The Opioid Epidemic and Opioid Use Disorder

What are safe consumption sites? How are they run?  What are the benefits according to the literature?  Do you believe their are potential problems associated with them?  

Please respond to one peer . https://youtu.be/NPlNCqBHPnE.               https://youtu.be/BLj9VMhDHeQ https://youtu.be/tNWU6XSjb-c.           https://youtu.be/mwizZOHqZ3M.    https://youtu.be/HMchXc5lemU

here’s 1 reply Bianca

safe consumption sites

COLLAPSE

Safe consumption sites are supervised spaces where people are given the opportunity to inject drugs cleanly, reducing the risk of spreading disease by sharing dirty needles. These sites can be considered as a harm reduction approach to addiction, because it decreases the added health risks that come with drug use in unsafe environments, like spreading diseases or overdosing. By having resources on site like trained staff who can immediately respond in the event of an overdose, and detox management for people who want to get better, safe consumption sites are helpful in fighting the opioid epidemic.

Despite the positives of safe consumption sites, there are also potential risks associated with them. For example, some may argue that they cause more overdoses and addiction, because they’re enabling people to freely use. While this can be true, these sites are not actually supplying people with the drugs, just giving them a place to use in a supervised manner to reduce other harmful effects of drug use, like contracting diseases or overdosing with no one around to provide assistance.

Here’s second Aliyah

Discussion Post 4

COLLAPSE

Safe consumption sites are locations where people addicted to drugs can use the drug safely among nurse staff within a clean environment. Individuals are not provided drugs at this facility, but they are provided with clean instruments such as needles to administer the drug themselves safely. The nurses who staff these consumption sites also help assist and are on standby in case one of these individuals were to overdose. Safe consumption sites are currently located in Canada and Europe, while they are projected to open soon in the U.S. in areas like New York and Denver (Bolger, 2021). The benefit of having these sites is that it helps limit the number of potential deaths that may occur through overdose or infection from unclean equipment. However, the potential problem associated with these sites is that in a way it enables drug users to keep using drugs given the fact that they are now supplied with the necessary means to administer drugs. Also having the safety net of having nurses available in case someone was to overdose probably gives individuals more incentive to want to use more drugs.

Works Cited

Bolger, Joy. (2021, Fall). The Opioid Epidemic and Opioid Use Disorder OUD. Retrieved October 24, 2022.

The Opioid Epidemic and Opioid Use Disorder OUD Joy Marie Bolger, M.S. LMHC Stony Brook University Department of Psychology Fall 2021 Psychology of Addiction PSY 349 What are Opioids? Opiates- are chemical compounds that are extracted or refined from natural plant matter (poppy sap and fibers). • Opium • Morphine • Codeine Opioids- are chemical compounds that not derived from natural plant matter. They are synthesized in the lab. Semisynthetic (chemically Modified Opiates) • Heroin • Hydrocodone • Oxycodone Synthetic (Man-made) • Fetanyl-approx. 100x stronger than morphine. • Isotonitazene, (ISO) 20x stronger than Fetanyl 1 pill laced with can be fatal. • Methadone • Demerol Drug Scheduling Categories Depend upon acceptable medical use and potential for abuse potential. Schedule I- No currently accepted medical use and a high potential for abuse. • Heroin • LSD • Cannabis Schedule II- High potential for abuse, dangerous and may lead to severe psychological or physical dependence. • Hydrocodone • Cocaine • Methamphetamine • Oxycodone (OxyContin), • Fentanyl, • Adderall Schedule III- Moderate to low potential for physical and psychological dependence. • (Tylenol with codeine), • Ketamine, Schedule IV- Low potential for abuse and low risk of dependence. • Xanax • Soma, • Valium • Ativan Schedule V- Lower potential for abuse than Schedule IV and contains limited quantities of certain narcotics. • Cough medication with less than 200 milligrams of codeine Opioid Use Disorder (OUD) In order to confirm a diagnosis of OUD, at least two of the following should be observed within a 12month period except for the LAST TWO: • Opioids are often taken in larger amounts or over a longer period than was intended. • There is a persistent desire or unsuccessful efforts to cut down or control opioid use. • A great deal of time is spent in activities necessary to obtain the opioid, use the opioid, or recover from its effects. • Craving, or a strong desire or urge to use opioids. • Recurrent opioid use resulting in a failure to fulfill major role obligations at work, school, or home. • Continued opioid use despite having persistent or recurrent social or interpersonal problems caused or exacerbated by the effects of opioids. • Important social, occupational, or recreational activities are given up or reduced because of opioid use. • Recurrent opioid use in situations in which it is physically hazardous. • Continued opioid use despite knowledge of having a persistent or recurrent physical or psychological problem that is likely to have been caused or exacerbated by the substance. • Exhibits tolerance. • Exhibits withdrawal. The last two diagnostic criteria, related to tolerance and withdrawal, are not considered to be met for individuals taking opioids solely under appropriate medical supervision. How did an epidemic evolve?: Opioid Prescriptions in US • • • • • In 2011 219 million prescriptions were dispensed. This suggests that almost every adult in the U.S. would have enough pills to treat themselves for one month. 2017 Declared a national PH emergency. 2.1 million people in the U.S. were dx with OUD. 6 fold increase in OD deaths since 1999 The Opioid Epidemic How did we get here? • Economic- Shifts from manufacturing to service economy in the 1980’s. Unemployment- major in parts of the country. Limited economic prospects. Helplessness- economic trauma. • Workers accessing disability and in order to get it must show they have significant pain. (prescriptions). • HMO’s: Less expensive to pay for prescription than give them complex pain management. • Industry produced mono-performing opioids without acetaminophen (liver toxicity) for end-of-life cancer patients. “War on Pain” 1970s- “Pain is a vital sign” • One article • Regulatory mandate to treat pain. Insurance won’t cover anything other than opioids. • Extended to all pain patients. • Oxycontin is on the scene in the 1990’s. • Pharmaceutical Companies targeted physicians in these regions. • “Get swingin with Oxycontin” Song sent with material. • PAYING OFF DOCTORS TO PRESCRIBE • Used 7 Cases of Patients that used were examples of benefits in a video. Follow up studies revealed in 2014- 2 were okay but still using, one addicted, one overdosed, two died from related complications. • 400 million dollar fine for unethical practices in marketing oxycontin. COVID-19 Pandemic • Rates of overdose and relapse for OUD significantly rise. • Non-fatal overdoses cause including brain hypoxia, decreased cognitive performance, clinical depression, and increased suicidal ideation. • Those who experience a non-fatal overdose are at greater risk of experiencing a subsequent overdose. Nonfatal Opioid Overdoses in Urban ED March 1 to June 30, 2019 & from March 1 to June 30, 2020 • The total number of nonfatal opioid overdose visits increased from 102 to 227 • In contrast the total number of common heart related diagnoses decreased from 41 to 31 • The total emergency department visits decreased from 36 565 to 26 061 in March through June 2020. Ochalek TA, Cumpston KL, Wills BK, Gal TS, Moeller FG. Nonfatal Opioid Overdoses at an Urban Emergency Department During the COVID-19 Pandemic. JAMA. 2020;324(16):1673–1674. doi:10.1001/jama.2020.17477 Not a New Problem • iatrogenic addiction to opioids: induced in a patient by the treatment • Between 1870 and 1880 American’s use of opiates almost tripled. (A prior epidemic!) Driver: Physicians disregarding the issues. 1889 James F.A. Adams published about toxicity and addiction • • “A greedy criminal druggist will sell you morphine or cocaine; we are not of that kind.” We have endogenous opioids, small painkilling peptides our bodies naturally produce: Endogenous Opioid System Endorphins Enkephalins Dynorphins These bind and activate opioid receptors- Widely distributed in the brain, spinal cord. (Also in the gastrointestinal system and other areas of the body). We will focus on the Mu receptor- Analgesia, Euphoria Analgesic and Reward Effects Opioids bind to receptors that are in high concentration in VTA and project to the NA Also, throughout the pain network- responsible for us processing pain. • Bind to these receptors, preventing signaling. Addiction is a result of opioids binding to receptors in Ventral tegmental area (VTA) and dopamine is released into the the nucleus accumbens. Supraphysiological response. Issue brief: Nation’s drug-related overdose and death epidemic continues to worsen “The nation’s drug overdose epidemic continues to change and become worse. One prevailing theme is the fact that the epidemic now is driven by illicit fentanyl, fentanyl analogs, methamphetamine, and cocaine, often in combination or in adulterated forms. There is an urgent need for policymakers’ action to increase access to evidence-based care for substance use disorders, pain and harm reduction measures..” AMA, February 15, 2022 BYSTANDER PRESENCE AND NALOXONE This is a form of:? Harm Reduction ”A focus on reducing the negative consequences of substance use for individuals, communities and societies…rather than focusing on decreasing or eliminating substance use” . • Needle distribution/recovery programs that distribute sterile needles. • MAT’s -Substitution therapies that substitute illegal heroin with legal, non-injection methadone or other prescribed opioids. • Naloxone programs that provide training to use an antidote to opioids to reverse an overdose thereby preventing brain injury, due to depressed breathing, and death. • Supervised consumption facilities that help prevent overdose deaths and other harms by providing a safer, supervised environment for people using substances. The ethical, legal and social context of harm reduction.Pauly B, Goldstone I, McCall J, Gold F, Payne S Can Nurse. 2007 Oct; 103(8):19-23. Supervised consumption sites (SCS) Supervised injection sites are also called safe injection facilities, overdose prevention centers and drug consumption rooms . This is where individuals with an addiction to IV drugs can inject the drug with the presence of nursing staff, and in a clean facility. • Recently a judge has ruled that the Philadelphia nonprofit group’s plan to open the first site in the U.S. does not violate federal drug laws-he Department of Justice sued and it never opened. • State legislators in California recently approved a bill to start a three-year pilot program in San Francisco. • In 2021- 2 sites in New York City- the first of their kind in America. • Some say we need more research they are helpful. • Exist in Canada and Europe. (about 100) • In Switzerland, France and other European countries, reports suggest they decrease rates of infectious disease, overdose and even consumption… and they supply the heroin. Safehouse –NP Group Supervised Consumption/Injection Sites Supplies Secure environment • Syringes • Free from criminal prosecution. • Sterile cookers • • Tourniquets Open booths keep clients in view of clinicians. • Legally sanctioned facilities are exempt from prosecution for having illicit drugs on the premises. • • Facilities that offer these provisions do not supply or inject clients with IV drugs. Pipes for crack use Support • Staff are equipped with crash kits to respond immediately to an overdose. • Detox management for people who request it. • Counselors and coordinators to bridge a gap to services Supervised Injection/ Consumption Sites Warning- Drugs are USED in this news report. Switzerland: First Heroin-Assisted Treatment Program 1994- Swiss legalized prescription heroin to combat fatal overdoses and HIV infection rates. • Controversial- Cause more deaths and addiction • Data suggests it was helpful • Decline in new users • 64% reduction in overdose deaths • Less use as less stress to find and use • 84% reduction in HIV • Related crime decreased “Staffthe at the heroin-assisted treatment facility in Geneva prepares injectable heroin before • Now HAT programs exist in Denmark, Germany, Luxembourg, the patients arrive.” Netherlands, the United Kingdom and Canada. Photo credit: Taylor Knopf Switzerland: Heroin-Assisted Treatment Program 1994- Swiss legalized prescription heroin to combat fatal overdoses and HIV infection rates. • HAT patients must have at least two years of opioid dependence before starting treatment. • Failed two other addiction treatments • 18 years old. “Staff at the heroin-assisted treatment facility in Geneva prepares injectable heroin before the patients arrive.” Photo credit: Taylor Knopf Medication-Assisted Treatments for OUD A type of Harm Reduction The use of FDA-approved medications, in combination with counseling and behavioral therapies, in the treatment of substance use disorders. Only about 20 percent of Americans who have an opioid use disorder are being treated with buprenorphine, naltrexone, or methadone, – this shows the extent of the barriers to treatment. Medication-assisted treatment (MAT) research suggests they are effective in treating OUDs. Substance Abuse and Mental Health Services Administration. “Medication Assisted Treatment for Opioid Addiction: Facts for Friends and Family.” HHS Publication No. (SMA) 09-4443. 2011 TREATING OPIOID DEPENDENCE WITH MEDICATIONS Agonist (replacement/substitution) Partial agonists Antagonist (blockade) Aversive (negative reinforcement) (Antabuse) 29 Medically/Medicine Assisted Treatment Methadone Agonist • • • Mu-opioid receptor full agonist. Opiate Schedule II controlled medication. • Reduces craving and withdrawal. • Blocks the effects of illicit opioids for 24 hours. • Steady release into the body to prevent withdrawal. • IV use will cross blood brain barrier quickly and will produce euphoria. • Liquid form is given at the clinic to avoid the sale of pills, which can be crushed and abused. Naltrexone Suboxone Antagonist Partial Agonist (Buprenorphine) • Used for AUD (alcohol use disorder) and OUD • Blocks the euphoric and sedative effects of opioids and alcohol • No abuse potential • If stopped and patient uses again, they are at risk for an overdose. Antagonist (Naloxone) • Delivers very diminished opioid doses to the patient who is addicted to a stronger opioid. • Provides a way for the patient to be gradually weaned heroin/opioids, minimizing withdrawal symptoms that would come from the process. VIVITROL® (naltrexone for extended-release injectable suspension). • Oral naltrexone has no proven effects for reducing opioid cravings • Injectable extended-release has been shown to reduce cravings. Why MAT’s? Medically Assisted Treatment (MAT) of opioid addiction is associated with: Decreases in the number of overdoses from heroin abuse. Increases retention of patients in treatment. Decreases drug use, infectious disease transmission. Decreases in criminal activity. (SAMHSA, 2014) MAT’s and the Criminal Justice System Only 30 out of 5,100 prisons and jails in the U.S. offered methadone or buprenorphine in 2017 Only 14 states offered methadone or buprenorphine maintenance for jail or prison inmates in 2018 Less than 50% of drug court participants with OUDs received MAT in a 2018 study. Methadone and buprenorphine are reliably proven to increase entry into and retention in treatment during incarceration and after release to the community. Medicaid Inmate Exclusion MAT’s in the Criminal Justice Settings The probability of being arrested increases from approximately 15% to 50% in people using opiates in the year prior to the arrest and approx. 75% for those using it in the form of heroin. Approximately 15% of state prison inmates and 20% of inmates report regularly using opiates. Approximately 35% of inmates report suffering from serious withdrawal symptoms. Prisoners and jail inmates released to the community are between 10 and 40 times more likely to die of an opioid overdose than the general population, within the first few weeks after reentering society.” A leading cause of death among formerly incarcerated individuals is drug overdose. There is strong correlational evidence indicates that providing methadone or buprenorphine both during custody and after release to the community is associated with substantially lower rates of opiate overdose and mortality. Substance Abuse and Mental Health Services Administration: Use of Medication-Assisted Treatment for Opioid Use Disorder in Criminal Justice Settings. HHS Publication No. PEP19-MATUSECJS Rockville, MD: National Mental Health and Substance Use Policy Laboratory. Substance Abuse and Mental Health Services Administration, 2019. Incarcerated Individuals and MAT Despite substantial evidence supporting MAT for the treatment of OUDs, few jails or prisons offer this treatment. Current Policies Do Not Support MAT Some jails and prisons have policies that prohibit the use of controlled substances (including the medications used in MAT). Detoxification and MATS are a condition of probation, yet many have difficulty obtaining them. Moore, K. E., Roberts, W., Reid, H. H., Smith, K. M. Z., Oberleitner, L. M. S., & McKee, S. A. (2019). Effectiveness of medication assisted treatment for opioid use in prison and jail settings: A meta-analysis and systematic review. Journal of Substance Abuse Treatment, 99, 32–43. Resistance to MAT’s • Lack of Understanding- Beliefthat MAT involves “substituting one drug for another” is something that exists in communities against MAT’s including the CJS. • Diverting the Medications/Abuse- Programs and facilities can implement procedures, to ensure medications are provided in a way that reduces risk for this. • Cost-The medication, training staff, storage, certifications etc. cost money. Also… remember- Stigma Start with Residential Treatment Patients with greater substance use severity Patients with limited social resources Patients with significant psychiatric and/or medical comorbidity Blonigen, Finney, Wilbourne, & Moos. (2015). Psychosocial treatments for substance use disorders. In Nathan and Gorman (Eds.), A Guide to Treatments That Work. London: Oxford University Press, 2015. 731-763. Out-Patient Treatment • Medically Assisted Treatments • Counseling- Motivational Interviewing/ ACT/CBT • Support Groups We will cover treatments for SUD’s in another module. bs_bs_banner HUMAN NEUR O IMAGING STUD doi:10.1111/adb.12182 Predicting subsequent relapse by drug-related cue-induced brain activation in heroin addiction: an event-related functional magnetic resonance imaging study Qiang Li1,2*, Wei Li1*, Hanyue Wang1*, Yarong Wang1, Yi Zhang3, Jia Zhu1, Ying Zheng1, Dongsheng Zhang1, Lina Wang1, Yongbin Li1, Xuejiao Yan1, Haifeng Chang1, Min Fan1, Zhe Li1, Jie Tian1,3, Mark S. Gold2, Wei Wang1 & Yijun Liu1,2,4 Department of Radiology, Tangdu Hospital, The Fourth Military Medical University, Xi’an, China1, School of Life Science and Technology, Xidian University, Xi’an, China3, Department of Psychiatry, McKnight Brain Institute, University of Florida College of Medicine, Gainesville, FL, USA2 and College of Engineering, Peking University, Beijing, China4 ABSTRACT Abnormal salience attribution is implicated in heroin addiction. Previously, combining functional magnetic resonance imaging (fMRI) and a drug cue-reactivity task, we demonstrated abnormal patterns of subjective response and brain reactivity in heroin-dependent individuals. However, whether the changes in cue-induced brain response were related to relapse was unknown. In a prospective study, we recruited 49 heroin-dependent patients under methadone maintenance treatment, a gold standard treatment (average daily dose 41.8 ± 16.0 mg), and 20 healthy subjects to perform the heroin cue-reactivity task during fMRI. The patients’ subjective craving was evaluated. They participated in a follow-up assessment for 3 months, during which heroin use was assessed and relapse was confirmed by self-reported relapse or urine toxicology. Differences between relapsers and non-relapsers were analyzed with respect to the results from heroin-cue responses. Compared with healthy subjects, relapsers and non-relapsers commonly demonstrated significantly increased brain responses during the processing of heroin cues in the mesolimbic system, prefrontal regions and visuospatial-attention regions. However, compared with non-relapsers, relapsers demonstrated significantly greater cue-induced craving and the brain response mainly in the bilateral nucleus accumbens/subcallosal cortex and cerebellum. Although the cue-induced heroin craving was low in absolute measures, the change in craving positively correlated with the activation of the nucleus accumbens/subcallosal cortex among the patients. These findings suggest that in treatment-seeking heroin-dependent individuals, greater cue-induced craving and greater specific regional activations might be related to reward/craving and memory retrieval processes. These responses may predict relapse and represent important targets for the development of new treatment for heroin addiction. Keywords Craving, fMRI, heroin addiction, relapse. Correspondence to: Wei Wang, Department of Radiology, Tangdu Hospital, The Fourth Military Medical University, 569 Xinsi Road, BaQiao District, Xi’an, Shaanxi 710038, China. E-mail: tdwangw@126.com INTRODUCTION Heroin addiction is a complex disorder of the brain, involving both affective and cognitive processes, characterized by a compulsive drive to take drugs regardless of serious negative consequences (Li & Sinha 2008). Despite the fact that most heroin-dependent individuals are willing to quit and that there are various heroin addiction treatments such as methadone maintenance treatment (MMT), which is deemed as an effective treatment for heroin addiction (Preston, Umbricht & Epstein 2000), relapse rates remain high. Studies showed that the *These authors contributed equally to this work. © 2014 Society for the Study of Addiction Addiction Biology, 20, 968–978 Cue responses predict relapse relapse rate is 70 percent once patients left MMT (Farrell et al. 1994). One major situation often inducing relapse is the confrontation with heroin-related cues that have been regularly associated with heroin consumption. The conditioned cues can evoke heroin craving or relapse even without the presence of heroin itself. Note that craving for heroin is often denied by detoxified and treated heroin-dependent individuals, although they still show high relapse rates. There is also a study demonstrating that responses to drug-related cues that occur before craving rather than subjective craving itself may have better predictive value in terms of relapse (Tiffany & Carter 1998). To date, there are few neuro-imaging studies assessing cue-induced brain responses that predict relapse in heroin addiction. Neuro-imaging techniques hold the potential to examine whether any specific pattern of brain responses to drug-related cues can predict treatment outcome and, more specifically, relapse to drug use (Kosten et al. 2006). Developing a better understanding of neurobiological mechanisms underlying heroin cue-reactivity and developing the means to identify relapse-vulnerable individuals would possibly reduce relapse rates and relative morbidity and mortality. Exposure to heroin-related versus neutral cues activates a wide range of brain regions, including mesolimbic system, prefrontal and visuospatial-attention regions such as nucleus accumbens (NAc), subcallosal cortex (SCC), amygdala, hippocampus, caudate, anterior cingulate cortex (ACC), medial prefrontal cortex, dorsolateral frontal cortex (DLPFC), orbitofrontal cortex (OFC), and temporal and parietal regions, as well as cerebellum (Daglish et al. 2001; Langleben et al. 2008; Zijlstra et al. 2009; Li et al. 2012, 2013). However, it is not clear whether these brain activations are closely related to subsequent relapse among treated heroindependent individuals. As drug addiction is a complex disorder of the brain, involving different networks such as the reward circuit (NAc, ventral tegmental area and ventral pallidum), conditioning/memory circuit (amygdala, medial OFC, hippocampus and dorsal striatum), executive control circuit (DLPFC, ACC and lateral OFC) and motivation/drive circuit (medial OFC, ventral ACC, ventral tegmental area, substantia nigra, dorsal striatum and motor cortex) (Volkow et al. 2011), it is unknown which circuit plays a more important role in relapse. The reward circuit (mainly including NAc) is viewed as an essential structure during the development of drug craving and likeliness to relapse (Filbey et al. 2009). Recently, there is a study reporting that heroin craving and relapse could be prevented with a memory retrievalextinction procedure (Xue et al. 2012). Therefore, the importance of the memory circuit is also highlighted. In the present study, we recruited 49 heroindependent patients and 20 healthy control subjects for © 2014 Society for the Study of Addiction 969 functional magnetic resonance imaging (fMRI) of the event-related cue-reactivity task, a behavioral paradigm reliably used to examine cue-induced brain response (Wang et al. 2011; Li et al. 2012, 2013). The aim of our study was to assess the relationship between subjective heroin craving and brain response in heroin-dependent individuals when exposed to heroin-related cues and relapse during a 3-month follow-up period. We hypothesized that brain reactivity during a heroin-related cuereactivity task can be used to predict relapse in heroindependent individuals. Specifically, we hypothesized that relapsing heroin-dependent patients relative to nonrelapsing individuals show greater craving for heroin and more intense brain activation in reward-related and memory-related brain regions. METHODS AND MATERIALS Subjects The present study was mainly among heroin-dependent patients under MMT in Baqiao MMT clinic, Xi’an, China, with a 3-month follow-up. Participants included 49 heroin-dependent male patients under MMT and 20 male healthy control individuals (Table 1). All of the subjects were smokers. Inclusion criteria for heroin-dependent patients were (1) DSM-IV criteria for heroin addiction for at least 1 year; (2) being under MMT for at least 6 months with a stable dose for at least 1 month; and (3) being right-handed. Exclusion criteria for all of the subjects were (1) use of cocaine or other illegal drug use except for heroin; (2) current or past psychiatric illness other than heroin and nicotine dependence; (3) neurological signs and/or history of neurological disease; (4) history of head trauma; (5) history of cardiovascular or endocrine disease; (6) current medical illness or recent medicine use; (7) presence of magnetically active objects in the body; and (8) claustrophobia or any other medical condition that would preclude the patient from lying in the MRI scanner for approximately 40 minutes. The Beck Depression Inventory II (BDI) (Beck et al. 1996) and Hamilton Anxiety Scale (HAMA) (Hamilton 1959) were used to evaluate the severity of depression and anxiety symptoms, respectively. All aspects of the research protocol were reviewed and approved by the ethics committee of Tangdu Hospital. All subjects provided written informed consent to participate in this study. Design and procedure We utilized a previously established event-related fMRI design in this study (Wang et al. 2011; Li et al. 2012, 2013). There were 48 trials in all, consisting of 24 heroin-related cues and 24 neutral cues. The heroinrelated cues included pictures of heroin injection, Addiction Biology, 20, 968–978 970 Qiang Li et al. Table 1 Demographic and clinical characteristics of participants. Characteristics Controls (n = 20) Relapsers (n = 23) Non-relapsers (n = 21) Group differences Age Years of education Cigarettes (per day) BDI scores HAMA scores Duration of heroin use (months) Average heroin dose (g/day) Total heroin dose (g) Duration of MMT (months) Average methadone dose (mg/day) Total methadone dose (mg) 35.2 ± 7.0 10.0 ± 2.3 13.7 ± 4.9 3.1 ± 4.4 2.9 ± 3.9 NA NA NA NA NA NA 31.3 ± 6.5 9.5 ± 2.3 18.3 ± 7.2 8.8 ± 9.3 7.4 ± 8.5 69.2 ± 68.5 0.5 ± 0.4 1130.5.2 ± 1693.2 18.3 ± 11.5 41.4 ± 14.0 23 269.5 ± 16 114.8 39.1 ± 7.6 9.2 ± 1.9 21.8 ± 9.3 10.0 ± 8.3 8.4 ± 8.7 92.3 ± 70.5 0.6 ± 0.6 1151.8 ± 1229.6 25.5 ± 17.3 41.0 ± 18.5 32 485.4 ± 32 130.3 F = 6.75 F = 0.84 F = 6.21 F = 4.76 F = 3.17 t = −1.19 t = −0.91 t = 0.05 t = −1.63 t = −0.09 t = −1.22 P = 0.002a P = 0.44 P = 0.004b P = 0.02b P = 0.05b P = 0.24 P = 0.37 P = 0.96 P = 0.11 P = 0.93 P = 0.23 Relapsers < non-relapsers, P < 0.05; controls versus relapsers, no significant difference; controls versus non-relapsers, no significant difference. Controls < relapsers, controls < non-relapsers, P < 0.05; relapsers versus non-relapsers, no significant difference. The total and average heroin dose was self-reported at baseline by heroin-dependent individuals. a b preparation and paraphernalia, and the neutral cues included pictures of household objects or chores. All of the cues were projected onto a mirror fixed on the scanner head coil and were presented in a pseudorandomized order with E-Prime 2.0 software (Psychology Software Tools, Inc., Pittsburgh, PA, USA). Picture cues were presented for 2 seconds with a variable 4- to 12-second inter-stimulus interval (mean = 8 seconds), during which a white cross hair with black background was displayed. The task began with a 10-second dummy scan followed by the first cue (heroin-related or neutral cue) and experimental scanning. The total task lasted for 490 seconds. Participants were placed in the scanner in a supine position using a foam head holder to lessen motion. Earplugs were used to reduce scanner noise. No use of caffeine, tea, alcohol and any other drug or medicine was allowed 12 hours prior to the time of the MRI scan. For heroin-dependent subjects, subjective heroin craving was evaluated by a 0–10 visual analog scale (Wang et al. 2011; Li et al. 2012, 2013) using the question, ‘To what extent do you feel the urge to use heroin?’ (0 for the least craving and 10 for the strongest craving). Craving ratings were acquired before and shortly after each fMRI scan. Heroin-dependent subjects were given a ‘talkdown’ to reduce heroin craving or subjective withdrawal symptoms after the fMRI scan, which may have been induced by heroin-related cues. Longitudinal clinical follow-up The procedures of longitudinal follow-up were similar to those described in Fatseas et al.’s (2011) study. All participants were given an appointment for a follow-up interview at 1, 2 and 3 months after the experimental session. Reminders were sent 3 days before each appointment. Heroin use was assessed at each follow-up appointment © 2014 Society for the Study of Addiction by the follow-up interview and urine screen. We used a sensitive method to capture any change in substance use patterns during the follow-up compared with the baseline evaluation. Substance use outcome was evaluated using both measures for heroin use and continuous measures for other substances (Fatseas et al. 2011). We thought that any heroin use and/or the increase of other substances (such as alcohol) used might be a symptom of relapse among MMT patients. Participants were considered relapsers if at any time during the 3-month follow-up period (1) they had used heroin defined by positive urine screen and/or self-reports of heroin use and (2) if they increased the number of days of self-reported use for other substances by at least 50 percent compared with the baseline evaluation (Fatseas et al. 2011). In addition, patients provided permission to contact people close to them who had knowledge of their heroin use, to get indirect information in the event that the patients were lost to follow-up. The assessors had no idea of cue-induced responses when the follow-up data were collected. MRI data acquisition All imaging data were acquired on a 3 T MRI scanner (GE Signa Excite HD, Milwaukee, WI, USA). The subjects underwent ‘mock scans’ for 1 minute prior to formal experimental scanning. This session served to familiarize subjects with the scanning environment. Following the mock scanning session, single-shot gradient-echo echo-planar imaging was used to acquire 240 T2*weighted image volumes. For each volume, 32 axial slices covering the whole brain were acquired with the following parameters: repetition time = 2000 milliseconds, echo time = 30 milliseconds, flip angle = 90°, matrix = 64 × 64, field of view = 256 × 256 mm2, slice thickness = 4 mm, gap = 0 mm, spatial resolution = 4 × 4 × 4 mm3. To facilitate co-registration of the fMRI data Addiction Biology, 20, 968–978 Cue responses predict relapse in standard space, a 166-slice high-resolution fast spoiled gradient-echo 3D T1-weighted image was also collected with the following parameters: repetition time = 7.8 milliseconds, echo time = 3.0 milliseconds, matrix = 256 × 256, field of view = 256 × 256 mm2, slice thickness = 1 mm, spatial resolution = 1 × 1 × 1 mm3. The structural data were carefully checked by an experienced radiologist to assure that there were no structural abnormalities. Data analysis The fMRI data analysis was conducted with SPM8 software (http://www.fil.ion.ucl.ac.uk/spm). Images were slice-time corrected, motion corrected, registered to the fast spoiled gradient-echo 3D T1-weighted images and then normalized to a standard SPM T1 template. The images were interpolated to 3-mm isotropic voxels and spatially smoothed (Gaussian kernel of 6-mm full width at half