View clinical trials related to Mild Cognitive Impairment.
Filter by:The purpose of the study is to examine the properties of the Automatic Story Recall Test (ASRT) and its parallel variants, as well as letter fluency and category fluency cognitive tests. Tests will be completed in crowdsourced populations, to derive normative data, and examine test properties in demographically diverse and cognitively impaired participants recruited and tested online.
The AMOR-Kentucky study will examine the impact of a pharmacist-physician patient-centered medication therapy management deprescribing intervention to address inappropriate medication use in patients with cognitive impairment in underserved, lower socioeconomic populations in rural Appalachian Kentucky. The results of this study will provide valuable insights on how to expand and implement deprescribing interventions using telemedicine to reduce the prevalence and the associated healthcare costs of medication-related problems in patients with mild cognitive impairment, Alzheimer's disease and other dementias in rural areas throughout the US. The investigators will assess the potential use of telemedicine in this population by performing an initial single arm, unblinded study of the medication therapy management (MTM) describing intervention in rural/underserved Kentucky Appalachian populations with cognitive impairment and/or dementia using potentially inappropriate medications (n=50). Following initial recruitment and clinical evaluation, engaged participants will have their medication list reviewed by a pharmacist-clinician team to identify targets for deprescribing intervention. The intervention will be engaged remotely with the participant and their caregiver, and the MTM team at 4 weeks post initial evaluation, and then reinforced at a 3-month timepoint. This approach will be carried forward through a telemedicine practice at University of Kentucky that is comprised of approximately 500 patient-caregiver dyads throughout rural areas of Appalachian Kentucky.
The primary objectives are to develop and validate a classifier using multimodal passive sensor data and metrics derived from normal iPhone and Apple Watch usage to distinguish individuals with normal cognition from those with mild cognitive impairment (MCI) and to develop and validate a cognitive wellness score that tracks fluctuations in cognitive performance over time using multimodal passive sensor data and metrics derived from normal iPhone and Apple Watch usage.
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 primary objective of the study is to evaluate whether a set of algorithms analysing acoustic and linguistic patterns of speech, can predict change in 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 AUC, 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 purpose of this platform study is to evaluate the effect of anti-inflammatory agents on cognition in early Alzheimer's disease. Additionally, the safety and tolerability of these anti-inflammatory agents and the effects on central and peripheral inflammation will be evaluated.
The purpose of this research study is to learn whether it is possible and useful for caregivers to report behavioral or psychological symptoms of people with mild memory problems or Alzheimer's disease through a smartphone mobile application. The investigators believe that monitoring these symptoms and having a tool, like a mobile application, can provide quicker accessibility to the patient's clinical care team, which could improve care for patients and caregivers. These types of symptoms found in patients with mild memory problems or Alzheimer's disease are any type of psychiatric symptoms or abnormal behaviors one might develop as the result of these brain illnesses. Examples of psychological and behavioral symptoms are depression, anxiety, insomnia, irritability, agitation, and hallucinations. These symptoms differ from regular psychiatric symptoms, because they are caused by mild memory problems or Alzheimer's disease (AD). These symptoms can cause a lot of distress for patients and caregivers, and can lead to greater use of healthcare services.
Individuals diagnosed with mild cognitive impairment (MCI) are at a high risk of developing dementia and are an important target population for interventions that may reduce the risk of cognitive decline. A diagnosis of MCI or dementia also has an important impact on caregivers, who show increased levels of stress, anxiety, and depression. Mindfulness meditation is a promising behavioural intervention that may have important benefits both for older adults with MCI and for caregivers. Previous research suggests that meditation may improve psychological wellbeing, reduce stress, and even improve cognitive function. Technology-based mindfulness meditation platforms may be a much-needed solution for promoting the adoption of mindfulness in these populations. The current study is a pilot randomized control trial of a mindfulness meditation intervention delivered via the Muse platform in two study populations: a) older adults diagnosed with MCI, and b) family caregivers of persons with MCI or neurodegenerative disorders. Muse is a mobile application for meditation that provides real-time feedback about the user's state of mindfulness during meditation via a headband containing electroencephalographic sensors (EEG) that the user wears while meditating. It is thought that this neurofeedback can promote learning and lead to faster improvements in meditation ability and, consequently, greater benefits from meditation practice. This aim of this pilot study is to establish the acceptability of the Muse platform as an intervention in the two study populations, to determine the feasibility of the randomized control trial designed to evaluate the effectiveness of a 6 week intervention with the Muse platform, and to evaluate the effect of neurofeedback on meditation. Participants will be randomly allocated to meditation with neurofeedback (NFB) or meditation without neurofeedback (no-NFB) and will complete daily meditation sessions for 6 weeks. An assessment visit before and after the intervention will evaluate participants' psychological well-being using questionnaires; their visual working memory, attention, and visual perception using behavioural tests; and their mindfulness ability using questionnaires and a behavioural measure. EEG will also be recorded using the Muse headband to examine changes in electrophysiological markers during cognitive tests and at rest.
This is an administrative supplement to an existing project "Using fMRI-guided TMS to increase central executive function in older adults: NCT02767323" This award allows extending our existing fMRI-TMS paradigm to patients with a prodromal form of Alzheimer's Disease (AD) known as amnestic Mild Cognitive Impairment (MCI), and investigate the role of brain health factors in mediating the TMS-related memory performance benefits associated with communication between a network of frontoparietal brain regions in these populations. The focus on focal neurostimulation at only a single site represents a fundamental gap in the approach of memory-based neurostimulation therapies. Neurostimulation affects multiple sites within a cortical network, but these global effects have not been used as targets for stimulation because of limited knowledge about what influence these localized sites have on global changes in brain state. To address this problem, multimodal neuroimaging tools and network modeling approaches developed though the parent U01 project will be used, to demonstrate how focal neurostimulation improves the efficacy of TMS for enhancing memory function. These goals will be addressed in the Administrative Supplement under our two specific aims. First, network-guided TMS will be applied to optimize memory success based in the frontoparietal network (FPN) in a new group of MCI patients. A new form of TMS targeting that involves modeling of the global network to understand how the controllability of a stimulation site evokes changes in widespread brain networks will be tested. Second, structural and functional factors affecting the efficacy of individualized network-guided TMS will be identified to ameliorate deficits in MCI. By creating a multimodal model of neural deficits related to MCI, network-guided TMS will be adjusted to demonstrate how the MCI brain might compensate for these neural deficits. The parent U01 project has made foundational advances towards these goals, as we have demonstrated the ability of to selectively enhance and reduce working memory performance in healthy older adults. In the current Administrative Supplement this paradigm will be extended to a group of MCI participants in order to test the hypothesis that excitatory rTMS to the working memory network can provide positive outcomes for patients with pre-clinical AD. The proposed work will provide an important tool for studying the stability and controllability of network connectivity of memory states in the aging brain, as well as new information on the effectiveness of brain stimulation technologies as a therapeutic approach for cognitive decline.
A phase 1 randomised, placebo-controlled, single-blind study to characterise the biomarker effects of the CSF-1 receptor antagonist JNJ-40346527 in participants with mild cognitive impairment. A maximum of 54 participants will be recruited to the two part study. The first part of the study will identify whether it is possible to identify biomarkers that may be used in future studies with JNJ-40346527 and part 2 will investigate a minimal efficacious JNJ-40346527 dose.