Effect of scanning duration and sample size on reliability in resting state fMRI dynamic causal modeling analysis.

Despite its widespread use, resting-state functional magnetic resonance imaging (rsfMRI) has been criticized for low test-retest reliability. To improve reliability, researchers have recommended using extended scanning durations, increased sample size, and advanced brain connectivity techniques. However, longer scanning runs and larger sample sizes may come with practical challenges and burdens, especially in rare populations. Here we tested if an advanced brain connectivity technique, dynamic causal modeling (DCM), can improve reliability of fMRI effective connectivity (EC) metrics to acceptable levels without extremely long run durations or extremely large samples. Specifically, we employed DCM for EC analysis on rsfMRI data from the Human Connectome Project. To avoid bias, we assessed four distinct DCMs and gradually increased sample sizes in a randomized manner across ten permutations. We employed pseudo true positive and pseudo false positive rates to assess the efficacy of shorter run durations (3.6, 7.2, 10.8, 14.4 min) in replicating the outcomes of the longest scanning duration (28.8 min) when the sample size was fixed at the largest (n = 160 subjects). Similarly, we assessed the efficacy of smaller sample sizes (n = 10, 20, …, 150 subjects) in replicating the outcomes of the largest sample (n = 160 subjects) when the scanning duration was fixed at the longest (28.8 min). Our results revealed that the pseudo false positive rate was below 0.05 for all the analyses. After the scanning duration reached 10.8 min, which yielded a pseudo true positive rate of 92%, further extensions in run time showed no improvements in pseudo true positive rate. Expanding the sample size led to enhanced pseudo true positive rate outcomes, with a plateau at n = 70 subjects for the targeted top one-half of the largest ECs in the reference sample, regardless of whether the longest run duration (28.8 min) or the viable run duration (10.8 min) was employed. Encouragingly, smaller sample sizes exhibited pseudo true positive rates of approximately 80% for n = 20, and 90% for n = 40 subjects. These data suggest that advanced DCM analysis may be a viable option to attain reliable metrics of EC when larger sample sizes or run times are not feasible.

Outcomes of Emergency Department Patients With Opioid Use Disorder Utilizing a Virtual Addiction Bridge Clinic: A Case Series.

OBJECTIVES: Within the last decade, there has been a dramatic increase in the rate of emergency department (ED) visits and death from opioid overdose. Those who present to the ED are at high risk for subsequent morbidity and mortality. Despite effective treatment, many patients do not get rapidly connected to outpatient care. The aim of this investigation was to describe outpatient treatment engagement after ED discharge among patients with opioid use disorder (OUD) enrolled in a virtual Addiction Bridge Clinic (ABC). METHODS: This was a retrospective case series describing an ED-initiated referral for rapid telehealth follow-up among patients with OUD. The primary outcome was addiction treatment engagement among those who completed the initial virtual ABC visit (engaged in ABC) vs. those who did not complete an ABC visit (Not engaged in ABC) at 1 week, 1 month, and 3 and 6 months timepoint intervals after the initial ED presentation. RESULTS: Of the N = 201 patients referred to the ABC between March and December 2021, a majority were Black (71%) and male (77%). Of the 201 referrals, 85 (42%) completed an initial ABC telehealth visit. Subsequent treatment engagement was 26% at 1 week, 26% at 1 month, 22% at 3 months, and 18% at 6 months after the index ED visit. CONCLUSIONS: A telehealth-enabled virtual addiction bridge clinic is one potential approach to reduce barriers to rapid treatment access. Strategies are needed to improve subsequent addiction treatment engagement after a virtual addiction bridge clinic visit.

Rising Racial Disparities in Opioid Mortality and Undertreatment of Opioid Use Disorder and Mental Health Comorbidities in Virginia.

