Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2023 May 12.
Published in final edited form as: Addict Behav. 2015 Jun 9;50:45–50. doi: 10.1016/j.addbeh.2015.06.003

Association of exposure to neighborhood drug activity, neurobehavioral traits, and marijuana use among at-risk African American females

Leah Floyd Campbell a,b,*, Kristin Wilmoth c, Michael Mason b
PMCID: PMC10176802  NIHMSID: NIHMS698703  PMID: 26101077

Abstract

Introduction:

Theories of relative deprivation suggest African Americans in disadvantaged communities are at increased risk for drug use. This increased risk may be due, in part, to exposure to drugs and drug subcultures. Given the significance of the prefrontal cortex (PFC) functioning in yielding behavior that is strategically guided rather than reactive to environmental demands, it is important to examine the relationship between PFC functioning, neighborhood drug activity and substance use among African Americans residing in high risk communities.

Methods:

A sample of 120 young adult African American females was recruited from high-risk neighborhoods. Each completed a modified version of neighborhood environment scale, a neurobehavioral assessment designed to measure apathy, behavioral disinhibition and executive dysfunction, and provided a urine sample that was tested for the presence of psychoactive drugs.

Results:

Logistic regression analyses indicated females with higher scores on behavioral disinhibition were 2.6 times more likely to test positive for marijuana (95%CI = 1.02, 6.57). Neither apathy nor executive dysfunction was related to marijuana use. No relationship emerged between neighborhood drug activity and marijuana use.

Conclusions:

Among the neurobehavioral traits considered only behavioral disinhibition was associated with marijuana use, suggesting different neurobehavioral domains may be uniquely related to marijuana use. For females living in high risk environments, the extent to which that they are able to control impulses may provide some protection against marijuana use. Future studies focused on the moderating effects of behavioral disinhibition on the association of exposure to risk environments and marijuana use may prove beneficial. Further, the study adds to the small base of literature supporting the Frontal Systems Behavior Scale as a brief assessment to evaluate frontally-mediated neurobehavioral traits relevant to substance use in community samples. However, future studies aimed at examining the influence of neighborhood drug activity might benefit from more precise measures of exposure to neighborhood drug activity. More research to replicate and expand on the present findings is warranted.

Keywords: marijuana, Frontal System Behavior Scale, behavioral disinhibition, neighborhood drug activity, African Americans, females

INTRODUCTION

National data indicate marijuana is the most commonly consumed illicit drug and current trends suggest both marijuana use and cannabis use disorders are increasing in African Americans (Chen & Jacobson, 2012; Compton, Grant, Colliver, Glantz, & Stinson, 2004; Johnston, O’Malley, Miech, Bachman, & Schulenberg, 2014). Despite the recent push for the legalization of marijuana, a substantial base of research has linked marijuana use to a range of adverse outcomes, including criminal activity, poor employment and high-risk sexual behaviors (Green, Doherty, Stuart, & Ensminger, 2010; Shook, Vaughn, Goodkind, & Johnson, 2011). Consequently, there remains a need to identify factors that are associated with or indicate risk for marijuana use among those at greatest risk for suffering these adverse outcomes (i.e., African Americans) (Zapolski, Pedersen, McCarthy, & Smith, 2014; Green et al., 2010 Kakade et al., 2012; Des Jarlais, McCarty, Vega, & Bramson, 2013).

According to the Social Cognitive Theory (SCT) (Bandura, 1986), human behavior results from the dynamic and ongoing interaction of personal, environmental and behavioral factors. In the field of substance abuse, intra- person factors, such as behavioral under-control (impulsivity), emotional dysregulation and deficits in executive functioning, have been linked to substance use disorders (Ridenour et al., 2009; Tarter et al., 2003; Verdejo-Garcia, Bechara, Recknor, & Perez-Garcia, 2006). Furthermore, neuroimaging and neuropsychological studies have identified several frontal lobe functional systems underlying behavioral regulation and executive control believed to be impaired in substance users (e.g., the medial prefrontal cortex (PFC), anterior cingulate cortex (ACC), the orbitofrontal cortex (OFC) and dorsolateral PFC) (Verdejo-Garcia, Bechara, Recknor, & Perez-Garcia, 2006; Ersche et al., 2005; Fishbein et al., 2005). For example, damage to the ACC, a frontal lobe region highly innervated with medial PFC connections, results in apathy and low motivation for natural reinforcers (Verdejo-Garcia, Bechara, Recknor, & Perez-Garcia, 2006). OFC dysfunction increases impulsive behavior (Everitt et al., 2007; Schoenbaum & Shaham, 2008; Wrase et al., 2007), and impairment of the dorsolateral PFC is associated poor planning, organization and working memory (Boeka & Lokken, 2011; Verdejo-Garcia, Bechara, Recknor, & Perez-Garcia, 2006). Frontal cortical dysfunction in these areas are thought to mediate substance use vulnerability (Ersche et al., 2005; Fishbein et al., 2005).

At the level of the environment, neighborhood factors have been shown to influence substance use and poor outcomes associated with use. One unique characteristic of economically disadvantaged African American neighborhoods that is conceptualized as a risk factor for substance use is visible drug market activity (Wallace & Muroff, 2002; Saxe et al., 2001; Crum, Lillie-Blanton, & Anthony, 1996; Wertz & Sayette, 2001). In lower socioeconomic (SES) neighborhoods with visible drug activity there are more opportunities to use drugs and drug sub-cultures likely reduce inhibitions about drug use, thereby increasing the likelihood of use (Wagner & Anthony, 2002; Linton, Jennings, Latkin, Gomez, & Mehta, 2014; Wertz & Sayette, 2001; Cochran, Grella & Mays, 2012; Rhodes et al., 2005).

