Active clinical trials for "Depressive Disorder"

The goal of this observational study is to determine the feasibility of using integrated Transcranial Doppler Ultrasonography or Near Infrared Spectroscopy to detect changes in cerebral autoregulation and neurovascular coupling in healthy, stroke, dementia, depression and delirium populations. We also aim to: Determine the optimal stimulus for neurovascular coupling To derive sample size estimates for a future study To develop a multilevel, multivariate model that can be applied to future datasets

There are many common pharmacological treatments for major depression disorder (MDD), however the efficacy of these drugs often fails in severe cases. Intravenous (IV) administered ketamine may offer the potential for remission of the symptoms in patients with MDD; however it has not yet been approved by FDA for this purpose. This study will make use of an electroencephalography (EEG) machine to measure the brain's activity and response while the IV ketamine is being delivered. The objective of this study is to characterize the change in EEG response of patients with MDD, during and 4 weeks after a course of IV ketamine infusions.

New working environments (digitalization, flexibilization) can lead to increased levels of stress. A balanced work-life balance is therefore important in order to prevent negative effects of stress on mental health. This study investigates, how stress, recovery, nutritional behavior, resilience and sleep behavior are related to the changed working conditions. how standardized nutritional training affects individual body composition (measured using Bioelectrical Impedance Analyses, BIA), stress perception, burn-out symptoms and sleep.

This study evaluates the use of an oral multi-strain probiotic in the treatment of depression in individuals with Parkinson's Disease. Participants will be randomized to either 12-week multi-strain probiotic treatment or placebo with an optional fMRI scan.

One in five people will present a major depressive episode (MDE) in their lifetime. While antidepressants (ADs) are currently the standard treatment for MDE, the first AD prescribed is effective in less than 40% of patients and a complete clinical response is only observed after several weeks. Identifying early biomarkers of the response to treatment with an AD could allow the clinician to rapidly identify patients in whom treatment will not be effective and therefore modify patient care. We have recently shown that the messenger RNA (mRNA) of two proteins, ELK1 and GPR56, were present in different amounts in the blood cells of "responder" compared to those of "non-respondent" patients. In this context, our main objective will be to determine whether ELK1 and GPR56 mRNAs, are very early biomarkers of the response to AD, i.e., biomarkers whose variation precedes the clinical response by several weeks. Secondary objectives will be to identify early phase changes in neurophysiological measures, cognitive and behavioral tasks, as well as levels of blood coding and non-coding RNAs, serum cytokine, mitochondrial and metabolic markers, neuroimaging markers as biomarkers of differential treatment outcomes to antidepressant treatment. Patients will be treated with SERTRALINE or FLUOXETINE or DULOXETINE or MAPROTILINE (in monotherapy) with or without adjunct benzodiazepine. Patients are identified as responders or non-responders based on their clinical assessment at 8 weeks after treatment onset. In addition, a second stage will collect data to address another important issue for the management of patients with a MDE: to discriminate those with a major depressive disorder (MDD) from those with a bipolar disorder (BD). BD diagnosis is one of the most common reasons of failure to response to ADs. Therefore, one of our secondary objectives will be to identify biomarkers to differentiate between these two categories of patients. To do this, we will follow patients for a period of 24 months to identify those who will present during this follow-up the diagnostic criteria of bipolarity.

This study will consist in a randomized controlled clinical trial (RCT) to test the efficacy of a blended group transversal protocol (BLGr-TP) compared to a face-to-face group transversal protocol (FFGr-TP). The main aims of the study are the following: To analyze the differential efficacy of the BLGr-TP versus the FFGr-TP for the treatment of emotional disorders in aspects of clinical measures, as well as in terms of retention and dropout rate and adherence. To analyze the differential acceptability of the BLGr-TP versus the FFGr-TP for the treatment of emotional disorders. In addition, it is intended to carry out a study of mediators and moderators of the efficacy of both interventions. The established hypotheses in relation to the main goals are: Both treatment modalities (FFGr-TP and BLGr-TP) will achieve improvements in the symptoms of emotional disorders, reflected in the scores of the clinical measures. The BLGr-TP will show equivalent efficacy to the FFGr-TP. The BLGr-TP will show an acceptability comparable to the face-to-face protocol. Both modalities will be well valued by the participants. In both treatment modalities, the changes achieved are expected to be maintained over time (3, 6 and 12 months).

