Show Summary Details

Page of

PRINTED FROM OXFORD HANDBOOKS ONLINE (www.oxfordhandbooks.com). © Oxford University Press, 2018. All Rights Reserved. Under the terms of the licence agreement, an individual user may print out a PDF of a single chapter of a title in Oxford Handbooks Online for personal use (for details see Privacy Policy and Legal Notice).

Subscriber: null; date: 19 January 2019

Agenda for Future Research and Concluding Comments

Abstract and Keywords

This concluding chapter highlights issues we see as especially important next-step agendas for the field. The issues we have highlighted concern (a) the implications that a developmental frame of reference provides in characterizing and parsing the etiology and course of addictive behavior; (b) the relevance of event-level predictors occurring in microtime and the extent to which they will supercede the more summative indicators that currently dominate the substance abuse field; (c) the increasing awareness, and characterization of drug-specific influences, and the degree to which these influences are useful in evaluating the vulnerability potential of drugs of abuse; (d) the differences in characterization of clinical symptomatology and course that have the potential to occur when evaluation of psychopathology and the details of intervention methods are unpacked with a specifically developmental lens; (e) the insights that new big data collection programs will create in understanding the cross-domain causal structure of substance abuse.

Keywords: developmental frame of reference, event-level prediction, drug-specific influences, big data, cross-level influences

The discourse of this Handbook covers an age period just prior to adulthood, when the majority of substance use begins, and when it escalates close to its peak level by the time of early adulthood (Jackson, this volume; McCabe et al., 2016). The volume covers 34 discrete areas of work, grouped within eight sections that were selected to describe the major domains of the field. It has a specifically developmental focus for several reasons. For one, substance use is a moving target that changes dramatically over the course of adolescence and has linkages extending far beyond the use of the substances themselves. Because this is a time frame where extraordinary physical, social, and biological changes are all taking place, it demands treatment at many levels of analysis that cut across a number of scientific disciplines. And because of the dynamic nature of the process, explanations and understanding are needed at multiple points in time. Despite these complexities, the period is often regarded as a unitary one, of “adolescence,” and by so explaining it, the nuances and the shifts in process are not seen. Our intent in this work is to focus a lens that destroys this unitary characterization, and that at the same time provides the insights that allow the reader to negotiate these different levels of process with greater understanding.

The Handbook’s chapters were selected to address these multiple levels of causal structure and also to summarize the applied work that has evolved to prevent and/or treat the clinical and social problems that result from substance abuse. In addition to summarizations of the literature in each of the areas, the authors were requested to comment on where the work needed to go next. Here we highlight the issues we see as especially important for the next generation of research to address. Our grouping of issues is organized based on judgement calls about their salience, and about the extent to which they share commonalities across multiple content areas. We also make some observations, based upon our reading of the full set of contributions, about what some of the overarching issues are that are essential next-step agendas for the field.

Implications of a Developmental Frame of Reference for Characterization of the Etiology and Course of Addictive Behavior

Two principles have organized the content of this Handbook. One, already noted earlier, is that adolescent substance abuse is a developmental phenomenon. This is more than a truism; it is a statement calling a major body of work into play which articulates that the passage of time is a dynamic process, involving multiple systems operating on the organism simultaneously, and which also articulates that outcomes from these multiple systems operating together produce multiple, differentiated pathways of behavior over time. The operation of these multiple systems requires both scientists and clinicians to be cognizant of the fact that onset and course of substance use (or alternatively, substance nonuse while the majority of others have started using) is a moving target occurring at different developmental waypoints. The impact of surrounding systems is likely to be different because they also are changing. For this reason, the possibility of producing different outcomes is large; these influences occur at different times and operate on subsystems that are differentially developed.

A good example of this is the development of nicotine dependence, wherein different patterns of symptomatology show up at different stages in the emergence of the disorder, and different social and biological factors predict attainment of different points in the dependence trajectory (see Myers & MacPherson, this volume; also Flay et al., 1998; Jester et al., in press; Kandel et al., 2009; Strong et al., 2012.) A similar pattern has been shown for the development of alcohol dependence (Buu et al., 2012a, 2012b). Another example pertaining to risk and protective factors for substance use is the recent work showing developmental changes in serotonin signaling which increase sensitivity to risky home environments but also amplify positive response to a nurturing environment (Brummelte et al., 2017). Perhaps the clearest illustration of developmental variations in risk is Dodge et al.’s (2008) dynamic cascade analysis of the development of severe violence. That work demonstrates the impact of developmentally adjacent influences, each of which has the potential to turn what begins early on as a trajectory to severe violence into one which has a number of developmentally shaped bifurcations along the way. Some of the influences move the developmental path “off-track” from the risky outcome, and some strengthen it.