Further, stress produced by living in lower SES neighborhoods primes an individual for substance use disorders (Sinha, 2001). Chronic stress is associated with PFC dysfunction (Qin, Hermans, van Marle, Luo, & Fernandez, 2009; Bondi, Rodriguez, Gould, Frazer, & Morilak, 2008; Brown, Henning, & Wellman, 2005; Cerqueira, Mailliet, Almeida, Jay, & Sousa, 2007; Mika, et al., 2012), which perpetuates impairments in emotional regulation (Li & Sinha, 2008). These and related deficits have been shown to increase vulnerability to substance use disorders (Medina et al., 2008; Verdejo-Garcia, Lopez-Torrecillas, Gimenez, & Perez-Garcia, 2004; Verdejo-Garcia, Rivas-Perez, Lopez-Torrecillas, & Perez-Garcia, 2006).

Given the important role of the PFC in yielding strategically-guided behavior (rather than behavior that is reactive to immediate environmental demands), coupled with evidence suggesting poor living conditions adversely affects PFC functioning, it is important to examine the relationship between PFC functioning and substance use among individuals residing in communities with high rates of drug activity. However, assessing neurocognitive factors often is not feasible given the time, effort and financial cost associated with administering assessments. As a result, few studies have examined the relationship between neurocognitive factors and substance use within community samples of young adults.

The purpose of this study was to address current gaps in the literature by focusing on the association of exposure to neighborhood drug activity, neurobehavioral traits and marijuana use among a sample of young adult females. Our focus on African Americans is critical given this group is exposed to multiple and unique risk factors (Wu, Temple, Shokar, Nguyen-Oghalai, & Grady, 2010; Finlay, White, Mun, Cronley, & Lee, 2012; Chen & Jacobson, 2012; Saxe et al., 2001), are among those least likely to receive treatment for substance use disorders (Alegria, Carson, Goncalves, & Keefe, 2011; Wells, Klap, Koike & Sherbourne, 2001), and suffers the highest rates of morbidities associated with drug use (Gil, Wagner, Tubman, 2004; Zapolski, Pedersen, McCarthy, & Smith, 2014; Green et al., 2010). Moreover, while comparing disadvantaged females to females from higher SES and African Americans to Whites is useful, these approaches provide limited information for designing tailored interventions. As a result, researchers are increasingly focusing on within group differences (see Wu et al., 2010). Therefore, using a sample of African American females from high risk communities we hypothesized: (1) performance on neurobehavioral measures will be associated with marijuana use and (2) exposure to neighborhood drug activity will be associated with marijuana use. Our goal is to identify contextual factors related to marijuana use and better understand who and what should be targeted for interventions within high-risk communities in an on-going effort to develop tailored interventions.

MATERIAL AND METHODS

Procedures

Data for this paper were drawn from a larger cross-sectional study that aimed to explain racial disparities in sexually transmitted diseases among young adult females by considering the influence of social and cultural neighborhood factors on sexual partnerships, sexual norms and behaviors of females residing in lower SES neighborhoods. The sample included 240 African American and White females aged 18–30 years recruited using snowball sampling techniques. Participants were recruited via street recruitment. Target areas included neighborhoods identified as low SES using census-based neighborhood social and economic indicators (e.g., mean household income and percent of female headed households). In addition, flyers were distributed at local community colleges and health clinics and we placed advertisements in local newspapers. To be eligible for participation in the study, females had to meet the following criteria:(a) identify as African American or White; (b) be between 18 and 30 years of age; (c) reside in lower SES neighborhoods in Baltimore City; and (d) have no history of regular drug use, excluding alcohol and marijuana.

Those who met the study’s eligibility criteria were asked to complete a face-to-face semi-structured interview with trained research assistants. After providing informed consent, participants answered detailed questions about drug use and sexual behaviors, social networks, psychosocial factors and socio-demographics. Participants also completed a neurobehavioral battery. Each participant provided a urine sample that was tested for the presence of psychoactive drugs. All assessments were conducted in a private interview room at the research site. All participants received remuneration for their time and effort. The Johns Hopkins Bloomberg School of Public Health Institutional Review Board approved the study.

For this paper, our focus is on a sub-sample of 120 African American females. The majority (88%) resided in neighborhoods where juvenile drug arrest rates ranged from 47 to 128 per 1,000. These rates were substantially higher than Baltimore City’s rate of 32 per 1,000 (Baltimore Neighborhood Indicators Alliance, 2008).

Measures

Demographic factors.

Participants provided information about age and education. Age was treated as a continuous variable. Education data were converted to a dichotomous variable (i.e., less than 12 years and Diploma/GED).

The Frontal Systems Behavior Scale (FrSBe):

The FrSBe consists of 46 items designed to measure: (1) apathy (i.e., low levels of initiation and persistence, score range 14–70), (2) disinhibition (i.e., low inhibitory control, score range 15–75) and (3) executive dysfunction (i.e., problems with attention, working memory, and planning, score range 17–85). Each subscale is intended to measure behavioral problems associated with specific frontally mediated cortical circuits: (1) the medial PFC (apathy subscale), (2) the OFC (disinhibition subscale) and (3) the dorsolateral PFC (executive dysfunction subscale). The FrSBe has demonstrated good reliability and validity in both normal and clinical populations (Carvalho, Ready, Malloy, & Grace, 2013; Grace & Malloy, 2001; Velligan, Ritch, Sui, DiCocco, & Huntzinger, 2002; Basso, et al., 2008) and has been used in substance use research (Lyvers, Jamieson, & Thorberg, 2013; Spinella, 2003; Verdejo-Garcia, Bechara et al., 2006; Verdejo-Garcia, Rivas-Perez et al., 2006). For the purposes of data analyses two groups were created for each subscale (i.e., persons scoring below the mean and persons scoring above the mean).