Introduction: There were more than 100,000 fatal drug overdoses in the U.S. in 2021 alone. In recent years, there has been a shift in opioid mortality from predominantly White rural communities to Black urban communities. This study aimed to identify the Virginia communities disproportionately affected by the overdose crisis and to better understand the systemic factors contributing to disparities in opioid mortality. Methods: Using the state all-payer claims database, state mortality records, and census data, we created a multivariate model to examine the community-level factors contributing to racial disparities in opioid mortality. We used generalized linear mixed models to examine the associations between socioecologic factors and fatal opioid overdoses, opioid use disorder diagnoses, opioid-related emergency department visits, and mental health diagnoses. Results: Between 2015 and 2020, racial disparities in mortality widened. In 2020, Black males were 1.5 times more likely to die of an opioid overdose than White males (47.3 vs 31.6 per 100,000; p<0.001). The rate of mental health disorders strongly correlated with mortality (ß=0.53, p<0.001). Black individuals are not more likely to be diagnosed with opioid use disorder (ß=0.01, p=0.002) or with mental health disorders (ß= -0.12, p<0.001), despite higher fatal opioid overdoses. Conclusions: There are widening racial disparities in opioid mortality. Untreated mental health disorders are a major risk factor for opioid mortality. Findings show pathways to address inequities, including early linkage to care for mental health and opioid use disorders. This analysis shows the use of comprehensive socioecologic data to identify the precursors to fatal overdoses, which could allow earlier intervention and reallocation of resources in high-risk communities.

Ghrelin receptor antagonist JMV2959 blunts cocaine and oxycodone drug-seeking, but not self-administration, in male rats.

The drug overdose crisis has spawned serious health consequences, including the increased incidence of substance use disorders (SUDs), conditions manifested by escalating medical and psychological impairments. While medication management is a key adjunct in SUD treatment, this crisis has crystallized the need to develop additional therapeutics to facilitate extended recovery from SUDs. The "hunger hormone" ghrelin acts by binding to the growth hormone secretagogue receptor 1α (GHS1αR) to control homeostatic and hedonic aspects of food intake and has been implicated in the mechanisms underlying SUDs. Preclinical studies indicate that GHS1αR antagonists and inverse agonists suppress reward-related signaling associated with cocaine and opioids. In the present study, we found that the GHS1αR antagonist JMV2959 was efficacious to suppress both cue-reinforced cocaine and oxycodone drug-seeking, but not cocaine or oxycodone self-administration in male Sprague-Dawley rats. These data suggest a role of the ghrelin-GHS1αR axis in mediating overlapping reward-related aspects of cocaine and oxycodone and premises the possibility that a GHS1αR antagonist may be a valuable therapeutic strategy for relapse vulnerability in SUDs.

Behavioral and neurocognitive factors distinguishing post-traumatic stress comorbidity in substance use disorders.

Significant trauma histories and post-traumatic stress disorder (PTSD) are common in persons with substance use disorders (SUD) and often associate with increased SUD severity and poorer response to SUD treatment. As such, this sub-population has been associated with unique risk factors and treatment needs. Understanding the distinct etiological profile of persons with co-occurring SUD and PTSD is therefore crucial for advancing our knowledge of underlying mechanisms and the development of precision treatments. To this end, we employed supervised machine learning algorithms to interrogate the responses of 160 participants with SUD on the multidimensional NIDA Phenotyping Assessment Battery. Significant PTSD symptomatology was correctly predicted in 75% of participants (sensitivity: 80%; specificity: 72.22%) using a classification-based model based on anxiety and depressive symptoms, perseverative thinking styles, and interoceptive awareness. A regression-based machine learning model also utilized similar predictors, but failed to accurately predict severity of PTSD symptoms. These data indicate that even in a population already characterized by elevated negative affect (individuals with SUD), especially severe negative affect was predictive of PTSD symptomatology. In a follow-up analysis of a subset of 102 participants who also completed neurocognitive tasks, comorbidity status was correctly predicted in 86.67% of participants (sensitivity: 91.67%; specificity: 66.67%) based on depressive symptoms and fear-related attentional bias. However, a regression-based analysis did not identify fear-related attentional bias as a splitting factor, but instead split and categorized the sample based on indices of aggression, metacognition, distress tolerance, and interoceptive awareness. These data indicate that within a population of individuals with SUD, aberrations in tolerating and regulating aversive internal experiences may also characterize those with significant trauma histories, akin to findings in persons with anxiety without SUD. The results also highlight the need for further research on PTSD-SUD comorbidity that includes additional comparison groups (i.e., persons with only PTSD), captures additional comorbid diagnoses that may influence the PTSD-SUD relationship, examines additional types of SUDs (e.g., alcohol use disorder), and differentiates between subtypes of PTSD.

Use of population health data to promote equitable recruitment for a primary care practice implementation trial addressing unhealthy alcohol use.