There are two larger implications of this work. One is that the etiologic trajectory of drug involvement is not likely to involve a single pathway, although the multiple pathways that flow from the effects of these influences at different developmental time points is not infinite. The second is that the time has come to incorporate developmentally informed gene-environment and gene-gene models in studies focusing on substance use and substance abuse risk. Windle (this volume) articulates some of the challenges such translational research faces, including the need to characterize the specific mediational role that genetic influences play, as well as detailing the manner in which such influences express themselves—through brain and endocrine systems. A parallel challenge involves characterizing the specific environments which maximize and minimize such influences. Our field is only in the very early stages of embracing such comprehensive models, with advances focusing upon understanding parts of the full model while ignoring others.

Event-Level Predictors: Action and Predictability in Microtime

The developmental perspective typically involves fairly long swaths of time. The variables examined within this framework are primarily within-person factors such as personality and temperament. Measurement of these factors involves summating behavioral expression in different situations and relationships. At the contextual or environmental level, assessment of the parallel factors also involves summative measurement of what happens—in a variety of specific situations that have a common environmental feature/attraction/threat. In both these spheres, what is sought is a marker index that averages across a variety of circumstances and that also shows a range of variation across these circumstances. But what is a useful tool from one perspective becomes a source of error from another. If one wishes to know precisely what will take place at a particular point in time (e.g., whether one will choose to take marijuana at a particular party on a particular evening; whether one decides to drive after having drunk), utilization of these measures provides only a loose approximation of what will take place. To address this gap, one needs to characterize what goes on at the event level, whether it be about decisions to use or not use a drug at a particular event, engaging in sexual behavior with a particular partner on a particular night, acting on an aggressive thought, and so on (e.g., Borodovsky et al., 2018; Moore et al., 2011).

Several Handbook authors have addressed this gap, including Peterson and Smith in their chapter on expectancies, and McCarty and McCarthy in their chapter on substance-impaired driving (SID). The expectancy studies make it very clear that it is the event-related evocation of memories, sometimes well formed and conscious, sometimes rudimentary and present as part of the cognitive background, that serves as the mediator between opportunity for use and actual use, and that this is one of the critical elements in the action chain that tends to be overlooked by behavioral researchers, whose focus is personality–behavior or temperament–behavior relationships. Similarly, McCarty and McCarthy note the need for work which will examine the specific decision-making process related to the choice to use a drug at a particular time. To do such work, one needs to turn to field-based data sampling in high-risk situations (e.g., in bars, at roadside stops), or—in the worst-case scenario—use timeline-follow-back methodology to approximate it. The timeline-follow-back methodology has been used to evaluate the event-level connections between alcohol, marijuana, cigarette use, and partner violence in dating and nondating situations (e.g., Epstein-Ngo et al., 2013). Smartphones have been used to assess current feelings in peer group situations (e.g., Kenny et al., 2016) and in prevention/treatment contexts, where monitoring of the symptomatology of the moment (e.g., anxiety, suicidal ideation, depression) can become the basis of help signals to an on-call treatment provider. Such assessments, utilizing social media, become very effective methodologies to reduce the problematic behavior (cf. Jander et al., 2014; Theedele et al., 2017).

McCarty and McCarty also make the more general point that all behavior is ultimately an interaction between contextual variations in risk and within-subject variability in intent/expectancy to use a drug, or conversely, intent/expectancy to avoid risk. They appropriately emphasize the need for “prospective examination of within-subjects and event-level influences on substance use, which would allow for the examination of interactions between psychological and environmental factors that determine risk for adolescent … (substance use) in a specific instance.” We note that an understanding of the momentary subjective experience/action that is taking place at this micro level provides the closest parallel to what is taking place at the neural level. Ultimately, these microtime variations are the building blocks of precision mechanistic cross-level models of the networks that organize behavior. Although this work is presently restricted to the computational neuroscience, which probes the networks of function in brain, it ultimately has extensions to behavioral variation. The units of precision for that work are currently far beyond what the behavioral field is capable of, but this level of understanding ultimately will be achieved, and when it does it will have implications for preventive intervention at a level of focused attack that is well beyond our current treatment capabilities (Menossi et al., 2013; Steele et al., 2018).

