View clinical trials related to Depressive Disorder.
Filter by:Depression is a disease that endangers the physical and mental health of all human beings. Only 30-40% of patients with initial episode depression are cured after treatment with antidepressants. Acupuncture is a widely recognized therapy to treat depression in clinical practice, and it can effectively relieve the depressive mood and improve related physical symtoms in patients with mild to moderate depression. This randomised controlled trial (RCT) is aimed to investigate the efficacy and safety of electroacupuncture (EA) in the treatment for patients with the first-episode of mild to moderate depression.
This project aims to improve the health care provided to people with major depressive disorder (MDD), a disease which is a top cause of disability worldwide. One of the main obstacles to a more effective health care in these patients is represented by clinical heterogeneity, which has not completely elucidated biological correlates. Using a large sample of people with MDD already recruited (n=29,400), the investigators develop a clustering algorithm based on genetic-environmental and brain imaging predictors aimed at identifying homogeneous MDD subgroups. The researchers will then link these subgroups with relevant health outcomes, such as disease recurrency and severity, well-being and functioning, risk of psychiatric and medical comorbidities (e.g. cardiovascular disorders). Replication in independent samples already recruited(n=1380) will prove the validity of the subgroups and expand their clinical characterization. The investigators will develop a classification tool to link the individual's characteristics to the relevant health outcomes and provide corresponding clinical recommendations. The prognostic support tool will be applied to newly recruited samples, feasibility and usefulness according to clinicians's opinion will be assessed (n=120, ongoing recruitment).
This project aims to investigate the effectiveness of existing common antidepressants and to provide new evidence for depressed children and adolescents who are not responding to their first treatment.
This study was conducted to determine the effect of baby massage on postpartum depression and maternal attachment in the postpartum period.
This is a study that will recruit patients from the neurosurgery clinic and the regular TMS clinic. It's a smaller study designed to collect brain imaging pre-treatment and then use image guided TMS to treat patient with a one week "accelerated" rTMS protocol using the research TMS machine that is housed in Dr. Sean Nestor's lab. The idea is to examine whether severe treatment resistant depression has a different brain signature than less severe/TRD and whether we can get a therapeutic response from patients that would otherwise undergo neurosurgery or will ultimately undergo neurosurgery.
There are 636,000 self-reported cases of sexual assault annually in Canada, and nine out of ten persons who have experienced sexual assault are women. Cognitive and behavioural therapies (CBT) are the treatment of choice for many psychological problems arising from sexual assault. However, accessing CBT is a significant challenge, especially for women who have experienced sexual assault who may be ashamed and not disclose the sexual assault. Online CBT is an effective option to circumvent these barriers. In addition to being accessible and less resource-intensive, studies report that patients are less inhibited and that the online environment provides greater emotional safety. There is also a growing body of evidence that online CBT programs requiring little or no contact with a mental health professional are effective, this having been demonstrated primarily with individuals with anxiety and mood disorders. But when it comes to treating the psychological symptoms of sexual assault in potentially vulnerable individuals, can we really suggest a self-care approach? There is no direct empirical evidence to support such a recommendation, and it is this important question that this project wishes to address. To compare the effectiveness, acceptability and user engagement in a self-managed treatment platform with or without the support of a therapist to reduce post-traumatic symptoms, depression and insomnia in people who have suffered one or more sexual assaults, 204 victims of sexual assault experiencing significant distress will be recruited and randomly assigned to either the self-managed or the therapist-assisted online treatment condition. Participants will complete measures assessing post-traumatic stress disorder, insomnia, depression, anxiety, and maladaptive beliefs before, during, after and 3 months after treatment. Secondary outcome will be and appreciation of the online treatment measures by a self-report questionnaire and a semi-structured interview. If effective in reducing symptoms, this treatment would offer the potential to support a self-care approach to treating a wide range of psychological symptoms resulting from sexual assault. The self-managed online platform would fill a service gap deplored by this population.
Clinical depression often includes a pessimistic view of things which have happened in the past and an impairment in the ability to experience pleasure or looking forward to things. A licensed drug called ketamine affects the levels of glutamate, a chemical messenger in the brain, and has been used as a treatment particularly for depression which hasn't got better with other types of medication. Glutamate plays a role in learning and memory so the investigators are interested in understanding how ketamine can affect how people with depression remember past negative and positive memories and how they experience reward. The investigators are conducting a study in depressed participants who did not improve with the standard antidepressant treatment to expand our understanding on how ketamine can influence memory, the way people understand emotions and learn from rewards and punishments. Study participants will undergo medical and psychiatric health screening, drug administration (ketamine or saline), questionnaires and computer tasks before and after the administration of the study drug, and an MRI scan after administration of the drug. MRI is a type of brain scan that allows us to see how the brain responds during for example memories of things which have happened in the past. This project will help us understand how NMDA antagonists may work in depression.
The purpose of this project is to determine if specific gut microbiome or gut-derived metabolites are associated with depression in patients with Multiple Sclerosis (pwMS). Mechanistically, the investigators further hypothesize that depression in pwMS is related to decreased abundance of gut bacteria with GABA-producing activities and/or with anti-inflammatory properties. To determine if the presence of depression in pwMS is associated with specific gut microbiome, gut-derived metabolites or peripheral blood immune profiles. The investigators will perform a cross-sectional study in clinically stable pwMS recruited at the John L. Trotter MS Center. The investigators will evaluate the presence of depression using the Quality of Life in Neurological Disorders (Neuro-Qol) depression scale, one of the 13 scales in the Neuro-Qol recently developed by the NIH using modern psychometric techniques and validated in pwMS. A total of 120 pwMS will be recruited: 60 with and 60 without depression based on the Neuro-Qol depression scale. At the study visit each participant will be asked to provide a stool sample for microbiome analyses and a blood sample for peripheral blood immunophenotyping. Potential confounders will be collected and treated as covariates in the analyses. These include: 1) degree of disability (EDSS); 2) treatment with anti-depressants and DMTs; 3) a 4-days food diary to evaluate diet composition; 4) weight and height to calculate the BMI; 5) fatigue; 6) level of physical activity; 7) sleep quality.
This study is a multicenter, open-label, single-arm Phase 2 clinical trial. Approximately 15 female participants with clinically diagnosed postpartum depression (PPD) will be included in this study. The participants will receive an individualized dosing regimen (IDR) with at least one and up to three doses of GH001 administered within a single day.
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.