HUMAN STUDIES CONTRIBUTING TO CAUSALITY

pmc

0213264

1117

Biol Psychiatry

Biological psychiatry

0006-32231873-2402

34561027

8776913

10.1016/j.biopsych.2021.07.013

NIHMS1744360

Article

Does Traumatic Brain Injury Cause Risky Substance Use or Substance Use Disorder?

Olsen

Christopher M.

Department of Pharmacology and Toxicology, Neuroscience Research Center, Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WisconsinCorrigan

John D.

Department of Physical Medicine & Rehabilitation, Wexner Medical Center, The Ohio State University, Columbus, Ohio

Address correspondence to Christopher M. Olsen, Ph.D., at colsen@mcw.edu.

11102021

0132022

2172021

0132023

915421437

There is a high co-occurrence of risky substance use among adults with traumatic brain injury (TBI), although it is unknown if the neurologic sequelae of TBI can promote this behavior. We propose that to conclude that TBI can cause risky substance use, it must be determined that TBI precedes risky substance use, that confounders with the potential to increase the likelihood of both TBI and risky substance use must be ruled out, and that there must be a plausible mechanism of action. In this review, we address these factors by providing an overview of key clinical and preclinical studies and list plausible mechanisms by which TBI could increase risky substance use. Human and animal studies have identified an association between TBI and risky substance use, although the strength of this association varies. Factors that may limit detection of this relationship include differential variability due to substance, sex, age of injury, and confounders that may influence the likelihood of both TBI and risky substance use. We propose possible mechanisms by which TBI could increase substance use that include damage-associated neuroplasticity, chronic changes in neuroimmune signaling, and TBI-associated alterations in brain networks.

Given the high co-occurrence of risky substance use and/or substance use disorders (SUDs) among adults with traumatic brain injury (TBI) (1–3), investigators have asked whether TBI causes SUD and/or SUD causes TBI. Several reviews have concluded that at-risk substance use is more likely to cause TBI than TBI is to cause SUD (4–6), while others have concluded that there is insufficient evidence to make definitive conclusions about the directionality of causal influences (1,2). There is little doubt that engaging in risky substance use can cause TBI. Intoxication, whether by alcohol, cannabis, or other drugs, increases the likelihood of injury, which can include a TBI (7). At least 2 studies have found that among those treated for an injury in an emergency department, the greater the alcohol intoxication, the more likely the injury included a TBI (8,9).

The more nuanced question is whether TBI causes risky substance use. Some authors have cited the tendency for TBI cohorts to reduce use immediately after injury, especially if the injury is associated with use (10–12), as evidence that TBI does not cause risky substance use. However, several studies have shown that most people who used alcohol before TBI eventually return to pre-injury patterns of use unless a medical condition precludes use (11–13). The logic of this argument is further undermined by what Corrigan et al. have called the chicken or the egg problem (14). The difficulty in examining a causal relationship between TBI and SUD is highly affected by the injury used to anchor the question. Previous investigators who concluded that SUD causes TBI but TBI does not cause SUD anchored their analyses in studies that used samples of patients in adult trauma or acute rehabilitation units. A large percentage of participants in these samples had histories of at-risk substance use or a diagnosable SUD that preceded their injury. However, these studies did not determine whether the TBI that led to their inclusion in the cohort was their first TBI, which ignores whether one or more TBIs earlier in life may have influenced their development of risky use behaviors. Several studies support the conclusion that childhood TBI could lead to the development of adolescent or adult at-risk substance use (15–19); thus, the conclusion that causality is only in the direction of substance use causing TBI appears flawed.

In this review, we will explore this more nuanced direction of causality—can TBI cause the development of at-risk substance use and/or SUD? To allow a focused consideration of this question, we will not address whether a TBI can make preexisting substance use worse. To confidently conclude that TBI is the cause for the development of at-risk substance use and/or SUD would require establishment of several relationships among these conditions. First, most obviously, the TBI would need to precede the development of the problematic substance use. Second, the relationship between TBI and problematic substance use could not be due to a confounder that precedes both and causes each. Confounders that have been hypothesized include childhood exposures (e.g., parental attributes, parenting, adverse childhood experiences, socioeconomic status, community risk factors) and a behavioral phenotype for risk taking (e.g., a personality trait that could lead to both TBI and problematic substance use). Finally, and perhaps most challenging, to establish causality, there would need to be a mechanism of action that provides a plausible explanation for how TBI could do so. In the following sections, we will explore first human, then preclinical evidence for the conditions required to conclude causality (summarized in Tables 1 and 2, respectively). Evidence from these two sources of findings is uneven, and surprisingly, despite the greater control afforded via preclinical studies, unequivocal evidence to inform this question is not easily derived.

HUMAN STUDIES CONTRIBUTING TO CAUSALITY

Human studies that allow scrutiny of a causal relationship between TBI and problematic substance use have been limited to examining the temporal onset and presence of confounders. These studies have used three methods: 1) eliciting lifetime exposure in TBI cohorts; 2) population surveys examining the association of the two conditions; and 3) birth cohorts examining both onset and association. TBI cohorts in which lifetime exposure was studied have included the TBI model systems (20), Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) (21), and Army Study to Assess Risk and Resilience in Servicemembers (STARRS) 22). Lifetime TBI identification has been done with standardized methods of self-report, and substance use is typically self-reported past-month behavior. Population surveys have used a variety of methods for detecting both TBI and substance use, with the former varying from single-item elicitation [i.e., New Haven, Connecticut (23); Southeast Australia (24); Ontario, Canada (25,26)] to protocols based on standardized methods of retrospective self-report [i.e., Colorado (27), Ohio (28,29), North Carolina (30)]. Only a small number of birth cohort studies have allowed examination of the onset and development of TBI and at-risk substance use, including those conducted in Christchurch, New Zealand (25); Northern Finland (31–33); and Avon, United Kingdom (34). TBI identification is typically based on medical records; substance use has been both medically documented or self-reported recent behavior. Each of these methodologies has strengths and weaknesses for examining the temporal onset and presence of confounders, as shown in Box S1.

Temporal Onset

Several studies support a relationship between childhood TBI and later adolescent or adult at-risk substance use (20–22). Studies in TBI cohorts that capture lifetime history of TBI have found that earlier life injuries are more common among those with alcohol use problems, although temporal ordering is not possible. Studies in 2 U.S. states did not find that children injured earlier in life (before age 15 and before age 18 years) than persons injured after those ages were not more likely to engage in risky alcohol use as adults (28,30). In contrast, Corrigan et al. found that adults who had experienced a mild TBI with loss of consciousness before age 20 were more likely to engage in binge drinking than those who had a first mild TBI with loss of consciousness at an older age (29). This risk was largely due to a first TBI being incurred both at 10 to 14 and 15 to 19 years. These two groups were equally likely to engage in adult binge drinking. Indeed, had either 15 or 18 years been used as the cut point for age at first TBI, as was done in the two other state population studies, the difference would not have been significant. While any adolescent onset of TBI seems to increase the likelihood of problem alcohol use in adulthood, unfortunately, it is not definitive that TBI preceded the problematic use.

The Christchurch birth cohort study found that children hospitalized with a mild TBI before the age of 6 were more likely to develop alcohol problems in adolescence than children with no TBI or those with a mild TBI that did not require hospitalization (35). The birth cohort study in Avon found that TBIs occurring before age 17 were associated with problem alcohol consumption at age 17 (34). The Northern Finland birth cohort study did not find a difference in heavy drinking for those with or without childhood TBI; however, those children who incurred a TBI before age 12 initiated heavy drinking 6 years earlier than those with a first TBI at 12 to 15 years old (33). Note that the Christchurch results would appear to un-equivocally support that TBI preceded problematic substance use, but the Avon cohort is more ambiguous due to the possibility that alcohol consumption could begin in early adolescence.

Potential Confounders

Studies using the Northern Finland birth cohort reported multiple risk factors associated with incurring a TBI, including that if parents misused alcohol, there was a twofold greater chance of childhood TBI (31), and that a first TBI after age 12 that occurred while drinking alcohol resulted in a fourfold greater risk of repeat TBI by age 34 (32). Findings noted above for the Christchurch and Avon birth cohort studies were significant after controlling for multiple demographic, parental, and developmental characteristics (34,35). The Avon birth cohort studies also compared those with a TBI to an orthopedic injury control group intended to represent a behavioral phenotype for risk taking. While the association with problem alcohol use was significantly higher in the orthopedic injury group than the uninjured comparison group, the TBI group was still significantly greater than the orthopedic control group. Their findings were specific to alcohol because the TBI and orthopedic injury groups did not differ significantly in their likelihood of smoking either cannabis or nicotine.

These few studies may lend support for the effect of TBI not being due to influences such as parental attributes, parenting, or socioeconomic status. Influences from adverse childhood experiences or community characteristics have not been explored. The single study that investigated a risk-taking behavioral phenotype still found an additional effect of TBI on drinking at age 17 (34). This finding is perhaps strengthened by risk taking having equivalent risk for the likelihood of cannabis and tobacco use. Still, it is a single study.

In summary, from human studies, a significant association is frequently found between TBI and at-risk substance use and/or SUD. These studies have been largely limited to alcohol. The strength of association observed may be small, thus contributing to variation in findings. The ability to detect the relationship may be masked by variations in manifestation of the influence of TBI, for instance, age at injury or context of the injury (e.g., whether it occurs during a period of stress). The utility of the human literature for establishing a causal relationship between TBI and risky substance use is specifically limited by uncertainty about temporal onset during adolescence. Without specificity about the age at injury and the age at initiation and/or risky use of substances, temporality will be difficult to ascertain. Finally, the study of confounders is limited by the ability to operationalize constructs such as adverse childhood experiences or behavioral phenotypes in population-based cohorts. However, studies that have controlled for characteristics of parents, parenting, and the home environment seem to consistently suggest that these factors are unlikely sources of confound.

PRECLINICAL STUDIES CONTRIBUTING TO CAUSALITYTemporal Onset

An advantage of preclinical research is that temporal ordering of TBI and exposure to drugs of abuse is controlled. To date, most preclinical research has focused on modeling the question of whether TBI can increase risky drug use (see Box S2 for commonly used models of drug reward/reinforcement; see Table 2 for a summary of experimental findings).

Early studies on the effects of TBI on subsequent drug use focused on alcohol. In mice, experimental TBI led to reduced alcohol intake within the first 2 weeks following injury (36,37). In rats, blast TBI resulted in similar alcohol intake during a 7-week course of two-bottle choice but divergent responses on a final 1-hour session (38). Similar results were found in mice after repeated blast TBI: the proportion of daily alcohol consumed in the first 2 hours was higher in injured mice (39). Weil and colleagues demonstrated that adolescent mice had elevated alcohol drinking 1 week after TBI (40). A subsequent study found that females but not males had higher alcohol consumption and conditioned place preference (CPP) (41).