Drug-Specific Influences: Vulnerability Potential and Potency Differences for Specific Drugs of Abuse

Although drug “addiction” is most often referred to as a singular phenomenon, the clinical evidence indicates that the addictive potential of pharmacological agents varies considerably across the drugs of abuse, and the evidence in this volume shows that their course, their effects upon behavior, and their vulnerability to influence by the social environment are by no means monolithic. The effort to understand these differentiating attributes better has taken place at multiple levels of analysis, ranging from the behavioral, to the physiological, to the neural, to the genetic. However, the key parameters that would link this variation together are currently unknown, so the field is left with a conceptual framework that is either monolithic (lumped under the rubric of “addictive drugs”) or that addresses characteristics of each of the drugs individually. The ability to integrate their disparate characteristics in a way that would have direct clinical relevance is only in its very early stages of development (Heilig et al., 2016). We briefly review some of the drug-specific features, and commonalities, that have been discussed in a number of chapters, with the intent to stimulate efforts toward this integration.

One powerful and discriminatory index of addictive potential is catch rate, the probability that addiction will result once use has been initiated. A number of epidemiologic studies, ranging as far back as 1994 (Anthony, Warner, & Kessler) and as recently as 2015 (Lopez-Quintero & Neumark), have noted major across-drug differences in this probability. Among the common drugs of abuse, cannabis is the drug with the lowest catch rate, with only 8.9% of those who begin use moving thereafter into dependence. Nicotine is the highest, with 67% eventually becoming dependent. Speed of progression from first use to dependence among those who eventually become dependent has a substantially different profile, with cocaine initiators being the most rapid in progression (half have become dependent in 4 years) and nicotine users being the slowest (27 years from onset) (Lopez-Quintero & Neumark, 2015; Wagner & Anthony, 2002). These differences in addiction potential are driven to a very substantial degree by differences in the pharmacologic action of each of the drugs as well as differences in their site of action (Koob & Volkow, 2010; Volkow & Morales, 2015). At the same time, although these drugs all have addictive potential, there are clear differences in neural signature and subsequent potential to ensnare. Different neuroadaptations are present among the common drugs of abuse (Badiani et al., 2011), and different behavioral and molecular alterations are produced after abstinence from cocaine vis-à-vis morphine, nicotine, cannabis, and alcohol (Becker, Kiefer, & LeMerrer, 2017). These variations are not sufficiently accounted for by either a dopaminergic model or a stress surfeit disorder model (Badiani et al., 2018).

Moreover, the epidemiologic evidence also indicates that once use has begun, the developmental course varies across classes of drugs over the interval between adolescence and early adulthood. Nonmedical use of opioids, sedatives, stimulants, and tranquilizers all shows peak use in late adolescence, with a consistent descending linear path of use into the mid-20s for nonmedical use of opioids and sedatives, and a much flatter pathway of use for stimulants and tranquilizers (McCabe et al., 2016). Level of use and pattern of decline vary by both sex and racial/ethnic group status, indicating that the social ecology of use has some impact upon the addictive characteristics of these significantly addictive substances. In addition, personality factors, in particular internalizing and externalizing behavior, predict patterns of drug use for some drugs but not others, and in some instances predict protection from use for some drugs, but not others (Colder et al., 2013). There are also noted drug–drug interactions that influence trajectory course. Binge drinking at age 18 is consistently associated with a slower rate of decline in frequency of nonmedical use of all classes of prescription drugs; conversely, marijuana use was only associated with a slower rate of decline in sedative and tranquilizer use (McCabe et al., 2016).

Genetic influences across the drugs of abuse also vary substantially, both in terms of type of influence on the phenotype and developmental phase of action. This is an issue that the field has struggled to understand for well over 20 years. In the 1990s, genetic studies suggested that the mechanisms of addiction involved a common genetic core, which accounted for the largest single component of variance in addiction potential (Tsuang et al., 1996). The underlying genetic disposition appeared to be common to both licit (alcohol and tobacco) and illicit (marijuana) drugs, and it involved a vulnerability to disinhibitory or externalizing behavior (Iacono et al., 2008; Kendler et al., 2003). Although the data indicated that the genetic contribution was large, the proportion of genetic factors influencing the pathway from use to abuse to addiction appeared to be stage specific (Agrawal & Lynskey, 2006). In short, the evidence indicated that there was also developmental variation in the mechanistic structures operating at each of these stages.

