Although medical care and law enforcement may intersect in an emergency situation, cross-communication and mutual education opportunities prior to the critical tipping point are currently sorely lacking. Our innovative partnership seeks to address these gaps by determining the specific steps dementia clinicians and law enforcement in WA state can take together to improve community health.
For Phase I of this project, we are initiating a direct collaboration between clinicians and law enforcement for dementia crisis intervention, in order to establish appropriate safety measures to be enacted in WA communities. An essential component of ensuring lasting impact can only be achieved by determining the local availability, usability, and effectiveness of proposed safety interventions.
UW dementia specialists will partner with law enforcement across Washington state to jointly identify the resources necessary for effective dementia crisis response. We seek to bridge the gap between medical care and community safety concerns, specifically at a crisis point when a community member feels compelled to summon law enforcement due to perceived significant threat or lack of awareness of other, more appropriate resources. We will conduct interviews with clinicians, police departments, and community stakeholders, review police call logs, and perform ride alongs. The information gathered will be analyzed for major themes related to knowledge and resource gaps, as well as any existing solutions. Three crisis response priorities will be identified, and corresponding “safety packet” content will be outlined in preparation for community partnership input, local adaptations, and ultimately state-wide dissemination.
Though the focus of most research on dementia is the pathogenesis of cognitive deficits, neuropsychiatric symptoms (NPS) are identified in >90% of those afflicted, resulting in hastened cognitive decline, worsened general health, reduced patient and caregiver quality of life, sooner institutionalization, and increased mortality. Affective symptoms, including depression, are the most common NPS in Alzheimer’s Disease (AD), and are present in over half of patients. Using the in-depth clinical phenotyping of participants in the National Alzheimer’s Coordinating Center (NACC) with matched plasma samples, we propose to determine the correlation between select cytokines/chemokines and T-cell differentiation with depression in dementia.
The primary objective of this study is to verify the clinical benefit of monthly doses of aducanumab in slowing cognitive and functional impairment as measured by changes in the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) score as compared with placebo in participants with early Alzheimer’s disease.
The reason for this study is to see how safe and effective the study drug donanemab is in participants with early Alzheimer’s disease.
The most salient and debilitating aspect of dementia is memory loss. Unfortunately, memory loss is also the most difficult to quantify because it relies on doctor-administered tests that cannot be repeated very often. Without frequent and accurate measurements, it is difficult for clinicians to make reliable diagnoses, for patients and their caretakers to prepare in advance and for researchers to better understand the relationship between brain changes and cognitive decline.
This project will recruit 100 patients who are just beginning to experience memory loss as well as 100 healthy controls. Their memory function will be measured weekly through a brief, online test that can be accessed through any device and performed in less than 10 minutes. Data from the test will be fed to a computer model that simulates how fast memories fade in each patient’s brain, and the parameter that represents each patient’s speed of forgetting will be tracked over time. While the model simulates the patient, it also adapts the difficulty of the weekly task, ensuring it remains engaging but doable as memory declines.
The weekly estimates will provide the first, detailed trajectories of how fast memory declines over time in healthy aging and in different forms of dementia. The trajectory of the rate of forgetting will be used to analyze MRI data, producing precise associations between different types of memory loss and different types of brain damage.
Alzheimer’s Disease (AD) is a degenerative condition that affected 5.8 million seniors in 2020 and is the sixth leading cause of death in the United States. Detecting mild cognitive impairment, often a precursor to AD, and predicting its advance to AD dementia are key clinical diagnostic problems. Early diagnosis can motivate early intervention with lifestyle changes that build cognitive reserve or reduce comorbidity and thus prolong functional independence. MRI scans and specialized tests for AD-related proteins in spinal fluid or on PET brain scans are available, but it is not known how best to deploy these expensive tests or combine the information from them. New computer-based “machine learning” software tools may provide a solution to these problems.
This project will explore the use of a machine learning technology called deep learning to diagnose the stage of AD and to predict its progression. We will use the data available from the scientifically open Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, which contains MRI, PET, risk genes, cerebrospinal fluid and other data. We will develop a deep learning model that performs its predictions using MRI data alone, and can also augment the MRI data with the other datatypes for improved performance at some expense. Our modern machine-learning methods are designed to be rationally factored in with other individualized clinical information to aid clinicians in these vital diagnostic decisions.
UW Medicine has amassed detailed patient treatment and business data in its electronic medical record (EMR). This information is a treasure trove that is not used to its full potential for two reasons: 1) For each clinical encounter, only a fraction of the information in the EMR is relevant, and virtually all of the information a clinician engages remains in a format that obscures patterns and trends; and 2) In groups of patients with the same illness, data from the EMR could be used to discern larger trends in the course of the disease or evaluate the effect of practice patterns on patient outcomes. The EMR currently does not provide a way to access this information in an agile way.
We have developed innovative software, “Leaf,” that allows medical providers to access population-based EMR data in real time. Leaf is now used at several academic medical centers nationally. In this project, we will collaborate with the UW Memory and Brain Wellness Center to design and evaluate “dashboards” that visualize how a patient’s history and trajectory compare to other, similar patients. For instance, daily function and cognitive testing data for a person with Alzheimer’s disease, already gathered over the course of several years, could be graphed and compared to the same information from all UW patients with Alzheimer’s disease. We will pilot these dashboards in Leaf and collect patient and provider feedback. We intend to publish our results and make code available as part of the open Leaf platform for rapid dissemination.
Positron emission tomography (PET) is an imaging technique that uses radioactive substances to visualize and assess the brain function. Apart from its heavy use in clinical oncology, PET is widely used in a variety of other conditions such as various neurological, psychiatric, neuropsychological, and cognitive disorders and is the gold standard for assessing neurodegeneration. In particular, PET is clinically used to distinguish Alzheimer’s disease from other dementias and assess the disease progression. Despite its clinical importance, PET imaging encounters barriers because of limited availability, expense and radiation exposure.
This project seeks to address this barrier to brain health using artificial intelligence to predict PET brain images from magnetic resonance imaging (MRI) data. Such a method would be extremely beneficial in clinical settings because unlike PET, MRI is widely available, non-invasive and relatively inexpensive. The approach essentially turns an MRI scanner into a PET scanner, opening up this technology to sites and applications in which PET is either unavailable or infeasible. Doing so would give millions of people access to initial screens for Alzheimer’s disease, assessment of disease progression and an easy way to monitor treatment.
Alzheimer’s disease and related dementias (ADRD) affect more than 10% of
adults who are age 65 and older, but the toll of ADRD is most devastating among
older African Americans. COVID-19 widened these disparities; in addition to
being more susceptible to COVID-19 infection and fatalities, older African
Americans are more likely to experience digital and technical inequities. This puts
them at risk for the development/worsening of depression, anxiety, cognitive
impairment and sleep disturbances.
This project will evaluate several traditional and mobile health tools for
remotely monitoring the effects of social isolation on cognition and mental
health in older African Americans with baseline cognitive complaints. By
testing three different strategies, we will identify the most effective,
feasible and subject-preferred approach to collecting cognitive and mental
health data which will help address brain health disparities.
We aim to determine the accuracy and specificity of Arterial spin labeling (ASL) — a non‐invasive perfusion technique used in MRI to track cerebral blood flow — in measuring vascular and glial‐dependent water transfer to establish whether it is a valuable clinical tool in Alzheimer’s disease. This simple and safe technique, already approved for use in a clinical setting, has potential to circumvent current invasive approaches in human subjects at risk for AD‐related dementias.
