Faculty Bibliography

BACKGROUND:Antipsychotic-induced weight gain (AIWG) is a clinically relevant and concerning adverse effect of contemporary antipsychotic medications. Lumateperone is a novel antipsychotic, which became commercially available in 2020 and received Food and Drug Administration approval for schizophrenia and bipolar disorder in 2019 and 2021, respectively. To date, no comprehensive review exists on its AIWG profile. This systematic review aims to assess the association between lumateperone and AIWG. METHODS:Data Sources: A comprehensive search of published studies on "lumateperone" OR "ITI-007" OR "Caplyta" was conducted on PubMed, CINAHL Complete, APA PsychInfo, Cochrane Library, and Embase databases until January 2022.Study Selection: A total of 149 articles in English were collected. After removing duplicates, all human trials on lumateperone were screened for the inclusion criteria.Data Extraction: Two reviewers conducted an independent screening followed by full-text analysis of extracted studies adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Third reviewer resolved the conflicts as tiebreaker. RESULTS:Primary search generated 77 articles, excluding 72 duplicates, of which 51 were deemed appropriate for exclusion. Full-text analysis of the remaining 26 articles concluded with 5 studies for finalized review per inclusion criteria. Excluded studies were manually reviewed for relevant citation of studies per inclusion criteria. Three randomized, double-blinded, placebo-controlled clinical trials and 2 open-label trials were derived from this systematic review. Lumateperone showed a favorable weight profile compared with placebo and alternate antipsychotics. CONCLUSIONS:Lumateperone displays minimal to no weight gain among participants in the studies reviewed.

Importance/UNASSIGNED:Although classic psychedelic medications have shown promise in the treatment of alcohol use disorder (AUD), the efficacy of psilocybin remains unknown. Objective/UNASSIGNED:To evaluate whether 2 administrations of high-dose psilocybin improve the percentage of heavy drinking days in patients with AUD undergoing psychotherapy relative to outcomes observed with active placebo medication and psychotherapy. Design, Setting, and Participants/UNASSIGNED:In this double-blind randomized clinical trial, participants were offered 12 weeks of manualized psychotherapy and were randomly assigned to receive psilocybin vs diphenhydramine during 2 day-long medication sessions at weeks 4 and 8. Outcomes were assessed over the 32-week double-blind period following the first dose of study medication. The study was conducted at 2 academic centers in the US. Participants were recruited from the community between March 12, 2014, and March 19, 2020. Adults aged 25 to 65 years with a DSM-IV diagnosis of alcohol dependence and at least 4 heavy drinking days during the 30 days prior to screening were included. Exclusion criteria included major psychiatric and drug use disorders, hallucinogen use, medical conditions that contraindicated the study medications, use of exclusionary medications, and current treatment for AUD. Interventions/UNASSIGNED:Study medications were psilocybin, 25 mg/70 kg, vs diphenhydramine, 50 mg (first session), and psilocybin, 25-40 mg/70 kg, vs diphenhydramine, 50-100 mg (second session). Psychotherapy included motivational enhancement therapy and cognitive behavioral therapy. Main Outcomes and Measures/UNASSIGNED:The primary outcome was percentage of heavy drinking days, assessed using a timeline followback interview, contrasted between groups over the 32-week period following the first administration of study medication using multivariate repeated-measures analysis of variance. Results/UNASSIGNED:A total of 95 participants (mean [SD] age, 46 [12] years; 42 [44.2%] female) were randomized (49 to psilocybin and 46 to diphenhydramine). One participant (1.1%) was American Indian/Alaska Native, 5 (5.3%) were Black, 16 (16.8%) were Hispanic, and 75 (78.9%) were non-Hispanic White. Of the 95 randomized participants, 93 received at least 1 dose of study medication and were included in the primary outcome analysis. Percentage of heavy drinking days during the 32-week double-blind period was 9.7% for the psilocybin group and 23.6% for the diphenhydramine group, a mean difference of 13.9%; (95% CI, 3.0-24.7; F1,86 = 6.43; P = .01). Mean daily alcohol consumption (number of standard drinks per day) was also lower in the psilocybin group. There were no serious adverse events among participants who received psilocybin. Conclusions and Relevance/UNASSIGNED:Psilocybin administered in combination with psychotherapy produced robust decreases in percentage of heavy drinking days over and above those produced by active placebo and psychotherapy. These results provide support for further study of psilocybin-assisted treatment for AUD. Trial Registration/UNASSIGNED:ClinicalTrials.gov Identifier: NCT02061293.

Elevated exposure to multiple trace metals can be neurotoxic even at relatively low levels. These findings are primarily evident from adult occupational studies as well as in children exposed prenatally or in early childhood. Less research has focused on the neurodevelopmental impacts of exposure to metals among school-aged children. We examined associations between exposure to a mixture of four metals (arsenic, cadmium, manganese, lead) measured in hair and markers of cognition, attention, and behavior among 222 6-12 year old children who participated in a 2009-2010 neurodevelopmental follow-up to the C8 Health Project. Using quantile-based g-computation we estimated the adjusted overall metal mixture effect ψ (95 % CI) as the change in outcome per decile increase in all metals in the mixture. Hair metal levels varied by metal, with cadmium being lowest (median 0.007, interquartile range (IQR) 0.013 μg/g) and lead the highest concentration (median 0.152, IQR 0.252 μg/g). Children's cognitive skills and development, attention/impulsivity, and behavior were all close to standardized population means. Each decile increase in all metals was associated with a Full Scale IQ reduction of 1.01 points (95 % confidence interval (CI) -1.88, -0.15) and Verbal IQ reduction of 1.11 points (95 % CI -1.97, -0.25), adjusted for child age, sex, secondhand smoke exposure, HOME score, maternal education, maternal IQ, and examiner. Maternal report of ADHD-like behaviors and executive functioning also showed adverse associations with the metal mixture. Our findings suggest that similar to exposure during prenatal and early childhood periods, recent exposure to metals during middle childhood is associated with adverse neurodevelopmental consequences. Middle childhood may also be a developmental window of susceptibility to the negative consequences of exposure to environmental neurotoxicants.

The Brain Imaging Data Structure (BIDS) is a specification accompanied by a software ecosystem that was designed to create reproducible and automated workflows for processing neuroimaging data. BIDS Apps flexibly build workflows based on the metadata detected in a dataset. However, even BIDS valid metadata can include incorrect values or omissions that result in inconsistent processing across sessions. Additionally, in large-scale, heterogeneous neuroimaging datasets, hidden variability in metadata is difficult to detect and classify. To address these challenges, we created a Python-based software package titled "Curation of BIDS" (CuBIDS), which provides an intuitive workflow that helps users validate and manage the curation of their neuroimaging datasets. CuBIDS includes a robust implementation of BIDS validation that scales to large samples and incorporates DataLad--a version control software package for data--as an optional dependency to ensure reproducibility and provenance tracking throughout the entire curation process. CuBIDS provides tools to help users perform quality control on their images' metadata and identify unique combinations of imaging parameters. Users can then execute BIDS Apps on a subset of participants that represent the full range of acquisition parameters that are present, accelerating pipeline testing on large datasets.

OBJECTIVES:Negative symptom studies frequently use single composite scores as indicators of symptom severity and as primary endpoints in clinical trials. Factor analytic and external validation studies do not support this practice but rather suggest a multidimensional construct. The current study used structural equation modeling (SEM) to compare competing dimensional models of negative symptoms to determine the number of latent dimensions that best capture variance in biological, psychological, and clinical variables known to have associations with negative symptoms. METHODS:Three independent studies (total n = 632) compared unidimensional, two-factor, five-factor, and hierarchical conceptualizations of negative symptoms in relation to cognition, psychopathology, and community functioning (Study 1); trait emotional experience and defeatist performance beliefs (Study 2); and glutamate and gamma-aminobutyric acid levels in the anterior cingulate cortex quantified using proton magnetic resonance spectroscopy (Study 3). RESULTS:SEM favored the five-factor and hierarchical models over the unidimensional and two-factor models regardless of the negative symptom measure or external validator. The five dimensions-anhedonia, asociality, avolition, blunted affect, and alogia-proved vital either as stand-alone domains or as first-order domains influenced by second-order dimensions-motivation and pleasure and emotional expression. The two broader dimensions sometimes masked important associations unique to the five narrower domains. Avolition, anhedonia, and blunted affect showed the most domain-specific associations with external variables across study samples. CONCLUSIONS:Five domains and a hierarchical model reflect the optimal conceptualization of negative symptoms in relation to external variables. Clinical trials should consider using the two dimensions as primary endpoints and the five domains as secondary endpoints.

Background and Aims/UNASSIGNED:Screen media activity (SMA) may impact neurodevelopment in youth. Cross-sectionally, SMA has been linked to brain structural patterns including cortical thinning in children. However, it remains unclear whether specific brain structural co-variation patterns are related to SMA and other clinically relevant measures such as psychopathology, cognition and sleep in children. Methods/UNASSIGNED:Adolescent Brain Cognitive Development (ABCD) participants with useable baseline structural imaging (N = 10,691; 5,107 girls) were analyzed. We first used the Joint and Individual Variation Explained (JIVE) approach to identify cortical and subcortical covariation pattern(s) among a set of 221 brain features (i.e., surface area, thickness, or cortical and subcortical gray matter (GM) volumes). Then, the identified structural covariation pattern was used as a predictor in linear mixed-effect models to investigate its associations with SMA, psychopathology, and cognitive and sleep measures. Results/UNASSIGNED:A thalamus-prefrontal cortex (PFC)-brainstem structural co-variation pattern (circuit) was identified. The pattern suggests brainstem and bilateral thalamus proper GM volumes covary more strongly with GM volume and/or surface area in bilateral superior frontal gyral, rostral middle frontal, inferior parietal, and inferior temporal regions. This covariation pattern highly resembled one previously linked to alcohol use initiation prior to adulthood and was consistent in girls and boys. Subsequent regression analyses showed that this co-variation pattern associated with SMA (β = 0.107, P = 0.002) and externalizing psychopathology (β = 0.117, P = 0.002), respectively. Discussion and Conclusions/UNASSIGNED:Findings linking SMA-related structural covariation to externalizing psychopathology in youth resonate with prior studies of alcohol-use initiation and suggest a potential neurodevelopmental mechanism underlying addiction vulnerability.

Objective: This review aims to provide a brief introduction of clinical decision support systems (CDSSs) and discuss the role of psychology for the integration of CDSSs for child mental health into pediatric subspecialty care. Methods: A review of literature regarding CDSSs used for physical and mental health services was conducted to lay the foundation for our recommendations. Results: Children with chronic physical illness are twice as likely to suffer from mental illness as compared to healthy controls. These mental health problems are often discovered in pediatric subspecialty care. Yet their mental health needs are often unaddressed when the sections of pediatric subspecialty care and mental health are not well coordinated. CDSSs could help subspecialty providers with evidence-based decision-making regarding mental health screening and referrals. That is, CDSSs could enhance the access to most effective mental health treatment for children. However, the implementation of CDSSs in pediatric subspecialty care is very limited. Conclusions: Psychologists could assist subspecialty providers with advocating CDSSs to administrative and information technology staff, as well as build, customize, evaluate, and update CDSSs.

Previous research has examined how mood affects individuals"™ judgment. Our study aims to extend this research to evaluate the influence of mood on judgment in vivo. Using a smartphone-based design, we prompted participants (N = 103) three times a day to rate their mood and perform one of three judgment tasks three times a day for one week: (1) evaluate their self-efficacy on a word unscrambling task, (2) identify face emotions, or (3) judge risk by pumping virtual balloons. Our results showed that, contrary to our hypotheses, mood did not have a significant effect on the judgments individuals made. Prior task performance and task trial were significant predictors of self-efficacy; sex and task trial were associated with face emotion recognition; and balloons popped the day prior, and task trial influenced how individuals evaluated risk. The in vivo design of this study is a novel and more ecologically valid than some earlier work, but it is not without limitations, including the self-reported nature of mood, and potential for unmeasured third variable effects. This research raises questions about the validity of lab-based studies of the relation between mood and judgment, and shows the capacity of in vivo research and technology to challenge and enhance our understanding of how mood influences behavior.