These time-dependent differences in genetic influence have suggested that the issue of drug vulnerability differences is not going to be accounted for by a single metric. Moreover, research involving a broader swath of drugs of abuse has indicated that the genetic disposition for licit and illict drugs is not a common one when a broader array of drugs is included in the analyses (cf. Kendler et al., 2007). As noted by Windle (this volume), “recent findings support the notion that there are important disorder-specific influences and environmental sources of variation that contribute significantly to the observed covariation.” Recent work has also demonstrated the interaction of environmental, developmental, and allelic variation that is drug specific (Trucco et al., 2018).

Taken together, the weight of this evidence across multiple domains strongly advocates for a differentiated view of “substance” abuse and “drug” addiction, and it indicates the need for drug-specific modifiers for our understanding of the mechanistic structure of this family of disorders. In a broader sense, the questions about the developmental course of use for these drugs; their specific genetic, neurophysiological, and sociocultural etiology; and the different requirements (special needs) involved in treatment and prevention for each of them raise a larger challenge that the chapters in this Handbook echo in many different ways. What the field needs is a more delineated understanding of what the drug-specific and non-drug-specific elements are among the drugs of abuse, as well as an understanding of the specific targets that differentiate the intervention/prevention strategies for each of these substances of abuse. To put this another way, given a differentiated knowledge of the critical components that lead to substance use, abuse, and addiction, our goal needs to be identification of which components are most central in advancement of the abuse process, and also to be able to specify when, developmentally, is the most sensitive time where such advancement can be halted. The field is now sufficiently differentiated, and the science is sufficiently elaborated such that this challenge is a feasible one to take on.

Clinical Implications of a Developmental Frame of Reference

There are also direct clinical implications stemming from a developmental frame of reference. Being able to characterize development more precisely will sharpen our ability to identify the time points of greatest vulnerability, and hence time points of greatest sensitivity to change. By ignoring developmental time, a core piece of the matrix of causation is overlooked, yet the ability to understand it is to take advantage of natural points of stability and change as places of opportunity, albeit requiring different strategies to have impact (see, for example, the classic work on turning points by Rutter, 1996, and a recent and more articulated version by Schulenberg and colleagues (this volume). At the moment, both clinical practice and prevention programming remain largely ignorant about these issues. The ability to begin to quantify these developmental locations has the potential to make intervention much more powerful, as an insulator against the impact of risk, on the one hand, and as a tool with greater potency for change. Moreover, the points of change are not just about timing; they typically imply a shift in activity and attention. To the extent these modality shifts can be woven into the intervention they have the potential for greater effect. To provide but one example, the shifts in adolescent brain responsivity to reward would suggest that more affectively framed messaging will have greater impact at this time.

Above and beyond the ability to identify time points of greatest vulnerability, there are other clinical practice implications stemming from an understanding of the developmental nature of substance use disorder (SUD). These implications are sketched out in a number of the Handbook chapters. Wilson and Janoff (this volume) emphasize that comorbidity means multiple etiologies, and that to address only the substance abuse in the presenting adolescent is likely to be ineffective in the long term for the treatment. For this reason, just as is the obverse case with SUD, understanding the course of the comorbidity and addressing it in the initial evaluation as well as in treatment is critical.

Understanding the developmental phase within which the adolescent makes the connection with treatment is also critical. Contexts are different, goals for the adolescent are different, and significant influences are likely to be different, at different points in adolescence. Different modalities of treatment are called for under these circumstances. A related clinical question is the time sequencing of onset and course for adolescents with SUD and one or more co-occurring clinical disorders. Wilson and Janoff make the case for an in-depth developmental understanding of the emergence of symptomatology for both clinical problems. They point to the commonly used treatment strategy of working to make the patient substance abuse free in order to ascertain the linkage between the co-occuring disorder and the SUD, and note that this involves an at-best crude understanding of the interrelationship. Although the diagnostic system implies that there are two separate entities, this is simply a labeling of categories to indicate the multiple symptomatologies that are present. From an individual standpoint, it is much more likely that the two disorders are woven together in their development. Without understanding this, and documentation of the patient’s account of what emerged when, one will not be able to identify the nodal points of the symptomatic course and will likewise not be able to devise an individualized treatment plan that has the potential to address the patient’s idiosyncratic etiology. Although Wilson and Janoff’s account is very practical, it simultaneously embraces a sophisticated developmental view of the clinical picture that goes far beyond a cross-sectional assessment of what presents at time of treatment entry. As they describe, this strategy is much more likely to lead to the design of an effective plan of recovery that has the potential to create long-term resolution of these complex cases.