Neighborhood drug activity.

Two items drawn from a modified version of neighborhood environment scale were used to assess perceived neighborhood drug activity: (1) In my neighborhood I see people using or selling drug; and (2) In my neighborhood drug dealers earn the most money (see Crum, et al., 1996). Response options included true or false. Participants endorsing one or both of the items were placed in the exposure category.

Marijuana use:

On-site urinalysis for drug metabolites was performed using the Multi-Drug 12 Panel Test for the rapid detection of THC/marijuana; cocaine and its metabolite, benzoylecgonine; PCP (phencyclidine); morphine and its related metabolites derived from opium (opiates); methamphetamines (including ecstasy); methadone; amphetamines; barbiturates; benzodiazepines and tricyclic antidepressants. In addition, participants responded to the following three item: (1) in the past seven days did you smoke marijuana; (2) in the past seven days on how many days did you smoke marijuana; and (3) how old were you the first time you used marijuana.

Data Analysis

Using SPSS 20 we employed descriptive statistics to obtain frequencies and mean scores. We used Pearson’s chi square tests and t-tests to examine differences among individuals who tested positive for marijuana and those who tested negative for marijuana on neurobehavioral traits and exposure to drug activity. Logistic regression analyses were executed to examine the association of neighborhood drug activity, neurobehavioral traits, and marijuana use. First unadjusted odds ratios (ORs) and 95% confidence intervals (CIs) were obtained for each covariate and the outcome variable, marijuana use measured by urinalysis. Next, a model that included all covariates (i.e., apathy, behavioral disinhibition, executive dysfunction, perceived neighborhood drug activity, age and education) was tested. Adjusted ORs and 95% CIs were obtained.

RESULTS

Of the 120 participants seven had missing data, therefore the final analytic sample included 113 females. The mean age was 23.6 (sd=3.5) years and 73% completed high school. Forty four percent of females tested positive for marijuana. The average age at first use was 14.6 years (sd=2.8). Sixty two percent reported exposure to neighborhood drug activity. Table 1 summarizes the sample characteristics.

Table 1.

Sample Characteristics (N=113)

Variable N (%)/M(sd)
Age 23.6 (3.5)
Education
 < 12 years 31 (27.4)
 Diploma/GED 82 (72.6)
Neighborhood drug activity 70 (61.9)
Marijuana use (urinalysis) 50 (44.2)
Apathy 25.8 (6.8)
Behavioral Disinhibition 28.4 (8.4)
Executive Dysfunction 33.7 (10.0)
Open in a new tab

Among those testing positive for marijuana (n=50), 55% reported smoking marijuana in the 24 hours prior to the assessment. Three out of four reported using marijuana in the week prior to the assessment. The mean number of days of marijuana use during the past week was 4.1 (sd=2.8). Compared to non-marijuana users, marijuana users had significantly high mean scores on behavioral disinhibition (32.2 (sd=7.9) vs. 25.3 (sd=7.6) (t=4.6, p< .001)) and executive dysfunction (36.9 (sd=10.1) vs 31.1(sd=9.2) (t=3.1, p<.01). The mean score for apathy among users of marijuana was 27.8 (sd=6.0) compared to 24.8 (sd=6.4) among females who did not test positive for marijuana (t=1.8; p<.10). Sixty percent of marijuana users compared to 64% of non-users reported exposure to neighborhood drug activity (X2=0.14, p=.70).

Results from the unadjusted logistic regression analysis yielded significant results for the association of behavioral disinhibition and marijuana use (OR = 3.2, 95%CI = 1.47, 6.96) and executive dysfunction and marijuana use (OR =2.6, 95%CI =1.20, 5.52). Apathy was not associated with marijuana use. No relationship emerged between neighborhood drug activity and marijuana use (see Table 2).

Table 2.

Results of logistic regression analyses examining the association of neighborhood drug activity, neurobehavioral traits and marijuana use (N=113)

Variable % Testing positive for marijuana Unadjusted OR (95% CI) Adjusted OR (95%CI)
Age -- 1.1 (0.97, 1.20) 1.1 (0.99, 1.27)
Education
 < 12 years 66.7 1.0 1.0
 Diploma/GED 39.3 3.1 (1.36, 7.02) 2.3 (0.88, 6.13)
Apathy
 Below mean 36.9 1.0 1.0
 Above mean 54.2 2.1 (0.94, 4.31) 0.99 (0.38, 2.52)
Behavioral Disinhibition
 Below mean 32.3 1.0 1.0
 Above mean 60.4 3.2 (1.47, 6.96)** 2.6 (1.02, 6.57)*
Executive Dysfunction
 Below mean 33.9 1.0 1.0
 Above mean 56.9 2.6 (1.20, 5.52)** 1.4 (0.52, 4.07)
Neighborhood drug activity
 No 47.7 1.0 1.0
 Yes 45.2 0.9 (0.43, 1.91) 1.0 (0.43, 2.30)
Open in a new tab
*

p<.05

**

p<.01

When all covariates were entered into the equation, simultaneously, only behavioral disinhibition emerged as a correlate of marijuana use (AOR = 2.6, 95%CI = 1.02, 6.57). The relationship between executive dysfunction and marijuana use was no longer significant (AOR = 1.4, 95%CI = 0.52, 4.07). No relationship emerged between exposure to neighborhood drug activity and marijuana or apathy and marijuana.

