Active clinical trials for "Alzheimer Disease"

The purpose of this study is to determine how well a blood test can detect amyloid beta, a protein involved in Alzheimer's disease. Participants will be asked to complete an initial blood collection and cognitive testing, and a subset of participants will be asked to complete a larger blood collection, amyloid PET imaging, and an MRI.

Today, the Alzheimer's disease (AD) diagnosis is founded not only on clinical criteria but also on complementary examinations to confirm a physiopathological process of AD. In complex cases, lumbar punction could be necessary in order to measure Aβ peptides and Total and phosphorylated Tau but new biomarkers could be useful. The main objective of this project is to conserve these cerebrospinal fluids, collected in usual practice in order to validate new biomarkers for diagnosis, prognosis or therapeutic following of Alzheimer's disease and other dementia.

The aim of this study is to create a repository of both cross-sectional and longitudinal data, including cognitive, linguistic, imaging and biofluid biological specimens, for neurodegenerative disease research and treatment.

Immediate family members shoulder the majority of care responsibilities for persons living with dementia. However, due to various societal changes, elder care responsibilities have expanded to extended family members, including grandchildren, siblings, nieces/nephews, siblings, and step-kin. The main objective of this study is to understand the caregiving journeys of various extended family members involved in dementia care. We aim to learn about caregivers' care management strategies; their use of home and community-based services and informal support; and barriers to service usage. We will use the results from the study to help enhance service delivery, alleviate care-related stress, and improve the quality of life of dementia patients and their caregivers. We will use a mixed-methods design to explore the challenges faced by caregivers as well as their service usage for the person living with dementia. Our methodology involves an initial telephone interview (approx. 70 minutes) that includes open-ended questions, standard items, and structured measures, followed by an 8-day semi-structured daily diary interview about daily care responsibilities and experiences with services (15-20 minutes each evening). This study will be conducted with 240 extended family members serving as one of the main caregivers for a person living with dementia in a community setting.

The purpose of this research study is to evaluate adult children of parents with and without Alzheimer's disease which represent an ideal population for investigating the biological changes that precede the clinical onset of AD. The investigators will be imaging the brain to detect the presence of amyloid deposits (plaques in the brain). Amyloid is a protein that may be related to dementia of Alzheimer's disease (DAT). This study will use a radioactive tracer called [11C]-Pittsburgh Compound B (11C]PIB), which is a tracer that binds to beta amyloid protein in the brain. This compound is considered investigational, which means that it has not been approved by the United States Food and Drug Administration (FDA).

The primary objective of the study is to evaluate whether a set of algorithms analysing acoustic and linguistic patterns of speech, can predict change in Preclinical Alzheimer's Clinical Composite with semantic processing (PACC5) between baseline and +12 month follow up across all four Arms, as measured by the coefficient of individual agreement (CIA) between the change in PACC5 and the corresponding regression model, trained on baseline speech data to predict it. Secondary objectives include (1) evaluating whether similar algorithms can predict change in PACC5 between baseline and +12 month follow up in the cognitively normal (CN) and MCI populations separately; (2) evaluating whether similar algorithms trained to regress against PACC5 scores at baseline, still regress significantly against PACC5 scores at +12 month follow-up, as measured by the coefficient of individual agreement (CIA) between the PACC5 composite at +12 months and the regression model, trained on baseline speech data to predict PACC5 scores at baseline; (3) evaluating whether similar algorithms can classify converters vs non-converters in the cognitively normal Arms (Arm 3 + 4), and fast vs slow decliners in the MCI Arms (Arm 1 + 2), as measured by the Area Under the Curve (AUC) of the receiver operating characteristic curve, sensitivity, specificity and Cohen's kappa of the corresponding binary classifiers. Secondary objectives include the objectives above, but using time points of +24 months and +36 months; and finally to evaluate whether the model performance for the objectives and outcomes above improved if the model has access to speech data at 1 week, 1 month, and 3 month timepoints.

The focus of this study is to examine the protein-plaque clearance (Aß) in relation to the blood-brain-barrier, the glymphatic system, brain lymphatic system and enzymatic degradation. In order to achieve this aim the investigators intend to study participants with a Subjective Cognitive Decline, Mild Cognitive Impairment and a mild Alzheimer's disease.

The purpose of the TRC-PAD study is to develop a large, well-characterized, biomarker-confirmed, trial-ready cohort to facilitate rapid enrollment into AD prevention trials utilizing the APT Webstudy and subsequent referral to in-clinic evaluation and biomarker confirmation. Participants with known biomarker status may have direct referral to the Trial-Ready Cohort. If you are interested in being selected for the TRC-PAD study, you should first enroll in the APT Webstudy (https://www.aptwebstudy.org/welcome).

Apheleia-001 is a prescreener that aims to identify and characterize participants with reported cognitive impairment using demographic information, clinical history, brief cognitive assessments, and blood-based biomarkers to distinguish appropriate participants for referral to a therapeutic AD clinical trial.

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