Cocaine use disorder remains a serious public health problem in the USA, with 2.2 million regular cocaine users in 2019 and over a million individuals with cocaine use disorder in the past year (SAMHSA, 2019). The use of cocaine is associated with substantial morbidity and elevated rates of healthcare utilization (Butler et al., 2017), and over the last 10 years, the number of cocaine-related deaths has quadrupled (Hedegaard et al., 2018). A key unanswered question for addiction research remains why casual patterns of drug consumption escalate to problematic patterns associated with high motivation and a compulsive-like pattern of drug use in some individuals, but not others and how sex may affect this trajectory (George and Koob, 2017; Piazza and Deroche-Gamonet, 2013). Although addiction affects both males and females, most preclinical studies have focused on males (Beery and Zucker, 2011; Shansky and Murphy, 2021). Even in studies that use males and females, low sample size often makes it difficult to draw reliable conclusions about the role of sex differences in addiction vulnerability (Becker and Koob, 2016; Carroll and Lynch, 2016). To investigate the critical questions about how sex and individual differences interact to produce the vulnerability to develop addiction-like large sample sizes are urgently needed.

Recently, the preclinical addiction field has moved from recognizing ‘compulsive drug seeking/use’ and ‘continued seeking/use despite negative consequences’ as two distinct aspects of addiction, to examining compulsive-like behavior nearly exclusively by models of continued seeking/use despite negative consequences (Chen et al., 2013; Domi et al., 2021; Siciliano et al., 2019; Timme et al., 2022). The three most prevalent behavioral measures used in animal models of addiction are an escalation of drug intake, increased motivation under a PR schedule of reinforcement, and continued drug use despite adverse consequences, such as a footshock. These measures are thought to capture different aspects of addiction-like behaviors. Some researchers argue that continued drug use despite adverse consequences is the most critical measure for identifying an addiction phenotype, as it reflects the compulsive nature of drug use (Deroche-Gamonet et al., 2004; Vanderschuren and Everitt, 2004). This view is supported by findings that responding despite adverse consequences is sometimes uncorrelated with drug taking/seeking (Belin et al., 2008; Chen et al., 2013; Domi et al., 2021; Pelloux et al., 2007; Siciliano et al., 2019; Timme et al., 2022), suggesting that it may measure a distinct psychological construct. Unfortunately, most of these studies have used low sample sizes and used animal models with limited access to the drug that are often not associated with significant levels of intoxication or dependence, making it difficult to draw definitive conclusions.

To address these issues, we analyzed addiction-like behaviors in >500 male and female rats that were phenotyped for an ongoing gene-wide association study. We used HS rats because they are the most highly recombinant rat intercross available and exhibit remarkable individual differences in addiction-like behaviors (Carrette et al., 2021; Carrette et al., 2022; Chitre et al., 2020; Duttke et al., 2022; Kallupi et al., 2020; Sedighim et al., 2021; Solberg Woods and Palmer, 2019). HS rats were created by interbreeding eight inbred strains and maintaining them as an outbred population in a way that minimizes inbreeding, thereby maximizing genetic diversity within the colony (Solberg Woods and Palmer, 2019). We used extended access to intravenous cocaine self-administration and characterized escalation of cocaine intake, breaking point under a progressive ratio schedule of reinforcement, continued use despite adverse consequences (footshock), and irritability-like behavior as a measure of negative affective state during withdrawal.

We characterized addiction-like behavior in 556 genetically diverse HS rats by establishing a large behavioral screening aimed at studying different cocaine-related behaviors (escalation, motivation, compulsive-like responding, and irritability-like behavior). A bimodal distribution was observed, with ~20% of rats maintaining low intake levels and ~80% escalating their cocaine intake when given long access. Females exhibited faster acquisition and higher levels of cocaine self-administration compared to males. Individual differences were found, with some rats categorized as resilient and others as vulnerable to addiction-like behaviors. Vulnerable rats showed an increased escalation of cocaine intake, motivation, and compulsive-like responding. Correlational and principal component analysis revealed that a single construct explained 48% of the behavioral variability, separating vulnerable and resilient rats. This construct included the variables associated with escalation of intake, breaking point under a progressive ratio, and continued responding despite adverse consequences under long access, but not irritability-like measures or self-administration measures under short access. Moreover, responding before the shock session strongly predicted continued responding despite adverse consequences during the shock session. The use of an Addiction Index calculated based on the escalation of intake, breaking point under a progressive ratio, and continued responses despite adverse consequences allows us to further identify different levels of severity of addiction-like behaviors from animals exhibiting resilience, low, moderate, and severe addiction-like behaviors.

The high throughput behavioral paradigm used a model of extended access to intravenous self-administration combined with behavioral characterization of compulsivity using progressive ratio responding and responding despite adverse consequences (contingent foot shocks). We chose the model of extended access to cocaine self-administration because it is highly relevant to cocaine use disorder (Edwards and Koob, 2013; George et al., 2014), and is associated with neuroadaptations that are also observed in humans with cocaine use disorder (Adinoff et al., 1990; Briand et al., 2008; George et al., 2008; George et al., 2012; Vendruscolo et al., 2012). After escalation, intake was bimodally distributed. While ~80% of rats (vulnerable) escalated their cocaine intake after being given long access to cocaine,~20% of rats (resilient) maintained a low and stable level of cocaine intake. The low and stable level of intake in resilient rats was not due to lack of catheter patency or failure to self-administer a reward (Sedighim et al., 2021).

It is important to note that while our study focused on the differences between resilient and vulnerable rats under long access conditions, the short access period may also be predictive of addiction-like behaviors, particularly in genetically diverse populations. The observed escalation of drug intake during short access in our study is not due to an acquisition issue, as rats start differentiating the active from the inactive levers on the first day of ShA1 (Figure 1—figure supplement 1B), with a 3–1 ratio between active/inactive pressing by ShA7. Rather, this early escalation in some individuals could be due to the genetic diversity of the HS rat population, which includes 8 different strains as founder parents. Previous studies have shown profound strain differences in vulnerability to cocaine self-administration and escalation of intake (Freeman et al., 2009; Kosten et al., 2007; Perry et al., 2006; Picetti et al., 2010; Valenza et al., 2016). Additionally, our use of a 2 hr short access period, compared to the more common 1 hr period, may have allowed for the detection of these individual differences that might otherwise be masked by a ceiling effect in shorter sessions.

The identified resilient group was enriched for males, as over 2/3 of the resilient animals were male. Moreover, we found that females not only showed a low percentage of resilient animals but also showed higher levels of cocaine intake, motivation, and aversion resistance than males in rats with moderate and severe addiction-like behaviors. The increased motivation and compulsive-like responses in females cannot simply be explained by the higher intake among females, as the significant difference remained when we compared only the vulnerable rats in both sexes (see Figure 3—figure supplement 1). Previous preclinical studies have reported mixed results on the detection of sex differences in addiction (Algallal et al., 2020; Becker, 2016; Roth and Carroll, 2004; Swalve et al., 2016; Templeton et al., 2023). In humans, cocaine use disorder is more prevalent in males than in females (SAMHSA, 2019), but this might be driven by societal factors. Women may actually be more vulnerable to the reinforcing effects of cocaine and acquire cocaine use disorder faster and at higher levels than males (Cummings et al., 2011; Jackson et al., 2006). Sex differences were found in HS rats for cocaine cue preference (King et al., 2021). The current study was able to replicate the presence of sex differences in cocaine addiction-like behaviors in this large and diverse population with small to moderate effect sizes. The smaller effect size of sex differences, compared to the large effect of drug intake, could explain the contradictory results in the field from underpowered studies. Indeed, a statistical power analysis using every significant sex difference observed in our study shows that studies would require 34–100 animals to find significant sex effects at alpha = 0.05 with 80% power; unfortunately, such sample sizes are rarely used in the field.

To further investigate potential sex differences in the behavioral constructs underlying addiction-like behaviors, we first examined male vs. female differences in the biplot represented in Figure 4E, which included the irritability index. This analysis, shown in Figure 4—figure supplement 1B, revealed no apparent sex differences in the overall structure of addiction-like behaviors. However, when we performed separate PCAs for males and females (Figure 4—figure supplement 1C, D), we found that the relationship between the Compulsivity Index and the other addiction-like behaviors differed between sexes. In males, compulsivity was more positively correlated with irritability, while in females, the relationship was the opposite (Figure 4—figure supplement 1C, D). However, when the irritability index was excluded from the analysis, the resulting PCA plots for males and females were almost identical (Figure 4—figure supplement 1E, F). These findings suggest that sex differences in the relationship between compulsivity and other addiction-like behaviors may be driven, in part, by differences in irritability. This observation highlights the importance of considering sex-specific patterns when investigating the complex relationships between different aspects of addiction-like behavior.

Withdrawal-induced irritability-like behavior was increased in rats with a history of cocaine self-administration compared to naïve aged-matched rats. This result is in line with previous evidence of a negative emotional state in the long-access model (Ahmed and Koob, 2005; Deroche-Gamonet et al., 2004; Mantsch et al., 2004). However, this increase in irritability-like behavior did not correlate with any self-administration measures and was orthogonal to them in the PCA. Animals taking more cocaine were not typically the ones that were more irritable, suggesting that irritability may not be a direct measure of withdrawal severity. Similarly, more irritable animals did not necessarily take more cocaine, indicating that irritability may not be a strong driver of escalation in this model, although it may contribute to relapse and craving. The identified increased negative emotional state may thus be independent, capturing a different aspect of addiction, or be due to non-specific effects associated with the protocol, including surgery, chronic catheter implant, and daily testing for approximately 2 months.

Despite the lack of correlation between irritability-like behavior and drug intake in our study, it is important to consider the translational relevance of irritability in the context of substance use disorders. In individuals with a history of substance use disorder, negative affective states, such as irritability, are thought to contribute to continued drug use and relapse through negative reinforcement processes (Baker et al., 2004; Koob and Le Moal, 2008). Specifically, the desire to alleviate or escape from these unwanted behavioral states may drive individuals to seek and use drugs, thus perpetuating the cycle of addiction (Baker et al., 2004; Solomon and Corbit, 1974). While our study focused on the relationship between irritability-like behavior and initial drug-seeking behavior, future research should investigate the degree to which irritability acts as a negative reinforcer in the context of drug relapse.

For the purpose of the addiction index, the irritability-like behavior measure was excluded to capture and maximize the diversity associated with PC1’s cocaine intake following escalation under progressive ratio and despite aversive footshock. This decision was based on the lack of correlation between irritability and drug intake measures. It is important to note that for the PCA presented in Figure 4E, we carefully selected only four key variables: the Z-scores for escalation (average intake at the end of LgA), motivation (intake under progressive ratio), compulsivity (continued responding despite adverse consequences), and irritability. This approach minimized variable redundancy and focused on essential measures of addiction-like behaviors. Furthermore, to ensure the robustness of our findings, we conducted an additional PCA excluding the irritability index, using only the escalation, motivation, and compulsivity indices for the same 377 animals. The results of this analysis (Figure 4—figure supplement 1A) corroborated our original findings, with these three variables loading onto a single factor that explained a significant portion of the variance in addiction-like behaviors. However, further studies are needed to differentiate between the possibilities that the increased negative emotional state may be an independent aspect of addiction or due to non-specific effects associated with the protocol, and to investigate the specific role of irritability-like behavior in the context of cocaine use disorder.

Addiction is commonly described as a chronic, recurring condition characterized by excessive drug consumption, compulsive drug seeking, and continued drug use despite negative consequences (SAMHSA, 2019). There is a prevailing notion in preclinical addiction research that the only way to identify addiction-like behaviors in individuals is by measuring compulsive drug use and seeking using continued responses despite adverse consequences. Continued drug use or seeking in the face of punishment, such as painful footshocks or aversive tastes, is widely considered the behavioral hallmark of compulsive-like drug-seeking behavior in preclinical models (Chen et al., 2013; Domi et al., 2021; Giuliano et al., 2019; Li et al., 2021; Siciliano et al., 2019; Timme et al., 2022). A key argument in favor of this hypothesis is that several studies have found that drug intake in the absence of an adverse consequence does not predict drug intake in the presence of an adverse consequence, suggesting that they are independent behavioral measures and that only measures of drug taking/seeking in the context of punishment can measure compulsive-like drug taking/seeking (Chen et al., 2013; Domi et al., 2021; Giuliano et al., 2019; Li et al., 2021; Siciliano et al., 2019; Timme et al., 2022). However, the results from the present report provide strong evidence that in heterogeneous outbred rats, continued responding despite adverse consequences is not an independent measure of compulsive-like responding that is orthogonal to other measures such as escalation of intake and motivation under a progressive ratio schedule of reinforcement. Both correlational and principal component analyses demonstrate that these three behaviors are highly correlated and load onto the same underlying construct.