Background: Recruiting underrepresented people and communities in research is essential for generalizable findings. Ensuring representative participants can be particularly challenging for practice-level dissemination and implementation trials. Novel use of real-world data about practices and the communities they serve could promote more equitable and inclusive recruitment. Methods: We used a comprehensive primary care clinician and practice database, the Virginia All-Payers Claims Database, and the HealthLandscape Virginia mapping tool with community-level socio-ecological information to prospectively inform practice recruitment for a study to help primary care better screen and counsel for unhealthy alcohol use. Throughout recruitment, we measured how similar study practices were to primary care on average, mapped where practices' patients lived, and iteratively adapted our recruitment strategies. Results: In response to practice and community data, we adapted our recruitment strategy three times; first leveraging relationships with residency graduates, then a health system and professional organization approach, followed by a community-targeted approach, and a concluding approach using all three approaches. We enrolled 76 practices whose patients live in 97.3% (1844 of 1907) of Virginia's census tracts. Our overall patient sample had similar demographics to the state for race (21.7% vs 20.0% Black), ethnicity (9.5% vs 10.2% Hispanic), insurance status (6.4% vs 8.0% uninsured), and education (26.0% vs 32.5% high school graduate or less). Each practice recruitment approach uniquely included different communities and patients. Discussion: Data about primary care practices and the communities they serve can prospectively inform research recruitment of practices to yield more representative and inclusive patient cohorts for participation.

Case report: Identification of AP-238 and 2-fluorodeschloroketamine in internet available powder samples sold as bucinnazine.

Novel synthetic opioids (NSOs) are a class of opioid agonists that include analogs of fentanyl and structurally distinct non-fentanyl compounds normally used as standalone products, heroin adulterants, or constituents of counterfeit pain pills. Most NSOs are not currently scheduled in the U.S., are predominantly illegally synthesized, and sold on the Darknet. Among them, the cinnamylpiperazine derivatives such as bucinnazine (AP-237), AP-238, and 2-methyl-AP-237 and the arylcyclohexylamine derivatives, analogs of ketamine, such as 2-fluoro-deschloroketamine (2 F-DCK) have appeared in several monitoring systems. Two white powders purchased on the internet as bucinnazine were first analyzed with polarized light microscopy followed by direct analysis in real time-mass spectrometry (DART-MS) and gas chromatography-mass spectrometry (GC-MS). Both powders were white crystals with no other significant microscopic properties. The DART-MS analysis showed the presence of 2-fluorodeschloroketamine in powder #1, and AP-238 in powder #2. Identification was confirmed by GC-MS. The purity of each substance was 78.0% for powder #1, and 88.9% for powder #2, respectively. The toxicological risk associated with the misuse of NSOs still needs further study. The absence of bucinnazine and the presence of different active compounds in internet purchased samples raises public health and safety concerns.

Rapid induction onto extended-release injectable buprenorphine following opioid overdose: A case series.

Background: Buprenorphine treatment has been associated with reduced non-prescribed opioid use and opioid related overdose (OD). We evaluated initial outcomes of rapid induction onto extended-release injectable buprenorphine (BUP-XR) within 7 days of emergency department presentation for unintentional OD. Methods: Between February 2019-February 2021, N = 19 patients with opioid use disorder received buprenorphine/naloxone (4/1 mg), followed by BUP-XR (300 mg) at induction and continued BUP-XR outpatient for 6 months. Primary outcomes included adverse events, repeat OD, and death. Results: For patients who received at least one dose of BUP-XR, there were no treatment related serious adverse events or symptoms of precipitated withdrawal. In addition, there were no repeat visits for ODs or deaths within 6 months of the initial OD. Discussion: These preliminary findings support the need for larger controlled clinical trials to examine the safety and efficacy of rapid induction of BUP-XR in patients with opioid use disorder at high risk of opioid OD. Rapid induction onto long-lasting injectable buprenorphine may be a promising and protective treatment approach in the future.

Executive control network resting state fMRI functional and effective connectivity and delay discounting in cocaine dependent subjects compared to healthy controls.

