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Background: Due to its anxiolytic and stimulating effects gammahydroxybutyrate (gamma-hydroxybutyric acid, GHB) has gained popularity among drug users. The rate of dangerous intoxications and withdrawal syndromes is very high.

Objective: This study was conducted to gain knowledge about the sociodemographic background of GHB users and their motives of consumption. Furthermore, we intended to collect data about the clinical course of withdrawal treatment, its risks, and complications.

Methods: A group of 34 patients who had been consecutively admitted to our hospital for withdrawal treatment was asked for informed consent. The participants fullfilled a semiquantative questionnaire giving information about their socioeconomic background and their history of addictive disease. We also collected clinical data about the course and complications of the withdrawal treatment in our clinic. All patients received treatment as usual.

Results: Most of the participants were male, well educated, and rather young (mean age 34). Very few had children of their own or lived in a partnership. None of them had a history of criminal activities. There were prior contacts with other drugs, especially amphetamines. During detoxification, the rate of withdrawal delirium was very high (40%).

Conclusion: In order to improve our treatment and prevention of GHB addictive disease, we need to learn more about the patient groups who are at risk of becoming addicted to GHB. It is further crucial to develop clinical treatment standards to reduce the high rates of delirium and premature treatment interruption.

Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive neuromodulation technique that has been established for several years in the treatment of various mental illnesses. The clinical effectiveness for depressive disorders has been proven with good evidence, so that clear guideline recommendations for the use of rTMS have been formulated. It is available as a treatment option in many hospitals and outpatient practices. This article provides a current overview on the use of rTMS in unipolar depression. In addition to the mechanisms of action, data on clinical effectiveness, guideline recommendations and aspects of practical implementation, more recent developments in the field are also presented.

Background: It remains unclear whether an opioid crisis similar to that in the United States can be expected in Germany. In September 2024, the federal government stated that it does not see any danger of this occurring in the country. Nevertheless, the number of drug-related deaths in 2023 reached an all-time high of 2227 victims, with opioids involved in 1626 of those cases. For a more comprehensive assessment, information from additional settings may be helpful.

Objectives: This study aimed to analyze the quantitative development of inpatient treatment cases of younger patients with opioid addiction from a large addiction clinic in Hamburg, as well as their consumption patterns.

Materials and methods: Based on data stored in electronic patient records, a comparison was made of the relative proportions of inpatient patients treated for opioid addiction in the age groups "18-30 years" vs. "≥ 31 years" between the years 2019 and 2023 using chi-square tests. Additionally, the substances consumed were analyzed.

Results: A significant increase in the relative proportion of young opioid-dependent patients was observed, rising from 17.2% in 2019 to 27.5% in 2023 (p < 0.001). Heroin remained the most commonly consumed substance, consistently inhaled or taken nasally. The use of oxycodone increased significantly. Additionally, a significant increase in pregabalin consumption was noted.

Conclusions: Although the collected data may not be representative of Germany as a whole, our study suggests a growing issue with opioid addiction. Possible causes may include psychological stress, potentially influenced by the aftermath of the COVID-19 pandemic. The results highlight the need for more comprehensive treatment options and preventive measures.

Effective treatment of affective disorders (AD) requires a deep understanding of the underlying neurobiological mechanisms. However, in machine-learning-based analyses, cross-sectional studies have failed to identify robust individual-level biomarkers. Research Domain A of CRC/TRR393 addresses this gap by implementing longitudinal, multimodal studies using real-time mobile assessments. Central to this effort is the identification of "inflection signals"-clinically meaningful symptom changes marking transitions from euthymia to depressive or (hypo)manic episodes. These critical windows are captured through digital phenotyping and ecological momentary assessments and followed up by in-depth neurobiological profiling. Six projects examine the dynamic interplay of behavioral, cognitive-emotional, molecular, immune, and neural mechanisms during these transitions. Project A01 validates early-warning models using digital phenotypes and machine learning. Project A02 maps structural and functional brain changes in relation to disease course and risk factors. Project A03 investigates the role of microglial immune activation in recurrent depression. Project A04 investigates neurobiological alterations after inflection signals using intensive, multimodal data acquisition conducted both in laboratory settings and in the participants' personal environments. Project A05 adds molecular and immunological profiling and integrates findings from human and animal data. Project A06 studies trajectories from bipolar at-risk states to full-blown illness. Together, these projects form the empirical foundation for mechanism-based interventions (Domain C) and theoretical modeling of symptom trajectories (Domain B).

Background: Although affective disorders are a major driver of disability worldwide, there is a lack of understanding of the mechanisms and modulating factors involved in the long-term disease trajectories.

Objectives: Our goal is to determine key cognitive-emotional mechanisms in the domains of emotion regulation, expectation, social cognition, and cognitive-behavioral rhythms and their neurobiological correlates in the progression of affective disorders, including recurrences and remissions, chronicity, and functional decline.

Materials and methods: In CRC/TRR 393, we will pursue a multi-level investigation of these four domains. Within the German Mental Health Cohort (GEMCO), these mechanisms and their influence on disease trajectories will be investigated longitudinally. Parallelized human and animal projects will enable an in-depth characterization of their neurobiological correlates.

Results: By leveraging recent advancement in the modeling of complex, dynamic systems and machine learning techniques, we will be able to integrate human and animal data on the key cognitive-emotional mechanisms and their interplay with stressors and other modifying factors across disease trajectories.

Conclusion: Gaining a deeper understanding of the cognitive-emotional mechanisms in the progression of affective disorders will help to predict symptom changes and course of illness as well as to identify key targets of intervention.

The recurrent and often unpredictable course of affective disorders poses a critical challenge for long-term patient care. The CRC/TRR 393 consortium has established an ambitious longitudinal study, the German Mental Health Cohort (GEMCO), to systematically investigate the trajectories of symptom recurrence and remission in affective disorders. This article provides an overview of the core structural projects of the CRC/TRR 393 consortium that underpin this effort. Project S02 orchestrates the GEMCO, recruiting 1500 participants (approximately 900 with major depressive disorder, 300 with bipolar disorder, 300 healthy controls) and conducting comprehensive phenotyping, neuroimaging, and biobanking at baseline and follow-up time points. Project S01 provides an innovative mobile health infrastructure for continuous monitoring of patients' mood, behavior, and environment in real time over a 2-year period, enabling detection of early warning signs ("inflection signals") of mood episodes. Project INF implements a centralized information infrastructure, ensuring high-quality data capture, multisite data integration, and open-science data sharing. Project S03 serves as the advanced data analysis hub, developing machine learning models to predict individual illness trajectories and outcomes from the rich multimodal data. A research training group (RTG) provides funding and infrastructure for early-career scientists. Together, these structural projects establish a state-of-the-art framework for studying affective disorder trajectories, with the ultimate goal of identifying predictors and mechanisms of relapse and remission, and paving the way toward mechanism-based clinical interventions.