Bipolar Affective Disorder

This observational study will characterise the general psychiatric and general medical phenotypes of 100 adults, sequentially admitted to NHS General Adult Psychiatry (GAP) "mental health" inpatient wards, providing the first detailed information on morbidity in this patient population.

The objective with this study is to conduct a 6-month RCT comparing effects of add-on melatonin versus add-on placebo on mood stabilisation and other critical patient outcomes in patients with BD and to test whether principal effects are antimanic, antidepressant and/or prophylactic against relapse

This project aims to evaluate feasibility, validity and clinically applicability to systematically measure fitness in patients with severe mental illness. The research project is a validation study using a cross-sectional design. The project will include 50 people with severe mental illness and 25 healthy controls without present or previous mental illness. The protocol includes a baseline visit where three different ways of measure fitness will be carried out, and a 14-day period of physical activity monitoring.

Initially, observational studies suggested a possible effect of Lacosamide on depressive and anxious symptoms in individuals with epilepsy, and later, an open-label study demonstrated the efficacy of lacosamide in improving depressive and manic symptoms in individuals with bipolar disorder (BD). The primary objective of this study is to evaluate the efficacy of combining lacosamide as an augmentation treatment to first- or second-line medication treatments in moderate to severe major depressive episodes of treatment-resistant BD I and II (failure of at least two adequate treatments during the current episode). The main hypothesis of the study is that lacosamide produces a greater reduction in depression scores compared to a placebo treatment and that both groups will exhibit similar rates of side effects and adverse events. We will conduct a double-blind, randomized, parallel-group pilot study, comparing the enhancement of the treatment that patients had been using with lacosamide and placebo, over a duration of 12 weeks. Forty subjects aged between 18 and 65 years with a diagnosis of BD (I or II) in a moderate or severe major depressive episode, despite the use of first- or second-line treatments, will be selected. The primary outcome will be the assessment of lacosamide efficacy through the difference in scores on the Hamilton Depression Rating Scale (HAMD-17) from the initial visit to the end of week 12 of intervention between the lacosamide and placebo groups.

The project proposes to develop a digital product based on cognitive behavioral therapy for the assisted treatment of depression. The digital cognitive behavioral therapy in this study is conducted based on a self-developed mobile applet. The therapy is developed by psychotherapists, which is conducted for a total of 8 weeks, with weekly sessions including AI-guided course work and homework. This study aims to evaluate the therapeutic effects of CBT-based digital products for depression in patients through a real-world study, and to explore its genetic and neuroimaging mechanisms.

Mental health issues represent a major public health and social problem that significantly impacts economic and social development. Compared to other diseases, mental disorders can impair various aspects of a patient' s life, including psychological, social, occupational, and educational functions, affecting their quality of life and daily living abilities. Particularly, severe mental disorders tend to have a chronic course, often resulting in diminished social functions and social withdrawal, making it difficult for patients to integrate into society. Repeated, systematic, and comprehensive rehabilitation training for patients with severe mental disorders can effectively control or delay disease recurrence, improve social functions, enhance quality of life, and facilitate patients' reintegration into society. In recent years, the scope of mental disorder rehabilitation has expanded to include enhancing patients' social functions and promoting their integration into society. Vocational rehabilitation and social skills training are widely used in the rehabilitation treatment of patients with severe mental disorders, and some physical intervention methods, such as neurofeedback training, have also proven to be significantly effective in the rehabilitation process. However, traditional rehabilitation techniques often lack specificity and fail to meet individualized needs of patients. Additionally, the rehabilitation process lacks long-term monitoring, making it challenging to continuously assess and adjust patients' rehabilitation outcomes. Furthermore, the assessment of rehabilitation effectiveness mainly relies on patients' subjective feelings and clinical observations, lacking high-quality evidence. Therefore, there is an urgent need to introduce new rehabilitation technologies and scientifically evaluate their effectiveness to address the shortcomings of traditional methods and provide more personalized, precise, and effective rehabilitation support. With the rise of digital health technologies, the field of mental health rehabilitation has encountered new opportunities. Compared to traditional therapies, digital health is revolutionizing the healthcare industry, moving away from traditional approaches to healthcare management to real-time personalized monitoring and therapeutic care.Technologies such as remote monitoring, virtual reality, and computer-assisted cognitive correction therapy are increasingly applied in rehabilitation. However, these methods still need improvements in data management and integration capabilities. A large amount of data accumulates in systems, recording only the training process and real-time effects of patients, without further evaluating their rehabilitation status, leading to resource waste. Therefore, there is an urgent need to develop a digital rehabilitation model that better meets the genuine needs of patients with severe mental disorders. This study aims to integrate multimodal technology, reinforcement learning, and agent-based modeling (ABM) into the research of mental health rehabilitation to more accurately assess and predict the rehabilitation status of mental disorder patients and to more effectively guide and support decision-making in mental rehabilitation treatment.

Disorganized speech, language and communication, also called 'formal thought disorder,' is a key part of severe mental illnesses like psychosis and mood disorders. When someone's communication is disorganized, it makes social interactions difficult, increases stigma and affect educational and employment opportunities. However, we do not know much about why this happens. This project, called DIALOG, aims to understand the brain's role in disorganization by studying everyday language use instead of traditional clinical ratings. The study will look at how our brain creates predictions during interactions and how these processes break down in psychosis. This international project also includes experts with personal experience of mental illness. The study will look at speech, thinking patterns, symptoms, and brain waves. The goal of the study is to see if brain waves are disrupted in psychosis, especially in language-related problems. Speech tasks, like describing pictures, talking about a significant event, and telling a story are administered. These tasks will be audio-recorded for analysis. Non-invasive brain imaging technologies such as Magnetoencephalography (MEG) and Magnetic Resonance Imaging (MRI) are utilized. MRI creates images of the brain's structure, while MEG records magnetic activity from neurons, shown as brain waves. The MRI machine uses a large magnet to create images, and MEG captures small magnetic field changes from brain activity. Participants will also undergo clinical and neurocognitive assessments. The study will combine Large Language Models (LLM) applied to speech recordings with large scale participant data from neuroimaging tools (MRI/MEG). The goal of DIALOG is to pioneer a computationally informed, molecular-to systems-level account of disorganisation, identifying the precise mechanisms that can be targeted with novel treatments. This project aims to gather speech and neuroimaging data from Montreal \[100 healthy volunteers and 50 patients with psychosis\], Groningen \[17 synaptic density PET scans\], Cardiff \[600 participants\] and Marburg \[1600 participants\] with schizophrenia, schizoaffective disorder or mood disorders and user acceptability data at Pavia and Melbourne.

This study aimed to understand patients' needs and preferences for antipsychotic dosage forms.