DISCUSSION

We examined the association of exposure to neighborhood drug activity, neurobehavioral traits, and current marijuana use among a sample of young adult African American females from disadvantaged communities. Consistent with extant literature, behavioral disinhibition was related to recent marijuana use (Bolla, Eldreth, Matochik, & Cadet, 2005; Lyvers et al., 2013; Piechatzek et al., 2009; Spinella, 2003). Previous longitudinal research has identified disinhibition as a plausible risk factor for cannabis use disorder (Tarter et al, 2003; Ridenour, Tarter, Reynolds, Mezzich, Kirisci, &Vanyukov, 2009). Unique to this study is the finding that behavioral disinhibition, as measured by the FrSBe, was positively associated with marijuana use, while controlling for the effects of exposure to neighborhood drug activity.

In the adjusted model, executive dysfunction was not associated with marijuana use. This finding is somewhat contradictory to results from previous neurocognitive work that demonstrated an association between marijuana use and deficits in information processing, decision making, attention, and working memory (Boggio et al., 2010; Bolla et al., 2005; Crane, Schuster, Fusar-Poli, & Gonzalez, 2013; Crane, Schuster, & Gonzalez, 2013; Fontes et al., 2011a, 2011b; Lyvers et al., 2013; Thames, Arbid, & Sayegh, 2014; Verdejo-Garcia et al., 2007; Verdejo-Garcia, Rivas-Perez et al., 2006). Discrepancies in findings may be due to methodological differences. Specifically, differences in duration and severity of drug use must be taken into consideration. In the current study, we used a brief questionnaire to measure neurobehavioral traits in a community sample of young adults. The majority of previous studies reporting an association of executive dysfunction and marijuana use have used clinical samples or older populations (Verdejo-Garcia et al., 2007; Verdejo-Garcia, Rivas-Perez et al., 2006) and employed comprehensive batteries of neuropsychological tests or neuroimaging (Bolla et al., 2005; Crane, Schuster, Fusar-Poli et al., 2013; Crane, Schuster, & Gonzalez 2013; Fontes et al., 2011a, 2011b; Martin-Santos et al., 2010; Thames et al., 2014).

Similarly, we did not find a relationship between apathy and marijuana use. However, a small base of research identifies apathy as a correlate of marijuana use (Verdejo-Garcia, Rivas-Perez et al. (2006; Cherek, Lane, & Dougherty, 2002; Lane, Cherek, Pietras, & Steinberg, 2005) and the apathy subscale was designed to assess everyday behavioral deficits associated with the ACC, an area of the brain known to be strongly affected by THC intoxication (Martin-Santos et al., 2010). More longitudinal research focused on apathy and marijuana use is needed.

Exposure to neighborhood drug activity and marijuana use were not related. Increased exposure to contextual disadvantages is presumed to place African Americans at heighten risk for poor health behaviors, such as drug use (Wilson, 1987; Rhodes 2002). Moreover, it is well documented that African Americans living in disadvantaged urban neighborhoods are exposed to drugs in their community more than their white counterparts or persons residing in more affluent neighborhoods (Crum et al., 1996). However, neighborhood drug activity has not been linked, consistently, to increased drug use among African American young persons (Ridenour et al., 2009; Crum et al., 1996; Furr-Holden et al., 2014; Lambert, Brown, Phillips, & Ialongo, 2004). Thus, while drugs may be more readily available in neighborhoods with drug activity, the simple assumption that exposure to drug activity increases drug use may not be correct (Crum, et al., 1996; Wagner & Anthony, 2002; Wertz & Sayette, 2001; Cochran, et al, 2012). This relationship is likely complex and requires consideration of the synergistic effects of exposure, intra-person factors (e.g., neurobehavioral traits), and other social and cultural factors (Mennis & Mason, 2012; Ridenour et al., 2009; Ridenour et al., 2013). Furthermore, the relationship may vary by type of drug, gender and race/ethnicity (Karriker-Jaffe, 2011).

Our findings must be considered in light of the study’s limitations. First, we cannot infer causality due to the study’s cross-sectional design. Second, our small sample size likely resulted in reduced power to detect relationships and limited our ability to test more comprehensive models (e.g., neurobehavioral traits as moderators of the association of exposure to drugs and marijuana use). In addition, our sampling strategy limited the generalizability of the findings. A longitudinal study design would allow us to better understand the relationship between neurobehavioral traits and marijuana use. Increasing the sample size would increase the power to detect relationships, allow for the testing moderation models and increase the validity of the findings. Next, using random sampling techniques would yield a more representative sample and, thereby, improve external validity. Measurement issues are also a concern, particularly as they relate the validity and reliability of our two item self-report measure of neighborhood drug activity. However, it should be noted that at present there is no gold standard for measuring neighborhood drug activity. Previous research has used objective measures (e.g., geocoded drug arrest data) (see Jennings, Taylor, Salhi, Furr-Holden, & Ellen, 2012; Linton et al., 2014) as well as self-report measures, which typically consists of one or two items, to assess exposure to neighborhood drug activity (see Crum et al., 1996 and Jennings et al., 2012).

CONCLUSIONS

In conclusion, the results suggest different neurobehavioral domains may be uniquely related to marijuana use. For African American females exposed to risk environments the extent to which that they are able to control impulses may provide some protection against engaging in marijuana use. Given a small base of research indicating intact neurocognitive functioning might serve as protective function against adverse outcomes among high risk populations (Mitchell Severtson, & Latimer, 2007), more research focused on the moderating effects of behavioral disinhibition on the association of exposure to neighborhood drug activity and marijuana use may prove beneficial. In addition, our findings support the utility of the FrSBe as a brief valid assessment for measuring frontally-mediated neurobehavioral traits relevant to substance use disorders (Winhusen et al., 2013; Lyvers et al., 2013). However, future studies aimed at examining the influence of neighborhood drug activity might benefit from more precise measures of neighborhood drug activity. More research to replicate and expand on the present findings is warranted.