White, Cronley, and Iyer (this volume) also focus on the dynamics of comorbidity issues in their review of the evidence for direction of effects in the relationship between delinquency and substance use. That work is most commonly focused on etiologic questions rather than clinical ones—that is, which is the causal agent and which the outcome—but the reality is that once the relationship is established, the behaviors (“symptoms”) coexist. Or to put the matter differently, each is comorbid with the other. The authors point out that the evidence for developmental complexity of this relationship is considerable. Comorbidity that originated by an effect in one direction can reverse causal flow at a later developmental way point; in other instances, the influences are reciprocal across both types of behavior. They review the studies showing these multiple directions of effect, and their conclusions offer a tantalizing invitation for clinical researchers to use this multidirectional developmental model to facilitate understanding of other clinical symptomatology as it is embedded in context. Their findings also provide an invitation for clinicians to probe more deeply about the issues of timing of first symptom appearance and tracking direction of effect as they relate both to etiology and current treatment. Finally, we note that adolescent drug use is newer and less practiced, and hence the behavior should be easier to modify.

At the same time, the clinician’s fallacy—shared as well in the field of medicine more generally—is that all trouble is ultimately addressable at the individual level. However, for some types of problems this may be impossible. As noted by Schwartz and colleagues (this volume), some problems are the result of forces impacting the individual from macro-level influences, such as racial prejudice, socioeconomic deprivation, and so on. These influences both create elevated stress and also, via a process of system justification, result in the discriminated group accepting its “inferior” status. It is likely that addressing these problems only at the individual level will ultimately lead to relapse, except for the most resilient. And even for those who are resilient, the macro-level processes create great stress. Unless macro-level effects can be loosened (e.g., by moving to another part of the community, and perhaps even in some instances, by migration) the same or other symptomatology is likely to appear. To address such issues, the clinician’s efforts need to move more toward public health action, with a special focus on social policy change (see especially Holder and Green, this volume).

Cross-Domain Causal Structure of Substance Abuse and the Challenge for New Data Collection Methodologies

The multiple empirical studies reviewed in this book utilize a very large number of methods to assess the variables hypothesized to lead to, or correlate with, the development of substance abuse. The variation being assessed covers multiple domains, crossing multiple levels of analysis. Understanding the relationships between these levels of function and how they affect one another is in its infancy, but the effort to establish these connections is driven by increasing sensitivity to the fact that mechanistic variation at any one level of analysis can explain only a small portion of the predictive variance (Karmiloff-Smith et al., 2014; McEwen & Akil, 2011). This volume’s authors indicate an awareness of this issue to varying degrees, with the acknowledgment being most obvious in the chapters that utilize a developmental psychopathology conceptual framework, a cross-species perspective, or are characterizing the mechanistic structure of substance abuse etiology at the extremes of the social neuroscience continuum (i.e., genetics and neuroimaging on the one hand and sociocultural influence on the other). Creation of a methodology to handle this diversity of causal influences is a daunting enterprise, but comprehensive templates are slowly emerging. The National Institutes of Health (NIH) collection of assessment methods for Research Domain Criteria (RDoC) Constructs (2016, RDoC) is one such broad-ranging array of instruments that was constructed with the goal to cover these domains. The PhenX Toolkit (Conway et al., 2014; Hendershot et al., 2015) is another.

