Active clinical trials for "Bipolar Disorder"

The main purpose of the study is to examine to which extent abnormalities in the dynamics of neural activities observed in patients with psychosis is related to difficulties at ordering simple visual stimuli and/or personal events.

The purpose of the National Pregnancy Registry for Atypical Antipsychotics is to determine the frequency of birth defects among infants exposed to atypical antipsychotics.

This study looks to identify genes that may affect a person's chances of developing bipolar disorder (BP) and related conditions.

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.

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.

There is increasing awareness of the importance of providing mental health services and support that promote a recovery-oriented and human rights-based approach. A mental health service system that is guided by a rehabilitation and recovery perspective places emphasis on treating the consequences of the illness rather than just the illness "per se", and on empowering people to regain control of their identity and life, and to have hope for the future. Within this philosophy, mental health policies in several countries advocate for the introduction of peer workers in mental health services, people with lived experience of mental health issues and recovery, who are employed to use their lived experience to support those who access mental health services. However, more effectiveness and implementation research is needed. Evidence also suggests that the period following hospital discharge is of high risk of treatment dropout for people with serious mental illness, thus interrupting their recovery process. Therefore, this vulnerable population may particularly benefit from more targeted interventions during this transitional period. The research project will conduct a randomized controlled trial to evaluate the effectiveness, feasibility and implementation of the Critical Time Intervention-Peer Support model, a recovery-oriented based model for people with serious mental illness discharged from inpatient psychiatric treatment facilities in Portugal. The randomized controlled trial (RCT) will be conducted in three psychiatric services in the Lisbon Metropolitan Area and their catchment areas. People with diagnoses of psychotic disorders discharged from inpatient psychiatric treatment facilities will be recruited and randomly divided into CTI-PS intervention or usual care. Those allocated to the intervention group will additionally receive CTI-PS rather than usual care alone over a 9-month period. Outcomes at baseline, 9- and 18-months will be analyzed by multilevel models, considering the observations clustered within sites. Longitudinal analyses will be used to examine trends over time of the outcomes of interest. The implementation of the CTI-PS model will introduce a novel approach to community mental health care that has not yet been tried in Portugal. This study aims to explore to what extent this intervention can be effective and implemented in countries with the characteristics of Portugal. Additionally, the proposed research aims to contribute to the global knowledge about peer interventions by investigating whether the CTI model maintains its effectiveness using peers.

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.

Bipolar disorders are mental illnesses characterized by the recurrence of mood-episodes, that can have a severe impact on the life of individuals. The effect of lithium, one of the main medications used to treat acute episodes or prevent them from happening, is very different from one individual to an-other. So far, there is no way to predict in advance for whom patient this treatment will be effective or for whom it will not. Finding markers that can predict as early as possible the efficiency of this treatment is a major field of current research in psychiatry, in order to avoid maintaining an inefficient treatment for several years that can have negative side-effects. Over the past decades, it has been shown by multiple studies that lithium can act on the biological clock, that regulates circadian rhythmicity of the body (i.e. rhythms that presents a 24 hours periods, such as rhythms of sleep and activity, feeding, social activities...). But it is still very unclear whether the effect of lithium in regulating the mood in bipolar disorders is mediated by this action. Melatonin is one of the key-regulator of circadian rhythmicity of the human body. Our hypothesis, based on some previous studies, is that the action of lithium in type-1 bipolar disorder (BD-I) is related to an action on melatonin secretion. To test that, we want in this study to compare the noctunal secretion of melatonin between BD-I individuals with a good response to lithium versus with a poor response to lithium.

Measurement based care (MBC) is an emerging best practice involving serial assessment of clinical status and using those findings to inform clinical decision making. However, there is a lack of research on how to best apply principles of MBC for patients with bipolar disorder. The proposed project goal is to assess the feasibility of comparing effectiveness of measurement-based care (MBC) to enhanced usual care in a randomized trial. Many individuals with bipolar disorder experience fluctuating depressive and manic symptoms which can impair functioning and reduce quality of life. The main hypothesis is that treatment adjustments will occur more often in the MBC group than the enhanced usual care group. The exploratory hypothesis is that symptoms of bipolar disorder will decrease more in the MBC group than the enhanced usual care group.

The purpose of this study is to better understand mental illness and will test the hypotheses that while viewing affective stimuli, patient groups will show increased blood oxygenation level dependent (BOLD) signal by fMRI after lorazepam. This study will enroll participants between the ages of 16 and 60, who have a psychotic illness (such as psychosis which includes conditions like schizophrenia, schizoaffective disorder, and mood disorders). The study will also enroll eligible participants without any psychiatric illness, to compare their brains. The study will require participants to have 3-4 sessions over a few weeks. The initial assessments (may be over two visits) will include a diagnostic interview and several questionnaires (qols) to assess eligibility. Subsequently, there will will be two separate functional magnetic resonance imaging (fMRI) sessions in which lorazepam or placebo will be given prior to the MRI. During the fMRI the participants will also be asked to answer questions. Additionally, the participants will have their blood drawn, women of child bearing potential will have a urine pregnancy test, vital signs taken, and asked to complete more qols.