Adolescent, but not adult, TBI resulted in elevated cocaine CPP in male mice (42–44), but female mice showed differential effects based on estrus status. Mice in met- or diestrus at the time of injury had significantly elevated cocaine CPP, while those in proestrus or estrus had no change (45). Moderate to severe frontal TBI increased cocaine self-administration (46), while mild TBI found no difference in self-administration, extinction, or cue- or cocaine-primed induced reinstatement (47). Repeated blast resulted in similar levels of oxycodone self-administration, but subsequent drug seeking was elevated in the injured group (48). Thus, there are several examples of increased susceptibility for substance use in rodent models, but outcomes differ based on factors such as injury mechanism and severity, age at the time of injury, the drug studied, and time after injury.

Potential Confounders

Although preclinical studies can be designed to reduce the impact of confounding variables, these may not always be considered. Unknown individual differences that are present prior to experiment onset could independently contribute to both TBI recovery and addiction-related outcomes. Many studies use outbred rats [e.g., (46,48)] or mice [e.g., (40)], and this genetic variability may explain individual differences in drug reward/reinforcement following TBI (38,46). Similarly, individual differences in traits such as impulsivity exist in experimental animals and, similar to humans, can explain differences in addiction-related behaviors (49–51). Consideration of these differences and testing models of human confounders [e.g., models of early-life adversity (52,53)] will be important to better understand human variability in the relationship between TBI and risky substance use.

Plausible Mechanisms of Action

TBI induces myriad neurochemical changes, the nature of which is affected by factors including injury mechanism and severity, genetics, age, and sex. This section will focus on biological effects of TBI that are linked with neuroplasticity and describe plausible mechanisms by which these effects could increase addiction liability (summarized in Table 3). The reader is referred to (54–57) for more comprehensive reviews on the neurochemical sequelae of TBI. For simplicity, we will discuss these mechanisms in 2 broad timeframes: acute (within the first few days of injury) and chronic (after the first few days). The acute stage will be discussed in terms of the initial effects of injury that can set the stage for enduring neuroplasticity relevant to addiction liability, and the chronic stage will present evidence that persistent sequelae could influence physiological responses to drugs of abuse and addiction liability.

Acute Effects: Damage-Associated Molecular Pattern Mediated Increases in Ca<sup>2+</sup>-Permeable AMPA Receptors.

Primary mechanisms of TBI involve impact-associated forces, which stretch tissue and shear axons (54). At impact, mechanical deformation and/or mechanoporation leads to ion flux that ultimately leads to profound membrane depolarization of neurons (54,58,59). This depolarization promotes neuronal excitotoxicity and necrosis, which releases damage-associated molecular patterns (DAMPs) (59–61). DAMPs promote activation of astrocytes and microglia (collectively referred to as gliosis), resulting in release of cytokines and chemokines (54,61). DAMP-associated TLR4 (toll-like receptor 4) signaling triggers robust synaptic plasticity, increasing synaptic levels of Ca²⁺-permeable AMPA receptors (CP-AMPARs) and increasing Ca²⁺ conductance through NMDA receptors (62). Both experimental TBI and injury to cultured neurons were found to elevate synaptic CP-AMPARs (63,64). In rodents, drugs of abuse also increase synaptic CP-AMPARs in areas of the brain involved in drug seeking, including the ventral tegmental area (VTA), nucleus accumbens, and prefrontal cortex (PFC), and these effects often persist for weeks or months (65,66). Elevated CP-AMPAR expression is observed in the VTA after exposure to morphine, cocaine, ethanol, and cannabis (65). CP-AMPARs are also increased in the nucleus accumbens after cocaine withdrawal, and reversal of this phenomenon is sufficient to reduce drug seeking (67,68). Thus, the DAMP→TLR4→CP-AMPAR cascade is one plausible mechanism by which TBI could promote subsequent substance use.

Acute Effects: Cytokine Regulation of Neuronal Transmission.

Glial and peripheral cytokines can influence synaptic plasticity and have been linked to addiction liability. Interleukin 1β (IL-1β) modulates neuronal ion flux via several mechanisms (69,70), and chronic IL-6 downregulates metabotropic glutamate 2/3 receptors (mGluR2/3) (71). Reduction of mGluR2/3 function in mesocorticolimbic brain regions is also observed following exposure to nicotine, cocaine, or alcohol, and treatment with mGluR2/3 agonists or positive modulators reduces the reinforcing effects and seeking of drugs (72–76). Tumor necrosis factor α strongly increases the balance of excitatory/inhibitory transmission by increasing cell surface CP-AMPARs and internalizing GABA_A (gamma-aminobutyric acid A) receptors (77). Although cytokine responses occur acutely following injury, there is extensive evidence for prolonged elevations in excitatory neurotransmission following immune challenge, including susceptibility to seizures (78–80). Persistent increases in neuronal excitability and seizure susceptibility are also common following TBI (81,82). Thus, IL-6–associated reduction of mGluR2/3 and tumor necrosis factor α–associated elevation of CP-AMPARs are plausible mechanisms by which TBI-associated cytokine signaling could promote subsequent substance use.

Chronic Effects.

Postmortem and neuroimaging studies demonstrate gliosis that persists months or years after injury (55,57), and elevated levels of IL-1β, major histocompatibility complex class II, and IL-6 have been reported several months after experimental TBI (55,57). Drug use has been linked to chronic gliosis. Alcohol (83), cocaine (84,85), methamphetamine (86), and opioids (87,88) engage innate immune signaling, and alcohol and methamphetamine can also induce gliosis via other mechanisms, such as the generation of reactive oxygen species (89,90). Methamphetamine users had elevated binding of the microglial activation marker (R)-[¹¹C]-PK11195, which was inversely proportional to abstinence duration (91). Similarly, upregulation of immune-related genes is a consistent finding in postmortem tissue from chronic alcohol users (92), and gliosis is prominent in animal studies of alcohol and opioid self-administration (92–94).

Chronic Effects: Microglial Priming—Implications for Drug Exposure, Craving, and Relapse.

TBI can lead to microglial priming, where the cells are hyperresponsive to subsequent immune challenge, even at distal time points (95–97). It is plausible that TBI-associated priming could also affect immune responses to drugs of abuse. Morphine and other opioids prolong recovery from nerve injury (98,99), and this has been proposed to be due to injury-induced priming of spinal microglia (100). Neuroimmune activation has previously been proposed to create the biology of addiction (101). In the context of TBI, injury-associated priming may contribute to addiction liability by altering biological responses to substances and triggers of drug craving: re-exposure to the drug, stress, and drug-associated cues (102–105).

Prenatal immune activation increased drug-primed reinstatement to methamphetamine seeking and CPP for amphetamine (106,107), and adult immune activation increased alcohol drinking (108,109). These data suggest that immune activation primed the response to these substances of abuse in a manner consistent with greater risk of risky substance use. In a model of comorbid TBI and cocaine use, cocaine intake was positively correlated with neuroinflammatory markers in the frontal cortex (46). Drug exposure itself can also prime neuroimmune responses. A history of cocaine primes the cocaine-induced increase in IL-1β, nuclear factor-κB, and CD11b messenger RNA in the VTA, and blockade of IL-1 receptors in the VTA suppresses cocaine-primed reinstatement of drug seeking (110). Similarly, rats exposed to morphine in adolescence had an exaggerated immune response to morphine-elevated morphine CPP in adulthood (111). The glial modulator ibudilast given during adolescent morphine exposure blocked the later increase in morphine CPP, suggesting that gliosis is a critical mediator of the effect (111). Supporting the notion that a TBI-primed response to drug can influence subsequent drug seeking, the steroidal anti-inflammatory drug dexamethasone reduced the TBI-associated elevation in cocaine CPP without affecting CPP in noninjured animals (43). Similarly, the glial modulator minocycline reduced TBI-associated increase in voluntary alcohol consumption but had no effect in uninjured animals (40). Complementing these studies that demonstrate the necessity of neuroimmune signaling for drug seeking, intra-VTA injection of the TLR4 agonist lipopolysaccharide was found to be sufficient to reinstate cocaine seeking (110).

Stress is the most-reported trigger of drug craving for several drugs of abuse (112). Stress triggers a neuroimmune response and has been proposed to act as a potential trigger for microglial priming (113,114). In mice, early-life stress increased central immune responses to cocaine (115), suggesting that stress-associated microglial priming is relevant to the immunologic response to drugs of abuse. Preclinical studies support the notion that stress-induced immune responses are important in drug seeking and craving: ibudilast blocked stress-induced reinstatement of methamphetamine seeking (116).

Exposure to drug cues (e.g., places and things associated with substance use) is also a powerful trigger of drug craving (117), and there is evidence of conditioned immunologic effects of drug-associated cues. Exposure to cocaine-associated cues elevated plasma tumor necrosis factor α (118), and heroin seeking evoked by exposure to the drug environment was suppressed by the non-opioid TLR4 antagonist (+)-naltrexone (119).

Chronic Effects: Changes in Function of Brain Networks.

Preclinical studies have identified TBI-associated increased network excitability in the cortex that emerges over time, and increased excitatory and decreased inhibitory synaptic inputs have been identified as putative mechanisms of increased excitability of pyramidal neurons (120–122). A rodent model of comorbid TBI and oxycodone abuse identified interactive effects of TBI and drug exposure on increasing widespread connectivity (123), a phenomenon associated with worse TBI outcomes (124,125) and abstinence from prior heroin use (126). The PFC is a region that is highly vulnerable to injury in TBI (127), and the same study found that structural and functional outcomes in the PFC correlated with drug seeking (123).

DISCUSSION

The relationship between TBI and risky substance use is difficult to study, and there is evidence for each to increase the incidence of the other (Figure 1). We have summarized human and animal studies that reflect on the question, does TBI cause risky substance use or SUD? However, there is not sufficient evidence to definitively conclude that TBI can cause such use. In general, preclinical studies outnumber human investigations. Both human and animal studies have identified an association between TBI and risky substance use, although the strength of this association varies. This variability may imply a weak signal or may be due to methodological limitations.

A weak signal also may be due to specific characteristics that modify the presence or strength of relationship. For instance, the relationship between TBI and substance use may vary by the substance studied. Human and animal studies have been almost exclusively about alcohol, although recent studies include other substances. Particularly pertinent may be the sex of the organism, as well as the developmental stage when injury and/or substances are introduced. There are both human and animal studies that suggest that TBI in adolescence shows a greater association to substance use proclivity. It should be noted that prior substance use itself may alter the ability of TBI to increase subsequent risky drug use. A plethora of human studies indicate that pre-injury misuse of substances increases the likelihood of substance misuse after. Similarly, male (but not female) rats with a history of alcohol drinking were found to increase intake following injury (128–130).