Concurrent to the need to develop a suitably broad-ranging methodology is the challenge to create studies that would be able to assess and then manage this extraordinarily large set of measures, and that likewise will have the expertise to take on the major analytic challenges such a massive, cross-domain matrix will present. At the time most of these chapters were written, such a project did not exist, although two were in fact either in the launch stage or in the planning stage. They are the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) Study (2015) and the Adolescent Brain Neurocognitive Development (ABCD) Study (Garavan et al., 2018; Jernigan & Brown, 2018; Volkow et al., 2017). NCANDA, currently in its fifth year of operation, is following 831 adolescents between the ages of 12 and 21, The ABCD study, currently in its third year of operation, is the largest developmental neuroimaging study worldwide, beginning at ages 9–10 and following a nationally representative sample of approximately 11,900 study participants for a period of 10 years. In was conceptualized using a population neuroscience perspective (Falk et al., 2013) and involves a high-dimensional data collection endeavor that utilizes assessments of structural and functional brain imaging, bioassay of key biological processes for genetic and epigenetic analysis, neurocognition, physical and mental health, substance use, social and emotional functions, culture, and environment. It is out of these longitudinal, cross-level matrices of data that new levels of understanding of the causes and course of substance use and abuse are most likely to emerge. Because they are multidomain as well as developmental, it is hard to predict what the shape of those understandings will be. But it is very clear that we are entering a new era of science that not only cuts across disciplines but also seeks to integrate them. In so doing, we will be constructing new models of the emergence of risk as well as the emergence of resilience.

References

Agrawal, A., & Lynskey, M. T. (2006). The genetic epidemiology of cannabis use, abuse and dependence. Addiction, 101, 801–812.Find this resource:

    Anthony, J. C., Warner, L. A., & Kessler, R. C. (1994). Comparative epidemiology of dependence on tobacco, alcohol, controlled substances, and inhalants: Basic findings from the National Comorbidity Survey. Experimental and Clinical Psychopharmacology, 2(3), 244.Find this resource:

      Badiani, A., Belin, D., Epstein, D., Calu, D., & Shaham, Y. (2011). Opiate versus psychostimulant addiction: The differences do matter. Nature Reviews Neuroscience, 12(11), 685.Find this resource:

        Badiani, A., Berridge, K. C., Heilig, M., Nutt, D. J., & Robinson, T. E. (2018). Addiction research and theory: A commentary on the Surgeon General’s Report on alcohol, drugs, and health. Addiction Biology, 23(1), 3–5.Find this resource:

          Becker, J. A., Kieffer, B. L., & Le Merrer, J. (2017). Differential behavioral and molecular alterations upon protracted abstinence from cocaine versus morphine, nicotine, THC and alcohol. Addiction Biology, 22(5), 1205–1217.Find this resource:

            Borodovsky J. T., Marsch L. A., & Budney A. J. (2018). Studying cannabis use behaviors with Facebook and Web surveys: Methods and insights. JMIR Public Health Surveillance, 4(2), e48. doi:10.2196/publichealth.9408Find this resource:

              Brummelte, S., McGlanaghy, E., Bonnin, A., & Oberlander, T. F. (2017). Developmental changes in serotonin signaling: Implications for early brain function, behavior and adaptation. Neuroscience, 7(342), 212–231. doi:10.1016/j.neuroscience.2016.02.037Find this resource:

                Buu, A., Wang, W., Schroder, S. A., Kalaida, N. L., Puttler, L. I., & Zucker, R. A. (2012a). Developmental emergence of alcohol use disorder symptoms and their potential as early indicators for progression to alcohol dependence in a high-risk sample: A longitudinal study from childhood to early adulthood. Journal of Abnormal Psychology, 121(4), 897.Find this resource:

                  Buu, A., Wang, W., Schroder, S. A., Kalaida, N. L., Puttler, L. I., & Zucker, R. A. (2012b). Correction to Buu et al. (2012a). Journal of Abnormal Psychology, 122(1), 25. doi:10.1037/a0025961. PMID: 21842966. PMCID: PMC3560403Find this resource:

                    Colder, C. R., Scalco, M., Trucco, E. M., Read, J. P., Lengua, L. J., Wieczorek, W. F., & Hawk, L. W. (2013). Prospective associations of internalizing and externalizing problems and their co-occurrence with early adolescent substance use. Journal of Abnormal Child Psychology, 41(4), 667–677.Find this resource:

                      Conway, K. P., Vullo, G. C., Kennedy, A. P., Finger, M. S., Agrawal, A., Bjork, J. M., … Huggins, W. (2014). Data compatibility in the addiction sciences: An examination of measure commonality. Drug & Alcohol Dependence, 141, 153–158. doi:10.1016/j.drugalcdep.2014.04.029Find this resource:

                        Dodge, K. A., Greenberg, M. T., Malone, P. S., & Conduct Problems Prevention Research Group. (2008). Testing an idealized dynamic cascade model of the development of serious violence in adolescence. Child Development, 79(6), 1907–1927.Find this resource:

                          Epstein-Ngo, Q. M., Cunningham, R. M., Whiteside, L. K., Chermack, S. T., Booth, B. M., Zimmerman, M. A., Walton, M. A. (2013). A daily calendar analysis of substance use and dating violence among high risk urban youth. Drug and Alcohol Dependence, 130(1–3), 194–200.Find this resource:

                            Falk, E. B., Hyde, L. W., Mitchell, C., Faul, J., Gonzalez, R., Heitzeg, M. M., …, Morrison, F. J. (2013). What is a representative brain? Neuroscience meets population science. Proceedings of the National Academy of Sciences, 110(44), 17615–17622.Find this resource:

                              Flay, B. R., Phil, D., Hu, F. B., & Richardson, J. (1998). Psychosocial predictors of different stages of cigarette smoking among high school students. Preventive Medicine, 27(5), A9–A18.Find this resource:

                                Garavan, H., Bartsch, H., Conway, K., Decastro, A., Goldstein, R. Z., Heeringa, S., … Zahs, D. (2018). Recruiting the ABCD sample: Design considerations and procedures. Developmental Cognitive Neuroscience, 32,16–22. doi:10.1016/j.dcn.2018.04.004Find this resource:

                                  Heilig, M., Epstein, D. H., Nader, M. A., & Shaham, Y. (2016). Time to connect: Bringing social context into addiction neuroscience. Nature Reviews. Neuroscience, 17(9), 592–599. doi:10.1038/nrn.2016.67Find this resource:

                                    Hendershot, T., Pan, H., Haines, J., Harlan, W. R., Marazita, M. L., McCarty, C. A., Ramos, E. M., & Hamilton, C. M. (2015). Using the PhenX Toolkit to add standard measures to a study. Current Protocols in Human Genetics, 86(1), 1–21.Find this resource:

                                      Iacono, W. G., Malone, S. M., & McGue, M. (2008). Behavioral disinhibition and the development of early-onset addiction: Common and specific influences. Annual Review of Clinical Psychology, 4(1), 325–348.Find this resource:

                                        Jander, A., Crutzen, R., Mercken, L., & De Vries, H. (2014). A Web-based computer-tailored game to reduce binge drinking among 16 to 18 year old Dutch adolescents: Development and study protocol. BMC Public Health, 14(1), 1054.Find this resource:

                                          Jernigan, T. L., Brown, S. A., & ABCD Consortium Coordinators. (2018). Introduction. Developmental Cognitive Neuroscience 32, 1–3.Find this resource:

                                            Jester, J. M., Glass, J. M., Bohnert, K., Nigg, J. T., Wong, M., & Zucker, R. A. (in press). Child and adolescent influences predicting degree of smoking involvement in emerging adulthood. Health Psychology.Find this resource:

                                              Kandel, D. B., Griesler, P. C., & Schaffran, C. (2009). Educational attainment and smoking among women: Risk factors and consequences for offspring. Drug & Alcohol Dependence, 104, S24–S33.Find this resource:

                                                Karmiloff-Smith, A., Casey, B. J., Massand, E., Tomalski, P., & Thomas, M. S. C. (2014). Environmental and genetic influences on neurocognitive development: The importance of multiple methodologies and time-dependent intervention. Clinical Psychological Science, 2(5), 628–637.Find this resource:

                                                  Kendler, K. S., Myers, J., & Prescott, C. A. (2007). Specificity of genetic and environmental risk factors for symptoms of cannabis, cocaine, alcohol, caffeine, and nicotine dependence. Archives of General Psychiatry, 64(11), 1313–1320.Find this resource:

                                                    Kendler, K. S., Prescott, C. A., Myers, J., & Neale, M. C. (2003). The structure of genetic and environmental risk factors for common psychiatric and substance use disorders in men and women. Archives of General Psychiatry, 60, 929–937.Find this resource:

                                                      Kenny, R., Dooley, B., & Fitzgerald, A. (2016). Ecological momentary assessment of adolescent problems, coping efficacy, and mood states using a mobile phone app: An exploratory study. JMIR Mental Health, 3(4), e51. doi:10.2196/mental.6361Find this resource:

                                                        Koob, G. F., & Volkow, N. D. (2010). Neurocircuitry of addiction. Neuropsychopharmacology, 35(1), 217–238.Find this resource:

                                                          Lopez-Quintero, C., & Neumark, Y. (2015). Prevalence and determinants of resistance to use drugs among adolescents who had an opportunity to use drugs. Drug & Alcohol Dependence, 149, 55–62.Find this resource:

                                                            McCabe, S. E., Kloska, D. D., Veliz, P., Jager, J., & Schulenberg, J. E. (2016). Developmental course of non‐medical use of prescription drugs from adolescence to adulthood in the United States: National longitudinal data. Addiction, 111(12), 2166–2176.Find this resource:

                                                              McEwen, B. S., & Akil, H. (2011). Introduction to social neuroscience: Gene, environment, brain, body. Annals of the New York Academy of Sciences, 1231, vii–ix.Find this resource:

                                                                Menossi, H. S., Goudriaan, A. E., Périco, C. D. A. M., Nicastri, S., de Andrade, A. G., D’Elia, Li, C. S., & Castaldelli-Maia, J. M. (2013). Neural bases of pharmacological treatment of nicotine dependence—Insights from functional brain imaging: A systematic review. CNS Drugs, 27(11), 921–941.Find this resource:

                                                                  Moore, T. M., Elkins, S. R., McNulty, J. K., Kivisto, A. J., & Handsel, V. A. (2011). Alcohol use and intimate partner violence perpetration among college students: Assessing the temporal association using electronic diary technology. Psychology of Violence, 1, 315–328. doi:10.1037/a0025077Find this resource:

                                                                    National Institutes of Health. (2016). Research Domain Criteria (RDoC) Matrix. Retrieved from https://www.nimh.nih.gov/research-priorities/rdoc/constructs/rdoc-matrix.shtml

                                                                    Rutter, M. (1996). Transitions and turning points in developmental psychopathology: As applied to the age span between childhood and mid-adulthood. International Journal of Behavioral Development, 19, 603–626.Find this resource:

                                                                      Steele, V. R., Maurer, J. M., Arbabshirani, M. R., Claus, E. D., Fink, B. C., Rao, V., Calhoun, V. D., & Kiehl, K. A. (2018). Machine learning of functional magnetic resonance imaging network connectivity predicts substance abuse treatment completion. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 3(2), 141–149. doi:10.1016/j.bpsc.2018.07.003Find this resource:

                                                                        Strong, D. R., Schonbrun, Y. C., Schaffran, C., Griesler, P. C., & Kandel, D. (2012). Linking measures of adult nicotine dependence to a common latent continuum and a comparison with adolescent patterns. Drug & Alcohol Dependence, 120(1), 88–98.Find this resource:

                                                                          Theedele D. A., Cushing C. C., Fritz A., Amaro C. M., & Ortega A. (2017). Mobile health interventions for improving health outcomes in youth: A meta-analysis. JAMA Pediatrics, 171(5), 461–469. doi:10.1001/jamapediatrics.2017.0042Find this resource:

                                                                            Trucco, E., Villafuerte, S., Hussong, A., Burmeister, M., & Zucker, R. A. (2018). Biological underpinnings of an internalizing pathway to alcohol, cigarette, and marijuana use. Journal of Abnormal Psychology, 127(1), 79–91.Find this resource:

                                                                              Tsuang, M. T., Lyons, M. J., Eisen, S. A., Goldberg, J., True, W., Lin, N., … Eaves, L. J., (1996). Genetic influences on abuse of illicit drugs: A study of 3,297 twin pairs. American Journal of Medical Genetics, 67, 473–477.Find this resource:

                                                                                Volkow, N. D., Koob, G. F., Croyle, R. T., Bianchi, D. W., Gordon, J. A., Koroshetz, W. J., … Deesds, B. G. (2017). The conception of the ABCD study: From substance use to a broad NIH collaboration. Developmental Cognitive Neuroscience, 32, 4–7.Find this resource:

                                                                                  Volkow, N. D., & Morales, M. (2015). The brain on drugs: From reward to addiction. Cell, 162(4), 712–725.Find this resource:

                                                                                    Wagner, F. A., & Anthony, J. C. (2002). From first drug use to drug dependence: Developmental periods of risk for dependence upon marijuana, cocaine, and alcohol. Neuropsychopharmacology, 26, 479–488.Find this resource: