Analyses

Demographic and baseline questionnaire measures were analyzed using one-way analysis of variance (ANOVA) for continuous variables and chi-square for categorical variables. Baseline and outcome variables were analyzed with an analysis of covariance (ANCOVA). As predictors, we used the participant's baseline scores on the same test (a continuous covariate) and Group (a fixed factor with three levels: Decoder, Bingo and No Game). For the CANTAB RVP, we also included prior regular cognitive training games/applications as a predictor (a fixed factor with two levels: Yes/No). This approach allowed us to assess the effects of gaming (Group) whilst controlling for within-group variance in pre-existing cognitive performance as well as the potential effects of other games/applications with purported cognitive benefits delivered using an iPad or mobile phone. Pairwise comparisons were made between groups following significant effects of gaming. Sidak correction was applied for adjustment of multiple comparisons.

#### RESULTS

#### Demographic Variables

The Decoder (n = 25), Bingo (n = 25) and No Game (n = 25) groups did not differ in basic demographic variables including age, gender, years in education and premorbid intelligence (**Table 1**). The groups also did not differ in personality traits including impulsiveness, impulsive behavior, sensation seeking, apathy, depression or behavioral avoidance/inhibition (**Table 1**). By design, frequencies of Internet use (X 2 = 0.25) and computer game play (X 2 = 0.33) were not significantly different between groups.

#### Attention

#### CANTAB RVP

Target sensitivity (A') significantly differed between groups at outcome, F(2,68) = 5.60, p = 0.006, η 2 = 0.14 (group means adjusted for baseline performance: Decoder = 0.96, SE = 0.01; Bingo = 0.93, SE = 0.01; No Game = 0.93, SE = 0.01; **Figure 1**). Pairwise comparisons showed that target sensitivity significantly differed between Decoder and Bingo, p = 0.04 and Decoder and No Game, p = 0.009, but not between Bingo and No Game, p = 0.95. There was an effect of regular cognitive training game/application experience, such that target sensitivity was significantly better in those without prior experience, p < 0.001 (group means adjusted for baseline performance:

FIGURE 1 | Target sensitivity [Cambridge Neuropsychological Test Automated Battery Rapid Visual Information processing (CANTAB RVP) A'] significantly differed between groups at outcome when controlling for baseline performance, F(2,68) = 5.60, p = 0.006. The Decoder group showed better detection of target sequences, with adjusted means differing significantly between Decoder and Bingo, p = 0.04, and Decoder and Control, p = 0.009. ∗ Indicates means significantly differed between groups.

Notes: NART, National Adult Reading Test; BIS, Barratt Impulsiveness Scale; UPPS, Impulsive Behavior Scale; SSS, Sensation Seeking Scale; AES, Apathy Evaluation Scale; BDI-II, Beck Depression Inventory-II; BIS/BAS, Behavioral Inhibition/Avoidance Scale.

with experience = 0.92, SE = 0.01; without experience = 0.96, SE = 0.96).

#### TMT

Time (seconds) taken to complete TMT numbers and letters was significantly different between groups at outcome, F(2,71) = 3.72, p = 0.03, η 2 = 0.06 (group means adjusted for baseline performance: Decoder = 32.28, SE = 2.02; Bingo = 39.73, SE = 2.02; No Game = 37.92, SD = 2.01; **Figure 2**). Pairwise comparisons showed that time to completion was significantly different between Decoder and Bingo, p = 0.03. Time to completion was not significantly different between Decoder and No Game, p = 0.15 or Bingo and No Game, p = 0.90.

The same pattern of result emerged when including gender as a covariate in the above models; there was no effect of gender for either task (p = 0.64 and p = 0.65).

#### Visual Analogue Scales

Across all hours of gameplay, the two game groups significantly differed in their average levels of enjoyment (Decoder = 6.57, SD = 2.08; Bingo = 4.70, SD = 2.18; t(48) = 3.11, p = 0.003), task-related motivation (Decoder = 7.07, SD = 2.12; Bingo = 4.64, SD = 2.77; t(48) = 3.59, p = 0.001) and alertness (Decoder = 6.35, SD = 2.12; Bingo = 4.59, SD = 2.18; t(48) = 2.90, p = 0.006). Positive mood did not significantly differ between the two groups after the 1st hour of game play (Decoder = 6.08, SD = 2.31; Bingo = 6.20, SD = 2.18; t(48) = 0.19, p = 0.85), but gradually increased for Decoder, and decreased for Bingo, and significantly differed after the 8th h of game play (Decoder = 6.92,

SD = 5.12; Bingo = 5.12, SD = 2.20; t(48) = 3.05, p = 0.004; **Figure 3**).

#### DISCUSSION

The main finding of this study was that attention/concentration was significantly improved by playing the Decoder game on an iPad, which was developed for cognitive training. This enhancement in attentional performance by the Cognitive Training Group was seen both in comparison to the passive control group and also the active control group who played Bingo for the same time period. In addition, subjective measures of enjoyment, motivation, alertness and positive mood remained at high levels in those who played Decoder after every hour of gameplay.

Attention prioritizes sensory processing according to task relevance (Sarter et al., 2001). Focused attention involves the ability to respond to specific stimuli (Commodari, 2017). It plays a key role in many cognitive functions such as problem solving and reasoning (Sohlberg and Mather, 1989). Divided attention refers to the optimal allocation of resources between different sets of input by rapid shifting of attentional focus, in an attempt to process information in parallel from multiple sources (Parasuraman, 1998). Different attentional processes have separate, but also overlapping neural networks (Nebel et al., 2005; Petersen and Posner, 2012; Esterman et al., 2014). It is possible that by improving one form of attention that it is at the expense of impairing another form of attention. Gazzaley and Rosen (2016) have argued that current styles of working and the multifaceted way that we engage with the environment is affecting our ability to sustain attention under conditions of distraction and interruptions which are often technology-related. Importantly, training of sustained attention/concentration did not impair performance on the TMT, which favors shifts in attention. Indeed, if anything, gameplay had a beneficial effect on this form of attention also. Decoder in the present study was aimed at healthy young adults wanting to enhance their attention or in those who easily get distracted. While we used iPads, the Decoder game will be available for all iPhone and iPad users of Peak Brain Training3 in January 2019. In addition, it has been noted that healthy women have attentional problems specifically associated with the menstrual phase (Merritt et al., 2007; Pletzer et al., 2014, 2017), which might be improved by attention/concentration training.

While healthy young adults were used as participants in the current study, the aim of cognitive training with Decoder for future studies is to assess whether it can improve attentional performance, a key cognitive domain that is impaired in ADHD, schizophrenia and brain injury (Pironti et al., 2014; Lustig and Sarter, 2016; Savulich et al., 2018). The CANTAB RVP test, the main outcome of the present study, has been shown to utilize a fronto-parietal neural network (Coull et al., 1996). Using this test, attention has been shown to be a cognitive endophenotype in ADHD, as performance was impaired in

3www.peak.net

both adult patients with ADHD and also their unaffected first degree relatives, compared with healthy volunteers (Pironti et al., 2014). Furthermore, neuroanatomical abnormalities in gray matter volume in the right inferior frontal gyrus and white matter volume in the caudal portion of the right inferior frontal gyrus and fronto-occipital fasciculus were shared between patients with ADHD and their unaffected relatives (Pironti et al., 2014). Performance on the CANTAB RVP test has also shown to be a useful cognitive endophenotype for ADHD genetic studies over and above other tasks of motor speed and visual search (Gau and Huang, 2014). In neuroimaging and lesion studies in healthy people and in patients with ADHD, the right inferior frontal gyrus has been identified as a key area in the neural circuitry underlying response inhibition (Aron et al., 2003; Hampshire et al., 2010; Fan et al., 2018). Future research could determine the underlying neural network changes following attention/concentration and flow training with Decoder. For children with ADHD, there are a few games aimed at self-regulation and social cognition (Prins et al., 2013; Bul et al., 2015).

In addition to ADHD, attentional function is commonly impaired following concussion and TBI (Stierwalt and Murray, 2002). Patients with TBI have been shown to have impairments in sustained attention as measured by the CANTAB RVP (Salmond et al., 2005; Manktelow et al., 2017). The gamification of cognitive training would also have advantages for patients with problems of apathy or negative symptoms, as it can maintain motivation during the training period (see e.g., Sahakian et al., 2015; Savulich et al., 2017b).

In conclusion, we have demonstrated significant improvements in healthy young adults in attention following cognitive training with a game on an iPad developed specifically for this purpose, using evidence-based neuropsychological and neuroimaging studies. It may be that this will be a suitable game for those healthy individuals who find sustaining attention difficult in the workplace and will assist them in maintaining the ''flow'' despite distraction and interference in the environment.

#### AUTHOR CONTRIBUTIONS

ET, GS and KP recruited and tested the participants. TP and BS designed the Decoder game and TP programmed it. GS and BS analyzed and interpreted the data. BS and JP supervised the study. All authors wrote the manuscript.

#### FUNDING

This research was supported by the NIHR Brain Injury Health Care Technology Co-operative (HTC) and the NIHR MedTech and in vitro diagnostic Co-operative (MIC). GS is funded by the Wallitt Foundation and Eton College, with support from the NIHR Cambridge Biomedical Research Centre (BRC) Mental Health theme. TP is funded by the NIHR Cambridge Biomedical Research Centre (BRC) Mental Health theme. BS received funding from the NIHR Cambridge Biomedical Research Centre (BRC) Mental Health theme.

#### REFERENCES

Faraone, S. V., Sergeant, J., Gillberg, C., and Biederman, J. (2003). The worldwide prevalence of ADHD: is it an american condition? World Psychiatry 2, 104–113.

**Conflict of Interest Statement**: BS consults for Cambridge Cognition and Peak. We have technology-transferred the App to Cambridge Enterprise who intends to technology-transfer the App to the games company Peak so that it can become widely available for use on mobile devices. This has not occurred yet.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Savulich, Thorp, Piercy, Peterson, Pickard and Sahakian. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Sub-dimensions of Alcohol Use Disorder in Alcohol Preferring and Non-preferring Rats, a Comparative Study

Ana Domi, Serena Stopponi, Esi Domi † , Roberto Ciccocioppo\* and Nazzareno Cannella

Pharmacology Unit, School of Pharmacy, University of Camerino, Camerino, Italy

#### Edited by:

David Belin, University of Cambridge, United Kingdom

#### Reviewed by:

Marco Venniro, National Institute on Drug Abuse (NIDA), United States Nathan James Marchant, University Medical Center Amsterdam, Netherlands

#### \*Correspondence:

Roberto Ciccocioppo<EMAIL_ADDRESS>
#### †Present address: Esi Domi,

Center for Social and Affective Neuroscience, Institute for Clinical and Experimental Medicine, Linkoping University, Linkoping, Sweden

> Received: 16 October 2018 Accepted: 07 January 2019 Published: 30 January 2019

#### Citation:

Domi A, Stopponi S, Domi E, Ciccocioppo R and Cannella N (2019) Sub-dimensions of Alcohol Use Disorder in Alcohol Preferring and Non-preferring Rats, a Comparative Study. Front. Behav. Neurosci. 13:3. doi: 10.3389/fnbeh.2019.00003 Recent animal models of alcohol use disorder (AUD) are centered in capturing individual vulnerability differences in disease progression. Here, we used genetically selected Marchigian Sardinian alcohol-preferring (msP) and Wistars rats to apply a multidimensional model of AUD adapted from a previously described DSM-IV/DSM-5 multisymptomatic cocaine addiction model. As proof of concept, we hypothesized that msP rats, genetically selected for excessive drinking, would be more prone to develop dependence-like behavior compared to Wistars. Before exposure of animals to alcohol, we monitored basal anxiety in the elevated plus maze (EPM). Animals were then trained in prolonged operant alcohol self-administration, consisting of 30-min daily sessions for 60 days in total. Each session consisted of two 10-min periods of alcohol reinforcement separated by 10-min interval of non-reinforcement. Following training, we applied three criteria of individual vulnerability for AUD: (1) persistence of lever pressing for alcohol when it was not available; (2) motivation for alcohol in a progressive ratio (PR) schedule of reinforcement; and (3) resistance to punishment when alcohol delivery was anticipated by a foot-shock (0.3 mA). We obtained four groups corresponding to the number of criteria met (0–3 crit). Rats in the 0crit and 1crit groups were characterized as resilient, whereas rats in the 2crit and 3crit groups were characterized as prone to develop a dependent-like phenotype. As predicted, the 2–3crit groups were enriched with msP rats while the 0–1crit groups were enriched in Wistar rats. In further analysis, we calculated the global addiction score (GAS) per subject by the sum of the normalized score (z-score) of each criterion. Results showed GAS was highly correlated with animal distribution within the 3 criteria. Specifically, GAS was negative in the 0–1crit groups, and positive in the 2–3crit groups. A positive correlation between basal anxiety and quantity of alcohol intake was detected in msP rats but not Wistars. In conclusion, we demonstrated that the 0/3criteria model is a suitable approach to study individual differences in AUD and that msP rats, selected for excessive-alcohol drinking, show a higher propensity to develop AUD compared to non-preferring Wistars.

Keywords: alcoholism, ethanol, self-administration, alcohol-seeking, punished responding, DSM-5

## INTRODUCTION

Alcohol Use Disorder (AUD) is associated with increased health risks and social harm with dramatic impact to the global disease burden (Rehm, 2011). In 2014, the World Health Organization reported that alcohol contributes to more than 200 diseases, such as alcohol dependence, liver cirrhosis and cancers, as well as alcohol related injuries (WHO, 2014). In attempting to capture the clinical condition of AUDs, a range of procedures have been developed to model alcohol dependence-related traits in rodents (Spanagel, 2000; Tabakoff and Hoffman, 2000; Hopf and Lesscher, 2014). In humans, addictive behavior is characterized by a shift from recreational to compulsive drug seeking as described in the DSM-IV (American Psychiatric Association, 2000). Long-term alcohol consumption induces neuroadaptations that are associated with loss of control, compulsive drug taking and negative emotional states (i.e., anxiety, depression; Wolffgramm and Heyne, 1995; Koob and Le Moal, 1997; Koob, 2013). Particularly, compulsivity, defined by DSM-IV/5 as use of alcohol despite harmful social, health and economic consequences, is a major component in the transition to alcoholism (Spanagel, 2009; Koob and Volkow, 2010; Hasin et al., 2013; McKim et al., 2016). Over the years, the characterization of different lines of rats and mice genetically predisposed to alcohol drinking have helped to elucidate several aspects of AUD neurobiology (McBride and Li, 1998; Bell et al., 2006; McBride et al., 2014). However, a clear understanding of the factors leading the development of dependence itself is still lacking, including compulsivity associated with disease progression (Crabbe, 2010). Such gaps between animal models and the human condition in AUDs are reflected in the limited efficacy of available pharmacological treatments to attenuate compulsive drinking (Volpicelli et al., 1995; Kranzler, 2000; Franck and Jayaram-Lindström, 2013).

Recent research has been oriented towards the development of preclinical models that more closely mimic the complexity of human alcohol addictive behaviors by going beyond the simple alcohol drinking procedures useful to study alcohol reward. By capturing multiple aspects that define AUD such as compulsive alcohol seeking and the inability to abstain from its use despite negative consequences, these models provide new insights into individual vulnerability to develop addictive-like behaviors (Hopf et al., 2011; Radwanska and Kaczmarek, 2012; Seif et al., 2013; Radke et al., 2017; Augier et al., 2018; Giuliano et al., 2018).

One of the features of drug addiction is the inter-individual vulnerability to lose control of drug consumption. This loss of control depends upon genetics, environment, personality traits, psychiatric comorbidities and the interplay of all these factors (Enoch, 2013; Morrow and Flagel, 2016; Egervari et al., 2018). In both humans and laboratory animals the predisposition to develop addiction-like behavior is present in only a small subpopulation of subjects (Anthony et al., 1994; Piazza and Deroche-Gamonet, 2013). In order to identify the interindividual differences in vulnerability to shift from controlled to compulsive drug intake that define this subpopulation, Deroche-Gamonet et al. (2004) developed a multidimensional animal model of drug addiction (Belin-Rauscent et al., 2016; Deroche-Gamonet et al., 2004; Belin et al., 2008, 2009, 2011). This model characterized a cocaine addiction-prone phenotype in rats, based on the DSM-IV diagnostic criteria of addiction (American Psychiatric Association, 2000), by measuring three traits: (1) inability to refrain from drug seeking; (2) high motivation for the drug; and (3) maintenance of drug use despite negative consequences (Deroche-Gamonet et al., 2004; Kasanetz et al., 2010; Belin et al., 2011). Here, we adapted the DSM-IV/5 based three-criteria model of cocaine addiction to characterize an alcohol-addiction prone phenotype in the rat. We used Marchigian Sardinian alcohol preferring (msP) rats and non-preferring Wistar rats to assess whether a genetic predisposition to ethanol preference contributes to the development of dependence-like behavior. msP rats represent an animal model of genetic predisposition to high ethanol drinking and relapse associated with anxious and depressive-like traits (Ciccocioppo et al., 1999, 2006; Hansson et al., 2006; Cippitelli et al., 2015; Stopponi et al., 2018). Based on these conceptualizations we predicted that msP rats, genetically selected for excessive drinking, would be more prone to develop dependence-like behavior compared to Wistars.

### MATERIALS AND METHODS

### Animals

Experiments were performed using male Wistar (n = 31; Charles River, Calco, Italy) and msP (n = 32; bred at the School of Pharmacy, University of Camerino) rats. Rats weighed 200–250 g at the beginning of the study. Rats were housed in pairs under a reversed 12:12-h light/dark cycle (lights off at 9:00 AM) with constant temperature (20–22◦C) and humidity (45–55%). Food and water were provided ad libitum. Ethanol (95%, Carsetti, Camerino, Italy) was diluted to 10% (v/v) in tap water for chronic, intermittent EtOH exposure and for self-administration behavioral testing. Animals were treated in accordance with the guidelines of the European Community Council Directive for Care and Use of Laboratory Animals. The experimental procedures were approved from the Italian Ministry of Health (authorization n◦ 414/2016-PR).

#### Elevated Plus-Maze

Before being exposed to alcohol, rats were tested in the elevated plus-maze (EPM) to measure anxiety-like traits. The apparatus was constructed of wood and painted black. It consisted of two open arms and two enclosed arms (40 cm high walls) arranged so that the similar arms were opposite each other. The maze, elevated 50 cm above the floor, was located in a sound attenuated room illuminated by a red dim light (∼30 lux). The 5 min test began placing the animal in the center of the maze, facing a closed arm. The number of open and closed-arm entries and the time spent in each arm was recorded. Data were expressed in percentage (open or closed time/total time × 100; open or closed entries/total entries × 100). The percentage of time spent in open arms and the number of open arm entries (with entries defined as placement of all four paws into the respective area) were used as measures of anxiety-like behavior, while the number of total arm entries was used as an indicator of general motor activity (Pellow et al., 1985; Cippitelli et al., 2011; Cannella et al., 2016; Domi et al., 2016; Stopponi et al., 2018).

## Alcohol Training Procedure

Prior to operant responding training, rats were exposed to an intermittent two-bottle choice alcohol drinking procedure (choice between 10% alcohol and water) for 3 weeks. This training protocol was adopted to avoid sucrose fading or water deprivation procedures and facilitate the acquisition of operant responding. Alcohol self-administration was performed in rat operant conditioning chambers (Med Associate St Albans, VT, USA) enclosed in sound-attenuating, ventilated, environmental cubicles. Each chamber was equipped with two retractable levers located in the front panel of the chamber with two stimulus lights placed above each lever in addition to a house light and a tone generator. The operant chambers were controlled, and data collected with MED-PCr IV windows-compatible software.

Rats were trained to press the active lever for EtOH 10% on a fixed-ratio 1 (FR1) schedule of reinforcement until a stable baseline was reached (one daily sessions for 7 days). Animals were then moved to a FR3 schedule of reinforcement until addiction criteria were tested. Training sessions were 30 min in duration, during which a 10-min reward-available period (drug-period) was followed by a 10-min reward-unavailable period (no-drug-period) which was followed by a second 10-min drug-period. Pressing the right (active) lever during the drug period resulted in the delivery of 0.1 ml of 10% ethanol in a receptacle connected to a syringe pump, which was followed by the activation of a cue light above the lever for 5 s and a 10 s time-out period. During the no-drug period, signaled by activation of the house light, pressing the active lever had no consequences. Responses on the left or ''inactive'' lever were recorded during the entire session but did not result in any programmed consequences.

### Evaluation of the Three Criteria for AUD Like-Behavior

At completion of the training we applied three main criteria to monitor individual vulnerability for AUD:

(i.e., the number of lever responses or the ratio required to receive one dose of 10% ethanol) was increased as follows: for each of the first four ethanol deliveries the ratio was increased by 1; for the next four deliveries the ratio was increased by 2 and for all of the following deliveries the ratio was increased by 4 (1, 1, 1, 1, 2, 2, 2, 2, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72 etc.; Economidou et al., 2006). Each alcohol delivery was paired with a 5 s illumination of the cue light. Sessions were terminated when 30 min had elapsed since the last reinforced response. The maximal number of responses that a rat produced to obtain one infusion was referred to as the break point.

3. To measure resistance to punishment, rats were placed for 10 min (corresponding to the first drug-period of a standard training session) in the SA chamber were the punishment was a foot-shock (0.3 mA, 0.5 s). The intensity of the shock was chosen based on a pilot study in which different cohorts of msP and Wistar rats were exposed to shock intensities of 0.1 mA, 0.3 mA and 0.6 mA. Results showed that rats were not sensitive to 0.1 mA intensity while at 0.6 mA foot shock completely abolished alcohol self-administration in all the animals. Here, in a FR3 schedule, the first active lever press led to the illumination of a new, different stimulus light (green light), signaling the presence of a shock session. The second active lever press produced a foot-shock of 0.3 mA via a metal grid connected to a shock generator. The third active lever press produced the delivery of 0.1 ml of 10% ethanol associated with the cue light. If within a minute, animals did not complete an FR3 the green light turned off and the sequence was reinitiated.

PR and punishment sessions were performed on days 45 and 55 respectively.

A rat was considered positive for a particular addiction-like criterion when the score for this behavior was in the top 34% percent of the distribution. This criterion was arbitrarily chosen based on seminal work from Deroche-Gamonet et al. (2004) and considering that a change of the selection threshold from 25 to 40% has minimal effect on individual rat-group allocation (Deroche-Gamonet and Piazza, 2014). We obtained four groups of rats (0crit, 1crit, 2crit and 3crit) defined by the number of positive criteria met.

As a second level of analysis, we measured the global addiction score (GAS) by calculating the sum of the normalized score (z-score) of each criterion for each subject (Belin et al., 2009).

### Statistical Analysis

Data are expressed as mean ± standard error (SEM). All behavioral experiments were analyzed by mean of Student's t-test comparison, one-way, factorial or repeated-measures analysis of variance (ANOVAs) and covariance (ANCOVA) according to experimental design. We examined for significant violations for assumptions of homogeneity of variances by using Levene's and Bartlett's test. In case of deviation from homogeneity of the variance was significantly detected, the Mann-Whitney and Kruskal Wallis non-parametric analysis were used (footshock resistance vs. genotype) and (footshock resistance vs. 0, 1, 2 and 3 criteria), respectively. Significant difference was set at p < 0.05. Post hoc comparisons were carried out by Newman-Keuls test when appropriate. To asses the escalation of alcohol seeking during the no-drug period we used a k-means cluster analysis with 10 iterations and with maximization of distances between groups defined a priori as 3. This approach was taken to verify the existence of a subgroup of animals that increased ''active'' lever presses over time (from day 1–44). Moreover, for each rat we calculated the slope of ''active'' responses during the no-drug period over the 44 days (divided in four intervals of 11 days each). Positive values of the slope represent an increase in lever presses over time while negative values reflect a decrease in lever presses over time (Dilleen et al., 2012; Ducret et al., 2016).