It is important to consider that the observed correlations between escalation of intake, increased motivation under progressive ratio, and responding despite negative consequences may be influenced by individual differences in baseline response rates. Previous research has shown that animals with higher baseline rates of responding tend to be less sensitive to punishment and exhibit higher levels of responding under progressive ratio schedules (Dews, 1955; Sanger and Blackman, 1976). While our findings suggest that these behaviors can be explained by a single psychological construct related to addiction vulnerability, we cannot rule out the possibility that individual differences in baseline response rates may contribute to the observed correlations. Future studies should investigate the relationship between baseline response rates and addiction-like behaviors to further clarify the underlying mechanisms.

A possible explanation as to why previous studies failed to observe this correlation between escalation, motivation, and aversion-resistance is that most of the previous studies used small sample sizes that may not provide sufficient statistical power to observe this relationship between variables. Another explanation is that previous studies often used animal models with limited access to the drug, where animals exhibit low levels of acute intoxication and very few, if any, signs of drug dependence (George et al., 2022). However, it is important to note that several behavioral and pharmacological studies have indicated that different measures may capture, at least to some degree, different aspects of addiction-like behavior in alcohol and opioid-dependent rodents (Aoun et al., 2018; Barbier et al., 2015; Marchette et al., 2023). While the present results suggest that escalation of drug intake highly predicts drug response despite adverse consequences in an animal model with long access to cocaine and evidence of drug dependence, further research is needed to determine the extent to which these findings generalize to other drugs of abuse and different stages of the addiction cycle.

It is important to acknowledge that several experimental factors could influence the outcomes of the PCA analysis. These factors include the schedule of reinforcement, the progression of the progressive ratio schedule, the shock intensity, the contingency of the shock, the cocaine unit dose, and the use of multiple punishment sessions (Belin et al., 2008; Deroche-Gamonet et al., 2004; Pelloux et al., 2007). In particular, learning effects may play a role when animals undergo multiple punishment or progressive ratio sessions. An animal’s response to punishment or its performance in progressive ratio sessions may change over time as it learns from its previous experiences (Marchant et al., 2013; Vanderschuren et al., 2017). While the present study utilized a large dataset obtained from a particular experimental design, it is essential to acknowledge that not finding differences in one dataset does not necessarily mean that these differences do not exist. Future studies should investigate the impact of these experimental factors, including learning effects, on the relationship between escalation, motivation, and aversion-resistance to further elucidate the underlying constructs of addiction-like behaviors.

Finally, this report demonstrates the feasibility of characterizing a very large population of heterogeneous rats for addiction-like behavior using an animal model of extended access to intravenous drug self-administration. Such a large-scale behavioral approach is required to unveil differences with smaller effect sizes like sex differences and the genetic basis of addiction-like behavior. The evaluation of different addiction-like behaviors is important, as previous research has suggested that multiple elements of addiction vulnerability may be independently heritable (Eid et al., 2019). While our current findings indicate that escalation, motivation, and compulsivity are highly correlated and load onto a single construct in our model, it is possible that distinct genes contribute to different aspects of addiction vulnerability. The high correlation between these behaviors in our study may reflect common underlying genetic influences, but it does not preclude the existence of additional, unique genetic factors that shape specific aspects of addiction-like behavior. Further research is needed to identify the specific genes that contribute to the overall construct of addiction vulnerability, as well as those that may influence distinct behavioral elements. The behavioral characterization of HS rats in this study provides a foundation for future genome-wide association studies (GWAS) aimed at identifying specific alleles and genes that contribute to vulnerability and resilience to cocaine addiction-like behavior (Chitre et al., 2020). Moreover, biological samples from animals that are resilient or have mild, moderate, or severe addiction-like behaviors are currently available through the Cocaine Biobank (https://www.drugaddictionresearch.org/) to facilitate collaboration and investigation on the biological mechanisms of addiction-like behaviors (Carrette et al., 2021).

In summary, this study examined addiction-like behaviors in a large population of genetically diverse male and female rats. We found that ~80% of the rats were vulnerable and escalated their cocaine intake, while ~20% was resilient and maintained low stable levels. The resilient group had two times more males than females. Females also had higher susceptibility to cocaine self-administration than males, both qualitatively and quantitively. Correlational and principal component analyses showed that escalation of intake, aversion-resistant responding, and breaking points are highly correlated measures of the same construct in animals with extended access to the drug. This study provides an in-depth characterization of heterogeneous rats with a variety of addiction-like profiles from resilience to mild, moderate, and severe addiction-like behaviors that can be used to investigate the biological mechanisms of addiction.

Detailed procedures associated with the experimental timeline in Figure 1A can be found in the George lab protocol repository on protocols.io (https://www.protocols.io/workspaces/george-lab).

All behavioral data generated and analyzed during this study are included in source data files accompanying this manuscript. The dataset comprises the numerical data used to generate all figures.

1. 1. Adinoff B
   2. Martin PR
   3. Bone GH
   4. Eckardt MJ
   5. Roehrich L
   6. George DT
   7. Moss HB
   8. Eskay R
   9. Linnoila M
   10. Gold PW

   (1990) Hypothalamic-pituitary-adrenal axis functioning and cerebrospinal fluid corticotropin releasing hormone and corticotropin levels in alcoholics after recent and long-term abstinence

   Archives of General Psychiatry 47:325–330.

   https://doi.org/10.1001/archpsyc.1990.01810160025004

   • PubMed
   • Google Scholar
2. 1. Ahmed SH
   2. Koob GF

   (1998) Transition from moderate to excessive drug intake: change in hedonic set point

   Science 282:298–300.

   https://doi.org/10.1126/science.282.5387.298

   • PubMed
   • Google Scholar
3. 1. Ahmed SH
   2. Koob GF

   (2005) Transition to drug addiction: a negative reinforcement model based on an allostatic decrease in reward function

   Psychopharmacology 180:473–490.

   https://doi.org/10.1007/s00213-005-2180-z

   • PubMed
   • Google Scholar
4. 1. Algallal H
   2. Allain F
   3. Ndiaye NA
   4. Samaha AN

   (2020) Sex differences in cocaine self-administration behaviour under long access versus intermittent access conditions

   Addiction Biology 25:e12809.

