View clinical trials related to Bipolar Disorder.
Filter by:This is a randomized, double-blind, placebo-controlled proof-of-concept clinical trial to assess the efficacy and safety of Magnesium-vitamin B6in combination with treatment as usual for treating symptoms of depression, stress, and anxiety in patients with first episode bipolar I disorder.
Mood disorders (including bipolar disorder and major depressive disorder) are chronic mental disorders with high recurrent rate. The more the number of recurrence is, the worse long-term prognosis is. This study aims to establish a prediction model of recurrence of manic and depressive episodes in mood disorders, with a hope to detect recurrence relapse as early as possible for timely clinical intervention. We will adopt wearable smart watch to collect heart rate, sleep pattern, activity level, as well as emotional status for one year long in 100 patients with bipolar disorder, and annotated their mood status (i.e., manic episode, depressive episode, and euthymic state). We expect to establish prediction models to predict the recurrence of mood episodes.
The COVID-19 pandemic significantly impacted primary care across Canada. Inequities in prevention activities and chronic disease management likely increased but the extent is unknown. Pragmatic interventions are required to prioritize patients and improve the quality of primary care post-COVID. In AFTERMATH, the investigators will conduct a pragmatic cluster randomized controlled trial (cRCT) at the largest primary care Practice-Based Research Network (PBRN) in Ontario, focused on a highly marginalized population: adults living with mental illness and one or more additional chronic diseases. The investigators will test an intervention that builds on the investigators' past work and combines data and supports to primary care providers to improve quality of life, reduce gaps in prevention activities and improve chronic disease management. The investigators' project will result in new evidence on ways to improve access to care and reduce inequities, and inform future efforts to use data beyond COVID-19.
The primary aim of this project is to test if OCOsense glasses can function as a digital phenotyping tool derived from behavioural and physiological signals related to facial expression and motion recorded using the glasses.
Family-focused therapy (FFT) is a comprehensive therapy approach applied to individuals and their families. In the present study, the researchers aimed to investigate the effects of family-focused therapy (FFT) in the early stages of psychotic disorder and bipolar disorder, regarding the psychiatric symptomatology, family communication skills, coping capacities, family burden and quality of life. A total of 34 young people diagnosed with bipolar disorder (BD) and 17 psychotic disorders (PD) will be included in the study.
Bipolar Disorder (BD) is a severe mood disorder affecting between 1% and 3% of the general population. It is characterized by the succession of depressive and manic episodes, with periods of stabilization during which patients may present "residual" depressive or anxious symptoms, which are characterized by sadness and emotional hyper-reactivity. Although subthreshold, these residual symptoms are very disabling for their daily lives and are associated with the risk of recurrence and poor global functioning. The effect of pharmacological and psychotherapeutic treatments is demonstrated in the management of acute episodes but remains insufficient on residual symptoms. Therefore, there are so far few therapeutic options to target the inter-episode residual symptoms in BD. One novel approach is the real-time functional magnetic resonance imaging (fMRI) neurofeedback (NFB), which has already been shown to be an efficient method for self-regulating brain function, behavior and treating depression. Hypothesis/Objective : This study aims at assessing the efficacy of 3-weeks neurofeedback training with real-time fMRI on the treatment of residual mood symptoms in patients with BD. The investigators will specifically target depressive symptoms by training the patients to regulate the emotional network hemodynamic response to emotional stimuli. Method : The investigators will include 64 stabilized patients with BD. The investigators will recruit them in three French expert centers for BD and will randomly assign them to the experimental group, receiving feedback from the emotional brain network hemodynamic activity, or to the control group, receiving the signal from control brain areas not involved in emotion processing. Both groups will be trained to regulate their brain activity while they are presented with negatively valenced emotional pictures, based on the neurofeedback shown immediately after the trial. They will continue their usual treatment (as prescribed) throughout the duration of the study. Clinical scales and cognitive tests will enable us to evaluate the symptomatic, emotional, and cognitive changes after NFB training. The investigators will also measure resting-state functional connectivity and brain morphology before and after NFB to assess brain plasticity and to explore the neural mechanisms associated with successful regulation.
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.
The purpose of this study is to test the hypothesis that the anti-depressant and anti-suicidal effects of the N-methyl-D-aspartate receptor (NMDAR) antagonist Ketamine is critically dependent on stimulation of Alpha-Amino-3-Hydroxy-5-Methyl-4-Isoxazole Propionic Acid receptors (AMPAR).
There is no currently-approved pharmacotherapy for patients with Treatment-Resistant Bipolar Depression and Suicidal Ideation or behavior. The purpose of this Expanded Access Treatment Protocol is to make NRX-101 available to patients who have depression and suicidal ideation despite treatment with currently approved medication and to gather information on safety and efficacy in a real-world data environment. Participants will be treated by their own practicing psychiatrist and will agree to periodic psychometric evaluations to assess depression, suicidal ideation, and side effects.
This is a study of only patients with idiopathic hypersomnia. It is a rare and still poorly understood pathology. In clinical practice, we have found that the treatment and care offered were not always effective. The idea of this study to improve knowledge of this pathology by studying the demographic characteristics of patients and other co-morbidities, in particular psychiatric patients, to see if we can identify common factor to our patients and useful in their medical care. The main objective of this research is to allow a quantitative study of the demographic and psychiatric characteristics of patients with idiopathic hypersomnia.