Ruling out confounding effects has been more difficult. Human studies controlling for parental and household factors suggest that these factors may not be a source of confound. A human population study using uninjured sibling control subjects to specifically address risky substance use would be a useful addition to these studies. It has been more difficult to rule out personality traits that may be sources of confound. Risk taking, conduct disorders, and childhood traumatization have all been posited. While animal studies could be designed to test such factors, we are not aware of any that have examined these factors in relationship to the effects of TBI on drug reward or seeking.

Finally, there are several plausible mechanisms of action for TBI to cause a predisposition for risky substance use. Acute and chronic effects of TBI can result in increased CP-AMPARs and decreased mGluR2/3 expression, hallmarks of prior drug exposure. Emerging evidence suggests that microglial priming is a strong candidate for TBI to alter responses to substances in a way that promotes future use. Although research on the immunologic interactions between TBI and drug use is still in its infancy, the therapeutic potential of neuroimmune modulation for the treatment of risky substance use (independent of brain injury) is under investigation (131,132). Another plausible mechanism is by altering the function of brain networks, especially those involving the PFC. The PFC is particularly vulnerable to TBI, and clinical and preclinical studies identify it as a key node in drug craving and seeking (133–135).

The question of whether TBI can cause risky substance use is important for human health: knowledge of prior TBI may be used to guide personalized substance use treatment (136), and there is evidence that prior TBI may change the therapeutic approach for SUD treatment in individuals with comorbid TBI [e.g., dexamethasone reduced cocaine CPP only in TBI animals (43)]. To the extent that childhood TBI may predispose to adult risky substance use, secondary prevention to reduce the likelihood of that outcome could become a target for future research, not unlike adverse childhood experience (137). Future research would benefit from population studies specifically designed to address this question, as well as preclinical studies to test potential therapeutics for substance use in animals with and without brain injuries.

Supplementary Material

ACKNOWLEDGMENTS AND DISCLOSURES

This work was supported by the United States Department of Veteran Affairs (Grant No. RX002931 [to CMO]); National Institutes of Health (Grant No. DA042792 [to CMO]); and the National Institute on Disability, Independent Living, and Rehabilitation Research (Grant No. 90DP0040 [to JDC]).

National Institute on Disability, Independent Living, and Rehabilitation Research is a Center within the Administration for Community Living (ACL), Department of Health and Human Services. The contents of this publication do not necessarily represent the policy of the United States Department of Veteran Affairs; National Institute on Disability, Independent Living, and Rehabilitation Research; Administration for Community Living; National Institutes of Health; or Department of Health and Human Services, and you should not assume endorsement by the Federal Government.

We thank Cole Vonder Haar, Ph.D., and Antje Kroner-Milsch, M.D., Ph.D., for thoughtful comments on prior versions of this manuscript.

Figure 1 was created with BioRender.com.

The authors report no biomedical financial interests or potential conflicts of interest.

Supplementary material cited in this article is available online at https://doi.org/10.1016/j.biopsych.2021.07.013.

REFERENCES1.

Bjork

, Grant

(2009): Does traumatic brain injury increase risk for substance abuse? J Neurotrauma 26:1077–1082.19203230

Graham

, Cardon

(2008): An update on substance use and treatment following traumatic brain injury. Ann N Y Acad Sci 1141:148–162.18991956

Parry-Jones

, Vaughan

, Miles Cox

(2006): Traumatic brain injury and substance misuse: A systematic review of prevalence and outcomes research (1994–2004). Neuropsychol Rehabil 16:537–560.16952892

Rogers

, Read

(2007): Psychiatric comorbidity following traumatic brain injury. Brain Inj 21:1321–1333.18066935

Institute of Medicine (2009): Gulf War and Health: Volume 7: Long-Term Consequences of Traumatic Brain Injury. Washington, DC: The National Academies Press.

Ponsford

, Alway

, Gould

(2018): Epidemiology and natural history of psychiatric disorders after TBI. J Neuropsychiatry Clin Neurosci 30:262–270.29939106

Macdonald

, Anglin-Bodrug

, Mann

, Erickson

, Hathaway

, Chipman

, Rylett

(2003): Injury risk associated with cannabis and cocaine use. Drug Alcohol Depend 72:99–115.14636965

Chen

, Yi

, Yoon

, Dong

(2012): Alcohol use at time of injury and survival following traumatic brain injury: Results from the National Trauma Data Bank. J Stud Alcohol Drugs 73:531–541.22630791

Savola

, Niemelä

, Hillbom

(2005): Alcohol intake and the pattern of trauma in young adults and working aged people admitted after trauma. Alcohol 40:269–273.

10.

Kreutzer

, Wltol

, Sander

, Cifn

, Martvitz

, Delmonico

(1996): A prospective longitudinal multicenter analysis of alcohol use patterns among persons with traumatic brain injury. J Head Trauma Rehabil 11:58–69.

11.

Adams

, Ketchum

, Nakase-Richardson

, Katz

, Corrigan

(2021): Prevalence of drinking within low-risk guidelines during the first 2 years after inpatient rehabilitation for moderate or severe traumatic brain injury. Am J Phys Med Rehabil 100:815–819.33782273

12.

Corrigan

, Cuthbert

, Harrison-Felix

, Whiteneck

, Bell

, Miller

, (2014): US population estimates of health and social outcomes 5 years after rehabilitation for traumatic brain injury. J Head Trauma Rehabil 29:E1–E9.

13.

Awan

, DiSanto

, Juengst

, Kumar

, Bertisch

, Niemeier

, (2020): Interrelationships between post-TBI employment and substance abuse: A cross-lagged structural equation modeling analysis. Arch Phys Med Rehabil 101:797–806.31821796

14.

Corrigan

, Adams

, Dams-O’Conner

(2021): At-risk substance use and substance use disorders. In: Zasler

, Katz

, Zafonte

, editors. Brain Injury Medicine: Principles and Practice, 3rd ed. New York: Springer Publishing Company, LLC, 1241–1251.

15.

Cannella

, McGary

, Ramirez

(2019): Brain interrupted: Early life traumatic brain injury and addiction vulnerability. Exp Neurol 317:191–201.30862466

16.

Corrigan

, Bogner

, Holloman

(2012): Lifetime history of traumatic brain injury among persons with substance use disorders. Brain Inj 26:139–150.22360520

17.

McKinlay

, Grace

, Horwood

, Fergusson

, Ridder

, MacFarlane

(2008): Prevalence of traumatic brain injury among children, adolescents and young adults: Prospective evidence from a birth cohort. Brain Inj 22:175–181.18240046

18.

Weil

, Karelina

, Corrigan

(2019): Does pediatric traumatic brain injury cause adult alcohol misuse: Combining preclinical and epidemiological approaches. Exp Neurol 317:284–290.30910407

19.

Winqvist

, Jokelainen

, Luukinen

, Hillbom

(2006): Adolescents’ drinking habits predict later occurrence of traumatic brain injury: 35-year follow-up of the northern Finland 1966 birth cohort. J Adolesc Health 39:275.e1–275.e7.

20.

Corrigan

, Bogner

, Mellick

, Bushnik

, Dams-O’Connor

, Hammond

, (2013): Prior history of traumatic brain injury among persons in the Traumatic Brain Injury Model Systems National Database. Arch Phys Med Rehabil 94:1940–1950.23770276

21.

Dams-O’Connor

, Spielman

, Singh

, Gordon

, Lingsma

, Maas

, (2013): The impact of previous traumatic brain injury on health and functioning: A TRACK-TBI study. J Neurotrauma 30:2014–2020.23924069

22.

Adams

, Campbell-Sills

, Stein

, Sun

, Larson

, Kessler

, (2020): The association of lifetime and deployment-acquired traumatic brain injury with postdeployment binge and heavy drinking. J Head Trauma Rehabil 35:27–36.31365436

23.

Silver

, Kramer

, Greenwald

, Weissman

(2001): The association between head injuries and psychiatric disorders: Findings from the New Haven NIMH Epidemiologic Catchment Area Study. Brain Inj 15:935–945.11689092

24.

Anstey

, Butterworth

, Jorm

, Christensen

, Rodgers

, Windsor

(2004): A population survey found an association between self-reports of traumatic brain injury and increased psychiatric symptoms. J Clin Epidemiol 57:1202–1209.15567638

25.

Ilie

, Adlaf

, Mann

, Ialomiteanu

, Hamilton

, Rehm

, (2015): Associations between a history of traumatic brain injuries and current cigarette smoking, substance use, and elevated psychological distress in a population sample of Canadian adults. J Neurotrauma 32:1130–1134.25496189

26.

Ilie

, Wickens

, Ialomiteanu

, Adlaf

, Asbridge

, Hamilton

, (2019): Traumatic brain injury and hazardous/harmful drinking: Concurrent and single associations with poor mental health and roadway aggression. Psychiatry Res 272:458–466.30611965

27.

Whiteneck

, Cuthbert

, Corrigan

, Bogner

(2016): Prevalence of self-reported lifetime history of traumatic brain injury and associated disability: A statewide population-based survey. J Head Trauma Rehabil 31:E55–E62.25931187

28.

Bogner

, Corrigan

, Yi

, Singichetti

, Manchester

, Huang

, Yang

(2020): Lifetime history of traumatic brain injury and behavioral health problems in a population-based sample. J Head Trauma Rehabil 35:E43–E50.31033748

29.

Corrigan

, Hagemeyer

, Weil

, Sullivan

, Shi

, Bogner

, Yang

(2020): Is pediatric traumatic brain injury associated with adult alcohol misuse? J Neurotrauma 37:1637–1644.32111142

30.

Waltzman

, Daugherty

, Sarmiento

, Proescholdbell

(2021): Lifetime history of traumatic brain injury with loss of consciousness and the likelihood for lifetime depression and risk behaviors: 2017 BRFSS North Carolina. J Head Trauma Rehabil 36:E40–E49.32769836

31.

Winqvist

, Jokelainen

, Luukinen

, Hillbom

(2007): Parental alcohol misuse is a powerful predictor for the risk of traumatic brain injury in childhood. Brain Inj 21:1079–1085.17852100

32.

Winqvist

, Luukinen

, Jokelainen

, Lehtilahti

, Näyhä

, Hillbom

(2008): Recurrent traumatic brain injury is predicted by the index injury occurring under the influence of alcohol. Brain Inj 22:780–785.18787988

33.

Timonen

, Miettunen

, Hakko

, Zitting

, Veijola

, von Wendt

, Räsänen

(2002): The association of preceding traumatic brain injury with mental disorders, alcoholism and criminality: The Northern Finland 1966 Birth Cohort Study. Psychiatry Res 113:217–226.12559478

34.

Kennedy

, Heron

, Munafò

(2017): Substance use, criminal behaviour and psychiatric symptoms following childhood traumatic brain injury: Findings from the ALSPAC cohort. Eur Child Adolesc Psychiatry 26:1197–1206.28314984

35.