Research Highlights.

  • Marijuana use was prevalent among this sample of young adult females.

  • Behavioral disinhibition was positively associated with recent marijuana use.

  • Exposure to neighborhood drug activity was not associated with marijuana use.

Acknowledgements.

The authors would like to thank Ms. April Lawson who assisted in the data collection and management of the study.

Role of Funding Sources.

Funding for this study was provided by National Institute on Drug Abuse (NIDA) (R21DA025543). NIDA had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.

This work was supported by grants from the National Institute on Drug Abuse (R21DA025543).

List of Abbreviations:

FrSBe

Frontal System Behavior Scale

ACC

anterior cingulate cortex

OFC

orbitofrontal cortex

PFC

prefrontal cortex

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Conflict of Interest. There are no conflicts of interest

REFERENCES

  1. Alegria M, Carson NJ, Goncalves M, & Keefe K (2011). Disparities in treatment for substance use disorders and co-occurring disorders for ethnic/racial minority youth. Journal of the American Academy of Child and Adolescent Psychiatry, 50, 22–31. doi: 10.1016/j.jaac.2010.10.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Baltimore Neighborhood Indicators Alliance Jacob France Institute. Retrieved from http://www.bniajfi.org.
  3. Bandura A (1986). Social foundations of thought and action : a social cognitive theory. Englewood Cliffs, N.J.: Prentice-Hall. [Google Scholar]
  4. Basso MR, Shields IS, Lowery N, Ghormley C, Combs D, Arnett PA, & Johnson J (2008). Self-reported executive dysfunction, neuropsychological impairment, and functional outcomes in multiple sclerosis. Journal of Clinical and Experimental Neuropsychology, 30, 920–930. doi: 10.1080/13803390801888733 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Boeka AG, & Lokken KL (2011). Prefrontal systems involvement in binge eating. Eating and Weight Disorders, 16, 121–126. doi: 10.1007/BF03325317 [DOI] [PubMed] [Google Scholar]
  6. Boggio PS, Zaghi S, Villani AB, Fecteau S, Pascual-Leone A, & Fregni F (2010). Modulation of risk-taking in marijuana users by transcranial direct current stimulation (tDCS) of the dorsolateral prefrontal cortex (DLPFC). Drug and Alcohol Dependence, 112, 220–225. doi: 10.1016/j.drugalcdep.2010.06.019 [DOI] [PubMed] [Google Scholar]
  7. Bolla KI, Eldreth DA, Matochik JA, & Cadet JL (2005). Neural substrates of faulty decision-making in abstinent marijuana users. NeuroImage, 26, 480–492. doi: 10.1016/j.nueroimage.2005.02.012 [DOI] [PubMed] [Google Scholar]
  8. Bondi CO, Rodriguez G, Gould GG, Frazer A, & Morilak DA (2008). Chronic unpredictable stress induces a cognitive deficit and anxiety-like behavior in rats that is prevented by chronic antidepressant drug treatment. Neuropsychopharmacology, 33, 320–331. doi: 10.1038/sj.npp.1301410 [DOI] [PubMed] [Google Scholar]
  9. Campbell CA, Hahn RA, Elder R, Brewer R, Chattopadhyay S, Fielding J, Naimi TS, Toomey T, Lawrence B, & Middleton JC (2009). The effectiveness of limiting alcohol outlet density as a means of reducing excessive alcohol consumption and alcohol-related harms. American Journal of Preventive Medicine, 37, 556–569. doi: 10.1016/j.amepre.2009.09.028 [DOI] [PubMed] [Google Scholar]
  10. Carvalho JO, Ready RE, Malloy P, & Grace J (2013). Confirmatory factor analysis of the Frontal Systems Behavior Scale (FrSBe). Assessment, 20, 632–641. doi: 10.1177/1073191113492845 [DOI] [PubMed] [Google Scholar]
  11. Cerqueira JJ, Mailliet F, Almeida OF, Jay TM, & Sousa N (2007). The prefrontal cortex as a key target of the maladaptive response to stress. The Journal of Neuroscience, 27, 2781–2787. doi: 10.1523/jneurosci.4372-06.2007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Chen P, & Jacobson KC (2012). Developmental trajectories of substance use from early adolescence to young adulthood: Gender and racial/ethnic differences. Journal of Adolescent Health, 50, 154–63. doi: 10.1016/j.jadohealth.2011.05.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Cherek DR, Lane SD, & Dougherty DM (2002). Possible amotivational effects following marijuana smoking under laboratory conditions. Experimental and Clinical Psychopharmacology, 10, 26–38. doi: 10.1037/1064-1297.10.1.26 [DOI] [PubMed] [Google Scholar]
  14. Cochran SD, Grella CE, Mays VM (2012). Do Substance use norms and perceived drug availability mediate sexual orientation differences in patterns of substance use? Results from the California Quality of Life Survey II. Journal of Studies on Alcohol and Drugs, 73(4), 675–685 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Compton WM, Grant BF, Colliver JD, Glantz MD, & Stinson FS (2004). Prevalence of marijuana use disorders in the United States: 1991–1992 and 2001–2002. The Journal of the American Medical Association, 291, 2114–2121. doi: 10.1001/jama.291.17.2114 [DOI] [PubMed] [Google Scholar]