maximum). Subjects with excessive head motion (more than 1.5 mm in translation or 1.5° in rotation) were excluded from the analysis. The fMRI data were filtered using a high-pass filter and cut-off at 128 seconds. A statistical model for each subject was computed by applying a canonical response function. Regionally specific condition effects were tested by employing linear contrasts for each subject and different conditions. The critical contrast of interest was the heroin-related versus neutral cues contrast which would reveal brain activities related to processing of heroin-related cues (Franklin et al. 2007). Because our main focus was on the difference between relapsers and non-relapsers, only using the healthy subjects as negative controls, we directly compared the different groups of subject (heroin-dependent individuals versus healthy controls, relapsers versus healthy controls, non-relapsers versus healthy controls and relapsers versus non-relapsers) using voxel-wise random effects two-sample t-tests to identify regions in which brain response to heroin-related > neutral cues differed between two groups. The age, index of smoking behavior, BDI scores and HAMA scores were taken as covariates into the test. In addition, the daily methadone dose and MMT duration were included as covariates in the analysis between the relapsers and non-relapsers. The significance threshold was set at P < 0.05, corrected for multiple comparison using AFNI Alphasim via Monte Carlo simulation correction program (Cox 1996). For all of the heroin-dependent participants, the region of interest (ROI)-based correlation analyses were conducted to assess the relationship between craving change and brain activation intensity between viewing heroin-related and neutral cues. We chose the peak coordinate voxels of each differential cluster observed between the relapser and non-relapser groups as centers of the sphere-shaped ROIs (radius = 3 mm). The raw data © 2014 Society for the Study of Addiction 971 within the ROIs of the heroin-dependent individuals were extracted and Pearson correlation analysis was conducted. To explore whether the intensity of heroinrelated cue-induced brain response would be related to the period between cue exposure and relapse, we performed further Pearson correlation analysis to examine the predictors in relation to the time of relapse. The daily methadone dose, MMT duration, age, index of smoking behavior, BDI scores and HAMA scores were taken as covariates into the correlation analyses. The significance threshold was set at P < 0.05 RESULTS Sample characteristics Of the 69 participants who completed the MRI scan, four patients did not complete follow-up and were not included in the relapse analyses. Data from one patient were discarded due to a scanning artifact, leaving 44 patients and 20 usable healthy control subjects. According to our defined model of relapse, 23 (52.3 percent) patients were considered as relapsers. Nine patients reported using heroin and/or had a positive screening for opiates at 1-month follow-up. Eight patients at the 2-month follow-up and six patients at the 3-month follow-up reported using heroin and/or had a positive screening for opiates. During the 3-month follow-up, the relapsers reported times of relapse averaged 2.4 ± 1.9 and dose of heroin used averaged 0.6 ± 0.6 g. There were no patients who had significantly increased other substance (alcohol) use during the follow-up phase. There were no differences between relapsers and non-relapsers with respect to demographical data, drug use and psychiatric symptoms, except for age (Table 1). Craving For the subsequent relapser group, the subjective craving scores before and after cue exposure and change in craving were 1.6 ± 1.8, 1.8 ± 2.1 and 0.2 ± 1.8, respectively. For the non-relapser group, the subjective craving scores before and after cue exposure and change in craving were 1.0 ± 1.2, 0.5 ± 0.9 and −0.5 ± 1.3, respectively. Meanwhile, the subsequent relapser group demonstrated significantly higher craving scores after cue exposure relative to the non-relapser group (t = 2.78, P = 0.01). No significant difference in the craving score before cue exposure (t = 1.22, P = 0.23) and craving change (t = 1.52, P = 0.14) was found between the two groups. No significant change in the craving score before and after cue exposure was found for the relapse and non-relapser groups, respectively (t = −0.47, P = 0.64; t = −1.86, P = 0.08) (Fig. 1). Addiction Biology, 20, 968–978 972 Qiang Li et al. fMRI results Heroin-dependent individuals versus healthy controls: heroin-related > neutral cues Compared with the healthy control group, the heroindependent group demonstrated significantly increased brain responses during the processing of heroin-related cues in the bilateral NAc/SCC, cerebellum, caudate, putamen, pallidum, DLPFC, OFC, parahippocampal gyrus, inferior parietal lobule, precuneus, inferior occipital gyrus, and inferior temporal gyrus, pons, and left ACC, MPFC, midbrain, superior parietal lobule, superior temporal gyrus, and right middle cingulate gyrus and fusiform (Table 2, Fig. 2 and Supporting Information Fig. S1). No significantly greater brain response for the healthy control group relative to the heroin group was found. Figure 1 Changes in subjective craving according to heroin-related cue exposure in heroin-dependent groups. *Significant difference (P < 0.05). Relapsers showed higher post-cue exposure craving for heroin than non-relapsers (P = 0.01) Table 2 Activated brain regions for the heroin-dependent group compared with control group in response to heroin-related > neutral cues. Peak location Brodmann’s area Brain regions NAC/SCC Cerebellum Caudate Putamen Midbrain Pallidum ACC Middle cingulate gyrus DLPFC MPFC OFC Parahippocampal gyrus Superior parietal lobule Inferior parietal lobule Precuneus Fusiform Pons Inferior occipital gyrus Superior occipital gyrus Inferior temporal gyrus Superior temporal gyrus R/L R/L R L R L R L L R L R L R L L R L R L L R L R R R/L L R L L R L 25 – – – – – – – – 24,32 23 48 44,48 32 11 45 28 28 7 40 40 30 37 – 18 19 18 37 37 38 x y z Peak t-score Voxel number −3 0 12 −12 18 −18 30 −9 −6 12 −9 6 −42 45 −9 −15 51 −11 14 −24 −42 42 −6 9 33 0 −18 33 −15 −57 60 −34 15 −60 −87 9 19 10 −6 −27 0 −3 45 −9 9 9 30 15 24 −1 −1 −72 −51 −48 −54 −51 −33 −33 −93 −81 −96 −63 −63 6 −12 −36 −27 5 9 −2 6 −12 −12 −12 0 33 24 21 39 −21 0 −20 −18 48 54 48 12 12 −24 −27 −12 −15 21 −9 −9 −19 4.33 4.92 4.23 4.21 4.95 3.87 4.17 4.95 4.68 5.30 4.93 3.86 4.36 5.42 4.37 5.12 3.99 3.95 4.56 4.42 4.75 4.52 4.40 3.79 4.95 4.37 4.23 4.12 5.50 4.32 4.59 4.18 93 41 19 42 76 37 29 10 29 15 57 10 29 75 44 39 33 20 19 13 39 47 35 10 27 18 13 15 16 45 31 10 ACC = anterior cingulate cortex; DLPFC = dorsolateral prefrontal cortex; L = left; MPFC = medial prefrontal