Research News Roundup: October 2, 2025

Journal: Drug and Alcohol Dependence, 2025, doi 10.1016/j.drugalcdep. 2025.112841

Authors: Ramin Mojtabai, Ryoko Susukida, Mehdi Farokhnia, Trang Quynh Nguyen, Lorenzo Leggio, Cecilia Bergeria, Kelly E. Dunn, & Masoumeh Aminesmaeili

Abstract:

Background There is limited research on the course of drug craving in treatment trials of stimulant use disorders. This study examined trajectories of cocaine craving and use and their associations with other outcomes of cocaine use disorder in pharmacotherapy trials of cocaine use disorder.

Methods In 1070 participants from 6 randomized controlled trials testing selegiline, baclofen, cabergoline, modafinil, reserpine, and tiagabine, we used multi-trajectory modeling to identify joint trajectories of weekly-measured cocaine craving and use. Association of these trajectories with Addiction Severity Index (ASI) health and social outcomes was assessed.

Results A 3-trajectory model with High craving/High use (40.0 %), Decreasing craving/High use (36.8 %), and Decreasing craving/Decreasing use (23.2 %) groups was the most parsimonious. Compared to the High craving/High use group, the Decreasing craving/Decreasing use group experienced significantly greater improvement on ASI domains of drug use (change score = −13.7 vs. −3.3), alcohol use (-7.3 vs. −4.4), psychiatric status (-5.6 vs. 0.7) and relationships status (-7.0 vs. −2.8) (all p < 0.05). Compared to placebo, those on modafinil 200 mg/day were more likely to be in the Decreasing craving/Decreasing use group (Relative Risk Ratio [RRR]=4.84, 95 % CI=1.38–17.00) or Decreasing craving/High use group (RRR=5.55, 2.04–14.08) than in the High craving/High use group.

Conclusions Trajectories of craving/drug use in clinical trials for cocaine use disorder are heterogeneous. Participants experiencing the greatest reduction in cocaine craving/use experienced the greatest improvement in other measures of drug use and psychosocial functioning, supporting the utility of reduced craving/use as a clinically relevant outcome in pharmacotherapy trials of people with cocaine use disorder.

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Journal: NeuroImage, 2025, doi 10.1016/j.neuroimage.2025.121450

Authors: Dahyeon Kang, Catharine E. Fairbairn, Jiaxu Han, & Kara D. Federmeier

Abstract:

Evidence for the integration of alcohol into social life dates to the beginning of recorded history. Humans’ tendency to combine social interaction with alcohol has been attributed to alcohol’s ability to shift social perception, with behavioral research suggesting alcohol fosters social connection and diminishes perceived social threat. Yet the acute effects of alcohol on brain responses in social context are as yet unexplored. Combining experimental alcohol-administration with an EEG hyperscanning paradigm, the current study examines the effect of alcohol on evaluation of self- and other-linked performance. Social drinkers (N = 128) were administered either an alcoholic (target BAC 0.08 %) or control beverage in pairs. Dyads engaged in a gambling task while event-related potential Feedback Effects (FEs) to wins and losses were assessed simultaneously in both participants. Findings indicated a significant correlation in FEs among players and observers. Results further revealed alcohol effects that emerged specifically in the social domain, with alcohol intoxication significantly reducing the magnitude of FEs among observers paired with a stranger. In contrast, alcohol’s impact on FEs was non-significant when participants observed a familiar partner, as well as when participants were actively engaged in playing. Taken together, findings provide evidence for core social (e.g., observational) dimensions of human cognition and further offer clues surrounding neural pathways supporting the widespread integration of alcohol into social life.

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Journal: JMIR Mental Health, 2025, doi: 10.2196/73852

Authors: Hua Min, Xia Jing, Cui Tao, Joel E. Williams, Sarah F. Griffin, Christianne Esposito-Smythers, & Bruce Chorpit

Abstract:

Background: Youth mental health issues have been recognized as a pressing crisis in the United States in recent years. Effective, evidence-based mental health research and interventions require access to integrated datasets that consolidate diverse and fragmented data sources. However, researchers face challenges due to the lack of centralized, publicly available datasets, limiting the potential for comprehensive analysis and data-driven decision-making.

Objective: This paper introduces a curated directory of publicly available datasets focused on youth mental health (less than 18 years old). The directory is designed to serve as critical infrastructure to enhance research, inform policymaking, and support the application of artificial intelligence and machine learning in youth mental health research.

Methods: Unlike a systematic review, this paper offers a brief overview of open data resources, addressing the challenges of fragmented health data in youth mental health research. We conducted a structured search using 3 approaches: targeted searches on reputable health organization websites (eg, National Institutes of Health [NIH] and Centers for Disease Control and Prevention [CDC]), librarian consultation to identify hard-to-find datasets, and expert knowledge from prior research. Identified datasets were curated with key details, including name, description, components, format, access information, and study type, with a focus on freely available resources.

Results: A curated list of publicly available datasets on youth mental health and school policies was compiled. While not exhaustive, it highlights key resources relevant to youth mental health research. Our findings identify major national survey series conducted by organizations such as the NIH, CDC, Substance Abuse and Mental Health Services Administration (SAMHSA), and the U.S. Census Bureau, which focus on youth mental health and substance use. In addition, we include data on state and school health policies, offering varying scopes and granularities. Valuable health data repositories such as ICPSR, Data.gov, Healthdata.gov, Data.CDC.gov, OpenFDA, and Data.CMS.gov host a wide range of research data, including surveys, longitudinal studies, and individual research projects.

Conclusions: Publicly accessible health data are essential for improving youth mental health outcomes. Compiling and centralizing these resources streamlines access, enhances research impact, and informs interventions and policies. By improving data integration and accessibility, it encourages interdisciplinary collaboration and supports evidence-based interventions.

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Journal: Computational Psychiatry, 2025, doi: 10.5334/cpsy.131

Authors: Marishka M. Mehta, Navid Hakimi, Orestes Pena, Taylor Torres, Carter M. Goldman, Claire A. Lavalley, Jennifer L. Stewart, … Ryan Smith

Abstract:

Psychiatric disorders are highly heterogeneous and often co-morbid, posing specific challenges for effective treatment. Recently, computational modeling has emerged as a promising approach for characterizing sources of this heterogeneity, which could potentially aid in clinical differentiation. In this study, we tested whether computational mechanisms of decision-making under approach-avoidance conflict (AAC) – where behavior is expected to have both positive and negative outcomes – may have utility in this regard. We first carried out a set of pre-registered modeling analyses in a sample of 480 individuals who completed an established AAC task. These analyses aimed to replicate cross-sectional and longitudinal results from a prior dataset (N = 478) – suggesting that mechanisms of decision uncertainty (DU) and emotion conflict (EC) differentiate individuals with depression, anxiety, substance use disorders, and healthy comparisons. We then combined the prior and current datasets and employed a stacked machine learning approach to assess whether these computational measures could successfully perform out-of-sample classification between diagnostic groups. This revealed above-chance differentiation between affective and substance use disorders (balanced accuracy > 0.688), both in the presence and absence of co-morbidities. These results demonstrate the predictive utility of computational measures in characterizing distinct mechanisms of psychopathology and may point to novel treatment targets.

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