  16. Crane NA, Schuster RM, Fusar-Poli P, & Gonzalez R (2013). Effects of cannabis on neurocognitive functioning: Recent advances, neurodevelopmental influences, and sex differences. Neuropsychology Review, 23, 117–137. doi: 10.1007/s11065-012-9222-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Crane NA, Schuster RM, & Gonzalez R (2013). Preliminary evidence for a sex-specific relationship between amount of cannabis use and neurocognitive performance in young adult cannabis users. Journal of the International Neuropsychological Society, 19, 1009–1015. doi: 10.1017/S135561771300088X [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Crum RM, Lillie-Blanton M, & Anthony JC (1996). Neighborhood environment and opportunity to use cocaine and other drugs in late childhood and early adolescence. Drug and Alcohol Dependence, 43, 155–161. doi: 10.1016/S0376-8716(96)01298-7 [DOI] [PubMed] [Google Scholar]
  19. Des Jarlais DC, McCarty D, Vega WA, & Bramson H (2013). HIV infection among people who inject drugs: The challenge of racial/ethnic disparities. American Psychologist, 68(4), 274–285. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Ersche KD, Fletcher PC, Lewis SJ, Clark L, Stocks-Gee G, London M, Deakin JB, Robbins TW, & Sahakian BJ (2005). Abnormal frontal activations related to decision-making in current and former amphetamine and opiate dependent individuals. Psychopharmacology, 180, 612–623. doi: 10.1007/s00213-005-2205-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Everitt BJ, Hutcheson DM, Ersche KD, Pelloux Y, Dalley JW, & Robbins TW (2007). The orbital prefrontal cortex and drug addiction in laboratory animals and humans. Annals of the New York Academy of Sciences, 1121, 576–597. doi: 10.1196/annals.1401.022 [DOI] [PubMed] [Google Scholar]
  22. Finlay AK, White HR, Mun E Cronley CC, & Lee C (2012) Racial differences in trajectories of heavy drinking and regular marijuana use from ages 13 to 24 among African-American and White males. Drug and Alcohol Dependence, 121; 1–2, 118–123. doi: 10.1016/j.drugalcdep.2011.08.020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Fishbein DH, Eldreth DL, Hyde C, Matochik JA, London ED, Contoreggi C, Kurian V, Kimes AS, Breeden A, & Grant S (2005). Risky decision making and the anterior cingulate cortex in abstinent drug abusers and nonusers. Cognitive Brain Research, 23, 119–136. doi: 10.1016/j.cogbrainres.2004.12.010 [DOI] [PubMed] [Google Scholar]
  24. Fontes MA, Bolla KI, Cunha PJ, Almeida PP, Jungerman F, Laranjeira RR, Bressan RA, & Lacerda ALT (2011a). Cannabis use before age 15 and subsequent executive functioning. The British Journal of Psychiatry, 198, 442–447. doi: 10.1192/bjp.bp.110.077479 [DOI] [PubMed] [Google Scholar]
  25. Fontes MA, Bolla KI, Cunha PJ, Almeida PP, Jungerman F, Laranjeira RR, Bressan RA, & Lacerda ALT (2011b). Frontal Assessment Battery (FAB) is a simple tool for detecting executive deficits in chronic cannabis users. Journal of Clinical and Experimental Neuropsychology, 33, 523–531. doi: 10.1080/13803395.2010.535505 [DOI] [PubMed] [Google Scholar]
  26. Furr-Holden CD, Lee MH, Johnson R, Milam AJ, Duncan A, Reboussin BA, Leaf PJ, & Ialongo NS (2014). Neighborhood Environment and Marijuana Use in Urban Young Adults. Prevention Science. doi: 10.1007/s11121-014-0497-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Gil AG, Wagner EF, Tubman JG (2004). Associations between early adolescent substance use and subsequent young-adult substance use disorders and psychiatric disorders among a multiethnic males sample in South Florida. American Journal of Public Health, 94, 1603–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Grace J, & Malloy P (2001). FrSBe, frontal systems behavior scale: Professional manual: Psychological Assessment Resources. [Google Scholar]
  29. Green KM, Doherty EE, Stuart EA, & Ensminger ME (2010). Does heavy adolescent marijuana use lead to criminal involvement in adulthood? Evidence from a multiwave longitudinal study of urban African Americans. Drug and Alcohol Dependence, 112, 117–125. doi: 10.1016/j.drugalcdep.2010.05.018 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Jennings JM, Taylor RB, Salhi RA, Furr-Holden C, & Ellen JM (2012). Neighborhood drug markets: A risk environment for bacterial sexually transmitted infections among urban youth. Social Science and Medicine, 74, 1240–1250. doi: 10.1016/j.socscimed.2011.12.040 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Johns M, Sacks R, Rane M, & Kansagra SM (2013). Exposure to tobacco retail outlets and smoking initiation among New York City adolescents. Journal of Urban Health, 90, 1091–1101. doi: 10.1007/s11524-013-9810-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Johnston LD, O’Malley PM, Bachman JG, Schulenberg JE & Miech RA (2014). Monitoring the Future national survey results on drug use, 1975–2013: Volume 2, College students and adults ages 19–55. Ann Arbor: Institute for Social Research, The University Of Michigan. [Google Scholar]
  33. Kakade M, Duarte CS, Liu X, Fuller CJ, Drucker E, Hoven CW, … Wu P (2012). Adolescent substance use and other illegal behaviors and racial disparities in criminal justice system involvement: Findings from a US national survey. American Journal of Public Health, 102(7), 1307–1310. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Karriker-Jaffe KJ (2011). Areas of disadvantage: A systematic review of effects of area-level socioeconomic status on substance use. Drug and Alcohol Review, 30, 84–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Lambert SF, Brown TL, Phillips CM, & Ialongo NS (2004). The relationship between perceptions of neighborhood characteristics and substance use among urban african american adolescents. American Journal of Community Psychology, 34, 205–218. doi: 10.1007/s10464-004-7415-3 [DOI] [PubMed] [Google Scholar]