cortex; NAc/SCC = nucleus accumbens/ subcallosal cortex; OFC = orbitofrontal cortex; R = right. © 2014 Society for the Study of Addiction Addiction Biology, 20, 968–978 Cue responses predict relapse 973 Figure 2 The differences relating to the ‘heroin-related > neutral cues’ contrast between heroin-dependent patients and healthy controls, between relapsers and healthy controls, between non-relapsers and healthy controls, and between relapsers and non-relapsers (P < 0.05, corrected for Monte Carlo simulations correction). L = left; NAc/SCC = nucleus accumbens/subcallosal cortex; R = right Relapsers versus healthy controls: heroin-related > neutral cues Compared with the healthy control group, the subsequent relapser group demonstrated significantly increased brain responses during the processing of heroin-related cues in the bilateral NAc/SCC, caudate, DLPFC, cerebellum, left ACC, MPFC, midbrain, superior parietal lobule, inferior parietal lobule, precuneus, inferior and superior temporal gyrus, and right pallidum and pons (Fig. 2 and Supporting Information Table S1 and © 2014 Society for the Study of Addiction Fig. S2). No significantly greater brain response for the healthy control group relative to the subsequent relapser group was found. Non-relapsers versus healthy controls: heroin-related > neutral cues Compared with the healthy control group, the nonrelapser group demonstrated significantly increased brain responses during the processing of heroin-related cues in the bilateral caudate, putamen, pallidum, DLPFC, Addiction Biology, 20, 968–978 974 Qiang Li et al. parahippocampal gyrus, cerebellum, inferior parietal lobule, and left ACC, hippocampus, midbrain, precuneus, superior parietal lobule and right middle cingulate gyrus, precentral gyrus (Fig. 2 and Supporting Information Table S2 and Fig. S3). No significantly greater brain response for the healthy control group relative to the nonrelapser group was found. Relapsers versus non-relapsers: heroin-related > neutral cues Compared with the non-relapser group, the subsequent relapser group demonstrated significantly increased brain responses during the processing of heroin-related cues in the bilateral NAc/SCC and cerebellum. No significantly greater brain response for the non-relapser group relative to the subsequent relapser group was found (Table 3 and Fig. 2). Correlation results For heroin-dependent patients, a significant positive correlation between changes in craving and brain activity to heroin-related cues was found for the NAC/SCC (r = 0.30, P = 0.04) (Fig. 3). No significant correlations were found Table 3 Activated brain regions for the relapser compared with non-relapser group in response to heroin-related > neutral cues. Brain regions Peak location Brodmann’s area x y z NAc/SCC R/L 25 Cerebellum R/L – Peak Voxel t-score number −3 15 −8 4.38 3 −60 −48 4.29 14 14 L = left; NAc/SCC = nucleus accumbens/subcallosal cortex; R = right. Figure 3 The correlation map between craving change and signal amplitude of nucleus accumbens/subcallosal cortex (NAc/SCC) relating to the ‘heroin-related > neutral cues’ contrast among the heroin-dependent patients (r = correlation coefficient; P = P-value) © 2014 Society for the Study of Addiction between the drug cue-induced brain activity and the time period between drug cue exposure and relapse. DISCUSSION To the best of our knowledge, this is the first neuroimaging study to assess brain responses that may predict relapse in heroin addiction. The present findings demonstrated that increased brain response in the NAc/SCC and cerebellum during processing of heroin-related cues is associated with the prospective relapse in treated heroindependent individuals. Our findings confirmed our hypothesis and suggested that greater heroin craving and brain activation in reward/craving-related and memoryrelated brain regions is associated with relapse. More generally, it also contributed to a growing body of literature in which drug cue-based baseline neuro-imaging evaluations are employed to predict future relapse (Janes et al. 2010; Beck et al. 2012; Moeller et al. 2012; Seo et al. 2013). Compared with the healthy control group, the subsequent relapser and non-relapser groups commonly demonstrated significantly increased brain responses during the processing of heroin-related cues in the mesolimbic system (caudate, pallidum), prefrontal regions (ACC and DLPFC), visuospatial-attention regions (precuneus, inferior parietal lobule and superior parietal lobule), midbrain and cerebellum. These results were in line with our previous research (Wang et al. 2011; Li et al. 2012, 2013) and others’ studies (Sell et al. 1999; Daglish et al. 2001; Langleben et al. 2008, 2014; Yang et al. 2009; Zijlstra et al. 2009; Walter et al. 2014) showing an enhanced cue-induced brain response in these areas. The ACC and DLPFC have been demonstrated to be involved in reward prediction, decision making, inhibitory control and salience attribution (Garavan et al. 2000; Miller 2000; Watanabe et al. 2002; Kalivas & Volkow 2005). The midbrain and pallidum have been demonstrated to play a role in reward (Wang et al. 2007). The precuneus and inferior and superior parietal lobules have been demonstrated to be involved in visuospatial attention (Due et al. 2002; Spanagel 2003). All of the results indicated that heroin-related cues can induce enhanced salience attribution among the heroin-dependent patients under MMT. However, more importantly, compared with the group of non-relapsers, the group of relapsers demonstrated significantly increased brain responses to heroinrelated > neutral cues in the bilateral NAc/SCC and cerebellum. The NAc/SCC is a highly dopamine-innervated brain region and plays an important role in the function of reward, subjective euphoria and craving (Breiter et al. 1997; Kilts et al. 2001). Pre-clinical studies in animals indicate that this region plays a key role in Pavlovian Addiction Biology, 20, 968–978 Cue responses predict relapse conditioning (Parkinson et al. 1999), control of instrumental behavior by Pavlovian cues (Corbit, Muir & Balleine 2001) and behavior of drug seeking by drugpaired cues (Ito, Robbins & Everitt 2004). Drugs of abuse lead to excessive dopamine neurotransmission in the ventral striatum where the NAc/SCC is located (Kalivas & Stewart 1991). The increase of dopamine neurotransmission in the NAc/SCC could even be induced by cues related to drugs such as amphetamine (Boileau et al. 2007). On the contrary, animal studies have demonstrated that the procedure of deep brain stimulation (DBS) of NAc is effective to modulate the behavior of alcoholism (Knapp et al. 2009), cocaine seeking (Vassoler et al. 2008) and opiate addiction (Liu et al. 2008). Further, clinical case reports have also shown that craving and risk of relapse in smoking (Kuhn et al. 