Resting state functional magnetic resonance imaging (fMRI) has been used to study functional connectivity of brain networks in addictions. However, most studies to-date have focused on the default mode network (DMN) with fewer studies assessing the executive control network (ECN) and salience network (SN), despite well-documented cognitive executive behavioral deficits in addictions. The present study assessed the functional and effective connectivity of the ECN, DMN, and SN in cocaine dependent subjects (CD) (n = 22) compared to healthy control subjects (HC) (n = 22) matched on age and education. This study also investigated the relationship between impulsivity measured by delay discounting and functional and effective connectivity of the ECN, DMN, and SN. The Left ECN (LECN), Right ECN (RECN), DMN, and SN functional networks were identified using FSL MELODIC independent component analysis. Functional connectivity differences between CD and HC were assessed using FSL Dual Regression analysis and FSLNets. Effective connectivity differences between CD and HC were measured using the Parametric Empirical Bayes module of Dynamic Causal Modeling. The relationship between delay discounting and functional and effective connectivity were examined using regression analyses. Dynamic causal modeling (DCM) analysis showed strong evidence (posterior probability > 0.95) for CD to have greater effective connectivity than HC in the RECN to LECN pathway when tobacco use was included as a factor in the model. DCM analysis showed strong evidence for a positive association between delay discounting and effective connectivity for the RECN to LECN pathway and for the DMN to DMN self-connection. There was strong evidence for a negative association between delay discounting and effective connectivity for the DMN to RECN pathway and for the SN to DMN pathway. Results also showed strong evidence for a negative association between delay discounting and effective connectivity for the RECN to SN pathway in CD but a positive association in HC. These novel findings provide preliminary support that RECN effective connectivity may differ between CD and HC after controlling for tobacco use. RECN effective connectivity may also relate to tobacco use and impulsivity as measured by delay discounting.

Benzodiazepine, Z-drug, and sleep medication prescriptions in male and female people with opioid use disorder on buprenorphine and comorbid insomnia: an analysis of multistate insurance claims.

STUDY OBJECTIVES: In adult populations, women are more likely than men to be prescribed benzodiazepines. However, such disparities have not been investigated in people with opioid use disorder (OUD) and insomnia receiving buprenorphine, a population with particularly high sedative/hypnotic receipt. This retrospective cohort study used administrative claims data from Merative MarketScan Commercial and MultiState Medicaid Databases (2006-2016) to investigate sex differences in the receipt of insomnia medication prescriptions among patients in OUD treatment with buprenorphine. METHODS: We included people aged 12-64 years with diagnoses of insomnia and OUD-initiating buprenorphine during the study timeframe. The predictor variable was sex (female versus male). The primary outcome was receipt of insomnia medication prescription within 60 days of buprenorphine start, encompassing benzodiazepines, Z-drugs, or non-sedative/hypnotic insomnia medications (e.g. hydroxyzine, trazodone, and mirtazapine). Associations between sex and benzodiazepine, Z-drug, and other insomnia medication prescription receipt were estimated using Poisson regression models. RESULTS: Our sample included 9510 individuals (female n = 4637; male n = 4873) initiating buprenorphine for OUD who also had insomnia, of whom 6569 (69.1%) received benzodiazepines, 3891 (40.9%) Z-drugs, and 8441 (88.8%) non-sedative/hypnotic medications. Poisson regression models, adjusting for sex differences in psychiatric comorbidities, found female sex to be associated with a slightly increased likelihood of prescription receipt: benzodiazepines (risk ratio [RR], RR = 1.17 [1.11-1.23]), Z-drugs (RR = 1.26 [1.18-1.34]), and non-sedative/hypnotic insomnia medication (RR = 1.07, [1.02-1.12]). CONCLUSIONS: Sleep medications are commonly being prescribed to individuals with insomnia in OUD treatment with buprenorphine, with sex-based disparities indicating a higher prescribing impact among female than male OUD treatment patients.

Combined Symptom Screening and At-Home Tests for COVID-19.

