Active clinical trials for "Depressive Disorder"

Major depressive disorder (MDD) is the most common psychiatric disease worldwide with a huge socio-economic impact. Pharmacotherapy represents the first-line treatment choice; however, only about one third of patients respond to the first trial and about 30% are classified as treatment-resistant depression (TRD). TRD is associated with specific clinical features and genetic/gene expression signatures. To date, single sets of markers have shown limited power in response prediction. The aim of this project is the development of a precision medicine algorithm that would help early detection of non-responder patients, who might be more prone to later develop TRD. In this phase of the project a naturalistic cohort of 300 MDD patients will be recruited. The data collected will be used to assess, in real-world conditions, the capability of an innovative algorithm (integrating clinical, omics and gender data of other 300 patients con MDD) to predict the treatment outcomes. This project represents a proof-of-concept study. The obtained results will provide information about the feasibility and usefulness of the proposed approach, with the perspective of designing future clinical trials in which algorithms could be tested as a predictive tool to drive decision making by clinicians, enabling a better prevention and management of MDD resistance.

The pathophysiology of Major Depression Disorder (MDD) is unclear, with several theories for its neurobiological mechanisms. One possible explanation is the presence of altered neuroplasticity, which can be studied by Transcranial Magnetic Stimulation (TMS). Using TMS to study these mechanisms is performed by applying electromagnetic stimuli to the motor cortex, to obtain measures of temporary cortical excitability modulation. It is known that depressed patients with higher cortical modulation are more responsive to a TMS treatment course. However, it is unknown if there are differences in cortical modulation between depressed patients and healthy subjects. Our goal is to answer this question and contribute towards clarification of the neuroplasticity mechanisms underlying MDD. Accordingly, the investigators will access cortical excitability modulation measures in both depressed patients and healthy volunteers and compare their results. The investigators will also re-assess these measures after 6 weeks of antidepressant treatment. Finally, the investigators will study the association between cortical excitability measures and cognitive processes using an innovative cognitive task.

Differentiation between major depressive disorder (MDD) and bipolar disorder (BD) as soon as possible in the patient journey represents a major clinical issue. When the patient is in a depressive phase, the symptoms are similar between the two pathologies and the current clinical scales fail in distinguishing them. Physicians often report this difficulty and as a consequence, the mean time from onset to bipolar disorder diagnosis is currently 7.5 years. These diagnostic delays and misdiagnosis lead to damaging consequences for patients and their loved ones: worsening of symptoms, comorbidities, suicide risk and inadequate care resulting in severe impairment in social and occupational functioning. Faced with these high expectations for accurate diagnostic methods for an earlier management of psychiatric patients, the combination of relevant clinical features and biomarkers could stand for a solution, leading to a personalised approach in patients with mood disorders. In a first clinical discovery study, a panel of RNA biomarkers in the blood of patients with a major depressive episode (MDE) has been identified, allowing to differentiate bipolar disorder from MDD (unipolar depression). These biomarkers are based on RNA modifications, namely RNA editing, that could be identified using molecular biology, NGS and artificial intelligence. This panel constitutes EDIT-B test, which is based on Alcediag's proprietary and patented biomarkers and algorithms. The present study aims to validate the biomarker signatures proposed by Alcediag by measuring the association between the modifications of the RNA editing and major depressive disorder/ bipolar disorder diagnosis, in patients with a MDE in real-life setting pilot centres.

Major depressive disorder (MDD) is a chronic disease with a prevalence around 8-12% and is considered one of the most debilitating disease worldwide. A recent orientation is the analysis of language in relation to the description of images with a high and varied semantic and emotional content. It can be studied that changes in the description of an image check if these changes are associated with the evolution of a person with probable impairment both in memory and cognitive as well as emotional, psychiatric, behavioral and even in their interaction with environmental factors especially those associated with socialization and loneliness. The present study has the objective of validating Accexible as a tool for Screening and Monitoring Depression.

The goal of this observational study focuses on understanding and addressing a subset of persistent neuropsychiatric symptoms occurring within 3 months after mild to moderate COVID-19 infection (COVID-DNP). COVID-DNP encompasses major depressive episodes (MDE) with or without additional neuropsychiatric symptoms.

This study looks at the role of the Nociceptin/Orphanin FQ receptor system in the brain of individuals with current or past major depressive disorder (MDD). It also examines how individuals with a history of depression make certain decisions and which brain regions are involved in such decisions. Information collected through MRI, PET, biospecimens (i.e., blood, saliva) and behavioral tasks will be used to predict depressive symptoms in the future.

The investigators will include patients suffering from Major Depressive Disorder (MDD), in episode. The patients will undertake neuropsychological tasks evaluating executive function and clinical assessment related to depressive symptoms, anxiety, transdiagnostic symptoms and psychological skills.

Objective: Wearable technology holds promising potential for mental health monitoring and detection. Samsung has developed an algorithm that they believe can detect signs of depression and anxiety in smartwatch users. They have used this algorithm to create a "Mindfulness Index," which is an easily understood visual index of mental health. The primary aim of the study is to evaluate the performance of Samsung's Mindfulness Index in identifying those who have received a diagnosis of Major Depressive Disorder (MDD) from a clinician-administered semi-structured diagnostic interview. Research Procedures: The target sample size is 75 individuals diagnosed with current Major Depressive Disorder and 75 healthy controls. To meet this target, the recruitment target is set at 215 participants. Participants will be assigned to the MDD condition, or the healthy control condition based on their score on the Beck Depression Inventory. Each subject will be followed for 3 months. Participants will be provided with a Samsung smartphone and Samsung smartwatch. Participants will be asked to wear the smartwatch 24 hours per day, except while charging. This smartwatch will collect data on heartrate, sleep time, and step count. During the study, each day participants will receive texts prompting a link to a "daily diary." These surveys will ask about depression and anxiety symptoms. Additionally, during the first 3 weeks of the study, participants will participate in ecological momentary assessment; texts will be sent 5 times per day prompting participants to fill out a survey about how they currently feel in that moment. These extra surveys will stop after the first 3 weeks of the study, but the daily diary surveys will continue throughout the study. Furthermore, virtual clinician visits will occur at weeks 4, 8, and 12.

The investigators propose a test of causality by examining a cohort of patients undergoing deep brain stimulation (DBS) within the ventral striatum. The investigators will examine behavior on and off stimulation across a range of tasks that index different forms of impulsivity. Patients will be studied in both the ON and OFF state - that is, they will be tested during active deep brain stimulation and 30 min to 1 hour after stimulation has been stopped (order of state will be counterbalanced across subjects). The investigators specific aim is to test the hypothesis that enhancing ventral striatal signaling (i.e. ON-state DBS) will cause more impulsive patterns of behavior across several impulsivity tasks. The investigators predict that ventral striatal DBS will increase stop-signal reaction time on the stop-signal task and commission errors on the Go/NoGo task, and increase delay aversion in a delay discounting paradigm.

The objective of this study is to collect data to finalize the development of MEB-001 softward as a medical device. The data collected in this study will be used to develop MEB-001 machine learning algorithms by training the algorithms to match the patient's demographic and clinical information, and the objective physiological signals (i.e., electroencephalogram (EEG) and electrocardiogram (ECG)) recorded during PSG with the diagnosis of cMDE performed through the MINI neuropsychiatric evaluation.