### RESULTS

### Anxiety-Like Behavior in msP and Wistar Rats

The msP rats exhibited significantly higher anxiety-like behavior spending less time in the open arms compared to Wistar rats (t(61) = 5.62, p < 0.001; **Figure 1A**). No effect was found in the total number of entries indicating no difference in locomotion (t (61) = 1.88, NS; **Figure 1B**).

#### Acquisition of Alcohol Self-Administration in msP and Wistar Rats

Rats were trained to self-administer alcohol for 60 days. Both msP and Wistar rats acquired and maintained stable alcohol self-administration levels. ANOVA of number of rewards earned revealed a significant effect of line (F(1,61) = 83.3; p < 0.001), significant effect of session (F(59,3599) = 100.31; p < 0.001) and a significant line × session interaction (F(53,3599) = 5.71; p < 0.001). msP rats self-administered significantly more ethanol compared to Wistars. Post hoc Neuman-Keuls test (**Figure 2A**) revealed a significand difference in the following days: day 2 (p < 0.05), 24 (p < 0.01), 3–23 and 25–60 (p < 0.001). Analysis of lever responses over training demonstrated that both msP and Wistar rats discriminated between active and inactive lever and the difference between rat lines was specific to the active lever [line (F(1,60) = 39.32; p < 0.001), lever (F(1,60) = 709.05; p < 0.001), session (F(59,3540) = 138.15; p < 0.001) rat line × lever × session interaction (F(59,3540) = 7, 65; p < 0.001); **Figure 2B**].

## Evaluation of the Three Criteria AUD Like-Behavior

#### Persistence in Alcohol Seeking

Persistence in alcohol seeking was measured as the number of active lever presses occurring during the 10 min of non-drug availability. We used a k-means cluster analysis with the number of clusters set a priori to three and the variables defined as the 44 days of self-administration (i.e., prior to the PR and foot-shock session tests) divided into 4 intervals of 11 days each (Int.1 = day 1–11, Int.2 = day 12–22, Int.3 = day 23–33, Int.4 = day 34–44; **Figure 3A**). In assessing whether during the 44 days of operant training animals had progressively increased their lever presses during the drug free period, ANOVA revealed a significant difference between intervals (F(3,180) = 22.02; p < 0.001) and a significant interval × cluster interaction (F(6,180) = 26.06; p < 0.001). Subjects in cluster 1 (14 cases: 8 msP and 6 Wistar rats) decreased their lever presses over time and were defined as low persistent (LP). Animals in cluster 2 (20 cases:12 msP and 8 Wistar rats) markedly increased persistence to response over time as revealed by the post hoc Neuman-Keuls analysis (Int.1 vs. Int. 4 p < 0.001) and were defined as high persistent (HP). Subjects in Cluster 3 (29 cases: 12 msP and 17 Wistar rats) defined as Intermediate (IM) maintained a consistent rate of lever presses during the 44 days of operant training. For the subsequent measure of this criterion, we considered for each rat the slope of lever presses during the no-drug period over the four intervals of time and compared it between msP and Wistars (**Figure 3B**). Despite the fact that msP rats had higher slope values in average (msP = 1.47 ± 0.39, Wistar = 1.11 ± 0.38), the Student ttest revealed no difference between msP and Wistar rats in persistence to response (t(61) = 0.67, p = ns).

#### Motivation

In PR contingency we compared the breakpoint reached by msP and Wistars by ANCOVA using the average intake during the last three self-administration sessions as covariate. ANCOVA found no significant effect of line (F(1,60) = 7.8; p = NS). However, the breakpoint was higher in msP rats than Wistar rats (msP = 39.75 ± 1.6, Wistar = 28.84 ± 2.06), indicating a higher motivation to self-administer alcohol in the alcohol preferring line. Indeed, since a higher motivation prompts the msP to self-administer higher amount of alcohol both during the acquisition and the test phase, it is not surprising that using the alcohol intake as covariate would cancel the difference in break point. Confirming this interpretation, when intake is not used as covariate a strong significant difference in break point between the two lines is detected (t(61) = 4.2, p < 0.001; **Figure 3C**).

#### Resistance to Punishment

In the punished reward test, animals were presented an aversive stimulus (foot-shock) associated with subsequent administration of alcohol. Observed at group level, punishing of operant responding decreased the motivation for alcohol in both genotypes, but at the individual level the number of rewards self-administered spanned from 0 to 100% of baseline (**Figure 3D**). The average rate of rewards earned in the punished reward tests were 42% ± 4.15 for msP and 39% ± 3.95 for Wistars calculated as the average of the first 10 min of the last four baseline sessions vs. the first 10 min of the punished schedule session. Mann-Whitney U-test revealed no difference between genotypes in alcohol seeking despite punishment expressed in percentage of their baseline (U = −0.85, two-sided exact p = 0.4).

#### Distribution of msP and Wistar Rats by Their Addiction-Like Behavior Score

msP and Wistar rats were scored for each addiction-like behavior and were assigned to a ''positive criterion'' subgroup if their individual score was in the top of 34% of the total distribution. Rats were then separated into four groups depending on the number of positive criteria met (from 0crit to 3crit). msP rats represented the majority of the 3crit [12.69% of the whole rats' cohort, 9.52% were msP (n = 6) and 3.17% were Wistars (n = 2)] and the 2crit groups [19.05% of the whole rats' cohort, 14.19% were msP (n = 9) and 4.76% where Wistars (n = 3)]. Conversely, Wistar rats represented the majority of the 1crit [34.92% of the whole rats' cohort; 12.70% were msP (n = 8) and 22.22% were Wistars (n = 14)] and the 0crit groups [33.33% of the whole rats cohort; 14.29% were msP

FIGURE 3 | (A) K-means clustering with three clusters of the total population (msP and Wistar rats) used to classify animals based on their performance in the drug free period and using as variables the 44 daily sessions divided in four intervals of 11 days each. Cluster 1 defined as low persistent (LP; 14 cases: 8 msP and 6 Wistar rats); cluster 2 defined as high persistent (HP; 20 cases: 12 msP and 8 Wistar rats); cluster 3 defined as intermediate (29 cases: 12 msP and 17 Wistar rats). Values are presented as mean (±SEM). ∗∗∗p < 0.001, significant between Int.1 and Int.4 in Cluster 2. (B) Slope values of the number of active lever presses during the drug free period in msP and Wistar rats over the 44 daily sessions divided in four intervals of 11 days each (Int.1, Int.2, Int.3 and Int.4 as points of the slope line). Values are presented as mean (±SEM). (C) Motivation for 10% ethanol measured by the break point during a progressive-ratio (PR) schedule of reinforcement in msP and Wistar rats. Values are presented as mean (±SEM). ∗∗∗p < 0.001, significant as compared to msP and Wistar rats in the break point achieved. (D) Number of reinforcements in a 10 min foot-shock session for msP and Wistar rats represented as percentage of the baseline (average of the first 10 min of days 42–44). Values are presented as mean (±SEM). The dotted line indicates the 66th percentile of distribution of (B–D).

(n = 9) and 19.05% were Wistars (n = 12); **Figure 4A**]. The criteria for which 1crit and 2crit rats were positive are shown in **Table 1**.

Based on the sum of normalized scores (z-scores) assigned to each criterion (**Figure 4B**), we obtained a GAS for individual rats. The average of this addiction score for each subpopulation was negative for the 0 and 1 crit groups (0crit = −2.07 and 1crit = −0.08) and positive for 2 and 3 criteria groups (2crit = 1.73 and 3crit = 3.1; **Figure 4C**). We found a main effect for addiction scores among the criteria subgroups as revealed by one-way ANOVA (F(3,59) = 37.81; p < 0.001). Neuman-Keuls post hoc test showed a significant difference between each addiction score (0crit vs. 1crit, 2crit and 3crit p < 0.001; 1crit vs. 2crit and 3crit P < 0.001; 2crit vs. 3crit p < 0.05).

### Differences in the Three Measures of AUD-Like Behavior in 0crit, 1crit, 2crit and 3crit Groups

One-way ANOVA applied to persistence in alcohol seeking revealed a significant difference between groups (F(3,59) = 14.83; p < 0.001; **Figure 4D**). The slope of active lever pressing during the no-drug-period was about nil in 0crit (−0.05 ± 0.33) and progressively increased as a function of the criteria met: 1crit (0.85 ± 0.38), 2crit (2.76 ± 0.46) and 3crit (3.87 ± 0.65). Neuman-Keuls post hoc test showed that 3crit rat exhibited higher active lever pressing slope compared to 0crit (p < 0.001) and 1crit (p < 0.001) groups but not compared to 2crit group (p = ns). The 1crit rats differed as well from 2crit rats (p < 0.001) but not from 0crit group (p = ns).

In the motivation for alcohol (**Figure 4E**), one-way ANOVA revealed a significant between groups difference in the breakpoint (F(3,59) = 11.71; p < 0.001). As shown by Neuman-Keuls post hoc test, 3crit rats exhibited higher breakpoint (43.5 ± 1.4) compared to the 0crit (26.48 ± 1.9; p < 0.001) and 1crit (33.36 ± 2.18; p < 0.01) groups but not compared to 2crit rats (44 ± 3.27; p = ns). In addition, the 2crit rats differed from 0crit (p < 0.01) and 1crit (p < 0.05) rats but not from 3crit rats (p = ns).

Kruskal Wallis H test of punished alcohol seeking revealed an overall effect of groups (H 3, N = 63 = 25,064 p < 0.001; **Figure 4F**). Multiple comparisons showed that 3crit rats presented a higher rate of punished reinforcers (71.32 ± 5.27) compared to 0crit (18.92 ± 3.4; p < 0.001), 1crit (47.01 ± 6.4; p < 0.01) and 2crit (46.86 ± 6.75; p < 0.05) rats. Moreover, 2crit group differed from 0crit group (p < 0.01) and 1crit group differed from 0crit group (p < 0.01).

TABLE 1 | Description of the positive criteria met by msP and Wistar rats within the 1crit and 2crit groups.

### Alcohol Consumption and AUD-Like Behavior

We evaluated the relationship between alcohol intake in msP and Wistar rats with the propensity to develop addiction-like behavior vs. resistance (2–3crit vs. 0–1crit) (**Figure 5**). Factorial ANOVA revealed a significant effect of genotype (F(1,59) = 20.77; p < 0.001) and criteria subgroup (F(1,59) = 11.00; p < 0.001) and a significant interaction between genotype and the criteria subgroup on alcohol intake (F(1,59) = 5.53; p < 0.05). Newman-Keuls post hoc test showed that 0–1crit Wistar rats had lower levels of alcohol intake compared to 2–3crit Wistar rats (p < 0.01) while the 0–1crit msP rats did not differ in alcohol intake from the 2–3crit msP rats (p = ns). Moreover, 0–1crit Wistar rats differ from 0 to 1crit msP (p < 0.001) while there is not a statistically significant difference between 2–3crit Wistar and 2–3crit msP rats on alcohol intake (p = ns).

### Anxiety and the Vulnerability to Develop Addiction-Like Behavior

In the literature, high anxiety behavior has been associated with the development of drug addiction (Stewart and Conrod, 2008; Ipser et al., 2015). As revealed by Pearson's analysis, a significant (r = −0.38, p < 0.05) negative correlation between alcohol intake during ethanol self-administration and the percentage of time spent in the open arms was found in msP rats in which high levels of alcohol drinking were associated with higher anxiety. No significant correlation was detected in Wistars (r = −0.04, p = ns). We also compared the three addiction-like criteria with the percentage of time spent in open arms of the EPM.

#### DISCUSSION

2–3crit msP rats.

Modeling human AUDs in rodents has been a challenge in preclinical studies since alcohol, unlike cocaine or opioids, is a weak reinforcer and requires protracted exposure for the development of dependence (Spanagel and Hölter, 1999). A valid animal model that attempts to reflect the human condition in addiction should capture multiple aspects that characterize substance use disorders (SUDs) such as compulsive drug seeking and taking, increased motivation for the drug, and continued intake despite negative consequences (Hopf and Lesscher, 2014).

In this study, based on the DSM-IV/5 diagnostic criteria, we used a multidimensional model including different behavioral features of addiction to characterize an alcohol addiction prone phenotype in rats. We also sought to evaluate the role of genetic predisposition to excessive drinking in shaping individual variability in developing alcohol addictive like-behavior by comparing alcohol preferring msP rats with non-preferring progenitor Wistar rats.

Our data showed an enhanced propensity of the msP rats to exhibit higher scores in the three defined criteria for individual vulnerability to AUD, as measured in: (1) persistence in alcohol seeking when alcohol is not available; (2) motivation for alcohol in a PR schedule of reinforcement; and (3) resistance to punishment when alcohol delivery is anticipated by a footshock. The percentage of rats positive in all three criteria was approximately 13% with msP rats representing the majority of the 3crit group compared to Wistar rats, 9.52% vs. 3.17%, respectively. The percentage of rats characterized as 3crit is similar to the small proportion of individuals that develop alcohol dependence after protracted exposure (Anthony et al., 1994; Wagner and Anthony, 2002). The 2crit group was also enriched in msP rats, while Wistars constituted the majority of the 0crit and 1crit groups. The inter-individual variability we observed was present not only between but also within genotypes demonstrating that this protocol is a suitable approach to study individual differences in alcohol dependence in largely homogeneous rat populations.

Persistence of alcohol seeking was evaluated daily throughout the training period. We ran a cluster analysis to identify a subgroup of rats that increased their response during the drug free period over time, as other laboratories have failed to capture this behavioral criterion (Waters et al., 2014). By clustering rats based on their persistence in lever pressing in the absence of alcohol availability, we were able to determine a subpopulation of rats (38% of msP and 26% of Wistars) that were highly persistent in lever pressing. Recent studies on rodent models of AUDs have interpreted the intrasession drug free period as a measure of alcohol seeking behavior (Jadhav et al., 2017; Radke et al., 2017). However, in those studies persistence to response when alcohol was not available was assessed only in three to five sessions making it difficult to evaluate how uncontrolled drug seeking develops over time (Belin et al., 2016). Here, in line with earlier studies using the 0/3crit model in cocaine use, we demonstrated that persistence of alcohol seeking is a trait that develops over time but only in a subset of animals.

In assessing motivation for alcohol by using the PR schedule of reinforcement, we found that msP rats, compared to Wistar rats, exhibited increased motivation to self-administer alcohol as shown by higher breakpoints during the PR session (Ciccocioppo et al., 2006). This schedule, more than the persistence of alcohol seeking or resistance to punishment, is linked to consummatory behavior, a trait used to select msP rats. It is not surprising, therefore, to observe such a remarkable difference from non-selected Wistars. Previous studies have also demonstrated that the breakpoint is sensitive to genetic selection procedures (Czachowski and Samson, 2002).

As described in the DSM-IV and DSM-5, compulsive drug seeking or drug-taking despite negative consequences is another hallmark of drug dependence (American Psychiatric Association, 2000, 2013).

A recent preclinical model developed in Cambridge laboratories, has been able to identify a subset of vulnerable individuals that display compulsive alcohol seeking in the face of punishment (Giuliano et al., 2018). By using probabilistic footshock punishment of the seeking response in a seeking-taking chained schedule of reinforcement they were able to distinguish the punisher from the reward. Indeed, it has been demonstrated that when the footshock co-occurs with the drug delivery its effectiveness as a punisher is reduced (Dickinson and Pearce, 1976; Pelloux et al., 2007). Several models use footshock punishment (various shock intensities have been used) paired with the delivery of a constant dose of alcohol (Seif et al., 2013; Jadhav et al., 2017; Radke et al., 2017; Augier et al., 2018). To distinguish the punishing from the reinforcing proprieties of alcohol here, rats were punished with 0.3 mA footshock that preceded alcohol taking response without pairing the shock with ethanol delivery. Results showed that punishing operant responding markedly decreased alcohol seeking in both msP and Wistar rats. This phenomenon was previously described when footshock punishment was used in rats trained in cocaine self-administration (Deroche-Gamonet et al., 2004; Kasanetz et al., 2010). However, individual animals showed different behavioral suppression levels that spanned from 0 to 100% of baseline. Most importantly, in both lines a subgroup of rats continued to self-administer alcohol despite the negative consequences of footshock.

Consistent, with previous works adopting the 0–3crit model of cocaine (Belin et al., 2008, 2011; Kasanetz et al., 2013; Cannella et al., 2017, 2018) and alcohol (Radke et al., 2017) addiction, here we used a single foot-shock session to assess resistance to punishment. As demonstrated by other studies, using multiple punishment sessions could have been an alternative to better separate the population in shock-resistant and shock-sensitive subgroups (Seif et al., 2013; Augier et al., 2018; Giuliano et al., 2018; Marchant et al., 2018). However, a single shock-test session is also informative of individual resistance to punishment. Moreover, a recent work demonstrated that in the context of the 0–3crit model applied to alcohol, multiple shock tests can compress rather than increase the range of distribution of resistance to punishment. This study also revealed that resistance to punishment of the 0crit to 3crit groups decreases over time, although the groups differences are maintained (Jadhav et al., 2017).

As a second level analysis, by summing the normalized score (z-score) applied to single criteria the GAS for individual subject was calculated. Results showed that each criteria group differed significantly in GAS. Specifically, GAS was negative in the 0crit/1crit groups identified as resistant whereas it was positive in the 2crit/3crit groups that were characterized as prone to develop an alcohol dependent-like phenotype. We found a high correlation between the GAS and the distribution of the animals within the three criteria, indicating the interdependence between these two measures.

Epidemiologic studies have suggested that genes play an important role in the vulnerability to alcohol abuse and the subsequent risk to develop alcohol dependence (Crabbe et al., 2006; Mayfield et al., 2008; Schuckit, 2009). In recent years there has been considerable debate on whether geneticallypredisposed alcohol drinking rodents may adequately model AUDs. To this end an ideal genetic animal model of alcoholism should carry the same genetic traits linked to alcoholism in humans, and ideally those traits should correlate with the expression of similar subphenotypic characteristics. One of the genetic traits of msP rats is the over-expression of the corticotropin-releasing factor system, linked to the presence of two single nucleotide polymorphisms (SNPs) of the CRF1 receptor (CRF1-R) gene leading to receptor overexpression (Hansson et al., 2006; Ayanwuyi et al., 2013; Cippitelli et al., 2015). Polymorphisms at level of the promoter region of the CRF1-R gene have been reported in humans with AUDs, suggesting a genetic trait in common with msP rats (Treutlein et al., 2006). Notably, resembling a large subset of alcoholic patients, msP rats also drink excessive amounts of alcohol (7–8 g/kg/day) and exhibit an anxious and depressive-like behavioral phenotype. In msP rats this phenotype is, at least in part, linked to a hyperactive CRF1- R function (Schuckit and Hesselbrock, 1994; Grant et al., 2004; Ayanwuyi et al., 2013; Cippitelli et al., 2015). Here, we confirmed that, compared to unselected Wistars, the msP rats have a higher basal level of anxiety. However, when we attempted to associate open arm time (a measure of anxiety) with animal distribution within the 3 criteria, no significant correlations were detected. This, together with earlier findings, suggest that high anxiety is linked to a genetic predisposition to excessive drinking but not with propensity to develop alcohol abuse traits (compulsive-like alcohol use, persistence in seeking, and motivation). Dilleen et al. (2012) demonstrated recently that a high anxiety trait predicts loss of control over cocaine, but not heroin self-administration, suggesting that outcome may depend on the psychoactive drug used.

msP rats represented the majority of subjects belonging to the 2crit and 3crit groups. However, Wistar rats satisfying the 2/3crit, self-administered as much alcohol as the msPs rats, and significantly more than 0/1crit Wistars. The msP rats in the 0/1crit group consumed the same high amount of alcohol of the 2/3crit group. These data indicate that a genetic predisposition to ethanol preference and excessive drinking may not be necessarily associated with a propensity to develop addictive-like traits modeled by the 0/3crit paradigm used here. Interestingly, in humans it has been shown that a large number (about 90%) of people with excessive drinking habits do not meet the criteria for alcohol dependence (Esser et al., 2014). Our results are therefore in line with human data and with results from another recent study which found that the propensity for P alcohol preferring rats to drink high levels of alcohol was dissociable from the development of compulsive alcohol seeking (Giuliano et al., 2018). Together these data suggest that the study of individual vulnerability is an important approach to investigate AUD, as it is able to dissociate excessive alcohol drinking from the propensity to develop the dependence, thus mimicking the human condition. Ultimately, the study of individual vulnerability and the employment of this model in pharmacological studies may help refine the investigation of novel chemical entities for AUD by exploring their efficacy on specific traits not limited to drinking, and that more closely mimic the human condition.

#### AUTHOR CONTRIBUTIONS

AD and RC were responsible for the study concept and design. AD performed the experiments. AD, NC, RC, ED and SS assisted with the data analysis, interpretation of findings and drafted the manuscript. All authors critically reviewed the content and approved the final version for publication.

### FUNDING

This study was supported by National Institute on Alcohol Abuse and Alcoholism (NIH) AA014351 (Friedbert Weiss and RC) and AA017447 (Marisa Roberto and RC).

## REFERENCES

### ACKNOWLEDGMENTS

We thank Prof. David Belin for his expert opinion on the evaluation of the ''persistence in alcohol seeking'' criterion and his comments on the statistical analysis. We thank Agostino Marchi and Rina Righi for the technical support and Marina Minucci for her contribution to the behavioral experiments.

and alcohol. Neuropsychopharmacology 26, 479–488. doi: 10.1016/s0893- 133x(01)00367-0

**Conflict of Interest Statement**: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Domi, Stopponi, Domi, Ciccocioppo and Cannella. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Impulsive Action and Impulsive Choice Are Differentially Associated With Gene Expression Variations of the GABAA Receptor Alfa 1 Subunit and the CB1 Receptor in the Lateral and Medial Orbitofrontal Cortices

#### Edited by:

Nuno Sousa, University of Minho, Portugal

#### Reviewed by:

Trevor W. Robbins, University of Cambridge, United Kingdom Sebastien Carnicella, INSERM U1216 Grenoble Institut des Neurosciences (GIN), France

\*Correspondence:

Emilio Ambrosio<EMAIL_ADDRESS>Alejandro Higuera-Matas<EMAIL_ADDRESS>
#### †Present address:

Ana Contreras, Laboratory of Pharmacology, Department of Health and Pharmaceutical Sciences, School of Pharmacy, Universidad CEU-San Pablo, Madrid, Spain

> Received: 08 November 2018 Accepted: 28 January 2019 Published: 20 February 2019

#### Citation:

Ucha M, Roura-Martínez D, Contreras A, Pinto-Rivero S, Orihuel J, Ambrosio E and Higuera-Matas A (2019) Impulsive Action and Impulsive Choice Are Differentially Associated With Gene Expression Variations of the GABAA Receptor Alfa 1 Subunit and the CB1 Receptor in the Lateral and Medial Orbitofrontal Cortices. Front. Behav. Neurosci. 13:22. doi: 10.3389/fnbeh.2019.00022 Marcos Ucha, David Roura-Martínez, Ana Contreras† , Sheyla Pinto-Rivero, Javier Orihuel, Emilio Ambrosio\* and Alejandro Higuera-Matas\*

Department of Psychobiology, School of Psychology, National University for Distance Education (UNED), Madrid, Spain

The orbitofrontal cortex (OFC) is a key brain region for decision-making, action control and impulsivity. Quite notably, previous research has identified a double dissociation regarding the role of this cortical territory in impulsive choice. While medial orbitofrontal lesions increase preference for a large but delayed reward, lateral orbitofrontal lesions have the opposite effect. However, there are no data regarding this anatomical dissociation in impulsive action. The neurochemical basis of impulsivity is still being elucidated, however, in recent years a role for the endocannabinoids and the related glutamatergic and GABAergic neurotransmitter systems has been suggested. Here, we submitted male Wistar rats to a delay-discounting task (DDT) or a two-choice serial reaction time task (2-CSRTT) and classified them as high impulsive or low impulsive in either task using cluster analysis. We then examined the gene expression of several elements of the endocannabinoid system or different subunits of certain glutamatergic or GABAergic ionotropic receptors (AMPA, NMDA, or GABAA) in the lateral or medial divisions of their orbitofrontal cortices. Our results confirm, at the gene expression level, the dissociation in the participation of the medial, and lateral divisions of the orbitofrontal cortex in impulsivity. While in the 2-CSRTT (inhibitory control) we found that high impulsive animals exhibited lower gene expression levels of the α1 GABAA receptor subunit in the lateral OFC, no such differences were evident in the medial OFC. When we analyzed DDT performance, we found that high impulsive animals displayed lower levels of CB1 gene expression in the medial but not in the lateral OFC. We propose that GABAergic dynamics in the lateral OFC might contribute to the inhibitory control mechanisms that are altered in impulsive behavior while endocannabinoid receptor gene transcription in the medial OFC may subserve the delay-discounting processes that participate in certain types of impulsiveness.

Keywords: impulsivity, orbitofrontal cortex, delay-discounting, two-choice serial reaction time task, inhibitory control, ionotropic receptors, GABA, endocannabinoid system

## INTRODUCTION

fnbeh-13-00022 February 18, 2019 Time: 15:58 # 2

Understanding the mechanisms behind the control of behavior is one of the biggest challenges of modern Neuroscience. The natural tendency to make rapid decisions without foresight is a multifaceted trait commonly known as impulsivity. The capacity to make rapid decisions and act quickly without hesitation can be beneficial in many situations. However, when this tendency becomes extreme it can be detrimental and symptomatic of several psychopathological conditions such as attention deficit hyperactivity disorder or substance abuse (Dalley and Robbins, 2017).

During the last decades, researchers have explored different approaches to objectively measure impulsivity in humans and other mammals. There is an ample variety of tests based on decision making (such as delay and probability discounting tasks) and tests based on inhibiting motor actions [such as the fivechoice serial reaction time task (5-CSRTT) or go-no go tasks]. Considering the last decade of research and on the grounds of the neuroanatomical circuits essential to each test, impulsiveness is categorized into "waiting impulsivity" [measured with the delay-discounting task (DDT) and the 5-CSRTT], "stopping impulsivity" or the difficulty to stop an already initiated action (go/no-go tasks) and the preference for uncertain but bigger outcomes, known as "risky impulsivity" (probability discounting tasks). Although all these kinds of impulsivity share some common neural mechanisms they also rely on independent pathways (for an excellent review read Dalley and Robbins, 2017).

Waiting impulsivity is usually assessed using delaydiscounting or choice reaction time-based tasks. Nevertheless, these tasks could actually be assessing distinct subtypes of waiting impulsivity as they rely on subtly different neural mechanisms. For example, although both tasks are mediated by the nucleus accumbens (NAcc), the capacity of delaying gratification is more dependent on the core, while the inhibition of premature responses relies on the integrity of the shell (Basar et al., 2010). In addition, both subtypes of waiting impulsivity predict different aspects of drug addiction (Belin et al., 2008; Diergaarde et al., 2008).

The OFC has long been associated with several functions related to decision making (Wallis, 2007), including impulsivity (Chudasama et al., 2003; Berlin et al., 2004), but the concrete role of this area remains elusive. This elusiveness could be a consequence of the functional dissociation of the lateral and medial OFC shown both in humans (Elliott et al., 2000; Sescousse et al., 2010) and primates (Noonan et al., 2010). In rodents, the study of Mar et al. (2011) revealed a similar functional dissociation between the lOFC and the mOFC. The lesions in the lOFC elicited an increase in waiting impulsivity in a DDT whereas lesions of the mOFC caused the opposite effect. It may be tempting to speculate that this orbitofrontal dissociation could be related to the aforementioned segregation of functions between the core and shell of the NAcc, however, it seems that in the rat (contrary to the monkey) the NAcc is almost devoid of proper orbitofrontal connections [only the lateral portions of the shell receive some projections from the lOFC (Schilman et al., 2008)].

In this study, we set out to assess whether the expression of genes related to glutamatergic, GABAergic or cannabinoid neurotransmission in the lOFC or mOFC was related to the two varieties of waiting impulsivity that are captured by the DDT or the two choice serial reaction time task (2-CSRTT).

Most of the previous studies regarding impulsivity and the OFC have focused on other neurotransmitters such as dopamine and serotonin (Winstanley et al., 2006; Dalley et al., 2008). We chose to study the gene expression of several subunits of glutamatergic and GABAergic ionotropic receptors because of their direct relationship with the excitation or inhibition status of the region where they are being expressed and because little is still known about their roles in impulsivity. In addition, we have assessed endocannabinoid related gene expression because the endocannabinoid system plays a key role in the modulation GABA and glutamate release from the presynaptic terminals.

## MATERIALS AND METHODS

#### Animals

Adult male Wistar rats (n = 36, 18 per experiment) (Charles River Laboratories) were housed in groups of 3 in a controlled facility with a temperature of 22 ± 2 ◦C and relative humidity of 50% ± 10 on an inverted 12 h/12 h light/dark cycle (lights on at 8:00 pm). The rats weighted around 300 g at the beginning of the experiments and were kept at around 90–95% of their original weight by restricting their access to food (standard commercial rodent diet A04/A03: Panlab). They had ad libitum access to water through all the duration of the experiments. All the animals were maintained and handled according to European Union guidelines for the care of laboratory animals (EU Directive 2010/63/EU governing animal experimentation).

#### Apparatus

The behavioral tests were performed using six operant conditioning chambers (l = 300 mm; w = 245 mm; h = 328 mm) (Med Associates). The front part of each box was equipped with two levers 14 cm apart and a pellet dispenser with a nose poke detector between them. There were also light cues above each lever, a house light close to the top of the boxes and a white noise generator. The chambers were controlled using the software MedPC by a computer connected to a compatible interface (Med Associates).

### Behavioral Tasks

#### Acquisition of Lever Press Response

All the rats received instrumental training sessions with food pellets (grain-based rodent tablet, TestdietTM) and a light cue indicating the active lever on a fixed ratio 1 schedule. The sessions lasted 30 min and continued daily until the animals developed an acceptable lever press behavior (at least 30 lever presses), and then the same training was performed with the other lever (the order of the levers was counterbalanced across the conditioning chambers). Once the animals reached the criterion for both levers, they were trained with both active levers simultaneously (both cue lights on/both levers reward) until the Left/Right lever ratio was 1:1 ± 10%.

#### Behavioral Measurements of Impulsivity

#### **Delay-discounting task**

fnbeh-13-00022 February 18, 2019 Time: 15:58 # 3

For the study of "impulsive choice," we used an adaptation of the protocol of the DDT described by Mar and Robbins (2007). Each session lasted 100 min and consisted of five blocks of 12 trials each. Trials are presented every 100 s (i.e., 60 trials in 100 min). One of the levers (the "immediate lever") initiated the delivery of one food pellet when pressed while the other (the "delayed lever") delivered four of them. The immediate and delayed levers were in the same location (left or right) for each animal, but their position was counterbalanced between animals. The delay between lever press and the delivery of the reward was always 0 s for the immediate lever, whereas the delay associated to the delayed lever was increased across blocks in order to assess the tolerance to delay of the rats. The first two trials of each block were forced (i.e., only one lever was active and its corresponding cue light was illuminated). During the rest of the trials both levers were available, a fact that was signaled by the illuminated cue lights above each lever. Once a lever was pressed within the 10 s interval given, the cue lights were turned off and an inter-trial interval (ITI) commenced. If the rat failed to respond during the 10 s period, all lights were turned off, punishing the omitted response. During the first training sessions, both levers delivered a reward immediately, and these sessions continued until the rats showed a clear preference for the lever that delivered the large reward (> 90% choice). Once the criterion was met, the rats started the test sessions in which the delay of delivery for the delayed lever was increased with every block change (0, 5, 10, 20, and 40 s, respectively). At the end of each block, a tone cue was presented to mark the beginning of the next block. The choice ratio for each block was calculated by dividing the number of delayed responses in all the free-choice trials of the block (a maximum of 10 free choice trials per block) by the number of free choice trials completed. We used the average of the choice ratio during three consecutive blocks as a reliable estimate of choice behavior.

The sessions were repeated daily until the rats achieved a stable delay-discounting performance. Due to the variability of discounting curves between rats, the criterion for stability was defined by the average behavior of all the rats. We performed a two-way repeated measures ANOVA with the average choice ratios during two contiguous 3-sessions blocks as the BLOCK dependent variable and 3-SESSIONS and DELAY as withinsubject factors. Stability was met when no significant effect of the 3 SESSION BLOCK was found but a significant effect of DELAY was observed. This was achieved after twenty sessions of delay-discounting training.

Waiting impulsivity was operationalized here by the k parameter, calculated by fitting the choice ratio of the last three sessions block to a non-linear exponential function (CR = e−k(DELAY) ). The k parameter determines the rate of decay of the exponential function, i.e., the rate at which the lever choice changes from delayed to immediate across delays. Consequently, larger k-values indicate a faster rate of lever choice change and more impulsive behavior (Odum, 2011). There are other methods to compare the behavior of delay-discounting curves across groups or subjects, like the normalized area under the curve (AUC) (Myerson et al., 2001) or the AUC without normalization (Magnard et al., 2018). Similarly to the k parameter, these two metrics provide an index that is comparable between studies. In addition to computing the k parameter, we have also extracted both AUCs measures and tried to cluster the rats using the two indices. The main correlation of this study was preserved using the both AUCs. However, the groups resulting from the clustering process had very different sample sizes and were not considered in this study (see **Supplementary Information**).

#### **Two choice serial reaction time task**

The two choice serial reaction time task (2-CSRTT) used here is an adaptation of the popular five-choice serial reaction time task (Bari et al., 2008). The 2-CSRTT has been shown to be sensitive to an amphetamine challenge which increased premature responding in the task while leaving other parameters unaffected (Van Gaalen et al., 2009). This task was carried out in the same conditioning boxes described for the DDT.

The task started once the nose poke detector sensed an entry in the pellet dispenser. One of the stimulus lights was turned on for a variable period of time. If the lever under the light was pressed during the response interval time (CORRECT response), a pellet was delivered and, after an ITI, the next trial started. If the rat pressed the wrong lever (ERROR response), pressed a lever before any stimulus (PREMATURE response), or did not press any lever at all (OMISSION response), then the house light was turned off and rewards were not available during 5 s as a punishment. The sessions finished after 100 trials or 30 min, whichever came first. Once a rat completed one session with more than 75% of correct responses and less than 20% of omissions, the next phase of the experiment started. The experiment consisted of 12 training phases and a test phase. As the phases progressed, the stimulus duration and response interval time were shortened, while the ITI was extended [as detailed in the excellent description of the protocol by Bari et al. (2008)]. In the test phase, the ITI was drastically increased to 9 s to increase the number of premature responses and unmask the latent impulsivity trait. We used this variable (number of premature responses during the test phase) as a measure of the motor component of waiting impulsivity of each rat.

#### Sample Processing

After the behavioral assessments, the animals of both experiments were left ad libitum in their home cages for one week, in order to prevent any effect of the behavioral tests on gene expression. Then, they were mildly anesthetized with isoflurane and euthanized by decapitation. Using tools and surfaces previously treated with RNAseZap (Ambion) to prevent RNA degradation, the brain was extracted and the mOFC and lOFC were dissected out of 1 mm slices obtained by using a brain matrix and the adequate equipment. The dissected areas are depicted in **Figure 1**. The samples were then snap frozen in dry ice and stored at −70◦C for further processing. Five brains of the delay-discounting experiment were lost due to a faulty freezer.

#### RT-qPCR

RNA was isolated using the commercial kit RNeasy Lipid Tissue Mini Kit (Qiagen). Samples were retrotranscribed using a commercial kit (Biorad iScriptTM cDNA Synthesis Kit). PCR assays were performed on a real-time PCR detection system (CFX9600, Bio-Rad) with an SSO Advanced SYBR mix (Bio-Rad) using the primers indicated in **Supplementary Table S1**. We assessed the expression of subunits of the NMDA glutamatergic receptor (R1 and 2A), AMPA receptor (GluA1 and GluA2), GABAA receptor (alpha 1, alpha 2, delta, and gamma 2) an of elements of the endocannabinoid system (the CB1 receptor, the anandamide synthesis enzyme NAPE-PLD, the anandamide-degrading enzyme FAAH, the 2-arachidonoyl glycerol (2-AG) synthesis enzyme diacylglycerol lipase and the 2-AG degrading enzyme monoacylglycerol lipase). The relative expression of the target genes was calculated according to Pfaffl (2001), using Gapdh as a reference gene and the reaction efficiencies were obtained using LinRegPCR software (Ruijter et al., 2009).

#### Statistical Analyses

The animals were classified according to their impulsivity using hierarchical cluster analysis with Ward's method. Although other approaches, like a quartile categorization, could be applied to isolate extreme sub-populations in our sample, we were interested in studying the whole population so that we could compare these results with those obtained in the correlational analysis (which must include the whole behavioral and neurochemical continuum of the entire population). We also refrained from using a quartile approach because doing so would incur in loss of power due to resulting smaller sample size.

We analyzed the differences in the behavior of the clustered groups with a two-way repeated measures linear mixed models approach with either lever preference (for DDT) or premature responses (for 2-CSRTT) as the dependent variable, CLUSTER as the between-subject factor and DELAY or SESSION as the within-subject factor. We also used Student t-tests to test if the averages for k or the premature responses during the day of the test were significantly different between the clustered groups. Subsequently, we checked for statistical differences in gene expression between both groups using either the Student's t-test for the homocedastic and normal data or Mann Whitney's U when the parametric assumptions were not met. We applied a false discovery rate (FDR) correction using the Benjamini-Hochberg procedure with an FDR level of 0.1. We report Cohen's d as the effect size estimator for parametric and r for nonparametric data. All the uncorrected p-values are available in the **Supplementary Materials**. Finally, we measured the relationship between the expression of the genes which were found to have differential expression between groups and either measure of impulsivity using Pearson's r when the populations of both variables were normally distributed and Kendall's τ for the nonparametric data.

All the statistical analyses were performed using SPSS 24 (IBM) or InVivoStat (Bate and Clark, 2011) and the level of significance was set to α = 0.05. All the graphs were designed using the PRISM 6 software (GraphPad Software, Inc.) or Photoshop (Adobe Systems Inc.).

#### RESULTS

#### Delay-Discounting

Regarding impulsivity measured with the DDT, we used the k-values of the animals to segregate them in two groups: 7 rats were assigned to the High Impulsive (HI-DD) group and 6 to the Low Impulsive (LI-DD) group (**Figures 2A,B**). As expected, HI-DD rats showed steeper discounting curves than LI-DD animals (significant CLUSTER∗DELAY interaction (F4,44 = 7.48; p < 0.001), significant effect of the CLUSTER factor (F1,11 = 12.57; p < 0.01) and significant DELAY factor

TABLE 1 | Results of the two-way repeated measures linear mixed model of the six last training sessions of the 2-CSRTT.

represented by the asterisks in the legend and in the horizontal axis. (C) k-value of high impulsive and low impulsive animals. ∗∗p < 0.01 as compared to the low impulsive group. Line and bar graphs represent the mean ± standard error of the mean. Symbols in bar graphs represent individual data points from each rat.

(F4,44 = 51.56 p < 0.0001). We also compared the average k-value of both groups and verified that they differed significantly (t11 = −5.77; p < 0.001; d = −3.16; **Figure 2C**).

After the FDR correction, we found that the rats of the HI-DD group expressed higher levels of Cnr1 in the mOFC than the rats of the LI-DD group (t8 = −4.13; p < 0.01; d = −2.71; **Figure 4A**). We also found a significant positive correlation between k and the expression of Cnr1 in the mOFC (r = 0.77; p < 0.01 uncorrected). Accordingly, the animals that expressed higher levels of expression of these genes displayed higher impulsivity in this task (**Figure 4B**). There were no differences in Cnr1 gene expression between HI-DD and LI-DD in the lOFC (**Figure 4C**) or in the Gabra1 gene expression in either territory of the OFC (**Figures 5D,E**).

#### Two-Choice Serial Reaction Time

We also sorted another set of rats that performed the 2-CSRTT according to their premature responses in the long-ITI test day, they clustered in two groups: a high impulsive group of 11 rats (HI-2C) and a low impulsive group of 7 rats (LI-2C) (**Figure 3A**). The repeated measures linear mixed model analysis revealed no differences between both groups in either the premature, correct, incorrect, omitted or premature responses (**Table 1** and **Figure 3B**). During the test, no differences were found between both groups in the number of omissions, incorrect or perseverative responses but the number of premature responses during the test was significantly different between both groups (t16 = −6.385; p < 0.001; d = −3.07; **Figure 3C**).

The analysis of the differences between the groups extracted by cluster analysis revealed that the expression of Gabra1 in the lOFC was lower in the HI-2C as compared to LI-2C rats (t15 = 3.19; p < 0.01; d = 1.79; **Figure 5A**). We also found that the premature responses during the test were inversely related to the expression of Gabra1 in the lOFC (r = −0.48; p < 0.05 uncorrected). The animals that expressed lower levels of Gabra1 were less prone to make premature responses and

hence, less impulsive (**Figure 5B**). There were no Gabra1 gene expression differences between HI-2C and LI-2C in the mOFC (**Figure 5C**) or in Cnr1 expression in either territory of the OFC (**Figures 4D,E**).

### DISCUSSION

This study was aimed at determining if the expression of certain genes related to glutamatergic, GABAergic or endocannabinoid neurotransmission was associated to two different components of waiting impulsivity (delay-discounting and premature responding) and if there was neuroanatomical segregation between the medial and lateral divisions of the OFC in this relationship. For this purpose, we classified two separate groups of rats according to their performance in each task. A hierarchical clustering approach was chosen as the sorting strategy because, as observed from the figures, there was not a large variance between groups. We then compared the expression of selected genes related to neurotransmission in the medial and lateral orbitofrontal cortices between the resulting groups, searching for potential differences that could be specific to each variety of impulsivity.

Our results suggest that the gene expression signature of these two elements of waiting impulsivity is indeed different. We have found that, at the level of the genes studied here, the motor impulsivity component measured in the 2-CSRTT was mostly related to GABAergic gene expression in the lOFC, while the choice impulsivity assessed in the DDT was correlated with endocannabinoid gene expression in the mOFC.

The OFC has been strongly implicated in impulsiveness, goaldirected behavior and decision making-processes, although its key role in these psychological phenomena has been recently challenged (Stalnaker et al., 2015). With regard to impulsive behavior, the lesion studies that have been performed using DDT measurements of impulsive choice show conflicting results (Mobini et al., 2002; Chudasama et al., 2003; Winstanley et al., 2004; Rudebeck et al., 2006; Mar et al., 2011). The functional heterogeneity in the OFC has been suggested to be one of the reasons for such discrepancies (Mar et al., 2011; Stopper et al., 2014).

The mOFC has been proposed to be a hub where the different value signals of subjective goals are integrated (Kable and Glimcher, 2009). Indeed, mOFC-lesioned monkeys have difficulty making choices when the value of two options is close (Noonan et al., 2010) and studies with human patients have shown that mOFC lesions affect reward valuation and selfcontrol in intertemporal choice tasks (Peters and D'Esposito, 2016). Rat lesion studies also provide evidence for a role of the mOFC in impulsive choice whereby mOFC damage increases

the preference for a large but delayed reward (Mar et al., 2011). We have found that expression of Cnr1 in the mOFC was directly related to the waiting impulsivity that is captured by the DDT. The relationship between the endocannabinoid system and the different varieties of impulsivity is complex [see Moreira et al. (2015) for an excellent review]. Some previous reports suggested that the activation of CB1 receptors in the OFC promote impulsive choice (Khani et al., 2015; Fatahi et al., 2018), however, these studies mainly targeted the lateral and ventral divisions of the OFC making any comparison to the present results problematic. There are also previous studies assessing the effects of systemic injections of CB1 receptor agonists that suggest that THC administration reduced choice impulsivity measured with the DDT (Wiskerke et al., 2011). Interestingly, another study showed no effect after treatment with a cannabinoid agonist WIN 55,512-2 (Pattij et al., 2007). It is important to note that CB1 receptors are mostly presynaptically localized in axon terminals, so the gene expression differences found here (arising from mRNAs in the cell bodies) could be modulating neurotransmission distally, in terminal areas such as the hippocampus, a structure that is strongly connected to the mOFC (Fettes et al., 2017). In any case, the higher levels of Cnr1 gene expression in high impulsive animals in the mOFC may suggest that this subpopulation could be especially vulnerable to the disrupting effects of cannabinoids on those cognitive processes that depend on the normal function of the mOFC, such as reward valuation or self-control. It could also mean that, based on their differential expression of cannabinoid receptors, high impulsive individuals might reduce their impulsivity (or at least the tolerance to delay component of impulsivity) to a higher degree than low impulsive individuals, after marihuana use. This hypothesis merits further testing.

Previous studies, both in humans (Elliott et al., 2000) and monkeys (Iversen and Mishkin, 1970; Noonan et al., 2010) have shown that the lOFC is specifically required when a response previously associated with reward has to be suppressed (but see Gourley et al., 2010) and, conversely, its inactivation leads to impaired adjustment of behavior after non-rewarded actions (Dalton et al., 2016). While lesions of the lOFC have been shown to increase impulsive choice (Mar et al., 2011), to the best of our knowledge, a clear (and specific) role for the lOF in premature responding has not yet been established.

Gabra1 expression was lower in the animals that made more premature responses in the 2-CSRTT. In forebrain pyramidal neurons, GABAA receptors containing the alpha 1 subunit are mainly expressed throughout the somatodendritic region while those containing the alpha 2 subunit are mostly localized to the axon initial segment (Nusser et al., 1996; Loup et al., 1998).

This differential expression of the subunit in high and low impulsive animals could translate into net differences in the cellular localization of the receptor in both populations and this might have implications for how inhibitory signals are integrated by the cortical pyramidal neurons where these receptors are expressed. There are other previous studies that have involved the GABAergic system in impulsive action. For example, Jupp et al. (2013) found that GABAA binding in the anterior cingulate cortex was negatively correlated with premature responding in the 5-CSRTT and Caprioli and co-workers established a role of the GABA synthesis enzyme GAD (glutamic acid decarboxylase) within the nucleus accumbens core in premature responding (Caprioli et al., 2014) In addition, GAD inhibition in the medial prefrontal cortex impaired impulse control measured in the 5- CSRTT (Paine et al., 2015).

### CONCLUSION

In conclusion, we here provide the first evidence for a dissociation between the medial and lateral division of the OFC in impulsive action and impulsive choice and suggest that CB1 receptors in the mOFC are positively coupled to the expression of impulsive choice while GABAA receptors in the lOFC are markers of impulsive action. Functional studies interfering with or augmenting the expression of these genes must now be conducted in order to ascertain if there is a causal relationship between the gene transcription variations here reported and the different varieties of waiting impulsivity that we have studied in this work.

### AUTHOR CONTRIBUTIONS

AH-M conceived the research and carried out the initial experiments. MU performed the experiments and analyzed the data. AC performed the impulsive action behavioral experiments and qPCRs. DR-M performed qPCR experiments and helped

### REFERENCES

with data analysis. SP-R helped with the qPCR experiments. JO assisted with data analysis and interpretation. AH-M, EA, and MU wrote the manuscript with the feedback of the rest of the authors.

## FUNDING

This work has been funded by the Spanish Ministry of Economy and Competitiveness (PSI2016-80541-P to EA and AH-M); Spanish Ministry of Health, Social Services and Equality (Network of Addictive Disorders – Project n◦ : RTA-RD12/028/0020 of the Institute of Health Carlos III to EA and Plan Nacional Sobre Drogas, Project n◦ : 2012I057 to EA and 2017I042 to AH-M); The General Directorate of Research of the Community of Madrid (Project n◦ : S-2011/BMD-2308; Program of I + D + I Activities CANNAB-CM to EA); The UNED (Plan for the Promotion of Research to EA and DR-M); the European Union (Project n◦ : JUST/2013/DPIP/AG/4823-EU MADNESS) and the BBVA Foundation (2017 Leonardo Grant for Researchers and Cultural Creators to AH-M). MU received a predoctoral fellowship awarded by the Ministry of Science and Innovation and DR-M received a predoctoral fellowship awarded by the Research Office of the National University for Distance Education (UNED).

### ACKNOWLEDGMENTS

We thank Rosa Ferrado, Luis Carrillo, and Gonzalo Moreno for their excellent technical assistance.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnbeh. 2019.00022/full#supplementary-material

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Ucha, Roura-Martínez, Contreras, Pinto-Rivero, Orihuel, Ambrosio and Higuera-Matas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Compulsivity in Alcohol Use Disorder and Obsessive Compulsive Disorder: Implications for Neuromodulation

Elisabetta Burchi1,2 \*, Nikolaos Makris3,4, Mary R. Lee5 , Stefano Pallanti6 and Eric Hollander1

1 Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, NY, United States, 2 Department of Health Sciences, University of Florence, Florence, Italy, 3 Center for Morphometric Analysis, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States, 4 Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States, 5 Section on Clinical Psychoneuroendocrinology and Neuropsychopharmacology, National Institute on Alcohol Abuse and Alcoholism and National Institute on Drug Abuse, Bethesda, MD, United States, 6 Department of Psychiatry and Behavioral Sciences, Stanford University Medical Center, Stanford, CA, United States

Alcohol use Disorder (AUD) is one of the leading causes of morbidity and mortality worldwide. The progression of the disorder is associated with the development of compulsive alcohol use, which in turn contributes to the high relapse rate and poor longer term functioning reported in most patients, even with treatment. While the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) defines AUD by a cluster of symptoms, parsing its heterogeneous phenotype by domains of behavior such as compulsivity may be a critical step to improve outcomes of this condition. Still, neurobiological underpinnings of compulsivity need to be fully elucidated in AUD in order to better design targeted treatment strategies. In this manuscript, we review and discuss findings supporting common mechanisms between AUD and OCD, dissecting the construct of compulsivity and focusing specifically on characteristic disruptions in habit learning and cognitive control in the two disorders. Finally, neuromodulatory interventions are proposed as a probe to test compulsivity as key pathophysiologic feature of AUD, and as a potential therapy for the subgroup of individuals with compulsive alcohol use, i.e., the more resistant stage of the disorder. This transdiagnostic approach may help to destigmatize the disorder, and suggest potential treatment targets across different conditions.

Keywords: alcohol use disorder, compulsivity, obsessive compulsive disorder, habit learning, cognitive control, neuromodulation, transcranial magnetic stimulation

#### INTRODUCTION

Alcohol use disorder (AUD), a problematic pattern of alcohol use accompanied by clinically significant impairment or distress (American Psychiatric Association, 2013), is one of the leading causes of morbidity and mortality worldwide (GBD 2016 Alcohol Collaborators, 2018). Globally, with 100.4 million estimated cases, AUD was the most prevalent substance use disorder (SUD) in 2016, with 99.2 million and three quarters of all substance use-attributable disability-adjusted

#### Edited by:

Walter Adriani, Istituto Superiore di Sanità (ISS), Italy

#### Reviewed by:

Erin Jane Campbell, The Florey Institute of Neuroscience and Mental Health, Australia Francesca Pacitti, University of L'Aquila, Italy

#### \*Correspondence:

Elisabetta Burchi<EMAIL_ADDRESS>
Received: 30 January 2019 Accepted: 22 March 2019 Published: 11 April 2019

#### Citation:

Burchi E, Makris N, Lee MR, Pallanti S and Hollander E (2019) Compulsivity in Alcohol Use Disorder and Obsessive Compulsive Disorder: Implications for Neuromodulation. Front. Behav. Neurosci. 13:70. doi: 10.3389/fnbeh.2019.00070

life-years (DALYs). In the United States, 12-month and lifetime AUD prevalence is, respectively, estimated to be up to 14 and 30% of the adult population, with 9.8% of all deaths attributable to acute or chronic alcohol use (World Health Organization, 2018).

Despite there being effective treatments available, only an estimated 21.3 and 34.7% of patients with severe (≥6 DSM criteria) 12-month and lifetime AUD seek treatment in the United States (Grant et al., 2016). Further, high rates of relapse and poor longer term functioning are reported in the minority of patients that get some treatment (Maisto et al., 2018). This is particularly true for the subgroup of individuals with severe AUD (Tuithof et al., 2013), that is also characterized by a longer duration of untreated AUD, a lower level of spontaneous recovery as well as higher rates of psychiatric comorbidity (Grant et al., 2016; Saha et al., 2018). In addition to low rates of treatment utilization, AUD is indeed a heterogeneous disorder (Jellinek, 1960; Moss et al., 2007) and current therapeutic approaches are not developed to address this clinical variation. Hence, the magnitude of the therapeutic effect of the available AUD interventions is, overall, relatively modest (Kranzler and Soyka, 2018).

Next to the need to educate the public and policy makers in order to destigmatize the disorder and increase treatment rates (Keyes et al., 2010), the gaps in research and clinical care call for a shared framework to further characterize this heterogeneous disorder and lead to the development of new therapeutics, ultimately giving the clinician a biologically based roadmap to guide assessment and prioritize treatment (National Institute on Alcohol Abuse and Alcoholism, 2017). In this direction, the National Institute on Alcohol Abuse and Alcoholism (NIAAA) has implemented an Alcohol Addiction Research Domain Criteria (AARDoC) in order to identify specific domains relevant for alcohol addiction (Litten et al., 2015). Recently, incentive salience, negative emotionality and executive function have been proposed as key dimensional domains for the assessment of SUDs and AUD, which map onto the three stage cycle of the development of addiction over time (Koob and Le Moal, 1997; Koob and Volkow, 2016; Kwako et al., 2018). The three stage cycle (Koob and Le Moal, 1997) represents a model that provides an understanding of the development of AUD overtime, and may help organize research in AUD complementing the atemporal RDoC framework. The three stage cycle is consistent with the conceptualization of alcohol addiction as an aberrant form of learning, where alcohol exposure leads in time to alteration in the neurocircuitry underlying stress response, reward and cognitive functioning, all of which ultimately leads to compulsive substance use (Wise, 1987; Di Chiara, 1999; Berke and Hyman, 2000; Everitt et al., 2001; Lubman et al., 2004). Hence, compulsive drug seeking has been identified as the central, defining property of alcohol and substance use disorders (Wise and Koob, 2014; NIAAA; National Institute of Drug Abuse). The development of repetitive drug patterns of use, in turn, has been proposed as a main pathophysiologic factor contributing to the high relapse rates characteristic of addiction (Heyman, 2013).

While the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) defines AUD by a cluster of symptoms, parsing its phenotype by domains of behavior, such as

compulsivity, may be a critical step to improve outcomes of this condition. On the other hand, neurobiological underpinnings of compulsivity still need to be fully elucidated in addiction -and AUD in particular- in order to design specific targeted treatment strategies.

Compulsivity (**Box 1**) is a construct that encompasses motor, cognitive, affective and motivational processes. Multiple models have addressed the central question regarding the transition from casual to compulsive drug use over the development of addiction. The traditional hedonic/withdrawal hypothesis of addiction explained compulsive drug use as earlier pleasure seeking and later attempts to avoid unpleasant withdrawal symptoms (Koob and Le Moal, 1997, 2001), but this could not fully explain the high rates of relapse after long period of drug abstinence (Robinson and Berridge, 1993, 2000). More recently this theory has been complemented by models showing how other phenomena take place in the development of addiction, such as incentive sensitization (Robinson and Berridge, 2003), aberrant learning (Robbins and Everitt, 2002), and loss of cognitive control (Jentsch and Taylor, 1999). At the brain connectivity level, these phenomena have demonstrated to map onto limbic-cortico-striatal networks (Ma et al., 2010), with important overlap with neuroimaging findings in OCD (van den Heuvel et al., 2016).

Following the characterization of the neural basis of compulsivity developed in the field of Obsessive Compulsive Disorder (OCD) research, we discuss the findings supporting common mechanisms between AUD and OCD, focusing on specific aspects of the construct, namely habit learning and cognitive control. Finally, neuromodulatory interventions are

proposed as a probe of this hypothesis and as a potential therapy for the subgroup of individuals with AUD who demonstrate compulsive alcohol seeking and use, i.e., the later more severe stage of AUD. In particular, deep transcranial magnetic stimulation (dTMS), recently approved by the Food and Drug Administration to treat compulsivity in OCD, is specifically discussed as a promising non-invasive intervention to treat compulsivity in AUD.

Such a transdiagnostic approach aims to destigmatize AUD, and to help clarify common pathophysiological mechanisms underpinning compulsive behavior across different disorders (Fineberg et al., 2016; Gillan et al., 2016). Consistent with a need for new paths of research in the field of treatments for addictions, the ultimate aim of this manuscript is to highlight and discuss the scientific rationale supporting the development of evidence based treatments for compulsivity in AUD, eventually as a model for other disorders of addiction.

#### THE HABIT MODEL OF COMPULSIVITY

Compulsivity has been defined as a behavioral trait in which actions are persistent and repeated despite adverse consequences (Robbins et al., 2012) (**Box 1**). An initial hypothesis regarding compulsive behavior in OCD -where compulsivity has traditionally found its paradigmatic expression- purported that it represents a goal-directed process associated with a "cognitive bias" or disrupted assignment of value toward available alternative behaviors which are performed to reduce the likelihood that a feared consequence will take place (Salkovskis, 1985; Rachman, 1997). Later, OCD was conceptualized as a disorder of maladaptive habit learning (Graybiel and Rauch, 2000) on the basis of overlap between the frontostriatal circuits underlying repetitive behavioral habits and OCD (Alexander et al., 1986). Data from subsequent preclinical and clinical studies have actually elucidated the neural basis of habit formation in humans or the transition from goal-directed to habitual behavior as a shift away from signaling in ventral associative frontostriatal circuits comprising ventromedial prefrontal cortex (vmPFC), medial orbitofrontal cortex (mOFC) and caudate, toward that in dorsal sensorimotor frontostriatal circuits, including posterior putamen and premotor cortex (Balleine and O'Doherty, 2010; Gillan and Robbins, 2014). In particular, estimated white matter strength in the vmPFC seeded from the caudate have been found to predict flexible goal-directed action, while estimated white matter tract strength in the premotor cortex seeded from the posterior putamen predicts vulnerability to habitual behavioral control in healthy humans (de Wit S. et al., 2012). Patients with OCD consistently exhibit a cognitive bias toward habit learning over goal-directed behavior (Gillan et al., 2011, 2014; Vaghi et al., 2019) associated with relative higher task-related activity in dorsal vs. ventral striatum and weakened resting state caudate-ventrolateral prefrontal cortex (vlPFC) and putamendorsolateral prefrontal cortex (dlPFC) connectivity (Banca et al., 2015; Vaghi et al., 2017). In particular, decreased activity in caudate-prefrontal circuits accompanied by hyperactivation of subthalamic nucleus/putaminal regions have been associated to symptom generation in OCD (Banca et al., 2015). Hyperactivity of putamen preceded deactivation during avoidance/relief events indicating a pivotal role of the putamen in regulation of behavior and habit formation in OCD (Banca et al., 2015). Consistently, task dependent functional connectivity during goal-directed planning showed reduced hypoactivation of the right dlPFC coupled with reduced functional connectivity between this latter and the putamen (Vaghi et al., 2017). Other findings indicate a dissociation between actual behavior and subjectively reported action-outcome knowledge, reinforcing the notion that OCD may be driven by a disrupted goal-directed system over habitual response, rather than by dysfunctional beliefs (Vaghi et al., 2019). This diminished outcome sensitivity has also been associated with diminished caudate-parietal connectivity (Harrison et al., 2013).

Based on these studies, evidence favors the habit hypothesis for compulsivity in OCD: this model posits that rather than goal-directed avoidance behaviors, compulsions are a result of excessive habit formation (Gillan and Robbins, 2014; Burguière et al., 2015). Irrational threat beliefs (obsessions) characteristic of OCD may be a consequence, rather than a cause, of compulsive behavior. In other words, obsessions may be an attempt to resolve the discrepancy between patients' value attribution and their cognitively inexplicable urge to perform compulsive responses to determined stimuli.

Notably, deficits in goal-directed control and over–reliance on habits are a model of compulsivity that is applicable to other psychiatric disorders where compulsions feature prominently (Hollander, 1993; Hollander and Wong, 1995; Fontenelle et al., 2011; Voon et al., 2015) in particular to drug addiction (Robbins and Everitt, 1999; Gerdeman et al., 2003; Everitt and Robbins, 2005; Hogarth et al., 2013).

### HABIT HYPOTHESIS OF COMPULSIVITY IN AUD

#### Compulsivity and Habit Learning in Addiction

Drug addiction has been defined as representing a dysregulation of motivational circuits caused by development of aberrant incentive salience and habit formation, accompanied by compromised executive function over the use of the substance (Koob and Volkow, 2016). Such trajectory correlates with a progression from reward-driven to habit-driven drug-seeking behavior overtime (Koob and Le Moal, 2005; Everitt and Robbins, 2013).

Borrowing from the habit hypothesis of OCD, the experience of "wanting" the drug, progressively detached from the experience of "liking" it (Berridge and Robinson, 2016), may be understood as post hoc cognitive rationalizations of a goalinsensitive, stimulus-driven behavior (Gillan and Robbins, 2014). While the expectancy of reward may be crucial to initiate drug self-administration in the early stage of the disorder, the drive toward consumption of the drug triggered by the cue becomes quite indipendent from the expectancy of pleasure in the late stages of the disorder. Thanks to conditioned reinforcement,

over time the "wanting" for the drug becomes triggered by the incentive motivational properties of drug cues, independently of a cognitive desire for a declarative goal (Berridge and Robinson, 2016). This shift in phenomenology has been associated with a cascade of neuroadaptations that engage the dorsal striatal habit system over the ventral striatal loop of reward and motivation mediated by phasic dopamine release in the dorsolateral striatum (see for review of clinical studies Volkow and Fowler, 2000; Newlin and Strubler, 2007; Dolan and Dayan, 2013; Grant and Kim, 2014; Koob and Volkow, 2016). In particular, translational studies have shown how a gradual progression from hedonic to habitual and compulsive drug use over time (i.e., a gradual progression from a response that is first dependent and then independent from evaluation of the relationship between action and outcomes eventually becomes a response despite adverse consequences) is associated with (i) habit overlearning and (ii) parallel reduction in goal-directed behaviors and inhibitory control that correspond to a shift in recruitment of ventral to dorsal regions of the striatum (Yin et al., 2004; Belin and Everitt, 2008; Balleine et al., 2009; Lesscher and Vanderschuren, 2012; Willuhn et al., 2012). Intermittent drug–induced dopamine (DA) signaling promotes the ability of drug-paired cues to increase DA levels and recruit striatal-globus pallidal-thalamocortical loops that engage the dorsal striatum. This shift from a system dedicated to updating predictions of value (ventral) to a system dedicated to the optimization of reward-related responses (dorsal) augments progression through the addiction cycle and helps explain craving and compulsive drug use (Koob and Volkow, 2016).

#### Compulsivity and Habit Learning in AUD

A confluence of preclinical and clinical data strongly supports that the pathogenesis of AUD involves a shift from associative striatum (caudate) to sensorimotor striatum (posterior putamen) in response to alcohol reward responding, likely driven by simultaneous reductions in goal-directed control over actions and increase in habit associations. Learning about response-outcome (R-O) associations has been investigated using instrumental learning paradigms (Balleine and Dickinson, 1998). In instrumental discrimination tasks, which have been developed to distinguish between goal-directed and habit-based learning (de Wit et al., 2007), stimuli (or cues) are congruent, unrelated or incongruent with subsequent outcomes. Whereas performance (or learning) on congruent and control trials can be supported by both the goal-directed (S-R-O) and habitual system (S-O), performance on the incongruent discrimination (in which each pictures functions as a stimulus and an outcome for opposing responses) relies solely on the habit system. The dominance of habitual control over flexible goal-directed responding has traditionally been assessed using revaluation tests, by manipulating outcome value and observing consequent effects on response (Dickinson, 1985). In a typical outcome devaluation methodology (Adams, 1982), after a training phase, the value of the reinforcer of action (O) is typically reduced (extinction phase) affecting internal or external motivational states, and the experimenter assesses if the behavior (R) appropriately updates, in light of this change, measuring the strength of the R-O association. An alcohol addiction model study conducted in rats showed that instrumental alcohol seeking became insensitive to devaluation after 4 weeks of training, mirrored by a shift in control from the dorsomedial striatum to the dorsolateral striatum (Corbit et al., 2012). Another preclinical study using the instrumental learning paradigm showed how alcohol reinforced behavior was less sensitive to devaluation compared to when behavior was reinforced with food, suggesting that alcohol consumption may be particularly susceptible to habit formation (Dickinson et al., 2002). An fMRI clinical study using a cuereactivity paradigm showed significantly higher cue-induced activation of the dorsal striatum in heavy drinkers compared to social drinkers and higher cue-induced activation of ventral striatum and prefrontal areas in social drinkers compared to heavy drinkers (Vollstädt-Klein et al., 2010). These findings were interpreted as an indirect indication of a shift from ventral to dorsal striatal involvement in the development of AUD, associated with the increasing role of habit–like drug seeking behavior over the course of the disorder. Another clinical study using neuroimaging and learning tasks showed how early abstinent patients presented greater habit formation compared to controls and a progressive shift toward greater goal-directed behaviors with prolonged abstinence (Voon et al., 2015). Another study conducted in AUD patients was the first providing direct behavioral and neurophysiological evidence for an imbalance between goal-directed and habitual control in humans with a substance use disorder (Sjoerds et al., 2013). Subject underwent fMRI during completion of an instrumental learning task characterized by a discrimination learning phase and an outcome-devaluation test phase designed to study the balance between goal-directed and habit learning: patients with AUD compared to healthy controls showed a strong engagement of the neural habit pathway, comprising dorsolateral/posterior parts of the striatum (posterior putamen, caudate tail/body) and a relatively weak engagement of the goal-directed pathway in the vmPFC and dorsomedial/anterior parts of the striatum (caudate head, anterior putamen) during instrumental learning even in the context of AUD-irrelevant stimuli. Moreover, vmPFC activation was negatively associated with AUD duration. Another study investigating the effects of alcohol on devaluation sensitivity for food reward suggested a general effect of alcohol toward habit-formation (Hogarth, 2012).

All these studies give direct (Sjoerds et al., 2013) and indirect (Dickinson et al., 2002; Vollstädt-Klein et al., 2010; Corbit et al., 2012; Hogarth et al., 2012) demonstration of a shift from ventral to dorsal striatal involvement in the development of AUD, associated with an increasing role of habit–like drug seeking behavior over the course of the disorder. Moreover, alcohol consumption has demonstrated to be particularly susceptible to habit formation over other reinforcers (Dickinson et al., 2002) and it has also shown to exert a general direct effect toward habit-formation attenuating goal directed control over action selection (Hogarth, 2012).

Of note, habit circuitry, including the dorsolateral striatum, is also implicated in punishment resistance (Jonkman et al., 2012), characteristic feature of compulsive behaviors, namely behaviors performed in spite of adverse consequences. Indeed,

it is important to notice that the construct of "habit" doesn't completely overlap with the construct of compulsivity, where habitual alcohol use involves behavior that continues despite outcome devaluation, whereas compulsive alcohol use encompasses continued use despite adverse consequences (Hopf and Lesscher, 2014; Marchant et al., 2018). Studies investigating neural correlates of compulsive alcohol seeking using rodent model of aversion-resistant alcohol seeking showed involvement of mPFC, insula and nucleus accumbens (Seif et al., 2013) and hyperactivation of these areas have been found in heavy drinkers as opposed to light drinkers when viewing threat-paired alcohol cues (Grodin et al., 2018).

Next to extensive preclinical evidence suggesting overlap in the striatal regions involved in habitual and compulsive behavior (Everitt and Robbins, 2005; Belin et al., 2009; Pierce and Vanderschuren, 2010; Jonkman et al., 2012; Seif et al., 2013), a recent study conducted on rats for the first time demonstrated the causal importance of the functional recruitment of the anterior dorsolateral striatum (aDLS), a region strongly associated with the consolidation and performance of stimulus-response habits (Yin et al., 2004; Zapata et al., 2010; Corbit et al., 2012), in the switch from controlled to compulsive alcohol use (Giuliano et al., 2019). This study found that individual differences in the reliance of alcohol seeking habits on aDLS dopamine predict and underlie the emergence of compulsive alcohol seeking, providing first evidence that compulsive alcohol seeking stems from an inability to disengage aDLS control over seeking behavior when faced with negative outcomes. This result shows that the maladaptive nature of alcohol seeking in those individuals that become compulsive lays in the rigidity of those aDLS dopamine-dependent habits.

### Compulsivity and Cognitive Control in AUD and OCD

From a neurocognitive point of view, AUD is characterized by an imbalance between overwhelming drive toward alcohol consumption and inability to inhibit alcohol consumption, i.e., a disruption in cognitive control over alcohol use (Baler and Volkow, 2006; Jentsch and Pennington, 2014; Koob and Volkow, 2016). The RdoC domain of cognitive control is defined as a modulatory system that supervises all the activities in the service of goal-directed behavior, when prepotent modes of responding are not adequate to meet the demands of current context. Cognitive (or effortful) control encompasses the ability to select relevant stimuli, inhibit responses influenced by distracting elements, select appropriate responses, monitor the outcome of those responses, and adjust behavior as needed in the face of changing situations.