  36. Lane SD, Cherek DR, Pietras CJ, & Steinberg JL (2005). Performance of heavy marijuana-smoking adolescents on a laboratory measure of motivation. Addictive Behaviors, 30, 815–828. doi: 10.1016/j.addbeh.2004.08.026 [DOI] [PubMed] [Google Scholar]
  37. Li CS, & Sinha R (2008). Inhibitory control and emotional stress regulation: Neuroimaging evidence for frontal-limbic dysfunction in psycho-stimulant addiction. Neuroscience and Biobehavioral Reviews, 32, 581–597. doi: 10.1016/j.neubiorev.2007.10.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Linton SL, Jennings JM, Latkin CA, Gomez MB, & Mehta SH (2014). Application of space-time scan statistics to describe geographic and temporal clustering of visible drug activity. Journal of Urban Health, 91, 940–956. doi: 10.1007/s11524-014-9890-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Lipperman-Kreda S, Mair C, Grube JW, Friend KB, Jackson P, & Watson D (2014). Density and proximity of tobacco outlets to homes and schools: relations with youth cigarette smoking. Prevention Science, 15, 738–744. doi: 10.1007/s11121-013-0442-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Lyvers M, Jamieson R, & Thorberg FA (2013). Risky cannabis use is associated with alexithymia, frontal lobe dysfunction, and impulsivity in young adult cannabis users. Journal of Psychoactive Drugs, 45, 394–403. doi: 10.1080/02791072.2013.844525 [DOI] [PubMed] [Google Scholar]
  41. Marin RS, Firinciogullari S, & Biedrzycki RC (1994). Group differences in the relationship between apathy and depression. Journal of Nervous and Mental Disease, 182, 235–239. [DOI] [PubMed] [Google Scholar]
  42. Martin-Santos R, Fagundo AB, Crippa JA, Atakan Z, Bhattacharyya S, Allen P, Fusar-Poli P, Borgwardt S, Seal M, Busatto GF, & McGuire P (2010). Neuroimaging in cannabis use: a systematic review of the literature. Psychological Medicine, 40, 383–398. doi: 10.1017/S0033291709990729 [DOI] [PubMed] [Google Scholar]
  43. Medina KL, McQueeny T, Nagel BJ, Hanson KL, Schweinsburg AD, & Tapert SF (2008). Prefrontal cortex volumes in adolescents with alcohol use disorders: unique gender effects. Alcoholism: Clinical and Experimental Research, 32, 386–394. doi: 10.1111/j.1530-0277.2007.00602.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Mennis J & Mason M (2012) The moderating effects of age and gender social networks. Social Networks. 34, 150–157. doi: 10.1016/j.socnet.2010.10.003 [DOI] [Google Scholar]
  45. Mika A, Mazur GJ, Hoffman AN, Talboom JS, Bimonte-Nelson HA, Sanabria F, & Conrad CD (2012). Chronic stress impairs prefrontal cortex-dependent response inhibition and spatial working memory. Behavioral Neuroscience, 126, 605–619. doi: 10.1037/a0029642 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Mitchell MM Severtson SG, & Latimer WW (2007). Interaction of cognitive performance and knowing someone who has died from AIDS. AIDS Education and Prevention, 19, 289–297 [DOI] [PubMed] [Google Scholar]
  47. Piechatzek M, Indlekofer F, Daamen M, Glasmacher C, Lieb R, Pfister H, Tucha O, Lange KW, Wittchen H-U, & Schütz CG (2009). Is moderate substance use associated with altered executive functioning in a population-based sample of young adults? Human Psychopharmacology: Clinical and Experimental, 24, 650–665. doi: 10.1002/hup.1069 [DOI] [PubMed] [Google Scholar]
  48. Qin S, Hermans EJ, van Marle HJ, Luo J, & Fernandez G (2009). Acute psychological stress reduces working memory-related activity in the dorsolateral prefrontal cortex. Biol Psychiatry, 66, 25–32. [DOI] [PubMed] [Google Scholar]
  49. Rhodes T (2002). The ‘risk environment’: a framework for understanding and reducing drug-related harm. International Journal of Drug Policy 13, 85–94. doi: 10.1016/S0955-3959(02)00007-5 [DOI] [Google Scholar]
  50. Ridenour TA, Reynolds M, Ahlqvist O, Zhai ZW, Kirisci L, Vanyukov MM, & Tarter RE (2013). High and low neurobehavior disinhibition clusters within locales: Implications for community efforts to prevent substance use disorder. American Journal of Drug and Alcohol Abuse, 39, 194–203. doi: 10.3109/00952990.2013.764884 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Ridenour TA, Tarter RE, Reynolds M, Mezzich A, Kirisci L, & Vanyukov M (2009). Neurobehavior disinhibition, parental substance use disorder, neighborhood quality and development of cannabis use disorder in boys. Drug and Alcohol Dependence, 102, 71–77. doi: 10.1016/j.drugalcdep.2009.01.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Saxe L, Kadushin C, Beveridge A, Livert D, Tighe E, Rindskopf D, Ford J, & Brodsky A, (2001). The visibility of illicit drugs: Implications for community-based drug control strategies. American Journal of Public Health 91, 1987–1994. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Schoenbaum G, & Shaham Y (2008). The role of orbitofrontal cortex in drug addiction: a review of preclinical studies. Biological Psychiatry, 63, 256–262. doi: 10.1016/j.biopsych.2007.06.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Shook JJ, Vaughn M, Goodkind S, & Johnson H (2011). An empirical portrait of youthful offenders who sell drugs. Journal of Criminal Justice, 39, 224–231. doi: 10.1016/j.jcrimjus.2011.02.014 [DOI] [Google Scholar]
  55. Sinha R (2001). How does stress increase risk of drug abuse and relapse? Psychopharmacology 158:343–359. doi 10.1007/s002130100917 [DOI] [PubMed] [Google Scholar]
  56. Spinella M (2003). Relationship between drug use and prefrontal-associated traits. Addiction Biology, 8, 67–74. doi: 10.1080/1355621031000069909 [DOI] [PubMed] [Google Scholar]
  57. Tarter RE, Kirisci L, Mezzich A, Cornelius JR, Pajer K, Vanyukov M, Gardner W, Blackson T, & Clark D (2003). Neurobehavioral disinhibition in childhood predicts early age at onset of substance use disorder. American Journal of Psychiatry, 160, 1078–1085. [DOI] [PubMed] [Google Scholar]
  58. Thames AD, Arbid N, & Sayegh P (2014). Cannabis use and neurocognitive functioning in a non-clinical sample of users. Addictive Behaviors, 39, 994–999. doi: 10.1016/j.addbeh.2014.01.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Velligan DI, Ritch JL, Sui D, DiCocco M, & Huntzinger CD (2002). Frontal Systems Behavior Scale in schizophrenia: Relationships with psychiatric symptomatology, cognition and adaptive function. Psychiatry Research, 113, 227–236. doi: 10.1016/S0165-1781(02)00264-0 [DOI] [PubMed] [Google Scholar]
  60. Verdejo-Garcia A, Bechara A, Recknor EC, & Perez-Garcia M (2006). Executive dysfunction in substance dependent individuals during drug use and abstinence: an examination of the behavioral, cognitive and emotional correlates of addiction. Journal of the International Neuropsychological Society, 12, 405–415. doi: 10.1017/S1355617706060486 [DOI] [PubMed] [Google Scholar]
  61. Verdejo-Garcia A, Benbrook A, Funderburk F, David P, Cadet JL, & Bolla KI (2007). The differential relationship between cocaine use and marijuana use on decision-making performance over repeat testing with the Iowa Gambling Task. Drug and Alcohol Dependence, 90, 2–11. doi: 10.1016/j.drugalcdep.2007.02.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Verdejo-Garcia A, Lopez-Torrecillas F, Gimenez CO, & Perez-Garcia M (2004). Clinical implications and methodological challenges in the study of the neuropsychological correlates of cannabis, stimulant, and opioid abuse. Neuropsychology Review, 14, 1–41. doi: 10.1023/B:NERV.0000026647.71528.83 [DOI] [PubMed] [Google Scholar]
  63. Verdejo-Garcia A, Rivas-Perez C, Lopez-Torrecillas F, & Perez-Garcia M (2006b). Differential impact of severity of drug use on frontal behavioral symptoms. Addictive Behaviors, 31, 1373–1382. doi: 10.1016/j.addbeh.2005.11.003 [DOI] [PubMed] [Google Scholar]
  64. Wagner FA, & Anthony JC (2002). Into the world of illegal drug use: Exposure opportunity and other mechanisms linking the use of alcohol, tobacco, marijuana, and cocaine. American Journal of Epidemiology, 155, 918–925. doi: 10.1093/aje/155.10.918 [DOI] [PubMed] [Google Scholar]
  65. Wallace JM, & Muroff JR, 2002. Preventing substance abuse among African American children and youth: Race differences in risk factor exposure and vulnerability. Journal of Primary Prevention. 22, 235–261 doi: 10.1023/A:1013617721016 [DOI] [Google Scholar]
  66. Wells K, Klap R, Koike A, Sherbourne C (2001). Ethnic disparities in unmet need for alcoholism, drug abuse and mental health care. American Journal of Psychiatry, 158, 2027–32. [DOI] [PubMed] [Google Scholar]
  67. Wertz JM, & Sayette MA (2001). A Review of the effects of perceived drug use opportunity on self-reported urge. Experimental Clinical Psychopharmacology, 9(1), 3–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Wilson WJ. The Truly Disadvantaged: The Inner City, the Underclass, and Public Policy. Chicago, IL: University of Chicago Press; 1987. [Google Scholar]
  69. Winhusen TM, Somoza EC, Lewis DF, Frankie B Kropp FB, Horigianc VE, Adinoffd B, Wrase J, Kahnt T, Schlagenhauf F, Beck A (2013),. Frontal systems deficits in stimulant-dependent patients: Evidence of pre-illness dysfunction and relationship to treatment response. Drug and Alcohol Dependence, 127, 94–10 doi: 10.1016/j.drugalcdep.2012.06.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Wrase ML, & Rolls ET (2004). The functional neuroanatomy of the human orbitofrontal cortex: evidence from neuroimaging and neuropsychology. Progress in Neurobiology, 72, 341–372. doi: 10.1016/j.pneurobio.2004.03.006 [DOI] [PubMed] [Google Scholar]
  71. Wu ZH, Temple JR, Shokar NK, Nguyen-Oghalai TU, & Grady JJ (2010). Differential racial/ethnic patterns in substance use initiation among young, low-income women. American Journal of Drug and Alcohol Abuse, 36, 123–9. doi: 10.3109/00952991003718072 [DOI] [PubMed] [Google Scholar]
  72. Zapolski TCB, Pedersen SL, McCarthy DM, & Smith GT (2014). Less drinking, yet more problems: Understanding African American drinking and related problems. Psychological Bulletin, 140, 188–223. doi: 10.1037/a0032113 [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES