Active clinical trials for "Cognitive Dysfunction"

The primary aim of the project is to map fatigue, cognitive and visual dysfunctions and possible underlying pathophysiological mechanisms in persons with long-term symptoms after a mild to moderate COVID-19 infection. Secondary goals are to study whether covarying factors such as depression and sleep disorders contribute to the results.

The goal of the proposed observational, multi-site study is to use fNIRS neurophysiological data recorded during a battery of tasks to detect MCI within a cohort consisting of patients and age-matched healthy controls. Furthermore, the investigators aim to explore whether they can measure the severity of MCI symptoms in the patient population. If successful, this approach enables clinicians to track the disease at its source-the brain; possibly allowing for earlier detection of MCI and its progression, and ultimately more efficient interventions.

Alzheimer's disease dementia (AD) is a debilitating and prevalent neurodegenerative disease in older adults globally. Cognitive impairment, a hallmark of AD, is assessed through verbal tests that require high specialization, and while accepted as screening tools for AD, general practitioners seldom use them. AD can be diagnosed with expensive, invasive neuroimaging and blood tests, but these are usually conducted when cognitive functioning is already severely impaired. Thus, finding a novel, non-invasive tool to detect and differentiate mild cognitive impairment (MCI) and AD is a prime public health interest. Self-figure drawings (a projective tool in which individuals are asked to draw a picture of themselves), are easy to administer and have been shown to differentiate between healthy and cognitively impaired individuals, including AD. Convolutional Neural Network (CNN) (a type of deep neural network, applied to analyze visual imagery) has advanced to assess health conditions using art products. Therefore, the proposed study suggests utilizing CNN-based methods to develop and test an application tailored to differentiate between drawings of individuals with MCI, AD, and healthy controls (HC) using 4,000 self-figure drawings. This

The purpose of this research study is to measure cerebrospinal fluid (CSF) clearance in subjects with and without high blood pressure. CSF cushions the brain from impact and carries waste products from the brain to the bloodstream. This process is known as clearance. Impaired removal of proteins from the aging brain causes their buildup and may contribute to an increased risk for Alzheimer's disease. It is also suspected that clearance may be related to the health of vessels carrying the blood throughout the brain. It is known that high blood pressure damages blood vessels and thus may impair clearance. In this project we will examine if having high blood pressure is related to impaired brain clearance and whether treating high blood pressure improves clearance and reduces buildup of Alzheimer's disease-related proteins. Participants will be asked to undergo a medical examination, testing of memory, brain imaging (both magnetic resonance and positron emission tomography, and spinal tap at the beginning of the study and 1 -2 years later.

Patients with mild cognitive impairment aged 50-80 years old were recruited in Shiyan City, Hubei Province, and divided into intervention group and placebo group. They were given yeast β-glucan capsules and starch capsules, respectively, for 6 months, in order to explore whether yeast β-glucan can improve cognitive function of patients with mild cognitive impairment by regulating gut microbiota and its metabolites.

The goal of this study is to gain a better understanding of why some individuals who have suffered a stroke experience post-stroke cognitive decline. Specifically this study is testing whether global disruption of the blood-brain barrier detected at the time of the stroke is informative about the risk of post-stroke cognitive decline over the next 3 years.

Patients with Psychotic disorders (Schizophrenia, Bipolar disorder and Schizoaffective disorder) often suffer from significant cognitive impairments, however how these develop and change over time before and after the first psychotic break is unclear. While there are data by several groups, showing that many future patients have significant cognitive impairments years before the onset of psychosis, many future patients seem to be doing well before the manifestation of psychosis, and decline steeply in functioning after their first psychotic episode. Hence the timing of the onset of cognitive impairment in patients with psychotic disorders requires further exploration. The current study will investigate the timing of cognitive impairment by using IQ tests before and after the first psychotic break

Cognitive impairment is one of the core early signs of dementia, and it is also a key stage for community-based dementia prevention. Accurate and convenient prediction of cognitive impairment can help the community to identify and manage the high-risk population of dementia. Previous studies had developed several dementia predicting models, but such models may be not suitable for cognitive impairment prediction. Based on the national representative follow-up data of Chinese Longitudinal Healthy Longevity Survey (CLHLS), this project aims to develop and validate a brief cognitive impairment prediction algorithm among the community-dwelling elderly, using machine learning methods (such as Logistic regression, Naïve Bayes model, Extreme Gradient Boosting Tree and so on). Finally, based on the constructed model, an easy-to-use online intelligent assessment tool for predicting cognitive impairment risk will be developed. The general practitioners, social workers and the elderly would be invited to use the tool and we will revise the tool according to their suggestions and comments. This project is expected to provide scientific basis and technical support for community-based dementia prevention, and will also be useful for the elderly to easily understand their cognitive health.

The purpose of this study is to test the adapted protocol, CAPABLE Family which builds upon the evidenced based CAPABLE program to address older adults with co-occurring physical disability and mild cognitive impairment or early stage dementia and the older adults' caregivers. It will consist of two phases - an open label pilot and waitlist control trial.

The KBASE2 is the second phase of the KBASE project, which consists of roll-over participants from the first phase of the KBASE as well as newly enrolled participants with varying degrees of cognitive functions (e.g. individuals with normal cognition, mild cognitive impairment, or AD dementia). In addition to the aims of the first phase of the KBASE, the KBASE2 will focus on new data collection and integrative analysis of the rich structural, functional, and molecular neuroimaging data in relation to whole genome sequencing and other -omics. Network analysis of disruption in brain connectivity in relation to clinical status and AD biomarker profiles also will be conducted.