2009), alcoholism (Heinze et al. 2009) and heroin addiction (Gao et al. 2003; Wu et al. 2010) can be promisingly decreased by means of DBS or ablation of the NAc. Our fMRI finding, showing a positive correlation between heroin-related cue-induced craving change and activation in the NAc/SCC among heroin-dependent patients, further supports the NAc/SCC in the human, with a potential role in incentive as well as expectation of reward. The significantly increased NAc/SCC activity and craving to heroin-related cues among the relapsers relative to non-relapsers suggested that, although under stable MMT, the more NAc/SCC activity and craving to the heroin-related cues heroin-dependent patients showed, the more relapse vulnerability they had. Therefore, our findings further highlighted the key role of NAc/ SCC in heroin relapse. The cerebellum had once been viewed only as a mediator of motor functions (Stein & Glickstein 1992). However, there is growing evidence demonstrating that the cerebellum also plays a role in memory retrieval and learning during performance of higher order cognitive tasks such as drug cue-induced craving (Buckner et al. 1996; Yacubian et al. 2007). The cocaine craving studies demonstrated that the learned memory associations of drug use are mediated by the cerebellum (Hariri et al. 2005; Zubieta et al. 2005). Recently, an animal study (Carbo-Gas et al. 2014) demonstrated that olfactory stimulus preference was directly associated with cFos expression in cells at the apical region of the granule cell layer of the cerebellar vermis in cocaine-addicted mice. The results also suggested that the cerebellum might be an important part of the neural circuits involved in generating, maintaining and/or retrieving drug memories. Our findings, showing greater heroin-related cues induced cerebellar activation in relapsers relative to nonrelapsers, suggested that the abnormal memory retrieval function of cerebellum may play an important role in relapse among treated heroin-dependent patients. As is © 2014 Society for the Study of Addiction 975 known, drug use and relapse involve learned associations between drug-associated environmental cues and drug effects (Xue et al. 2012). The significantly increased cerebellar activation to heroin-related cues among the relapsers relative to non-relapsers suggested that the cue-induced cerebellar activity might also be a potential biomarker of relapse. It further suggested that the learned memory of drug use experience plays an important role in subsequent relapse even when heroin-dependent patients are under stable MMT. However, more fMRI studies are needed to understand the memory retrieval role of the cerebellum in heroin addiction. The current findings have some clinical implications. Our findings suggest that future therapies for heroin addiction should assess cue-induced brain responses prior to treatment as an indicator of relapse potential. Moreover, changes in drug cue-induced brain responses after a certain therapy may be a potentially reliable marker of treatment efficacy. Therapies for heroin addiction that would block such responses to heroin-related cues would presumably reduce vulnerability of relapse. Recently, Langleben et al. (2014) and Walter et al. (2014) demonstrated significantly changes in the patterns of brain response to drug-related cues after administration of naltrexone and pharmaceutical heroin (diacetylmorphine). These two studies further confirmed the potential value of drug cue-response measures in the evaluation of the efficacy of therapies. In addition, our findings support the notion that NAc/SCC and cerebellum may be targets for the development of addiction treatment. Some caveats apply to this study. First, the heroindependent individuals averaged more than 5 years of heroin use and were all relapsers in the past. Therefore, patients who did not relapse at 3 months would most likely relapse to heroin use with a longer follow-up period. These considerations suggest that the present cohort may not be ideal to examine neural predictors of relapse given neural-plastic adaptations as a result of long-term heroin exposure. Second, these heroin-dependent patients under long-term MMT focused on relapse prevention and had a stable dose of methadone treatment. The effect of methadone may influence our results. Various laboratory- and clinical-based studies have demonstrated that methadone plays a role in suppressing heroin craving, alleviating withdrawal symptoms and, in turn, reducing the relapse rates (Kreek 2000). However, the phenomena of relapse still exist (Fatseas et al. 2011). In summary, we found that increased cue-induced activation in the NAc/SCC and cerebellum can predict subsequent relapse among heroin-dependent individuals. Our findings shed light on the development of treatment targeting the NAc/SCC and cerebellum for preventing relapse in heroin addiction. Addiction Biology, 20, 968–978 976 Qiang Li et al. Acknowledgements This work was supported by grants from the National Natural Science Foundation of China (Nos. 81201081, 81371532, 81071142, 81071143, 81271549 and 61131003). We thank Paula J. Edge for her editorial contribution to the manuscript. Disclosure/Conflict of Interest All of the authors state that they have no conflicts of interest and have nothing to declare. Authors Contribution WW was responsible for the study design. JZ, YZ, DZ, LW, YL, XY, HC, MF and ZL contributed to the acquisition of fMRI and demographical data. HW and QL performed the data analysis. WL and YZ assisted with data analysis and interpretation of findings. QL drafted the manuscript. YL, JT, YW and MG provided critical revision of the manuscript for important intellectual content. All authors critically reviewed content and approved final version for publication. References Beck A, Wustenberg T, Genauck A, Wrase J, Schlagenhauf F, Smolka MN, Mann K, Heinz A (2012) Effect of brain structure, brain function, and brain connectivity on relapse in alcoholdependent patients. Arch Gen Psychiatry 69:842–852. 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R, right, L, left Figure S2 The differences relating to the ‘heroinrelated > neutral cues’ contrast between relapsers and © 2014 Society for the Study of Addiction healthy controls (P < 0.05, corrected for Monte Carlo simulations correction). R, right, L, left Figure S3 The differences relating to the ‘heroinrelated > neutral cues’ contrast between nonrelapsers and healthy controls (P < 0.05, corrected for Monte Carlo simulations correction). R, right, L, left Table S1 Activated brain regions for the relapser compared with control group in response to heroinrelated > neutral cues Table S2 Activated brain regions for the nonrelapsers compared with control group in response to heroin-related > neutral cues Addiction Biology, 20, 968–978