   https://doi.org/10.1111/adb.12809

   • PubMed
   • Google Scholar
5. 1. Aoun EG
   2. Jimenez VA
   3. Vendruscolo LF
   4. Walter NAR
   5. Barbier E
   6. Ferrulli A
   7. Haass-Koffler CL
   8. Darakjian P
   9. Lee MR
   10. Addolorato G
   11. Heilig M
   12. Hitzemann R
   13. Koob GF
   14. Grant KA
   15. Leggio L

   (2018) A relationship between the aldosterone-mineralocorticoid receptor pathway and alcohol drinking: preliminary translational findings across rats, monkeys and humans

   Molecular Psychiatry 23:1466–1473.

   https://doi.org/10.1038/mp.2017.97

   • PubMed
   • Google Scholar
6. 1. Baker TB
   2. Piper ME
   3. McCarthy DE
   4. Majeskie MR
   5. Fiore MC

   (2004) Addiction motivation reformulated: an affective processing model of negative reinforcement

   Psychological Review 111:33–51.

   https://doi.org/10.1037/0033-295X.111.1.33

   • PubMed
   • Google Scholar
7. 1. Barbier E
   2. Tapocik JD
   3. Juergens N
   4. Pitcairn C
   5. Borich A
   6. Schank JR
   7. Sun H
   8. Schuebel K
   9. Zhou Z
   10. Yuan Q
   11. Vendruscolo LF
   12. Goldman D
   13. Heilig M

   (2015) DNA methylation in the medial prefrontal cortex regulates alcohol-induced behavior and plasticity

   The Journal of Neuroscience 35:6153–6164.

   https://doi.org/10.1523/JNEUROSCI.4571-14.2015

   • PubMed
   • Google Scholar
8. 1. Becker JB

   (2016) Sex differences in addiction

   Dialogues in Clinical Neuroscience 18:395–402.

   https://doi.org/10.31887/DCNS.2016.18.4/jbecker

   • PubMed
   • Google Scholar
9. 1. Becker JB
   2. Koob GF

   (2016) Sex differences in animal models: focus on addiction

   Pharmacological Reviews 68:242–263.

   https://doi.org/10.1124/pr.115.011163

   • PubMed
   • Google Scholar
10. 1. Beery AK
    2. Zucker I

    (2011) Sex bias in neuroscience and biomedical research

    Neuroscience and Biobehavioral Reviews 35:565–572.

    https://doi.org/10.1016/j.neubiorev.2010.07.002

    • PubMed
    • Google Scholar
11. 1. Belin D
    2. Mar AC
    3. Dalley JW
    4. Robbins TW
    5. Everitt BJ

    (2008) High impulsivity predicts the switch to compulsive cocaine-taking

    Science 320:1352–1355.

    https://doi.org/10.1126/science.1158136

    • PubMed
    • Google Scholar
12. 1. Belin D
    2. Balado E
    3. Piazza PV
    4. Deroche-Gamonet V

    (2009) Pattern of intake and drug craving predict the development of cocaine addiction-like behavior in rats

    Biological Psychiatry 65:863–868.

    https://doi.org/10.1016/j.biopsych.2008.05.031

    • PubMed
    • Google Scholar
13. 1. Briand LA
    2. Flagel SB
    3. Seeman P
    4. Robinson TE

    (2008) Cocaine self-administration produces a persistent increase in dopamine D2 High receptors

    European Neuropsychopharmacology 18:551–556.

    https://doi.org/10.1016/j.euroneuro.2008.01.002

    • PubMed
    • Google Scholar
14. 1. Butler AJ
    2. Rehm J
    3. Fischer B

    (2017) Health outcomes associated with crack-cocaine use: Systematic review and meta-analyses

    Drug and Alcohol Dependence 180:401–416.

    https://doi.org/10.1016/j.drugalcdep.2017.08.036

    • PubMed
    • Google Scholar
15. 1. Carrette LLG
    2. de Guglielmo G
    3. Kallupi M
    4. Maturin L
    5. Brennan M
    6. Boomhower B
    7. Conlisk D
    8. Sedighim S
    9. Tieu L
    10. Fannon MJ
    11. Velarde N
    12. Martinez AR
    13. Kononoff J
    14. Kimbrough A
    15. Simpson S
    16. Smith LC
    17. Shankar K
    18. Ramirez FJ
    19. Chitre AS
    20. Lin B
    21. Polesskaya O
    22. Solberg Woods LC
    23. Palmer AA
    24. George O

    (2021) The cocaine and oxycodone biobanks, two repositories from genetically diverse and behaviorally characterized rats for the study of addiction

    eNeuro 8:ENEURO.0033-21.2021.

    https://doi.org/10.1523/ENEURO.0033-21.2021

    • PubMed
    • Google Scholar
16. 1. Carrette LLG
    2. Corral C
    3. Boomhower B
    4. Brennan M
    5. Crook C
    6. Ortez C
    7. Shankar K
    8. Simpson S
    9. Maturin L
    10. Solberg Woods LC
    11. Palmer AA
    12. de Guglielmo G
    13. George O

    (2022) Leptin protects against the development and expression of cocaine addiction-like behavior in heterogeneous stock rats

    Frontiers in Behavioral Neuroscience 16:832899.

    https://doi.org/10.3389/fnbeh.2022.832899

    • PubMed
    • Google Scholar
17. 1. Carroll ME
    2. Lynch WJ

    (2016) How to study sex differences in addiction using animal models

    Addiction Biology 21:1007–1029.

    https://doi.org/10.1111/adb.12400

    • PubMed
    • Google Scholar
18. 1. Chen BT
    2. Yau HJ
    3. Hatch C
    4. Kusumoto-Yoshida I
    5. Cho SL
    6. Hopf FW
    7. Bonci A

    (2013) Rescuing cocaine-induced prefrontal cortex hypoactivity prevents compulsive cocaine seeking

    Nature 496:359–362.

    https://doi.org/10.1038/nature12024

    • PubMed
    • Google Scholar
19. 1. Chitre AS
    2. Polesskaya O
    3. Holl K
    4. Gao J
    5. Cheng R
    6. Bimschleger H
    7. Garcia Martinez A
    8. George T
    9. Gileta AF
    10. Han W
    11. Horvath A
    12. Hughson A
    13. Ishiwari K
    14. King CP
    15. Lamparelli A
    16. Versaggi CL
    17. Martin C
    18. Pierre CL
    19. Tripi JA
    20. Wang T
    21. Chen H
    22. Flagel SB
    23. Meyer P
    24. Richards J
    25. Robinson TE
    26. Palmer AA
    27. Solberg Woods LC