Based on robust evidence from literature, the investigators hypothesize the presence of disease-specific neurobiological underpinnings for bipolar and unipolar disorder, which may serve as biomarkers for differential diagnosis. However, the group comparison approaches adopted in psychiatric research fail to translate the emerging knowledge to the diagnostic routine. How can physicians predict differential diagnosis and treatment response by using cutting-edge knowledge obtained in the last decade? How can such extensive knowledge be useful and applicable in clinical practice? With this project, the investigators propose a solution to these challenges by developing a software tool that integrates the available clinical, biological, genetic and imaging data to predict diagnosis and outcome of new individual patients. The decision support platform will employ artificial intelligence, specifically machine learning techniques, which will be "trained" through data in order to predict the category to which a new observation belongs to. By doing this, existing and newly acquired multimodal datasets of bipolar and unipolar patients will be translated into predictors for personalized patient diagnosis and prognosis. The project can have a great impact on psychiatric community and healthcare system. Identifying predictive biomarkers for UD and BD will provide an essential tool in the early stages of the disease, ensuring accurate diagnosis, enhancing prognosis and limiting health care costs. The investigators will recruit 80 bipolar patients, 80 unipolar patients and 80 healthy controls for the MRI study. Clinical, genetic and inflammation data will be acquired from all subjects. The following data will be obtained: age, gender, number of episodes, recurrence, age of illness onset, lifetime psychosis, BD or UD familiarity, tempted suicide, medication, scores at HDRS, Beck Depression Inventory and BACS battery. MRI will be performed on 3.0 Tesla scanners. MRI acquisitions will include SE EPI DTI, T1-weighted 3D MPRAGE and fMRI sequences during resting state and a face matching paradigm, which previously allowed defining the connectivity in mood disorder. Blood samples samples will be collected and plasma will be extracted and stored at -80. Pro- and anti-inflammatory cytokines will be measured using the Bioplex human cytokines 27-plex. Genetic variants associated considered for differential diagnosis will be evaluated using the Infinium PsychArray-24 BeadChip. This cost-effective, high-density microarray was developed in collaboration with the Psychiatric Genomics Consortium for large-scale genetic studies focused on psychiatric predisposition and risk. The relevance of the single clinical, genetic, molecular and image-based features as bipolar and unipolar disorder signatures will be evaluated by considered the cutting-edge literature and estimated on a independent already existing dataset (30 subjects per group). General Linear Model analyses followed by two sided t-tests will be used to identify whether each parameter significantly differs among groups, while removing the contribution of age, gender, length of illness and other confounding factors. A multiple kernel learning (MKL) algorithm will project the multisource features to a higher-dimensional space where the three subject groups will be maximally separated. The selected features will be used both separately and in combination. The nuisance effects of age, gender, length of illness and MRI system will be corrected during the training phase of the algorithm. The MKL classifier will be tested using a k-fold nested cross-validation strategy with hyperparameter tuning. The training dataset is already made available and includes about 550 subjects. The software architecture will be designed in Matlab environment by integrating quantitative imaging methods, machine learning algorithm and statistical analyses as separate modules in a user-friendly interface, which will facilitate the sharing of computational resources in the clinical community.

Rationale: Although there are effective treatments available for anxiety and depression, there is a large group of clients that does not benefit sufficiently from first-choice treatment. For this group of clients, no suitable alternative exists yet. One of the main hypothesis about maintaining factors is that there are underlying personality features that impede recovery. Schema focused therapy (SFT) is a transdiagnostic therapy focusing on underlying personality features. It has been proven to be an effective therapy for people with personality disorders and there are initial indications that SFT is also an effective treatment for anxiety and depressive symptoms. A short-term schema focused group therapy (SFGT) has been developed within GGZ-NHN, which is expected to benefit clients with persistent anxiety and depressive symptoms, but has not yet been studied. Objective of the study: In the present study the effectiveness of short-term SFGT (protocol of De Jager, Burger & Groot) on (1) persistent anxiety and depressive symptoms, and (2) early maladaptive schemas (EMS), experiential avoidance and the mode of the healthy adult will be investigated.

The goal of this clinical trial is to determine the impact of omega-3 fatty acids on the production of anti-inflammatory effects and clinical improvement in people with depression who have not responded well to standard antidepressant treatment. The main questions it seeks to answer are: Do omega-3 fatty acids added to ineffective antidepressant treatment increase production of compounds that reduce inflammation? Is the increase in these anti-inflammatory compounds associated with a stronger antidepressant effect? Participants taking antidepressants that have not worked completely will be assigned at random for a 12-week period to one of the following: an omega-3 preparation an inactive placebo During the course of the study, blood tests will be obtained for compounds associated with inflammation, and questionnaires to measure clinical improvement in depressive symptoms will be administered.

The purpose of this study is to evaluate the efficacy and safety of Ammoxetine hydrochloride enteric-coated tablets in subjects with depression.