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.
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.
Text messaging holds promise as a strategy for engaging older adults in depression treatment. The purpose of this project will be to develop and pilot test a text messaging intervention delivered in primary care settings practicing integrated care. Recent data shows that the vast majority of older adults have a cell phone and that about half have sent an email or text within the past month. Among that latter group, about one third reported they did so “most days.” There is research showing that text messaging with older adults is feasible that is focused on mental health was well received and effective. None of the prior studies looked at text messaging as an adjunct to mental health treatment delivered in primary care. This project will address that gap.
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.
This study will explore the feasibility of implementation of a technology-enhanced Evidence-Based Psychosocial Behavioral Intervention entitled Mobile Motivational Physical Activity Targeted Intervention (MobMPATI) by frontline primary care staff (e.g., nurses, medical assistants) to expand workforce capacity to deliver acceptable, sustainable, and effective treatment for depression in older adults.