McKinlay

, Grace

, Horwood

, Fergusson

, MacFarlane

(2009): Adolescent psychiatric symptoms following preschool childhood mild traumatic brain injury: Evidence from a birth cohort. J Head Trauma Rehabil 24:221–227.19461369

36.

Poznanski

, Lesniak

, Korostynski

, Sacharczuk

(2020): Ethanol consumption following mild traumatic brain injury is related to blood-brain barrier permeability. Addict Biol 25:e12683.30334599

37.

Lowing

, Susick

, Caruso

, Provenzano

, Raghupathi

, Conti

(2014): Experimental traumatic brain injury alters ethanol consumption and sensitivity. J Neurotrauma 31:1700–1710.24934382

38.

Lim

, Meyer

, Shah

, Budde

, Stemper

, Olsen

(2015): Voluntary alcohol intake following blast exposure in a rat model of mild traumatic brain injury. PLoS One 10:e0125130.25910266

39.

Schindler

, Baskin

, Juarez

, Janet Lee

, Hendrickson

, Pagulayan

, (2021): Repetitive blast mild traumatic brain injury increases ethanol sensitivity in male mice and risky drinking behavior in male combat veterans. Alcohol Clin Exp Res 45:1051–1064.33760264

40.

Karelina

, Nicholson

, Weil

(2018): Minocycline blocks traumatic brain injury-induced alcohol consumption and nucleus accumbens inflammation in adolescent male mice. Brain Behav Immun 69:532–539.29395778

41.

Weil

, Karelina

, Gaier

, Corrigan

(2016): Juvenile traumatic brain injury increases alcohol consumption and reward in female mice. J Neurotrauma 33:895–903.26153729

42.

Merkel

, Razmpour

, Lutton

, Tallarida

, Heldt

, Cannella

, (2017): Adolescent traumatic brain injury induces chronic mesolimbic neuroinflammation with concurrent enhancement in the rewarding effects of cocaine in mice during adulthood. J Neurotrauma 34:165–181.27026056

43.

Merkel

, Andrews

, Lutton

, Razmpour

, Cannella

, Ramirez

(2017): Dexamethasone attenuates the enhanced rewarding effects of cocaine following experimental traumatic brain injury. Cell Transplant 26:1178–1192.28933216

44.

Cannella

, Andrews

, Tran

, Razmpour

, McGary

, Collie

, (2020): Experimental traumatic brain injury during adolescence enhances cocaine rewarding efficacy and dysregulates dopamine and neuroimmune systems in brain reward substrates. J Neurotrauma 37:27–42.31347447

45.

Cannella

, Andrews

, Razmpour

, McGary

, Corbett

, Kahn

, Ramirez

(2020): Reward and immune responses in adolescent females following experimental traumatic brain injury. Behav Brain Res 379:112333.31682867

46.

Vonder Haar

, Ferland

, Kaur

, Riparip

, Rosi

, Winstanley

(2019): Cocaine self-administration is increased after frontal traumatic brain injury and associated with neuroinflammation. Eur J Neurosci 50:2134–2145.30118561

47.

Muelbl

, Slaker

, Shah

, Nawarawong

, Gerndt

, Budde

, (2018): Effects of mild blast traumatic brain injury on cognitive- and addiction-related behaviors. Sci Rep 8:9941.29967344

48.

Nawarawong

, Slaker

, Muelbl

, Shah

, Chiariello

, Nelson

, (2019): Repeated blast model of mild traumatic brain injury alters oxycodone self-administration and drug seeking. Eur J Neurosci 50:2101–2112.30456793

49.

Ferland

, Winstanley

(2017): Risk-preferring rats make worse decisions and show increased incubation of craving after cocaine self-administration. Addict Biol 22:991–1001.27002211

50.

Belin

, Mar

, Dalley

, Robbins

, Everitt

(2008): High impulsivity predicts the switch to compulsive cocaine-taking. Science 320:1352–1355.18535246

51.

Cervantes

, Laughlin

, Jentsch

(2013): Cocaine self-administration behavior in inbred mouse lines segregating different capacities for inhibitory control. Psychopharmacology (Berl) 229:515–525.23681162

52.

Levis

, Bentzley

, Molet

, Bolton

, Perrone

, Baram

, Mahler

(2019): On the early life origins of vulnerability to opioid addiction [published online ahead of print Dec 10]. Mol Psychiatry.

53.

Ordoñes Sanchez

, Bavley

, Deutschmann

, Carpenter

, Peterson

, Karbalaei

, (2021): Early life adversity promotes resilience to opioid addiction-related phenotypes in male rats and sex-specific transcriptional changes. Proc Natl Acad Sci U S A 118: e2020173118.33593913

54.

Giza

, Hovda

(2014): The new neurometabolic cascade of concussion. Neurosurgery 75(suppl 4):S24–S33.25232881

55.

Kumar

, Loane

(2012): Neuroinflammation after traumatic brain injury: Opportunities for therapeutic intervention. Brain Behav Immun 26:1191–1201.22728326

56.

Pearn

, Niesman

, Egawa

, Sawada

, Almenar-Queralt

, Shah

, (2017): Pathophysiology associated with traumatic brain injury: Current treatments and potential novel therapeutics. Cell Mol Neurobiol 37:571–585.27383839

57.

Simon

, McGeachy

, Bayır

, Clark

, Loane

, Kochanek

(2017): The far-reaching scope of neuroinflammation after traumatic brain injury. Nat Rev Neurol 13:171–191.28186177

58.

Kilinc

, Gallo

, Barbee

(2008): Mechanically induced membrane poration causes axonal beading and localized cytoskeletal damage. Exp Neurol 212:422–430.18572167

59.

Farkas

, Lifshitz

, Povlishock

(2006): Mechanoporation induced by diffuse traumatic brain injury: An irreversible or reversible response to injury? J Neurosci 26:3130–3140.16554464

60.

Wolf

, Stys

, Lusardi

, Meaney

, Smith

(2001): Traumatic axonal injury induces calcium influx modulated by tetrodotoxin-sensitive sodium channels. J Neurosci 21:1923–1930.11245677

61.

Corps

, Roth

, McGavern

(2015): Inflammation and neuroprotection in traumatic brain injury. JAMA Neurol 72:355–362.25599342

62.

Korgaonkar

, Li

, Sekhar

, Subramanian

, Guevarra

, Swietek

, (2020): Toll-like receptor 4 signaling in neurons enhances calcium-permeable alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor currents and drives post-traumatic epileptogenesis. Ann Neurol 87:497–515.32031699

63.

Spaethling

, Klein

, Singh

, Meaney

(2008): Calcium-permeable AMPA receptors appear in cortical neurons after traumatic mechanical injury and contribute to neuronal fate. J Neurotrauma 25:1207–1216.18986222

64.

Bell

, Park

, Ai

, Baker

(2009): PICK1-mediated GluR2 endocytosis contributes to cellular injury after neuronal trauma. Cell Death Differ 16:1665–1680.19644508

65.

Lüscher

(2016): The emergence of a circuit model for addiction. Annu Rev Neurosci 39:257–276.27145911

66.

Wolf

(2016): Synaptic mechanisms underlying persistent cocaine craving. Nat Rev Neurosci 17:351–365.27150400

67.

Loweth

, Tseng

, Wolf

(2013): Using metabotropic glutamate receptors to modulate cocaine’s synaptic and behavioral effects: mGluR1 finds a niche. Curr Opin Neurobiol 23:500–506.23385114

68.

Pascoli

, Terrier

, Espallergues

, Valjent

, O’Connor

, Lüscher

(2014): Contrasting forms of cocaine-evoked plasticity control components of relapse. Nature 509:459–464.24848058

69.

Vezzani

, Viviani

(2015): Neuromodulatory properties of inflammatory cytokines and their impact on neuronal excitability. Neuropharmacology 96:70–82.25445483

70.

Zhu

, Okada

, Yoshida

, Mori

, Ueno

, Wakabayashi

, Kaneko

(2006): Effects of interleukin-1beta on hippocampal glutamate and GABA releases associated with Ca2+-induced Ca2+ releasing systems. Epilepsy Res 71:107–116.16806825

71.

Vereyken

, Bajova

, Chow

, de Graan

, Gruol

(2007): Chronic interleukin-6 alters the level of synaptic proteins in hippocampus in culture and in vivo. Eur J Neurosci 25:3605–3616.17610580

72.

Kasanetz

, Lafourcade

, Deroche-Gamonet

, Revest

, Berson

, Balado

, (2013): Prefrontal synaptic markers of cocaine addiction-like behavior in rats. Mol Psychiatry 18:729–737.22584869

73.

, Uejima

, Gray

, Bossert

, Shaham

(2007): Systemic and central amygdala injections of the mGluR(2/3) agonist LY379268 attenuate the expression of incubation of cocaine craving. Biol Psychiatry 61:591–598.16893525

74.

Hao

, Martin-Fardon

, Weiss

(2010): Behavioral and functional evidence of metabotropic glutamate receptor 2/3 and metabotropic glutamate receptor 5 dysregulation in cocaine-escalated rats: Factor in the transition to dependence. Biol Psychiatry 68:240–248.20416862

75.

Moussawi

, Kalivas

(2010): Group II metabotropic glutamate receptors (mGlu2/3) in drug addiction. Eur J Pharmacol 639:115–122.20371233

76.

Caprioli

, Justinova

, Venniro

, Shaham

(2018): Effect of novel allosteric modulators of metabotropic glutamate receptors on drug self-administration and relapse: A review of preclinical studies and their clinical implications. Biol Psychiatry 84:180–192.29102027

77.

Stellwagen

, Beattie

, Seo

, Malenka

(2005): Differential regulation of AMPA receptor and GABA receptor trafficking by tumor necrosis factor-alpha. J Neurosci 25:3219–3228.15788779

78.

Vezzani

, French

, Bartfai

, Baram

(2011): The role of inflammation in epilepsy. Nat Rev Neurol 7:31–40.21135885

79.

Vezzani

, Maroso

, Balosso

, Sanchez

, Bartfai

(2011): IL-1 receptor/toll-like receptor signaling in infection, inflammation, stress and neurodegeneration couples hyperexcitability and seizures. Brain Behav Immun 25:1281–1289.21473909

80.

Galic

, Riazi

, Pittman

(2012): Cytokines and brain excitability. Front Neuroendocrinol 33:116–125.22214786

81.

Frey

(2003): Epidemiology of posttraumatic epilepsy: A critical review. Epilepsia 44:11–17.

82.

Bruns

Jr, Hauser

(2003): The epidemiology of traumatic brain injury: A review. Epilepsia 44:2–10.

83.

Marshall

, McClain

, Kelso

, Hopkins

, Pauly

, Nixon

(2013): Microglial activation is not equivalent to neuroinflammation in alcohol-induced neurodegeneration: The importance of microglia phenotype. Neurobiol Dis 54:239–251.23313316

84.

Calipari

, Godino

, Peck

, Salery

, Mervosh

, Landry

, (2018): Granulocyte-colony stimulating factor controls neural and behavioral plasticity in response to cocaine. Nat Commun 9:9.29339724

85.

Kashima

, Grueter

(2017): Toll-like receptor 4 deficiency alters nucleus accumbens synaptic physiology and drug reward behavior. Proc Natl Acad Sci U S A 114:8865–8870.28760987

86.

Wang

, Northcutt

, Cochran

, Zhang

, Fabisiak

, Haas

, (2019): Methamphetamine activates toll-like receptor 4 to induce central immune signaling within the ventral tegmental area and contributes to extracellular dopamine increase in the nucleus accumbens shell. ACS Chem Neurosci 10:3622–3634.31282647

87.

Schwarz

, Smith

, Bilbo

(2013): FACS analysis of neuronalglial interactions in the nucleus accumbens following morphine administration. Psychopharmacology (Berl) 230:525–535.23793269

88.

Hutchinson

, Bland

, Johnson

, Rice

, Maier

, Watkins

(2007): Opioid-induced glial activation: Mechanisms of activation and implications for opioid analgesia, dependence, and reward. ScientificWorldJournal 7:98–111.

89.

Crews

, Nixon

(2009): Mechanisms of neurodegeneration and regeneration in alcoholism. Alcohol 44:115–127.

90.

Krasnova

, Cadet

(2009): Methamphetamine toxicity and messengers of death. Brain Res Rev 60:379–407.19328213

91.

Sekine

, Ouchi

, Sugihara

, Takei

, Yoshikawa

, Nakamura

, (2008): Methamphetamine causes microglial activation in the brains of human abusers. J Neurosci 28:5756–5761.18509037

92.

Erickson

, Grantham

, Warden

, Harris

(2019): Neuroimmune signaling in alcohol use disorder. Pharmacol Biochem Behav 177:34–60.30590091

93.

Crews

, Lawrimore

, Walter

, Coleman

Jr (2017): The role of neuroimmune signaling in alcoholism. Neuropharmacology 122:56–73.28159648

94.

Zhang

, Liang

, Levran

, Randesi

, Yuferov

, Zhao

, Kreek

(2017): Alterations of expression of inflammation/immune-related genes in the dorsal and ventral striatum of adult C57BL/6J mice following chronic oxycodone self-administration: A RNA sequencing study. Psychopharmacology (Berl) 234:2259–2275.28653080

95.

Witcher

, Eiferman

, Godbout

(2015): Priming the inflammatory pump of the CNS after traumatic brain injury. Trends Neurosci 38:609–620.26442695

96.

Block

, Zecca

, Hong

(2007): Microglia-mediated neurotoxicity: Uncovering the molecular mechanisms. Nat Rev Neurosci 8:57–69.17180163

97.

Loane

, Kumar

(2016): Microglia in the TBI brain: The good, the bad, and the dysregulated. Exp Neurol 275:316–327.26342753

98.

Green-Fulgham

, Ball

, Kwilasz

, Fabisiak

, Maier

, Watkins

, Grace

(2019): Oxycodone, fentanyl, and morphine amplify established neuropathic pain in male rats. Pain 160:2634–2640.31299018

99.

Ellis

, Grace

, Wieseler

, Favret

, Springer

, Skarda

, (2016): Morphine amplifies mechanical allodynia via TLR4 in a rat model of spinal cord injury. Brain Behav Immun 58:348–356.27519154

100.

Grace

, Strand

, Galer

, Urban

, Wang

, Baratta

, (2016): Morphine paradoxically prolongs neuropathic pain in rats by amplifying spinal NLRP3 inflammasome activation. Proc Natl Acad Sci U S A 113:E3441–E3450.27247388

101.

Crews

, Zou

, Qin

(2011): Induction of innate immune genes in brain create the neurobiology of addiction. Brain Behav Immun 25(suppl 1):S4–S12.21402143

102.

Mantsch

, Baker

, Funk

, Lê

, Shaham

(2016): Stress-induced reinstatement of drug seeking: 20 years of progress. Neuropsychopharmacology 41:335–356.25976297

103.

Farrell

, Schoch

, Mahler

(2018): Modeling cocaine relapse in rodents: Behavioral considerations and circuit mechanisms. Prog Neuropsychopharmacol Biol Psychiatry 87:33–47.29305936

104.

Peck

, Ranaldi

(2014): Drug abstinence: Exploring animal models and behavioral treatment strategies. Psychopharmacology (Berl) 231:2045–2058.24633446

105.

Shaham

, Shalev

, Lu

, de Wit

, Stewart

(2003): The reinstatement model of drug relapse: History, methodology and major findings. Psychopharmacology (Berl) 168:3–20.12402102

106.

Richtand

, Ahlbrand

, Horn

, Chambers

, Davis

, Benoit

(2012): Effects of prenatal immune activation and peri-adolescent stress on amphetamine-induced conditioned place preference in the rat. Psychopharmacology (Berl) 222:313–324.22290326

107.

Borçoi

, Patti

, Zanin

, Hollais

, Santos-Baldaia

, Ceccon

, (2015): Effects of prenatal immune activation on amphetamine-induced addictive behaviors: Contributions from animal models. Prog Neuropsychopharmacol Biol Psychiatry 63: 63–69.26051209

108.

Randall

, Vetreno

, Makhijani

, Crews

, Besheer

(2019): The toll-like receptor 3 agonist poly(I:C) induces rapid and lasting changes in gene expression related to glutamatergic function and increases ethanol self-administration in rats. Alcohol Clin Exp Res 43:48–60.30403408

109.

Warden

, Azzam

, DaCosta

, Mason

, Blednov

, Messing

, (2019): Toll-like receptor 3 activation increases voluntary alcohol intake in C57BL/6J male mice. Brain Behav Immun 77:55–65.30550931

110.

Brown

, Levis

, O’Neill

, Northcutt

, Fabisiak

, Watkins

, Bachtell

(2018): Innate immune signaling in the ventral tegmental area contributes to drug-primed reinstatement of cocaine seeking. Brain Behav Immun 67:130–138.28813640

111.

Schwarz

, Bilbo

(2013): Adolescent morphine exposure affects long-term microglial function and later-life relapse liability in a model of addiction. J Neurosci 33:961–971.23325235

112.

Sinha

(2001): How does stress increase risk of drug abuse and relapse? Psychopharmacology (Berl) 158:343–359.11797055

113.

Frank

, Weber

, Watkins

, Maier

(2015): Stress sounds the alarmin: The role of the danger-associated molecular pattern HMGB1 in stress-induced neuroinflammatory priming. Brain Behav Immun 48:1–7.25816800

114.

Niraula

, Sheridan

, Godbout

(2017): Microglia priming with aging and stress. Neuropsychopharmacology 42:318–333.27604565

115.

Lo Iacono

, Catale

, Martini

, Valzania

, Viscomi

, Chiurchiù

, (2018): From traumatic childhood to cocaine abuse: The critical function of the immune system. Biol Psychiatry 84:905–916.30029767

116.

Beardsley

, Shelton

, Hendrick

, Johnson

(2010): The glial cell modulator and phosphodiesterase inhibitor, AV411 (ibudilast), attenuates prime- and stress-induced methamphetamine relapse. Eur J Pharmacol 637:102–108.20399770

117.

Shulman

(1989): Experience with the cocaine trigger inventory. Adv Alcohol Subst Abuse 8:71–85.2750578

118.

Kubera

, Filip

, Budziszewska

, Basta-Kaim

, Wydra

, Leskiewicz

, (2008): Immunosuppression induced by a conditioned stimulus associated with cocaine self-administration. J Pharmacol Sci 107:361–369.18719314

119.

Theberge

, Li

, Kambhampati

, Pickens

, St Laurent

, Bossert

, (2013): Effect of chronic delivery of the toll-like receptor 4 antagonist (+)-naltrexone on incubation of heroin craving. Biol Psychiatry 73:729–737.23384483

120.

Hånell

, Greer

, Jacobs

(2015): Increased network excitability due to altered synaptic inputs to neocortical layer V intact and axotomized pyramidal neurons after mild traumatic brain injury. J Neurotrauma 32:1590–1598.25789412

121.

Carron

, Alwis

, Rajan

(2016): Traumatic brain injury and neuronal functionality changes in sensory cortex. Front Syst Neurosci 10:47.27313514

122.

Smith

, Xiong

, Elkind

, Putnam

, Cohen

(2015): Brain injury impairs working memory and prefrontal circuit function. Front Neurol 6:240.26617569

123.

Muelbl

, Glaeser

, Shah

, Chiariello

, Nawarawong

, Stemper

, (2021): Repeated blast mild traumatic brain injury and oxycodone self-administration produce interactive effects on neuroimaging outcomes. bioRxiv. 10.1101/2020.11.18.388421.

124.

Hayes

, Bigler

, Verfaellie

(2016): Traumatic brain injury as a disorder of brain connectivity. J Int Neuropsychol Soc 22:120–137.26888612

125.

Sharp

, Scott

, Leech

(2014): Network dysfunction after traumatic brain injury. Nat Rev Neurol 10:156–166.24514870

126.

Sun

, Wang

, Lin

, Lu

, Shu

, Meng

, (2017): Disrupted white matter structural connectivity in heroin abusers. Addict Biol 22:184–195.26177615

127.

Eierud

, Craddock

, Fletcher

, Aulakh

, King-Casas

, Kuehl

, LaConte

(2014): Neuroimaging after mild traumatic brain injury: Review and meta-analysis. Neuroimage Clin 4:283–294.25061565

128.

Stielper

, Fucich

, Middleton

, Hillard

, Edwards

, Molina

, Gilpin

(2021): Traumatic brain injury and alcohol drinking alter basolateral amygdala endocannabinoids in female rats. J Neurotrauma 38:422–434.32838651

129.

Fucich

, Mayeux

, McGinn

, Gilpin

, Edwards

, Molina

(2019): A novel role for the endocannabinoid system in ameliorating motivation for alcohol drinking and negative behavioral affect after traumatic brain injury in rats. J Neurotrauma 36:1847–1855.30638118

130.

Mayeux

, Teng

, Katz

, Gilpin

, Molina

(2015): Traumatic brain injury induces neuroinflammation and neuronal degeneration that is associated with escalated alcohol self-administration in rats. Behav Brain Res 279:22–30.25446758

131.

Bachtell

, Jones

, Heinzerling

, Beardsley

, Comer

(2017): Glial and neuroinflammatory targets for treating substance use disorders. Drug Alcohol Depend 180:156–170.28892721

132.

Bachtell

, Hutchinson

, Wang

, Rice

, Maier

, Watkins

(2015): Targeting the toll of drug abuse: The translational potential of toll-like receptor 4. CNS Neurol Disord Drug Targets 14:692–699.26022268

133.