    (2020) Genome-wide association study in 3,173 outbred rats identifies multiple loci for body weight, adiposity, and fasting glucose

    Obesity 28:1964–1973.

    https://doi.org/10.1002/oby.22927

    • PubMed
    • Google Scholar
20. 1. Cummings JA
    2. Gowl BA
    3. Westenbroek C
    4. Clinton SM
    5. Akil H
    6. Becker JB

    (2011) Effects of a selectively bred novelty-seeking phenotype on the motivation to take cocaine in male and female rats

    Biology of Sex Differences 2:3.

    https://doi.org/10.1186/2042-6410-2-3

    • PubMed
    • Google Scholar
21. 1. de Guglielmo G
    2. Matzeu A
    3. Kononoff J
    4. Mattioni J
    5. Martin-Fardon R
    6. George O

    (2017) Cebranopadol Blocks the Escalation of Cocaine Intake and Conditioned Reinstatement of Cocaine Seeking in Rats

    The Journal of Pharmacology and Experimental Therapeutics 362:378–384.

    https://doi.org/10.1124/jpet.117.241042

    • PubMed
    • Google Scholar
22. 1. Deroche-Gamonet V
    2. Belin D
    3. Piazza PV

    (2004) Evidence for addiction-like behavior in the rat

    Science 305:1014–1017.

    https://doi.org/10.1126/science.1099020

    • PubMed
    • Google Scholar
23. 1. Dews PB

    (1955)

    Studies on behavior. I. Differential sensitivity to pentobarbital of pecking performance in pigeons depending on the schedule of reward

    The Journal of Pharmacology and Experimental Therapeutics 113:393–401.

    • PubMed
    • Google Scholar
24. 1. Domi E
    2. Xu L
    3. Toivainen S
    4. Nordeman A
    5. Gobbo F
    6. Venniro M
    7. Shaham Y
    8. Messing RO
    9. Visser E
    10. van den Oever MC
    11. Holm L
    12. Barbier E
    13. Augier E
    14. Heilig M

    (2021) A neural substrate of compulsive alcohol use

    Science Advances 7:eabg9045.

    https://doi.org/10.1126/sciadv.abg9045

    • PubMed
    • Google Scholar
25. 1. Duttke SH
    2. Montilla-Perez P
    3. Chang MW
    4. Li H
    5. Chen H
    6. Carrette LLG
    7. de Guglielmo G
    8. George O
    9. Palmer AA
    10. Benner C
    11. Telese F

    (2022) Glucocorticoid receptor-regulated enhancers play a central role in the gene regulatory networks underlying drug addiction

    Frontiers in Neuroscience 16:858427.

    https://doi.org/10.3389/fnins.2022.858427

    • PubMed
    • Google Scholar
26. 1. Edwards S
    2. Koob GF

    (2013) Escalation of drug self-administration as a hallmark of persistent addiction liability

    Behavioural Pharmacology 24:356–362.

    https://doi.org/10.1097/FBP.0b013e3283644d15

    • PubMed
    • Google Scholar
27. Preprint

    1. Eid M
    2. Pullmann D
    3. Li H
    4. Thomas A
    5. Jhou TC

    (2019) Multiple elements of addiction vulnerability are independently heritable in rats

    bioRxiv.

    https://doi.org/10.1101/566018

    • Google Scholar
28. 1. Freeman KB
    2. Kearns DN
    3. Kohut SJ
    4. Riley AL

    (2009) Strain differences in patterns of drug-intake during prolonged access to cocaine self-administration

    Behavioral Neuroscience 123:156–164.

    https://doi.org/10.1037/a0013727

    • PubMed
    • Google Scholar
29. 1. George O
    2. Mandyam CD
    3. Wee S
    4. Koob GF

    (2008) Extended access to cocaine self-administration produces long-lasting prefrontal cortex-dependent working memory impairments

    Neuropsychopharmacology 33:2474–2482.

    https://doi.org/10.1038/sj.npp.1301626

    • PubMed
    • Google Scholar
30. 1. George O
    2. Sanders C
    3. Freiling J
    4. Grigoryan E
    5. Vu S
    6. Allen CD
    7. Crawford E
    8. Mandyam CD
    9. Koob GF

    (2012) Recruitment of medial prefrontal cortex neurons during alcohol withdrawal predicts cognitive impairment and excessive alcohol drinking

    PNAS 109:18156–18161.

    https://doi.org/10.1073/pnas.1116523109

    • PubMed
    • Google Scholar
31. 1. George O
    2. Koob GF
    3. Vendruscolo LF

    (2014) Negative reinforcement via motivational withdrawal is the driving force behind the transition to addiction

    Psychopharmacology 231:3911–3917.

    https://doi.org/10.1007/s00213-014-3623-1

    • PubMed
    • Google Scholar
32. 1. George O
    2. Koob GF

    (2017) Individual differences in the neuropsychopathology of addiction

    Dialogues in Clinical Neuroscience 19:217–229.

    https://doi.org/10.31887/DCNS.2017.19.3/gkoob

    • PubMed
    • Google Scholar
33. 1. George O
    2. Ahmed SH
    3. Gilpin NW

    (2022) Are we compulsively chasing rainbows?

    Neuropsychopharmacology 47:2013–2015.

    https://doi.org/10.1038/s41386-022-01419-w

    • PubMed
    • Google Scholar
34. 1. Giuliano C
    2. Belin D
    3. Everitt BJ

    (2019) Compulsive alcohol seeking results from a failure to disengage dorsolateral striatal control over behavior

    The Journal of Neuroscience 39:1744–1754.

    https://doi.org/10.1523/JNEUROSCI.2615-18.2018

    • PubMed
    • Google Scholar
35. 1. Hansen C
    2. Spuhler K

    (1984) Development of the national institutes of health genetically heterogeneous rat stock

    Alcoholism, Clinical and Experimental Research 8:477–479.

    https://doi.org/10.1111/j.1530-0277.1984.tb05706.x

    • PubMed
    • Google Scholar
36. 1. Hedegaard H
    2. Bastian BA
    3. Trinidad JP
    4. Spencer M
    5. Warner M

    (2018)

    Drugs most frequently involved in drug overdose deaths: United States, 2011-2016

    National Vital Statistics Reports 67:1–14.

    • PubMed
    • Google Scholar
37. 1. Jackson LR
    2. Robinson TE
    3. Becker JB

    (2006) Sex differences and hormonal influences on acquisition of cocaine self-administration in rats

    Neuropsychopharmacology 31:129–138.

    https://doi.org/10.1038/sj.npp.1300778

    • PubMed
    • Google Scholar
38. 1. Kallupi M
    2. Carrette LLG
    3. Kononoff J
    4. Solberg Woods LC
    5. Palmer AA
    6. Schweitzer P
    7. George O
    8. de Guglielmo G

    (2020) Nociceptin attenuates the escalation of oxycodone self-administration by normalizing CeA-GABA transmission in highly addicted rats

    PNAS 117:2140–2148.

    https://doi.org/10.1073/pnas.1915143117

    • PubMed
    • Google Scholar
39. 1. Kallupi M
    2. Kononoff J
    3. Melas PA
    4. Qvist JS
    5. de Guglielmo G
    6. Kandel ER
    7. George O

    (2022) Deep brain stimulation of the nucleus accumbens shell attenuates cocaine withdrawal but increases cocaine self-administration, cocaine-induced locomotor activity, and GluR1/GluA1 in the central nucleus of the amygdala in male cocaine-dependent rats

    Brain Stimulation 15:13–22.

    https://doi.org/10.1016/j.brs.2021.11.003

    • PubMed
    • Google Scholar
40. 1. Kimbrough A
    2. de Guglielmo G
    3. Kononoff J
    4. Kallupi M
    5. Zorrilla EP
    6. George O

    (2017) CRF₁ receptor-dependent increases in irritability-like behavior during abstinence from chronic intermittent ethanol vapor exposure

    Alcoholism, Clinical and Experimental Research 41:1886–1895.

    https://doi.org/10.1111/acer.13484

    • PubMed
    • Google Scholar
41. 1. King CP
    2. Tripi JA
    3. Hughson AR
    4. Horvath AP
    5. Lamparelli AC
    6. Holl KL
    7. Chitre AS
    8. Polesskaya O
    9. Ishiwari K
    10. Solberg Woods LC
    11. Palmer AA
    12. Robinson TE
    13. Flagel SB
    14. Meyer PJ

    (2021) Sensitivity to food and cocaine cues are independent traits in a large sample of heterogeneous stock rats

    Scientific Reports 11:2223.

    https://doi.org/10.1038/s41598-020-80798-w

    • PubMed
    • Google Scholar
42. 1. Kononoff J
    2. Melas PA
    3. Kallupi M
    4. de Guglielmo G
    5. Kimbrough A
    6. Scherma M
    7. Fadda P
    8. Kandel DB
    9. Kandel ER
    10. George O

    (2018) Adolescent cannabinoid exposure induces irritability-like behavior and cocaine cross-sensitization without affecting the escalation of cocaine self-administration in adulthood

    Scientific Reports 8:13893.

    https://doi.org/10.1038/s41598-018-31921-5

    • PubMed
    • Google Scholar
43. 1. Koob GF
    2. Le Moal M

    (2008) Addiction and the brain antireward system

    Annual Review of Psychology 59:29–53.

    https://doi.org/10.1146/annurev.psych.59.103006.093548

    • PubMed
    • Google Scholar
44. 1. Kosten TA
    2. Zhang XY
    3. Haile CN

    (2007) Strain differences in maintenance of cocaine self-administration and their relationship to novelty activity responses

    Behavioral Neuroscience 121:380–388.

    https://doi.org/10.1037/0735-7044.121.2.380

    • PubMed
    • Google Scholar
45. 1. Li Y
    2. Simmler LD
    3. Van Zessen R
    4. Flakowski J
    5. Wan JX
    6. Deng F
    7. Li YL
    8. Nautiyal KM
    9. Pascoli V
    10. Lüscher C

    (2021) Synaptic mechanism underlying serotonin modulation of transition to cocaine addiction

    Science 373:1252–1256.

    https://doi.org/10.1126/science.abi9086

    • PubMed
    • Google Scholar
46. 1. Mantsch JR
    2. Yuferov V
    3. Mathieu-Kia AM
    4. Ho A
    5. Kreek MJ

    (2004) Effects of extended access to high versus low cocaine doses on self-administration, cocaine-induced reinstatement and brain mRNA levels in rats

    Psychopharmacology 175:26–36.

    https://doi.org/10.1007/s00213-004-1778-x

    • PubMed
    • Google Scholar
47. 1. Marchant NJ
    2. Li X
    3. Shaham Y

    (2013) Recent developments in animal models of drug relapse

    Current Opinion in Neurobiology 23:675–683.

    https://doi.org/10.1016/j.conb.2013.01.003

    • PubMed
    • Google Scholar
48. 1. Marchette RCN
    2. Carlson ER
    3. Said N
    4. Koob GF
    5. Vendruscolo LF

    (2023) Extended access to fentanyl vapor self-administration leads to addiction-like behaviors in mice: Blood chemokine/cytokine levels as potential biomarkers

    Addiction Neuroscience 5:100057.

    https://doi.org/10.1016/j.addicn.2022.100057

    • PubMed
    • Google Scholar
49. 1. Pelloux Y
    2. Everitt BJ
    3. Dickinson A

    (2007) Compulsive drug seeking by rats under punishment: effects of drug taking history

    Psychopharmacology 194:127–137.

    https://doi.org/10.1007/s00213-007-0805-0

    • PubMed
    • Google Scholar
50. 1. Perry JL
    2. Morgan AD
    3. Anker JJ
    4. Dess NK
    5. Carroll ME

    (2006) Escalation of i.v. cocaine self-administration and reinstatement of cocaine-seeking behavior in rats bred for high and low saccharin intake

    Psychopharmacology 186:235–245.

    https://doi.org/10.1007/s00213-006-0371-x

    • PubMed
    • Google Scholar
51. 1. Piazza PV
    2. Deroche-Gamonet V

    (2013) A multistep general theory of transition to addiction

    Psychopharmacology 229:387–413.

    https://doi.org/10.1007/s00213-013-3224-4

    • PubMed
    • Google Scholar
52. 1. Picetti R
    2. Ho A
    3. Butelman ER
    4. Kreek MJ

    (2010) Dose preference and dose escalation in extended-access cocaine self-administration in Fischer and Lewis rats

    Psychopharmacology 211:313–323.

    https://doi.org/10.1007/s00213-010-1899-3

    • PubMed
    • Google Scholar
53. 1. Roth ME
    2. Carroll ME

    (2004) Sex differences in the escalation of intravenous cocaine intake following long- or short-access to cocaine self-administration

    Pharmacology, Biochemistry, and Behavior 78:199–207.

    https://doi.org/10.1016/j.pbb.2004.03.018

    • PubMed
    • Google Scholar
54. Report

    1. SAMHSA

    (2019)

    Results from the 2018 National Survey on Drug Use and Health: Summary of national findings: Annual Report

    SAMHSA.

    • Google Scholar
55. 1. Sanger DJ
    2. Blackman DE

    (1976) Rate-dependent effects of drugs: a review of the literature

    Pharmacology, Biochemistry, and Behavior 4:73–83.

    https://doi.org/10.1016/0091-3057(76)90178-7

    • PubMed
    • Google Scholar
56. 1. Sedighim S
    2. Carrette LL
    3. Venniro M
    4. Shaham Y
    5. de Guglielmo G
    6. George O

    (2021) Individual differences in addiction-like behaviors and choice between cocaine versus food in Heterogeneous Stock rats

    Psychopharmacology 238:3423–3433.

    https://doi.org/10.1007/s00213-021-05961-1

    • PubMed
    • Google Scholar
57. 1. Shansky RM
    2. Murphy AZ

    (2021) Considering sex as a biological variable will require a global shift in science culture

    Nature Neuroscience 24:457–464.

    https://doi.org/10.1038/s41593-021-00806-8

    • PubMed
    • Google Scholar
58. 1. Siciliano CA
    2. Noamany H
    3. Chang CJ
    4. Brown AR
    5. Chen X
    6. Leible D
    7. Lee JJ
    8. Wang J
    9. Vernon AN
    10. Vander Weele CM
    11. Kimchi EY
    12. Heiman M
    13. Tye KM

    (2019) A cortical-brainstem circuit predicts and governs compulsive alcohol drinking

    Science 366:1008–1012.

    https://doi.org/10.1126/science.aay1186

    • PubMed
    • Google Scholar
59. 1. Solberg Woods LC
    2. Palmer AA

    (2019) Using heterogeneous stocks for fine-mapping genetically complex traits

    Methods in Molecular Biology 2018:233–247.

    https://doi.org/10.1007/978-1-4939-9581-3_11

    • PubMed
    • Google Scholar
60. 1. Solomon RL
    2. Corbit JD

    (1974) An opponent-process theory of motivation. I. Temporal dynamics of affect

    Psychological Review 81:119–145.

    https://doi.org/10.1037/h0036128

    • PubMed
    • Google Scholar
61. 1. Swalve N
    2. Smethells JR
    3. Carroll ME

    (2016) Sex differences in the acquisition and maintenance of cocaine and nicotine self-administration in rats

    Psychopharmacology 233:1005–1013.

    https://doi.org/10.1007/s00213-015-4183-8

    • PubMed
    • Google Scholar
62. 1. Templeton TJ
    2. Diarra S
    3. Gilpin NW

    (2023) Sex differences in cocaine self-administration by Wistar rats after predator odor exposure

    Advances in Drug and Alcohol Research 3:11245.

    https://doi.org/10.3389/adar.2023.11245

    • PubMed
    • Google Scholar
63. 1. Timme NM
    2. Ma B
    3. Linsenbardt D
    4. Cornwell E
    5. Galbari T
    6. Lapish CC

    (2022) Compulsive alcohol drinking in rodents is associated with altered representations of behavioral control and seeking in dorsal medial prefrontal cortex

    Nature Communications 13:3990.

    https://doi.org/10.1038/s41467-022-31731-4

    • PubMed
    • Google Scholar
64. 1. Valenza M
    2. Picetti R
    3. Yuferov V
    4. Butelman ER
    5. Kreek MJ

    (2016) Strain and cocaine-induced differential opioid gene expression may predispose Lewis but not Fischer rats to escalate cocaine self-administration

    Neuropharmacology 105:639–650.

    https://doi.org/10.1016/j.neuropharm.2016.01.004

    • PubMed
    • Google Scholar
65. 1. Vanderschuren L
    2. Everitt BJ

    (2004) Drug seeking becomes compulsive after prolonged cocaine self-administration

    Science 305:1017–1019.

    https://doi.org/10.1126/science.1098975

    • PubMed
    • Google Scholar
66. Book

    1. Vanderschuren LJ
    2. Minnaard AM
    3. Smeets JA
    4. Lesscher HM

    (2017) Punishment models of addictive behavior

    In: Vanderschuren LJ, editors. Current Opinion in Behavioral Sciences. Elsevier. pp. 77–84.

    https://doi.org/10.1016/j.cobeha.2016.10.007

    • Google Scholar
67. 1. Vendruscolo LF
    2. Barbier E
    3. Schlosburg JE
    4. Misra KK
    5. Whitfield TW Jr
    6. Logrip ML
    7. Rivier C
    8. Repunte-Canonigo V
    9. Zorrilla EP
    10. Sanna PP
    11. Heilig M
    12. Koob GF

    (2012) Corticosteroid-dependent plasticity mediates compulsive alcohol drinking in rats

    The Journal of Neuroscience 32:7563–7571.

    https://doi.org/10.1523/JNEUROSCI.0069-12.2012

    • PubMed
    • Google Scholar

Author details

1. Giordano de Guglielmo

   Department of Psychiatry, University of California, San Diego, La Jolla, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Supervision, Writing – original draft, Writing – review and editing

   Contributed equally with

   Lieselot Carrette

   For correspondence

   gdeguglielmo@health.ucsd.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4782-7430

2. Lieselot Carrette

   Department of Psychiatry, University of California, San Diego, La Jolla, United States

   Contribution

   Data curation, Formal analysis, Investigation, Writing – original draft, Writing – review and editing

   Contributed equally with

   Giordano de Guglielmo

   Competing interests

   No competing interests declared

3. Marsida Kallupi

   Department of Psychiatry, University of California, San Diego, La Jolla, United States

   Contribution

   Supervision

   Competing interests

   No competing interests declared

4. Molly Brennan

   Department of Psychiatry, University of California, San Diego, La Jolla, United States

   Contribution

   Supervision, Methodology

   Competing interests

   No competing interests declared

5. Brent Boomhower

   Department of Psychiatry, University of California, San Diego, La Jolla, United States

   Contribution

   Supervision

   Competing interests

   No competing interests declared

6. Lisa Maturin

   Department of Psychiatry, University of California, San Diego, La Jolla, United States

   Contribution

   Supervision

   Competing interests

   No competing interests declared

7. Dana Conlisk

   Department of Neuroscience, The Scripps Research Institute, La Jolla, San Diego, United States

   Contribution

   Supervision

   Competing interests

   No competing interests declared

8. Sharona Sedighim

   Department of Psychiatry, University of California, San Diego, La Jolla, United States

   Contribution

   Supervision

   Competing interests

   No competing interests declared

9. Lani Tieu

   Department of Psychiatry, University of California, San Diego, La Jolla, United States

   Contribution

   Supervision

   Competing interests

   No competing interests declared

10. McKenzie J Fannon

    Department of Psychiatry, University of California, San Diego, La Jolla, United States

    Contribution

    Supervision

    Competing interests

    No competing interests declared

11. Angelica R Martinez

    Department of Psychiatry, University of California, San Diego, La Jolla, United States

    Contribution

    Supervision

    Competing interests

    No competing interests declared

12. Nathan Velarde

    Department of Psychiatry, University of California, San Diego, La Jolla, United States

    Contribution

    Supervision

    Competing interests

    No competing interests declared

13. Dyar Othman

    Department of Psychiatry, University of California, San Diego, La Jolla, United States

    Contribution

    Supervision

    Competing interests

    No competing interests declared

14. Benjamin Sichel

    Department of Psychiatry, University of California, San Diego, La Jolla, United States

    Contribution

    Supervision

    Competing interests

    No competing interests declared

15. Jarryd Ramborger

    Department of Psychiatry, University of California, San Diego, La Jolla, United States

    Contribution

    Supervision

    Competing interests

    No competing interests declared

16. Justin Lau

    Department of Psychiatry, University of California, San Diego, La Jolla, United States

    Contribution

    Supervision

    Competing interests

    No competing interests declared

17. Jenni Kononoff

    Department of Neuroscience, The Scripps Research Institute, La Jolla, San Diego, United States

    Contribution

    Supervision

    Competing interests

    No competing interests declared

18. Adam Kimbrough

    Department of Psychiatry, University of California, San Diego, La Jolla, United States

    Contribution

    Supervision

    Competing interests

    No competing interests declared

19. Sierra Simpson

    Department of Psychiatry, University of California, San Diego, La Jolla, United States

    Contribution

    Supervision

    Competing interests

    No competing interests declared

20. Lauren C Smith

    1. Department of Psychiatry, University of California, San Diego, La Jolla, United States
    2. Department of Neuroscience, The Scripps Research Institute, La Jolla, San Diego, United States

    Contribution

    Supervision

    Competing interests

    No competing interests declared

21. Kokila Shankar

    1. Department of Psychiatry, University of California, San Diego, La Jolla, United States
    2. Department of Neuroscience, The Scripps Research Institute, La Jolla, San Diego, United States

    Contribution

    Supervision

    Competing interests

    No competing interests declared

22. Selene Bonnet-Zahedi

    1. Department of Psychiatry, University of California, San Diego, La Jolla, United States
    2. Institut de Neurosciences de la Timone, Aix-Marseille Université, Marseille, France

    Contribution

    Supervision

    Competing interests

    No competing interests declared

23. Elizabeth A Sneddon

    Department of Psychiatry, University of California, San Diego, La Jolla, United States

    Contribution

    Supervision

    Competing interests

    No competing interests declared

24. Alicia Avelar

    1. Department of Psychiatry, University of California, San Diego, La Jolla, United States
    2. Department of Neuroscience, The Scripps Research Institute, La Jolla, San Diego, United States

    Contribution

    Supervision

    Competing interests

    No competing interests declared

25. Sonja Lorean Plasil

    Department of Psychiatry, University of California, San Diego, La Jolla, United States

    Contribution

    Supervision

    Competing interests

    No competing interests declared

26. Joseph Mosquera

    Department of Psychiatry, University of California, San Diego, La Jolla, United States

    Contribution

    Supervision

    Competing interests

    No competing interests declared

27. Caitlin Crook

    Department of Psychiatry, University of California, San Diego, La Jolla, United States

    Contribution

    Supervision

    Competing interests

    No competing interests declared

28. Lucas Chun

    Department of Psychiatry, University of California, San Diego, La Jolla, United States

    Contribution

    Supervision

    Competing interests

    No competing interests declared

29. Ashley Vang

    Department of Psychiatry, University of California, San Diego, La Jolla, United States

    Contribution

    Supervision

    Competing interests

    No competing interests declared

30. Kristel K Milan

    Department of Psychiatry, University of California, San Diego, La Jolla, United States

    Contribution

    Supervision

    Competing interests

    No competing interests declared

31. Paul Schweitzer

    Department of Psychiatry, University of California, San Diego, La Jolla, United States

    Contribution

    Supervision

    Competing interests

    No competing interests declared

32. Bonnie Lin

    Department of Psychiatry, University of California, San Diego, La Jolla, United States

    Contribution

    Conceptualization

    Competing interests

    No competing interests declared

33. Beverly Peng

    Department of Psychiatry, University of California, San Diego, La Jolla, United States

    Contribution

    Conceptualization

    Competing interests

    No competing interests declared

34. Apurva S Chitre

    Department of Psychiatry, University of California, San Diego, La Jolla, United States

    Contribution

    Conceptualization

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-1709-9214

35. Oksana Polesskaya

    1. Department of Psychiatry, University of California, San Diego, La Jolla, United States
    2. Institute for Genomic Medicine, University of California, San Diego, La Jolla, United States

    Contribution

    Conceptualization

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-3024-114X

36. Leah C Solberg Woods

    Department of Internal Medicine, Section on Molecular Medicine, Wake Forest University School of Medicine, Winston-Salem, United States

    Contribution

    Resources, Methodology

    Competing interests

    No competing interests declared

37. Abraham A Palmer

    1. Department of Psychiatry, University of California, San Diego, La Jolla, United States
    2. Institute for Genomic Medicine, University of California, San Diego, La Jolla, United States

    Contribution

    Resources, Formal analysis, Visualization

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-3634-0747

38. Olivier George

    Department of Psychiatry, University of California, San Diego, La Jolla, United States

    Contribution

    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Project administration, Supervision, Writing – review and editing

    For correspondence

    olgeorge@health.ucsd.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-3700-5003

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