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Medication Treatment of Opioid Use Disorder


Opioid use disorder (OUD) is a chronic, relapsing condition, often associated with legal, interpersonal, and employment problems. Medications demonstrated to be effective for OUD are methadone (a full opioid agonist), buprenorphine (a partial agonist), and naltrexone (an opioid antagonist). Methadone and buprenorphine act by suppressing opioid withdrawal symptoms and attenuating the effects of other opioids. Naltrexone blocks the effects of opioid agonists. Oral methadone has the strongest evidence for effectiveness. Longer duration of treatment allows restoration of social connections and is associated with better outcomes. Treatments for OUD may be limited by poor adherence to treatment recommendations and by high rates of relapse and increased risk of overdose after leaving treatment. Treatment with methadone and buprenorphine has the additional risk of diversion and misuse of medication. New depot and implant formulations of buprenorphine and naltrexone have been developed to address issues of safety and problems of poor treatment adherence. For people with OUD who do not respond to these treatments, there is accumulating evidence for supervised injectable opioid treatment (prescribing pharmaceutical heroin). Another medication mode of minimizing risk of overdose is take-home naloxone. Naloxone is an opioid antagonist used to reverse opioid overdose, and take-home naloxone programs aim to prevent fatal overdose. All medication-assisted treatment is limited by lack of access and by stigma. In seeking to stem the rising toll from OUD, expanding access to approved treatment such as methadone, for which there remains the best evidence of efficacy, may be the most useful approach.

The Negative Affect of Protracted Opioid Abstinence: Progress and Perspectives From Rodent Models


Opioid use disorder (OUD) is characterized by the development of a negative emotional state that develops after a history of long-term exposure to opioids. OUD represents a true challenge for treatment and relapse prevention. Human research has amply documented emotional disruption in individuals with an opioid substance use disorder, at both behavioral and brain activity levels; however, brain mechanisms underlying this particular facet of OUD are only partially understood. Animal research has been instrumental in elucidating genes and circuits that adapt to long-term opioid use or are modified by acute withdrawal, but research on long-term consequences of opioid exposure and their relevance to the negative affect of OUD remains scarce. In this article, we review the literature with a focus on two questions: 1) Do we have behavioral models in rodents, and what do they tell us? and 2) What do we know about the neuronal populations involved? Behavioral rodent models have successfully recapitulated behavioral signs of the OUD-related negative affect, and several neurotransmitter systems were identified (i.e., serotonin, dynorphin, corticotropin-releasing factor, oxytocin). Circuit mechanisms driving the negative mood of prolonged abstinence likely involve the 5 main reward-aversion brain centers (i.e., nucleus accumbens, bed nucleus of the stria terminalis, amygdala, habenula, and raphe nucleus), all of which express mu opioid receptors and directly respond to opioids. Future work will identify the nature of these mu opioid receptor-expressing neurons throughout reward-aversion networks, characterize their adapted phenotype in opioid abstinent animals, and hopefully position these primary events in the broader picture of mu opioid receptor-associated brain aversion networks.

Social Bayes: Using Bayesian Modeling to Study Autistic Trait-Related Differences in Social Cognition


BACKGROUND: The autistic spectrum is characterized by profound impairments of social interaction. The exact subpersonal processes, however, that underlie the observable lack of social reciprocity are still a matter of substantial controversy. Recently, it has been suggested that the autistic spectrum might be characterized by alterations of the brain's inference about the causes of socially relevant sensory signals. METHODS: We used a novel reward-based learning task that required integration of nonsocial and social cues in conjunction with computational modeling. Thirty-six healthy subjects were selected based on their score on the Autism-Spectrum Quotient (AQ), and AQ scores were assessed for correlations with cue-related model parameters and task scores. RESULTS: Individual differences in AQ scores were significantly correlated with participants' total task scores, with high AQ scorers performing more poorly in the task (r = -.39, 95% confidence interval = -0.68 to -0.13). Computational modeling of the behavioral data unmasked a learning deficit in high AQ scorers, namely, the failure to integrate social context to adapt one's belief precision -the precision afforded to prior beliefs about changing states in the world-particularly in relation to the nonsocial cue. CONCLUSIONS: More pronounced autistic traits in a group of healthy control subjects were related to lower scores associated with misintegration of the social cue. Computational modeling further demonstrated that these trait-related performance differences are not explained by an inability to process the social stimuli and their causes, but rather by the extent to which participants consider social information to infer the nonsocial cue.

Ventral Hippocampal Afferents to Nucleus Accumbens Encode Both Latent Vulnerability and Stress-Induced Susceptibility

期刊: BIOLOGICAL PSYCHIATRY, 2020; 88 (11)

