Active clinical trials for "Dementia"

This research aims to collect comprehensive data on the clinical trial experience of dementia patients. Its goal is to identify the factors that limit patients' ability to join or complete a trial successfully. Clinical trial participation often favors specific demographic groups, and limited research exists on the impact of trial attributes on participation. Therefore, this study aims to analyze data from various demographic groups and identify any recurring trends that could provide valuable insights for future dementia patients.

IONA LCS is a research project that will allow the testing of multiple new therapies as well as diagnostic tests for neurodegenerative diseases. IONA LCS acts primarily as a readiness cohort for interventional studies targeting the treatment of neurodegenerative disease but also provides data as the reference standard to validate new diagnostic tests. As such, it is a foundational element to support a range of research projects undertaken by Scottish Brain Sciences and our partners.

This project's main goal is to use state-of-the-art passive sensing techniques to identify digital biomarkers that relate to bioenergetic changes in the brain due to nicotinamide riboside supplementation in those with mild cognitive impairment and mild Alzheimer's dementia.

Mild cognitive impairment (MCI) represents a transitional stage between healthy aging and dementia, and affects more than 15% of the population over the age of 60 in China. About 15% patients with MCI could progress into dementia after two years and about one-third develop into dementia within five years, which will lead to suffering, as well as staggering economic and care burden. So, exploring the predicting biomarkers from MCI to dementia to identify and delay progression to dementia at an early stage is of great social and clinical significance. Some reports based on a single neural biomarker suggest that risk models can predict the conversion of MCI to dementia, but no widely recognized prediction models basing on multiple complex markers have been used in clinical practice. The objectives of this study are to outline the spectrum of MCI transforming into dementia through a 5-year prospective longitudinal cohort study; Secondly, screening biomarkers for MCI transmit to dementia are based on clinical symptoms, neuropsychology, neuroimaging, neuroelectrophysiology, and humoral markers tests data.

Evaluate cognitive improvement pre amd post acupuncture treatment in patients with probable alzheimer's dementia as measured by MOCA score and also per form A( measure of patient's personal information). Also caregiver input.

Dementia with Lewy body disease (DLB) is the second leading cause of degenerative cognitive disorder after Alzheimer's disease (AD). Its variable clinical expression makes diagnosis difficult. To date, there is no validated DLB diagnostic biomarker, despite several biomarkers in development (EEG, MRI, biology). Studies have shown that an improvement in diagnostic performance could be obtained by combining different modalities biomarkers using machine learning. The aim of this research is to identify the best combination of multimodal biomarkers for the diagnosis of DLB (EEG, MRI, biology, cognitive scores), using a machine learning approach applied to a clinical cohort.

The main purpose of this study is primarily the translation and cross-cultural adaptation into Greek and then the investigation of validity and reliability in the Greek target population.

This observational study will examine the association of chronic traumatic cerebrovascular injury and cardiovascular risk factors with TBI-related cognitive impairment and vascular dementia. Cerebrovascular, inflammatory, and neurodegenerative blood biomarkers as well as clinical and neuroimaging data

Dementia is associated with a variety of neurovascular and neurometabolic abnormalities. Traditional imaging techniques used to investigate such abnormalities, such as Positron Emission Tomography and functional Magnetic Resonance Imaging, are not always well tolerated, have expensive start up and running costs, and are limited with regards to the types of experiments that can be performed as they can be highly sensitive to movement, are noisy, and have physical restrictions. Near-infrared spectroscopy (NIRS) is a non-invasive neuroimaging technique which uses light in the near-infrared spectrum to detect relative changes in concentration of oxygenated and deoxygenated haemoglobin, and the oxidation state of Cytochrome C Oxidase. As such, NIRS can provide measures of brain oxygenation and metabolism. NIRS is less sensitive to movement, is well tolerated and has few contraindications. It is thus a promising candidate for use in clinics or in peoples' homes for monitoring dementia. In the present study, the investigators aim to use both dual-wavelength and broadband NIRS in a range of dementia subtypes, including Alzheimer's Disease and Dementia with Lewy Bodies, and severities, including Mild Cognitive Impairment, to identify how brain oxygenation and metabolism is altered in dementia and across various clinical subgroups. The investigators also aim to determine the relationship between brain oxygenation and metabolism in dementia, and use machine learning approaches to identify optical biomarkers for dementia.

The causes of neurodegenerative dementias such as Frontotemporal Dementia, Lewy Body Disease and Alzheimer's disease are still largely unknown. While the contribution of some genetic mutations and polymorphisms is associated with autosomal dominant patterns of inheritance of these dementias, in many cases, the specific causative mutation in these families is not yet identified. Further, in many patients, polygenic risk is thought to give rise to pathophysiologic changes, but which specific genes affect risk are largely yet unknown. By examining genotypes in patients that present to our Cognitive Neurology and Alzheimer's Research Clinic with suspected or confirmed neurodegenerative dementia, or have a history of a familial dementia, we aim to help identify and characterize genetic mutations or polymorphisms that give rise to neurodegenerative diseases.