View clinical trials related to Bipolar Disorder.
Filter by:The main aim of this research is to explore the effects that ketamine has on the functional connectivity of the brain in participants with treatment resistant depression (TRD). This study will investigate the relationship between these changes and response to treatment as measured by clinical scales, as well as examining drug induced changes in reward and emotion based brain activity, structural connectivity, cerebral blood flow, cognition, metabolism and blood markers of brain plasticity.
Bipolar disorder (BD) is a lifelong condition characterized by recurrent episodes of depression and (hypo) mania. Periods of chronic and recurring depressive episodes are more common and can be severely disabling. Effective treatments exist; however, a significant portion of bipolar depressed patients do not respond to or have difficulty tolerating many of these interventions and thus look beyond established treatments to achieve symptom relief. Cannabidiol (CBD), a chemical from the Cannabis sativa plant has shown to have some beneficial effects on mood symptoms in a few small studies which assessed its effects in other mental and physical health conditions, but no large studies have been conducted to assess the safety and efficacy in bipolar depression. Additionally, several clinical studies have shown CBD to be safe and tolerable. The primary objective of this study is to assess the effectiveness, safety and tolerability of Cannabidiol in patients with bipolar depression (BD I or BD II) who have not responded to adequate trials with at least one first-line treatment for bipolar depression in comparison to those who will be treated with placebo. Placebo is an inactive substance that looks identical to the study medication but contains no therapeutic ingredient. This study is a randomized (like the flip of a coin), double-blind (you and the study team will not know which treatment arm you receive) study in which participants will receive either CBD or placebo added to their current treatment. Participants will have 5 clinical appointments and a phone appointment over a period of 10 weeks.
The goal of this case series is to investigate whether a brief compassion-focused intervention is a safe, acceptable and feasible therapy for clients with bipolar affective disorder. The secondary questions are whether a brief compassion focused intervention for BPD clients is associated with changes in bipolar mood symptoms and/ or with changes in psychological processes linked to mood symptoms in bipolar, including: Self-compassion, perfectionism, social comparison and social safeness. Four visual analogue scales will also be completed daily by each participant throughout the project. These scales will measure domains relevant to BPAD symptomology and self-compassion. Participants will complete a 4 session Compassion-Focused Therapy Intervention. The first session will involve completion of the psychoeducation and formulation work which was started during the initial assessment session. Each intervention session will also involve the introduction and practice of CFT techniques or exercises. The trial therapist will introduce the exercise and practice it together with the participant during the session. Participants will then be asked to continue practicing the exercises for homework. Their experience of the practice and any difficulties can then be discussed at the start of the next session.
This study will evaluate the effectiveness of valbenazine on patient- and clinician-reported outcomes assessing health-related quality of life, functioning, and treatment effect in participants with tardive dyskinesia (TD) who are receiving valbenazine for up to 24 weeks.
Evidence suggests that mGLUR5 availability may play a key role in the biology of mood disorders. This study aimed to investigate the changes in metabotropic glutamate receptor 5 (mGLUR5) availability and clinical symptoms in patients with MDD and bipolar disorder(BD) after two months of vortioxetine treatment. The investigators hypothesized that patients with MDD and BP have abnormal mGluR5 availability in certain brain regions, and baseline mGLUR5 availability can predict prognosis the prognosis of MDD and BD. fMRI and NODDI are also used to evaluate the function or neurite condition at baseline and 8 week
Alcohol use disorders (AUDs) affect up to 60% of individuals with bipolar disorder during their lifetime and is associated with worse illness outcomes, yet few studies have been performed to clarify the causes of this comorbidity. Understanding biological risk factors that associate with and predict the development of AUDs in bipolar disorder could inform interventions and prevention efforts to reduce the rate of this comorbidity and improve outcomes of both disorders. Identifying predictors of risk requires longitudinal studies in bipolar disorder aimed at capturing the mechanisms leading to the emergence of AUDs. Previous work in AUDs suggest that subjective responses to alcohol and stress-related mechanisms may contribute to the development of AUDs. In bipolar disorder, altered developmental trajectory of critical ventral prefrontal networks that modulate mood and reward processing may alter responses to alcohol and stressors; consequently, the disruption in typical neurodevelopment may be an underlying factor for the high rates of comorbidity. No longitudinal data exist investigating if this developmental hypothesis is correct. To address this gap, the investigators will use a multimodal neuroimaging approach, modeling structural and functional neural trajectories of corticolimbic networks over young adulthood, incorporating alcohol administration procedures, clinical phenotyping, and investigating effects of acute stress exposure and early life stress. Research aims are to identify biological risk factors-i.e., changes in subjective response to alcohol and associated neural trajectories-that are associated with the development of alcohol misuse and symptoms of AUDs over a two-year longitudinal period in young adults with bipolar disorder and typical developing young adults. Longitudinal data will be collected on 160 young adults (50% with bipolar disorder, 50% female; aged 21-26). This study is a natural extension of the PI's K01 award. How acute exposure to stress and childhood maltreatment affects subjective response to alcohol and risk for prospective alcohol misuse and symptoms of AUDs will be investigated. The investigators will test our hypothesis that developmental differences in bipolar disorder versus typical developing individuals disrupt corticolimbic networks during young adulthood, increase sensitivity to stress, and lead to changes in subjective response to alcohol and placebo response increasing risk for developing AUDs.
This study examines the feasibility and acceptability of a virtual tumor board for cancer and mental illness for patients with serious mental illness and a new cancer diagnosis. The study also examines the impact on patient care, psychiatric symptoms, and clinician self-efficacy in managing this population.
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.
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.