BACKGROUND: Stress is a major risk factor for depression, but not everyone responds to stress in the same way. Identifying why certain individuals are more susceptible is essential for targeted treatment and prevention. In rodents, nucleus accumbens (NAc) afferents from the ventral hippocampus (vHIP) are implicated in stress-induced susceptibility, but little is known about how this pathway might encode future vulnerability or specific behavioral phenotypes. METHODS: We used fiber photometry to record in vivo activity in vHIP-NAc afferents during tests of depressive- and anxiety-like behavior in male and female mice, both before and after a sex-specific chronic variable stress protocol, to probe relationships between prestress neural activity and behavior and potential predictors of poststress behavioral adaptation. Furthermore, we examined chronic variable stress-induced alterations in vHIP-NAc activity in vivo and used ex vivo slice electrophysiology to identify the mechanism of this change. RESULTS: We identified behavioral specificity of the vHIP-NAc pathway to anxiety-like and social interaction behavior. We also showed that this activity is broadly predictive of stress-induced susceptibility in both sexes, while prestress behavior is predictive only of anxiety-like behavior. We observed a stress-induced increase in in vivo vHIP-NAc activity coincident with an increase in spontaneous excitatory postsynaptic current frequency. CONCLUSIONS: We implicate vHIP-NAc in social interaction and anxiety-like behavior and identify markers of vulnerability in this neural signal, with elevated prestress vHIP-NAc activity predicting increased susceptibility across behavioral domains. Our findings indicate that individual differences in neural activity and behavior play a role in predetermining susceptibility to later stress, providing insight into mechanisms of vulnerability.

The Landscape of Circular RNA Expression in the Human Brain


BACKGROUND: Circular RNAs (circRNAs) are enriched in the mammalian brain and upregulated in response to neuronal differentiation and depolarization. These RNA molecules, formed by noncanonical back-splicing, have both regulatory and translational potential. METHODS: Here, we carried out an extensive characterization of circRNA expression in the human brain, in nearly 200 human brain samples, from both healthy controls and autism cases. RESULTS: We identified hundreds of novel circRNAs and demonstrated that circRNAs are not expressed stochastically, but rather as major isoforms. We characterized interindividual variability of circRNA expression in the human brain and showed that interindividual variability is less pronounced than variability between the cerebral cortex and cerebellum. Finally, we identified a circRNA coexpression module upregulated in autism samples, thereby adding another layer of complexity to the transcriptome changes observed in the autism brain. CONCLUSIONS: These data provide a comprehensive catalog of circRNAs, as well as a deeper insight into their expression in the human brain, and are available as a free resource in browsable format (http://www.voineagulab.unsw.edu.au/circ_rna).

Longitudinal Cortical Thickness Changes in Bipolar Disorder and the Relationship to Genetic Risk, Mania, and Lithium Use


BACKGROUND: Bipolar disorder (BD) is a highly heritable psychiatric disorder characterized by episodes of manic and depressed mood states and associated with cortical brain abnormalities. Although the course of BD is often progressive, longitudinal brain imaging studies are scarce. It remains unknown whether brain abnormalities are static traits of BD or result from pathological changes over time. Moreover, the genetic effect on implicated brain regions remains unknown. METHODS: Patients with BD and healthy control (HC) subjects underwent structural magnetic resonance imaging at baseline (123 patients, 83 HC subjects) and after 6 years (90 patients, 61 HC subjects). Cortical thickness maps were generated using FreeSurfer. Using linear mixed effects models, we compared longitudinal changes in cortical thickness between patients with BD and HC subjects across the whole brain. We related our findings to genetic risk for BD and tested for effects of demographic and clinical variables. RESULTS: Patients showed abnormal cortical thinning of temporal cortices and thickness increases in visual/so-matosensory brain areas. Thickness increases were related to genetic risk and lithium use. Patients who experienced hypomanic or manic episodes between time points showed abnormal thinning in inferior frontal cortices. Cortical changes did not differ between diagnostic BD subtypes I and II. CONCLUSIONS: In the largest longitudinal BD study to date, we detected abnormal cortical changes with high anatomical resolution. We delineated regional effects of clinical symptoms, genetic factors, and medication that may explain progressive brain changes in BD. Our study yields important insights into disease mechanisms and suggests that neuroprogression plays a role in BD.

The Electroretinogram May Differentiate Schizophrenia From Bipolar Disorder


BACKGROUND: The retina is recognized as an approachable part of the brain owing to their common embryonic origin. The electroretinogram (ERG) has proved to be a valuable tool to investigate psychiatric disorders. We therefore investigated its accuracy as a tool to differentiate schizophrenia (SZ) from bipolar disorder (BP) even after balancing patients for their main antipsychotic medication. METHODS: ERG cone and rod luminance response functions were recorded in 150 patients with SZ and 151 patients with BP and compared with 200 control subjects. We created a subgroup of subjects-45 with SZ and 45 with BP-balanced for their main antipsychotic medication. RESULTS: A reduced cone a-wave amplitude and a prolonged b-wave latency were observed in both disorders, whereas a reduced cone b-wave amplitude was present in SZ only. Reduced mixed rod-cone a- and b-wave amplitudes were observed in both disorders. Patients with SZ were distinguishable from control subjects with 0.91 accuracy, 77% sensitivity, and 91% specificity with similar numbers for patients with BP (0.89, 76%, and 88%, respectively). Patients with SZ and patients with BP could be differentiated with an accuracy of 0.86 (whole sample) and 0.83 (subsamples of 45 patients with 80% sensitivity and 82% specificity). Antipsychotic dosages were not correlated with ERG parameters. CONCLUSIONS: The ERG waveform parameters used in this study provided a very accurate distinction between the two disorders when using a logistic regression model. This supports the ERG as a tool that could aid the clinician in the differential diagnosis of SZ and BP in stabilized medicated patients.

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