Koob

, Volkow

(2016): Neurobiology of addiction: A neuro-circuitry analysis. Lancet Psychiatry 3:760–773.27475769

134.

Reiner

, Fredriksson

, Lofaro

, Bossert

, Shaham

(2019): Relapse to opioid seeking in rat models: Behavior, pharmacology and circuits. Neuropsychopharmacology 44:465–477.30293087

135.

Kalivas

, O’Brien

(2008): Drug addiction as a pathology of staged neuroplasticity. Neuropsychopharmacology 33:166–180.17805308

136.

Corrigan

(2021): Traumatic brain injury and treatment of behavioral health conditions [published online ahead of print Feb 24]. Psychiatr Serv.

137.

Purewal Boparai

, Au

, Koita

, Oh

, Briner

, Burke Harris

, Bucci

(2018): Ameliorating the biological impacts of childhood adversity: A review of intervention programs. Child Abuse Negl 81:82–105.29727766

138.

McKinlay

, Corrigan

, Horwood

, Fergusson

(2014): Substance abuse and criminal activities following traumatic brain injury in childhood, adolescence, and early adulthood. J Head Trauma Rehabil 29:498–506.24263173

139.

Hearing

(2019): Prefrontal-accumbens opioid plasticity: Implications for relapse and dependence. Pharmacol Res 139:158–165.30465850

140.

Marinelli

, Rudick

, Hu

, White

(2006): Excitability of dopamine neurons: Modulation and physiological consequences. CNS Neurol Disord Drug Targets 5:79–97.16613555

141.

Beattie

, Ferguson

, Bresnahan

(2010): AMPA-receptor trafficking and injury-induced cell death. Eur J Neurosci 32:290–297.20646045

142.

Kigerl

, de Rivero Vaccari

, Dietrich

, Popovich

, Keane

(2014): Pattern recognition receptors and central nervous system repair. Exp Neurol 258:5–16.25017883

143.

Burnstock

, Krügel

, Abbracchio

, Illes

(2011): Purinergic signalling: From normal behaviour to pathological brain function. Prog Neurobiol 95:229–274.21907261

144.

Mattson

, Camandola

(2001): NF-kappaB in neuronal plasticity and neurodegenerative disorders. J Clin Invest 107:247–254.11160145

145.

Maroso

, Balosso

, Ravizza

, Liu

, Aronica

, Iyer

, (2010): Toll-like receptor 4 and high-mobility group box-1 are involved in ictogenesis and can be targeted to reduce seizures. Nat Med 16:413–419.20348922

146.

Northcutt

, Hutchinson

, Wang

, Baratta

, Hiranita

, Cochran

, (2015): DAT isn’t all that: Cocaine reward and reinforcement require toll-like receptor 4 signaling. Mol Psychiatry 20:1525–1537.25644383

147.

Pando-Naude

, Toxto

, Fernandez-Lozano

, Parsons

, Alcauter

, Garza-Villarreal

(2021): Gray and white matter morphology in substance use disorders: A neuroimaging systematic review and meta-analysis. Transl Psychiatry 11:29.33431833

148.

Büki

, Povlishock

(2006): All roads lead to disconnection?–Traumatic axonal injury revisited. Acta Neurochir (Wien) 148:181–193; discussion 193–194.

16362181

149.

McGinn

, Povlishock

(2016): Pathophysiology of traumatic brain injury. Neurosurg Clin N Am 27:397–407.27637392

150.

Goldstein

, Volkow

(2011): Dysfunction of the prefrontal cortex in addiction: Neuroimaging findings and clinical implications. Nat Rev Neurosci 12:652–669.22011681

151.

Jasinska

, Chen

, Bonci

, Stein

(2015): Dorsal medial prefrontal cortex (MPFC) circuitry in rodent models of cocaine use: Implications for drug addiction therapies. Addict Biol 20:215–226.24620898

152.

Aoki

, Inokuchi

(2016): A voxel-based meta-analysis of diffusion tensor imaging in mild traumatic brain injury. Neurosci Biobehav Rev 66:119–126.27133211

153.

Zhang

, Volkow

(2019): Brain default-mode network dysfunction in addiction. Neuroimage 200:313–331.31229660

154.

Büttner

, Rohrmoser

, Mall

, Penning

, Weis

(2006): Widespread axonal damage in the brain of drug abusers as evidenced by accumulation of beta-amyloid precursor protein (beta-APP): An immunohistochemical investigation. Addiction 101:1339–1346.16911734

155.

Shlosberg

, Benifla

, Kaufer

, Friedman

(2010): Blood-brain barrier breakdown as a therapeutic target in traumatic brain injury. Nat Rev Neurol 6:393–403.20551947

156.

Chodobski

, Zink

, Szmydynger-Chodobska

(2011): Blood-brain barrier pathophysiology in traumatic brain injury. Transl Stroke Res 2:492–516.22299022

157.

Hammad

, Westacott

, Zaben

(2018): The role of the complement system in traumatic brain injury: A review. J Neuroinflammation 15:24.29357880

158.

Presumey

, Bialas

, Carroll

(2017): Complement system in neural synapse elimination in development and disease. Adv Immunol 135:53–79.28826529

159.

Lacagnina

, Rivera

, Bilbo

(2017): Glial and neuroimmune mechanisms as critical modulators of drug use and abuse. Neuropsychopharmacology 42:156–177.27402494

160.

Williamson

, Bilbo

(2013): Chemokines and the hippocampus: A new perspective on hippocampal plasticity and vulnerability. Brain Behav Immun 30:186–194.23376170

161.

Trotti

, Danbolt

, Volterra

(1998): Glutamate transporters are oxidant-vulnerable: A molecular link between oxidative and excitotoxic neurodegeneration? Trends Pharmacol Sci 19:328–334.9745361

162.

Beckhauser

, Francis-Oliveira

, De Pasquale

(2016): Reactive oxygen species: Physiological and physiopathological effects on synaptic plasticity. J Exp Neurosci 10:23–48.27625575

163.

Nennig

, Schank

(2017): The role of NFkB in drug addiction: Beyond inflammation. Alcohol 52:172–179.

164.

Chan

, Yang

, Lin

, Chang

, Lin

, Lo

(2015): Inflammatory response in heroin addicts undergoing methadone maintenance treatment. Psychiatry Res 226:230–234.25660662

165.

Araos

, Pedraz

, Serrano

, Lucena

, Barrios

, García-Marchena

, (2015): Plasma profile of pro-inflammatory cytokines and chemokines in cocaine users under outpatient treatment: Influence of cocaine symptom severity and psychiatric co-morbidity. Addict Biol 20:756–772.24854157

166.

Bland

, Beckley

, Watkins

, Maier

, Bilbo

(2010): Neonatal Escherichia coli infection alters glial, cytokine, and neuronal gene expression in response to acute amphetamine in adolescent rats. Neurosci Lett 474:52–57.20223277

167.

Linker

, Cross

, Leslie

(2019): Glial mechanisms underlying substance use disorders. Eur J Neurosci 50:2574–2589.30240518

168.

Miller

, Haroon

, Raison

, Felger

(2013): Cytokine targets in the brain: Impact on neurotransmitters and neurocircuits. Depress Anxiety 30:297–306.23468190

169.

Santello

, Volterra

(2012): TNFalpha in synaptic function: Switching gears. Trends Neurosci 35:638–647.22749718

170.

Merkel

, Cannella

, Razmpour

, Lutton

, Raghupathi

, Rawls

, Ramirez

(2017): Factors affecting increased risk for substance use disorders following traumatic brain injury: What we can learn from animal models. Neurosci Biobehav Rev 77:209–218.28359860

171.

Volkow

, Fowler

, Wang

, Swanson

, Telang

(2007): Dopamine in drug abuse and addiction: Results of imaging studies and treatment implications. Arch Neurol 64:1575–1579.17998440

172.

Liddelow

, Barres

(2017): Reactive astrocytes: Production, function, and therapeutic potential. Immunity 46:957–967.28636962

173.

Takahashi

, Jin

, Prince

(2018): Gabapentin prevents progressive increases in excitatory connectivity and epileptogenesis following neocortical trauma. Cereb Cortex 28:2725–2740.28981586

174.

Kutlu

, Brady

, Peck

, Hofford

, Yorgason

, Siciliano

, (2018): Granulocyte colony stimulating factor enhances reward learning through potentiation of mesolimbic dopamine system function. J Neurosci 38:8845–8859.30150359

175.

Ferrini

, De Koninck

(2013): Microglia control neuronal network excitability via BDNF signalling. Neural Plast 2013:429815.24089642

176.

Wise

, Robble

(2020): Dopamine and addiction. Annu Rev Psychol 71:79–106.31905114

177.

Willuhn

, Burgeno

, Groblewski

, Phillips

(2014): Excessive cocaine use results from decreased phasic dopamine signaling in the striatum. Nat Neurosci 17:704–709.24705184

178.

, Wanat

(2016): Phasic dopamine transmission reflects initiation vigor and exerted effort in an action- and region-specific manner. J Neurosci 36:2202–2211.26888930

179.

Donnemiller

, Brenneis

, Wissel

, Scherfler

, Poewe

, Riccabona

, Wenning

(2000): Impaired dopaminergic neurotransmission in patients with traumatic brain injury: A SPECT study using 123I-beta-CIT and 123I-IBZM. Eur J Nucl Med 27:1410–1414.11007526

180.

Wagner

, Scanlon

, Becker

, Ritter

, Niyonkuru

, Dixon

, (2014): The influence of genetic variants on striatal dopamine transporter and D2 receptor binding after TBI. J Cereb Blood Flow Metab 34:1328–1339.24849661

181.

Fox

, D’Sa

, Kimmerling

, Siedlarz

, Tuit

, Stowe

, Sinha

(2012): Immune system inflammation in cocaine dependent individuals: Implications for medications development. Hum Psychopharmacol 27:156–166.22389080

182.

Heneka

, McManus

, Latz

(2018): Inflammasome signalling in brain function and neurodegenerative disease. Nat Rev Neurosci 19:610–621.30206330

183.

Cadet

, Bisagno

, Milroy

(2014): Neuropathology of substance use disorders. Acta Neuropathol 127:91–107.24292887

184.

Egervari

, Akpoyibo

, Rahman

, Fullard

, Callens

, Landry

, (2020): Chromatin accessibility mapping of the striatum identifies tyrosine kinase FYN as a therapeutic target for heroin use disorder. Nat Commun 11:4634.32929078

185.

Kovacs

, Horvath

, Majtenyi

, Lutz

, Hurd

, Keller

(2015): Heroin abuse exaggerates age-related deposition of hyperphosphorylated tau and p62-positive inclusions. Neurobiol Aging 36:3100–3107.26254956

186.

Flanagan

, Larson

, Walker

, Keene

, Postupna

, Cholerton

, (2018): Associations between use of specific analgesics and concentrations of amyloid-beta 42 or phospho-tau in regions of human cerebral cortex. J Alzheimers Dis 61:653–662.29226863

Figure 1.

Theoretical framework of the relationship between TBI and substance use. CP-AMPARs, Ca²⁺-permeable AMPA receptors; mGluR2/3, metabotropic glutamate 2/3 receptors; TBI, traumatic brain injury.

Table 1.

Human Studies of the Association Between TBI and Risky Substance Use

Study	Sample and Population	Method	Risky Alcohol Use or Alcohol Use Disorder	Use of Other Substances
Corrigan et al. (20)	N = 4464 adults 1–20 years after acute rehabilitation for TBI and enrolled in TBI model systems	Prospective cohort (retrospective self-report of TBI; past-month [preindex injury] or past-year [postindex injury] use)	Those with history of TBI vs. those without more likely to engage in risky drinking before index injury (42.4% vs. 31.3%) and after index injury (22.8% vs. 13.5%)	Those with history of TBI vs. those without more likely to use illicit drugs before index injury (27.9% vs. 20.3%) and after index injury (17.8% vs. 8.1%)
Dams-O’Connor et al. (21)	N = 586 adults treated for TBI and enrolled in the TRACK-TBI study	Prospective cohort (retrospective self-report of TBI; past-month use)	Those with history of TBI vs. those without more likely to engage in risky drinking before index injury (56.6% vs. 37.1%)	Those with history of TBI vs. those without more likely to use illicit drugs before index injury (35.3% vs. 14.9%)
Adams et al. (22)	N = 4645 army soldiers following combat deployment who participated in Army STARRS study	Prospective cohort (retrospective self-report of TBI; past-month drinking)Adjusted for predeployment drinking, predeployment psychiatric diagnosis, combat/deployment stress severity, personal life stress during deployment, PTSD symptoms during deployment, and sociodemographic and military service characteristics	Three months after deployment, lifetime TBI, but not deployment-acquired TBI, associated with increased binge (AOR = 1.39, 95% CI: 1.20–1.60) and heavy drinking (AOR = 1.28, 95% CI: 1.09–1.49). Having both predeployment lifetime history and a deployment-acquired TBI increased heavy drinking 6 months later (AOR = 1.42, 95% CI: 1.03–1.95)	Not studied
Silver et al. (23)	N = 5034 adults from greater metropolitan New Haven, Connecticut	Population survey (retrospective self-report of lifetime TBI; use disorders from standardized interview)Adjusted for sociodemographics and health-related quality of life variables	Alcohol abuse or dependence (AOR = 2.2, 95% CI: 1.7–2.8)	Drug abuse or dependence (AOR = 1.8, 95% CI: 1.2–2.5)
Anstey et al. (24)	N = 7485 adults from 2 southeast Australian metropolitan areas	Population survey (retrospective self-report of lifetime TBI; past-month drinking)Adjusted for age group, gender, financial problems, physical health, and psychoticism	No difference for TBI vs. no TBI except young adult females (p = .045)	Not studied
Ilie et al. (25)	N = 1988 adults in the province of Ontario, Canada	Population survey (retrospective self-report of lifetime TBI; past-month substance use)Adjusted for age, sex, marital status, family income, and education	Not studied	Uses marijuana (AOR = 2.80, 95% CI: 1.79–4.39)Uses nonprescription opioids (AOR = 2.90, 95% CI: 1.50–5.59)
Ilie et al. (26)	N = 6074 adults in the province of Ontario, Canada	Population survey (retrospective self-report of lifetime TBI; past-month harmful or hazardous drinking)	Of persons with history of TBI, 21.4% reported harmful or hazardous drinking vs. 13.2% of no TBI respondents	Not studied
Whiteneck et al. (27)	N = 2701 noninstitutionalized adults residing in the state of Colorado	Population survey (retrospective self-report of lifetime TBI history; past-month drinking)Adjusted for place of medical care, age, sex, and race	At-risk alcohol use no more prevalent for persons with history of TBI than general population	Not studied
Bogner et al. (28)	N = 6996 noninstitutionalized adults residing in the state of Ohio	Population surveyBehavioral Risk Factors Surveillance System (retrospective self-report of lifetime TBI; past-month alcohol use)Adjusted for age, sex, and race/ethnicity	Binge drinking (AOR = 1.5, 95% CI: 1.1–2.0)Heavy drinking (AOR = 1.7, 95% CI: 1.1–2.6)Those with first TBI before age 15 years: no difference in binge or heavy drinking	Not studied
Corrigan et al. (29)	N = 2935 noninstitutionalized adults with history of TBI with loss of consciousness residing in the state of Ohio	Population surveyBehavioral Risk Factors Surveillance System (retrospective self-report of lifetime TBI; past-month alcohol use)Adjusted for age, sex, and race/ethnicity	Those with first TBI before age 20 more likely to binge drink in adulthood (28.5% vs. 20.4%, p = .003)Those with first mild TBI before age 20 more likely to binge drink in adulthood (31.9% vs. 19.3%, p < .001)	Not studied
Waltzman et al. (30)	N = 3605 noninstitutionalized adults with history of TBI with loss of consciousness residing in the state of North Carolina	Population surveyBehavioral Risk Factors Surveillance System (retrospective self-report of lifetime TBI; past-month alcohol use)Adjusted for sex, age, veteran status, marital status, educational attainment, employment status, and annual income	Those with history of TBI more likely to binge drink (AOR = 1.7, 95% CI: 1.2–2.4)Those with first TBI before age 18: no difference in binge drinking	Not studied
McKinlay et al. (138)	N = 1265 children born in 1977 in Christchurch, New Zealand	Birth cohort followed to age 25 (TBI from medical record abstraction of prospectively collected medical records; standardized structured interview to determine substance use disorder)Adjusted for sociodemographic factors, early behavioral problems, and parental substance abuse	Alcohol dependence n.s. for each group of age at first injury (0–5, 6–15, 16–21 years old)	Drug dependence for first injury 0–5 years old: AOR = 2.85, 95% CI: 1.11–7.32Drug dependence for first injury 6–15 years old: AOR n.s.Drug dependence for first injury 16–21 years old: AOR = 2.55, 95% CI: 1.07–6.12
Timonen et al. (33)	N = 10,934 children born in 1966 in Northern Finland	Birth cohort followed to age 31 (medically diagnosed TBI; heavy drinking defined by diagnosis of alcohol abuse or dependence [DSM-III criteria] or having at least 2 registered drunk driving offenses)Adjusted for mother’s marital status and father’s social class at time of birth and urban vs. rural residence at approximately age 14	Heavy alcohol use no different for childhood TBI vs. no childhood TBIAmong heavy drinkers, children with first TBI before age 12 began heavy drinking 6 years before those with first TBI at 12–15 years old	Not studied
Kennedy et al. (34)	N = 11,412 children born 1991–1992 in South West region of England (n = 800 with mild TBI; n = 2305 with orthopedic injury; n = 8307 uninjured children)	Birth cohort followed to age 17 (medically diagnosed injuries through age 16; self-reported recent use)Adjusted for prebirth sociodemographic factors, family environment and parenting style, and history of criminal activity	Alcohol use disorder vs. orthopedic injured: AOR = 1.69, 95% CI: 1.17–2.45Alcohol use disorder vs. general population: AOR = 1.31, 95% CI: 0.94–1.82	Cannabis misuse vs. orthopedic injured (n.s.)Nicotine dependence vs. orthopedic injured (n.s.)

AOR, adjusted odds ratio; CI, confidence interval; n.s., nonsignificant; PTSD, posttraumatic stress disorder; STARRS, Study to Assess Risk and Resilience in Servicemembers; TBI, traumatic brain injury; TRACK-TBI, Transforming Research and Clinical Knowledge in Traumatic Brain Injury.

Table 2.

Summary of Preclinical Studies of TBI and Drug Reward, Reinforcement, and Seeking

TBI Model	Single/Repeated Injury	Drug of Abuse	Reward/Reinforcement Model	Species/Strain	Sex	Age at Time of Injury	TBI→Testing Delay	Effect of TBI	Reference
TBI→Drug
CCI (closed head)	Single	Alcohol	Two-bottle choice (drinking in the dark)	Mouse/C57BL/6	M	6–7 wk	4–10 days	Reduced intake	(37)
Weight drop	Single	Alcohol	Two-bottle choice	Mouse/Swiss Webster (selectively bred for stress analgesia)	NS	8 wk	7–19 days	Reduced intake (only in LA mice)	(36)
Weight drop	Single	Alcohol	Two-bottle choice	Mouse/Swiss Webster (selectively bred for stress analgesia)	NS	8 wk	62–72 days	No effect	(36)
Blast	Single	Alcohol	Two-bottle choiceTwo-bottle choice (alcohol deprivation and quinine adulteration tests)	Rat/Sprague Dawley	M	8 wk	1–7 wk	No effect	(38)
							8 wk	Increased 1-hour intake in upper 50% after median split
							10–24 wk	No effect
CCI (closed head)	Single	Alcohol	Two-bottle choice	Mouse/Swiss Webster	M	21 days	10–22 days	Increased intake	(40)
CCI (closed head)	Single	Alcohol	Two-bottle choice	Mouse/Swiss Webster	M	21 days	8–9 wk	No effect	(41)
					F	21 days	8–9 wk	Increased intake
					M	60 days	3–4 wk	No effect
					F	60 days	3–4 wk	No effect
CCI (closed head)	Single	Alcohol	CPP	Mouse/Swiss Webster	M	21 days	8–9 wk	No effect	(41)
CCI (closed head)	Single	Alcohol	CPP	Mouse/Swiss Webster	F	21 days	8–9 wk	Increased CPP	(41)
Blast	Single Repeated (3×)	Alcohol	Two-bottle choice	Mouse/C57BL/6	M	12–16 wk	4–8 wk	No effectDecreased 24-hour intakeIncreased 2-hour proportional intake	(39)
CCI (open head)	Single	Cocaine	CPP	Mouse/C57BL/6	M	6 wk	2–3 wk	Increased CPP	(42,43)
CCI (open head)	Single	Cocaine	CPP	Mouse/C57BL/6	M	6 wk	2–3 wk	Increased CPP	(44)
CCI (open head)	Single	Cocaine	CPP	Mouse/C57BL/6	M	8 wk	2–3 wk	No effect	(44)
CCI (open head)	Single	Cocaine	CPP	Mouse/C57BL/6	F (E/P)^aF (M/D)^b	6 wk	2–3 wk	No effectIncreased CPP	(44)
CCI (open head)	Single	Cocaine	SA	Rat/Long-Evans	M	12 wk	2–4 wk	Increased SA	(46)
Blast	Single	Cocaine	SA	Rat/Sprague Dawley	M	8 wk	4–8 wk	No effect on SA	(47)
							10–11 wk	No effect on extinction
							12–14 wk	No effect on cue- or drug-primed reinstatement
Blast	Repeated (3×)	Oxycodone	SA	Rat/Sprague Dawley	M	8 wk	4–8 wk	No effect on FR-1 SA	(48)
							5–7 wk	Reduced FR-2 SA; no effect on FR-4 SA
							7–9 wk	Increased drug seeking in FR-4 extinction sessions
							10–23 wk	No effect on FR-1 extinction or cue reinstatement of drug seeking
Blast	Repeated (3×)	Oxycodone	SA	Rat/Sprague Dawley	M	8 wk	6 wk	Increased drug seeking	(123)
Drug→TBI→Drug
LFP	Single	Alcohol	Operant alcohol SA	Rat/Wistar	F	16 wk	2–10 days	No effect	(128)
LFP	Single	Alcohol	Operant alcohol SA	Rat/Wistar	M	16 wk	9 days	Increased breakpoint for alcohol	(129)
LFP	Single	Alcohol	Operant alcohol SA	Rat/Wistar	M	16 wk	2–15 days	Increased alcohol SA: preinjury intake positively correlated with postinjury increase in intake	(130)

CCI, controlled cortical impact; CPP, conditioned place preference; F, female; FR, fixed ratio; LA, low analgesia; LFP, lateral fluid percussion; M, male; NS, not specified; SA, self-administration; TBI, traumatic brain injury.

Phase at time of injury; E/P, estrus/proestrus.

Phase at time of injury; M/P, metestrus/diestrus.

Table 3.

Biological Effects of TBI That Are Linked With Neuroplasticity and Plausible Mechanisms by Which These Effects Could Increase Risky Substance Use

Time	Biological Processes	Downstream Effects/Processes	Link to Neuroplasticity	Plausible Link to Risky Substance Use	Clinical Evidence for Mechanistic Link	References
Immediate	Mechanical deformation/mechanoporation–induced ion flux: K⁺ efflux, Na⁺ and Ca²⁺ influx	Membrane depolarizationExcitotoxicity	Increased intrinsic excitability of neurons	PFC: increase or decrease in intrinsic excitability during cocaine or remifentanil abstinence (sex and treatment regimen dependent)NAc: decrease in intrinsic excitability during cocaine abstinenceVTA: increased intrinsic excitability of dopamine neurons during cocaine abstinence		(54,65,139,140)
Minutes/Hours	Necrosis	Release of DAMPs/engagement of TLR2, 3, 4, 7, 9; engagement of purinergic receptors, NLRs, RLRs, ALRsEnergy crisis	DAMP-associated TLR4 signaling: increased insertion of CP-AMPARs, increased NMDA-mediated Ca²⁺ flux; decrease in dopamine cell firing rateDAMP-associated TLR3, 7 signaling: growth cone collapse, inhibition of neurite outgrowth, neurodegenerationATP-associated P2X signaling: release of glutamate from astrocytesNLR signaling: see Downstream Effects/processes/inflammasome activationTLR4 signaling: increased NF-κB → modulation of LTD; antiapoptotic gene regulationTLR4 signaling: increased IL-6 →Short IL-6 exposure: increased intrinsic excitabilityLong IL-6 exposure: reduced depolarization-induced Na⁺ and Ca²⁺ currents, downregulation of presynaptic mGluR2/3	NAc: increase in CP-AMPARs during cocaine abstinence; reduction of NAc CP-AMPARs reduces drug seekingVTA: increase in CP-AMPARs after nicotine, cocaine, amphetamine, morphine, heroin, ethanol, and cannabisIntra-VTA LPS (TLR4 agonist) sufficient to induce reinstatement of cocaine seekingTLR4 agonist sufficient to increase alcohol consumptionTLR4 antagonist suppressed incubation of heroin cravingReduction of mGluR2/3 function in mesocorticolimbic brain regions is also observed following exposure to nicotine, cocaine, or alcoholTreatment with mGluR2/3 agonists or positive modulators can reduce the reinforcing effects and seeking of drugs	Lower volumes of gray matter in substance use disorder: largest effects in putamen, thalamus, insula, and anterior cingulate cortexPolymorphisms of human NF-κB1 associated with chronic alcohol useElevated NF-κB in brains of chronic alcohol users	(64–66,72–76,92,110,119,141–147)
Minutes/Hours	Axonal injury (shearing, calpain-associated proteolysis of cytoskeletal proteins)	Impaired synaptic transmission, necrosis/apoptosis	See Downstream Effects/processes/necrosis/apoptosis	Impaired prefrontal connectivity in TBI and risky substance use	Hypofrontality observed in substance use disorder with many drugs of abuseAxonal injury in polydrug abuse	(148–154)
Biphasic: Minutes/Hours, Then Days and Beyond	Increased BBB permeability	Extravasation of peripheral immune cells and immunogensActivation of the complement system	See Biological Processes/gliosisComplement signaling: synapse loss, neuroprotection against excitotoxicity, increased neurogenesis	Increased alcohol intake following BBB disruption	Reduced BBB integrity in postmortem tissue from risky alcohol use	(36,155–159)
Biphasic: Minutes/Hours, Then Days and Beyond	Generation of reactive oxygen species	Inflammasome activation → generation of IL-1β, IL-18; induction of pyroptosisInhibition of astrocytic transport of glutamateDecreased NMDA receptor–mediated Ca²⁺ fluxElevated NF-κB	IL-1β: reduction of Nav, L- and N-type Cav, and Kv channel activity; increased NMDA-mediated Ca²⁺ flux; increased or decreased GABA_A-mediated Cl⁻ current (cell-type specific); increased Ca²⁺-dependent glutamate release, decreased Ca²⁺-dependent GABA release; inhibition of LTPPyroptosis: see Biological Processes/apoptosisElevated extracellular glutamate→promotion of excitotoxicityNF-κB: modulation of LTD; antiapoptotic gene regulation; increase in expression of mu, delta opioid receptors and NK1Rs, increase in expression of β-endorphin and enkephalin propeptides, reduction of dynorphin expression	IL-1β antagonist in VTA reduced cocaine seeking in ratsTLR4 antagonist in VTA reduced cocaine-primed drug seeking in ratsIntra-NAc NF-κB inhibitor reduced morphine and cocaine CPP NF-κB inhibitor reduced naloxone-precipitated morphine withdrawal severity	Peripheral IL-1β positively correlated with duration of methadone maintenance in former heroin usersPeripheral IL-1β elevated in abstinent severe cocaine users with comorbid psychiatric diagnosisPolymorphisms of human NF-κB1 associated with chronic alcohol useElevated NF-κB in brains of chronic alcohol users	(69,101,110,142,160–165)
Days–Weeks	Gliosis	Potentiation of drug-associated dopamine increaseShift to IDO metabolismCytokine disruption of tetrahydrobiopterin (BH4)Cytokine increase in levels and activity of SERTCytokine reduction in DATCytokine-induced release of astrocytic glutamate and reduced astrocytic glutamate uptakeCytokine-induced increase in cell surface AMPA and NMDA receptorsCytokine-induced decrease in cell surface GABA_A receptorsCytokine-induced increase in levels and activity of acetylcholinesterase and decrease in acetylcholine releaseThrombospondin-mediated increase in axonal sproutingAstroglial scar formation	IDO-biased metabolism reduces serotonin production and produces neuroactive metabolites kynurenic acid (antagonist of AMPA, kainite, and NDMA glutamate receptors) and quinolinic acid (agonist of NMDA receptors)BH4 disruption reduces production of dopamine, norepinephrine, and epinephrineCytokine-associated changes in glutamate and GABA receptor function produce a net increase in synaptic strengthAstroglial scar formation associated with cognitive/motor deficitsTNF-α: Involved in synaptic scaling: increases mEPSC frequency and amplitude. Pathological concentrations of TNF-α inhibit LTPInduction of glutamate release from astrocytes and microglia, which acts on extrasynaptic GluN2B containing NMDA receptorsBDNF reduction of inhibitory transmission via decrease of Cl⁻ transport	Pharmacological shift of IDO metabolism from quinolinic acid to kynurenic acid pathway abolished the alcohol intake in 2 preclinical models and reduced cue-induced cocaine and alcohol seekingReduced tonic dopamine and norepinephrine reduces motivational arousalReduced phasic dopamine in striatum associated with increased cocaine self-administration, reversed by L-DopaG-CSF increases cocaine self-administration and increases evoked dopamine releaseTBI-primed immune response may prime immune response to drugs of abuseDexamethasone reduced TBI-associated increase in cocaine CPPMinocycline reduced TBI-associated increase in alcohol drinking	Lower kynurenic acid in patients with alcohol use disorderReduced DAT in brain injury and methamphetamine abuseUpregulation of immune-related genes is a consistent finding in postmortem tissue from chronic alcohol usersStress and cue-triggered drug craving elevated plasma TNF-α in cocaine-dependent individualsMicrogliosis elevated in orbital frontal cortex, striatum, and midbrain of methamphetamine users, inversely proportional to abstinence duration	(40,55,69,84,91,92,159,166–181)
Weeks	Demyelination	Impaired action potential propagation	See Biological Processes/axonal injury	See Biological Processes/axonal injury		(54)
Months	Network-level changes	Decreased communication between the default mode network and salience networkWidespread elevations in functional connectivity				(56)
Months	Neurodegenerative processes	Phospho-tau accumulationβ-amyloid accumulation	See Downstream Effects/processes/inflammasome activation	Elevated phospho-tau in rat striatum following heroin self-administration	Elevated phospho-tau in postmortem tissue from heroin abusersElevated phospho-tau in postmortem tissue from individuals with high levels of prescription opioid useElevated β-amyloid in polydrug users	(154,182–186)

ALR, AIM2-like receptor; ATP, adenosine triphosphate; BBB, blood-brain barrier; BDNF, brain-derived neurotrophic factor; CP-AMPAR, Ca²⁺-permeable AMPA receptor; CPP, conditioned place preference; DAMP, damage-associated molecular pattern; DAT, dopamine transporter; GABA, gamma-aminobutyric acid; G-CSF, granulocyte colony-stimulating factor; IL, interleukin; LPS, lipopolysaccharide; LTD, long-term depression; LTP, long-term potentiation; mEPSC, miniature excitatory postsynaptic current; mGluR, metabotropic glutamate receptor; NAc, nucleus accumbens; NF-κB, nuclear factor-κB; NK1R, neurokinin 1 receptor; NLR, NOD-like receptor; PFC, prefrontal cortex; RLR, rig-1 like receptor; SERT, serotonin transporter; TBI, traumatic brain injury; TLR, toll-like receptor; TNF-α, tumor necrosis factor α; VTA, ventral tegmental area.