BACKGROUND AND OBJECTIVE: At-home rapid antigen tests provide a convenient and expedited resource to learn about severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection status. However, low sensitivity of at-home antigen tests presents a challenge. This study examines the accuracy of at-home tests, when combined with computer-facilitated symptom screening. METHODS: The study used primary data sources with data collected during 2 phases at different periods (phase 1 and phase 2): one during the period in which the alpha variant of SARS-CoV-2 was predominant in the United States and another during the surge of the delta variant. Four hundred sixty-one study participants were included in the analyses from phase 1 and 374 subjects from phase 2. Phase 1 data were used to develop a computerized symptom screening tool, using ordinary logistic regression with interaction terms, which predicted coronavirus disease-2019 (COVID-19) reverse transcription polymerase chain reaction (RT-PCR) test results. Phase 2 data were used to validate the accuracy of predicting COVID-19 diagnosis with (1) computerized symptom screening; (2) at-home rapid antigen testing; (3) the combination of both screening methods; and (4) the combination of symptom screening and vaccination status. The McFadden pseudo-R2 was used as a measure of percentage of variation in RT-PCR test results explained by the various screening methods. RESULTS: The McFadden pseudo-R2 for the first at-home test, the second at-home test, and computerized symptom screening was 0.274, 0.140, and 0.158, respectively. Scores between 0.2 and 0.4 indicated moderate levels of accuracy. The first at-home test had low sensitivity (0.587) and high specificity (0.989). Adding a second at-home test did not improve the sensitivity of the first test. Computerized symptom screening improved the accuracy of the first at-home test (added 0.131 points to sensitivity and 6.9% to pseudo-R2 of the first at-home test). Computerized symptom screening and vaccination status was the most accurate method to screen patients for COVID-19 or an active infection with SARS-CoV-2 in the community (pseudo-R2 = 0.476). CONCLUSION: Computerized symptom screening could either improve, or in some situations, replace at-home antigen tests for those individuals experiencing COVID-19 symptoms.

Testing the Acceptability and Usability of an AI-Enabled COVID-19 Diagnostic Tool Among Diverse Adult Populations in the United States.

BACKGROUND AND OBJECTIVES: Although at-home coronavirus disease-2019 (COVID-19) testing offers several benefits in a relatively cost-effective and less risky manner, evidence suggests that at-home COVID-19 test kits have a high rate of false negatives. One way to improve the accuracy and acceptance of COVID-19 screening is to combine existing at-home physical test kits with an easily accessible, electronic, self-diagnostic tool. The objective of the current study was to test the acceptability and usability of an artificial intelligence (AI)-enabled COVID-19 testing tool that combines a web-based symptom diagnostic screening survey and a physical at-home test kit to test differences across adults from varying races, ages, genders, educational, and income levels in the United States. METHODS: A total of 822 people from Richmond, Virginia, were included in the study. Data were collected from employees and patients of Virginia Commonwealth University Health Center as well as the surrounding community in June through October 2021. Data were weighted to reflect the demographic distribution of patients in United States. Descriptive statistics and repeated independent t tests were run to evaluate the differences in the acceptability and usability of an AI-enabled COVID-19 testing tool. RESULTS: Across all participants, there was a reasonable degree of acceptability and usability of the AI-enabled COVID-19 testing tool that included a physical test kit and symptom screening website. The AI-enabled COVID-19 testing tool demonstrated overall good acceptability and usability across race, age, gender, and educational background. Notably, participants preferred both components of the AI-enabled COVID-19 testing tool to the in-clinic testing. CONCLUSION: Overall, these findings suggest that our AI-enabled COVID-19 testing approach has great potential to improve the quality of remote COVID testing at low cost and high accessibility for diverse demographic populations in the United States.

New approaches to deep phenotyping in addictions.

OBJECTIVE: The causes of substance use disorders (SUDs) are largely unknown and the effectiveness of their treatments is limited. One crucial impediment to research and treatment progress surrounds how SUDs are classified and diagnosed. Given the substantial heterogeneity among individuals diagnosed with a given SUD (e.g., alcohol use disorder [AUD]), identifying novel research and treatment targets and developing new study designs is daunting. METHOD: In this article, we review and integrate two recently developed frameworks, the National Institute on Drug Abuse's Phenotyping Assessment Battery (NIDA PhAB) and the Hierarchical Taxonomy of Psychopathology (HiTOP), that hope to accelerate progress in understanding the causes and consequences of psychopathology by means of deep phenotyping, or finer-grained analysis of phenotypes. RESULTS AND CONCLUSIONS: NIDA PhAB focuses on addiction-related processes across multiple units of analysis, whereas HiTOP focuses on clinical phenotypes and covers a broader range of psychopathology. We highlight that NIDA PhAB and HiTOP together provide deep and broad characterizations of people diagnosed with SUDs and complement each other in their efforts to address widely known limitations of traditional classification systems and their diagnostic categories. Next, we show how NIDA PhAB and HiTOP can be integrated to facilitate optimal rich phenotyping of addiction-related phenomena. Finally, we argue that such deep phenotyping promises to advance our understanding of the neurobiology of SUD and addiction, which will guide the development of personalized medicine and interventions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Initial Validation of a Behavioral Phenotyping Model for Substance Use Disorder.

Standard nosological systems, such as DSM-5 or ICD-10, are relied upon as the diagnostic basis when developing treatments for individuals with substance use disorder (SUD). Unfortunately, the vast heterogeneity of individuals within a given SUD diagnosis results in a variable treatment response and/or difficulties ascertaining the efficacy signal in clinical trials of drug development. Emerging precision medicine methods focusing on targeted treatments based on phenotypic subtypes rather than diagnosis are being explored as alternatives. The goal of the present study was to provide initial validation of emergent subtypes identified by an addiction-focused phenotyping battery. Secondary data collected as part of a feasibility study of the NIDA phenotyping battery were utilized. Participants completed self-report measures and behavioral tasks across six neurofunctional domains. Exploratory and confirmatory factor analysis (EFA/CFA) were conducted. A three-factor model consisting of negative emotionality, attention/concentration, and interoception and mindfulness, as well as a four-factor model adding a second negative emotion domain, emerged from the EFA as candidate models. The CFA of these models did not result in a good fit, possibly resulting from small sample sizes that hindered statistical power.

Sex Specific Sleep Parameters Among People With Substance Use Disorder.

Introduction: Sleep can have substantial impacts in substance use disorder (SUD) pathogenesis, treatment, and recovery. Sex differences exist in both sleep and SUD, but how sleep is uniquely associated with SUD by sex is not known. The study objective was to compare, within sex, sleep parameters between individuals with SUD and non-substance misusing controls. Methods: Secondary analyses of a parent cross-sectional study examining the feasibility and acceptability of a novel neurocognitive phenotyping assessment battery were completed. SUD and control subjects were recruited through local advertising and an established research registry. Subjects with SUD were also recruited through a university-based outpatient SUD treatment clinic. Self-reported sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). Sex-stratified t-tests compared sleep between SUD and control subjects while Crosstab analyses explored group differences in the proportion of individuals reporting poor sleep (defined as PSQI ≥ 5). Results: Data from 162 males (44 controls, 118 SUD) and 146 females (64 controls, 82 SUD) were included in the present study. For females only, a significantly lower proportion of controls reported PSQI-defined poor sleep than individuals with any SUD or specifically with opioid use disorder. Male, but not female, controls reported shorter sleep latency, longer sleep duration, and less sleep disturbance than males with each SUD type. Discussion/Implications: Sleep holds promise as an avenue to address SUD within a biopsychosocial model. Future work at the intersection of SUD and sleep should prioritize investigations of their interplay with sex to identify targets for tailored SUD interventions.

Virtual engagement of under-resourced communities: Lessons learned during the COVID-19 pandemic for creating crisis-resistant research infrastructure.

The COVID-19 pandemic led to an increased need to conduct research and community engagement using digital methods. Unfortunately, the shift away from in-person research activities can make it difficult to engage and recruit participants from under-resourced communities that lack adequate digital infrastructure. At the beginning of the pandemic, our team recognized that imminent lockdowns would significantly disrupt ongoing engagement with low-income housing resident community partners and that we would ultimately bear responsibility if that occurred. This manuscript outlines the development of methods designed to create capacity for virtual engagement with a community advisory board that were subsequently applied to a longitudinal mixed-methods study. We describe how our experience engaging low-income housing residents during the height of the pandemic influenced the approach and offer guidelines useful for engaging under-resourced communities regardless of setting. Of these, a strong commitment to providing technology, unlimited data connectivity, and basic digital literacy training/technical support is most important. While each of these is essential and failure in any one area will reduce overall effectiveness of the effort, providing adequate technical support while maintaining ongoing relationships with community members is the most important and resource-intensive.

A serotonergic biobehavioral signature differentiates cocaine use disorder participants administered mirtazapine.

Cocaine use disorder (CUD) patients display heterogenous symptoms and unforeseeable responses to available treatment approaches, highlighting the need to identify objective, accessible biobehavioral signatures to predict clinical trial success in this population. In the present experiments, we employed a task-based behavioral and pharmacogenetic-fMRI approach to address this gap. Craving, an intense desire to take cocaine, can be evoked by exposure to cocaine-associated stimuli which can trigger relapse during attempted recovery. Attentional bias towards cocaine-associated words is linked to enhanced effective connectivity (EC) from the anterior cingulate cortex (ACC) to hippocampus in CUD participants, an observation which was replicated in a new cohort of participants in the present studies. Serotonin regulates attentional bias to cocaine and the serotonergic antagonist mirtazapine decreased activated EC associated with attentional bias, with greater effectiveness in those CUD participants carrying the wild-type 5-HT2CR gene relative to a 5-HT2CR single nucleotide polymorphism (rs6318). These data suggest that the wild-type 5-HT2CR is necessary for the efficacy of mirtazapine to decrease activated EC in CUD participants and that mirtazapine may serve as an abstinence enhancer to mitigate brain substrates of craving in response to cocaine-associated stimuli in participants with this pharmacogenetic descriptor. These results are distinctive in outlining a richer "fingerprint" of the complex neurocircuitry, behavior and pharmacogenetics profile of CUD participants which may provide insight into success of future medications development projects.

Attentional function and inhibitory control in different substance use disorders.

Attentional function in substance use disorder (SUD) is not well understood. To probe attentional function in SUD as a function of primary substance of abuse, we administered the attentional network task (ANT) to 44 individuals with Cocaine Use Disorder (CoUD), 49 individuals with Cannabis Use Disorder (CaUD), 86 individuals with Opioid Use Disorder (OUD), and 107 controls with no SUD, along with the stop-signal task (SST). The ANT quantifies the effects of (temporal) alerting cues and (spatial) orienting cues to reduce reaction time (RT) to targets, as well as probing how conflicting (target-incongruent) stimuli slow RT. The SST quantifies individuals' ability to inhibit already-initiated motor responses. After controlling for sex representation and age, OUD and CaUD participants showed blunted alerting effects compared to controls, whereas CaUD and CoUD participants showed greater stimulus conflict (flanker) effects. Finally, CoUD participants showed a trend toward increased orienting ability. In SST performance, no SUD group showed a prolonged stop-signal reaction compared to controls. However, the OUD group (and CoUD group at trend level) showed prolonged "go" RT to targets and reduced hit rates. These data indicate differences in attentional function in persons with SUD as a function of the primary substance use.

Stemming the tide of distrust: A mixed-methods study of vaccine hesitancy.

Public distrust in the US pandemic response has significantly hindered its effectiveness. In this community-based participatory research mixed-methods study, based on two datasets, we examined how distrust in COVID-19 vaccines relates to institutional distrust. We found that the Johnson & Johnson vaccine pause undermined trust in COVID-19 vaccines in general. Findings also suggest that vaccine distrust developed after participating in a study on COVID-19 testing. Increased distrust may be an unintended consequence of how healthcare and public health activities are presented and delivered, and research participation is structured. Both will continue without proactively addressing the root causes of distrust.

Geostatistical modeling of positive-definite matrices: An application to diffusion tensor imaging.

Geostatistical modeling for continuous point-referenced data has extensively been applied to neuroimaging because it produces efficient and valid statistical inference. However, diffusion tensor imaging (DTI), a neuroimaging technique characterizing the brain's anatomical structure, produces a positive-definite (p.d.) matrix for each voxel. Currently, only a few geostatistical models for p.d. matrices have been proposed because introducing spatial dependence among p.d. matrices properly is challenging. In this paper, we use the spatial Wishart process, a spatial stochastic process (random field), where each p.d. matrix-variate random variable marginally follows a Wishart distribution, and spatial dependence between random matrices is induced by latent Gaussian processes. This process is valid on an uncountable collection of spatial locations and is almost-surely continuous, leading to a reasonable way of modeling spatial dependence. Motivated by a DTI data set of cocaine users, we propose a spatial matrix-variate regression model based on the spatial Wishart process. A problematic issue is that the spatial Wishart process has no closed-form density function. Hence, we propose an approximation method to obtain a feasible Cholesky decomposition model, which we show to be asymptotically equivalent to the spatial Wishart process model. A local likelihood approximation method is also applied to achieve fast computation. The simulation studies and real data application demonstrate that the Cholesky decomposition process model produces reliable inference and improved performance, compared to other methods.