View clinical trials related to Unipolar Depression.
Filter by:The study aims at investigating if tDCS applied to left DLPFC or to right OFC to treatment as usual is effective in reducing severe suicidal ideas in major depressive episode.
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.
Many different forms of depression exist. It is difficult to predict to what treatment a given patient with depression responds. Studies demonstrate that biomarkers can help to distinguish different forms of depression. Simple markers, like aldosterone/cortisol in body fluids, blood pressure and inflammation markers , have been identified as predictors of therapy resistance in depression. Enoxolone is a molecule derived from the licorice plant and has demonstrated an effect on these biomarkers, which may imply an improved response. The current randomized placebo controlled study is assessing whether the presence of markers of therapy resistance can predict a preferential effect of enoxolone vs. placebo on clinical outcome. Secondarily, it is tested whether these markers change differentially in the treatment groups. Finally, the relationship between the change of the markers and clinical change will be assessed.
Pragmatic, randomised, controlled, parallel-group, superiority trial of ketamine vs. midazolam as an adjunctive therapy for depression. The main purpose of the trial is to assess the mood-rating score difference between ketamine and midazolam from before the first infusion to 24 hours after the final infusion, supplemented by a 95% confidence interval. There will also be a 24-week follow-up after the final infusion session.
This is a longitudinal study where individual with Major Depressive Disorder (MDD) will be monitored for 12 weeks. The study aims to develop an objective, sensor-based, algorithm able to detect the presence of depression as well as predict treatment response. Measurement-based treatment is considered optimal and the development of a valid passive, objective, behavioral and biological assessment of depressive symptoms that does not rely on clinician interviews will improve monitoring and ultimately improve treatment significantly.
This is an observational neuroimaging study assessing the effects of ECT on the brains of patients with unipolar and bipolar depression.
Background: Electroconvulsive therapy (ECT) is used to treat people with severe depression. During ECT, the brain is given electric pulses that cause a seizure. Although it is effective, it can cause side effects, including memory loss. Researchers want to study a new way to give ECT called iLAST. Objective: To see if iLAST is safe and feasible in treating depression. Eligibility: People ages 22 70 years old who have major depressive disorder and are eligible for ECT Design: Participants will be screened under protocol 01-M-0254. This includes: Medical and psychiatric history and exam Blood and urine tests Participants will be inpatients at the Clinical Center. They study has 3 phases and will last up to 20 weeks. Phase I will last 1 week. It includes: MRI: Participants will lie in a scanner that takes pictures of the body MEG: A cone over the participant s head will record brain activity. TMS: A wire coil placed on the participant s scalp will produce an electrical current to affect brain activity. SEP: An electrode on the participant s wrist will give a small electrical shock to test nerve function. Phase II will last 2 and a half weeks. It includes: Seven sessions of iLAST under general anesthesia. Participants may also get standard ECT. EEG: A small electrode placed on the participant s scalp will record brain waves. Interviews about mood, symptoms, and side effects. Participants facial expressions may be video recorded. TMS Phase III will last at least 1 week. It will include: MRI EEG TMS MEG Standard ECT if needed. Participants will have sessions every other day, 3 times a week. Sponsoring Institution: National Institute of Mental Health ...
This trial aims to assess the efficacy and tolerability of Magnetic Seizure Therapy (MST) and two different forms of electroconvulsive therapy (ECT) in sustaining response during and after a course of continuation treatment.
Background: In the last 30 years mindfulness-based approaches have been extensively used for a variety of mental disorders, including affective disorders characterized by emotional instability. Mindfulness based cognitive therapy (MBCT) is an 8-week intervention that combines mindfulness practices with cognitive behavioural therapy. Although changes in emotional processing have been postulated as one of mindfulness mechanisms of action, the effects of mindfulness-based programs on objective tasks of emotional processing have been poorly studied. Objective: To explore the effects of mindfulness-based cognitive therapy (MBCT) on emotional processing in a sample of individuals with emotional instability attended in a mental-health public service. Methods: The sample (n = 30) will be recruited from public mental-health outpatient centers in Barcelona, Spain. Inclusion criteria: 1) high emotional instability defined as scores above 96 on the Difficulties in Emotion Regulation Scale (DERS), 2) age between 18 and 65 years, 4) no prior experience with mindfulness/meditation. Exclusion criteria: 1) risk of suicide (attempted suicide in the last year or current suicidal ideation). Psychotropic medication will be permitted, as long as there are no changes in dose/type during the study period. Measures Diagnostic measures (MINI, DERS, CGI) and a complete medical register will be collected prior inclusion in the study. Additional measures on personality (ZKPQ) and temperament (Temps-A) will be collected as well. Primary outcome: Emotional processing task: Participants will complete the FERT task at two time points: baseline (pre-treatment) and 8 weeks (post-treatment). Secondary outcomes: In addition, participants will complete the following assessments (pre-and-post- treatment): - Depressive Symptoms (QIDS-16) - Anxiety Symptoms (STAI) - Mindfulness (FFMQ and EQ). Treatment Mindfulness-based cognitive therapy (MBCT) is a manualized, 8 weeks-group-based training derived from mindfulness-based stress reduction (MBSR) and cognitive-behavioral therapy (CBT). In MBCT participants are trained in how to be more aware of their bodily sensations, thoughts and feelings associated with negative emotional states. Mindfulness exercises and cognitive-behavioral skills are practiced both during the sessions and through homework assignments. The program is structured in 2.30 h group sessions over 8 consecutive weeks.
The study focuses on the identification of clinical, physiological and morphological markers that could predict the response to antidepressant in elderly suffering from unipolar depressive disorder.