Older adults are less likely to receive the recommended standard of care for preventative services, chronic diseases and geriatric concerns such as complex care navigation. Late-life depression is a common chronic disease, and older adults face multiple barriers obtaining depression care from healthcare settings, especially if things like fragility, social needs, and transportation limit access to primary care. Offering depression care in non-traditional healthcare settings is one way to increase access. Community health workers (CHWs) are trusted community members who increase the health of communities through care coordination, health education and outreach. One approach is to task-shift the Care Manager (CM) role of a Collaborative Care framework to CHWs in the community. Global health work has demonstrated that non-clinicians can conduct low-intensity psychosocial interventions for depression. However, task-shifting the Care Manager role in a non-clinical setting requires additional skills and poses added challenges. We have gathered prior formative work among CHWs on what they think about being trained and supported in the skills of CM. We now seek to understand Collaborative Care stakeholders’ perspectives on this proposed role expansion of CHWs to CHW Care Managers (CHW-CMs) to understand how to design this role.
Patient Population: Older Adults
Deciphering Mechanisms of ECT Outcomes and Adverse Effects (DECODE)
Electroconvulsive therapy (ECT) is one of the most effective antidepressant non-invasive brain stimulation therapies for adults with major depression. However, a number of patients fail to respond despite adequate trials, and while clinically beneficial, ECT can produce adverse cognitive effects including amnesia, executive dysfunction, and verbal dysfluency.
In this prospective study, we propose the first investigation integrating multiple units of analysis including clinical and cognitive phenotyping, whole-brain neuroimaging, EEG, and E-field modeling to establish the mechanisms underlying ECT-induced antidepressant response (response biomarkers) and cognitive adverse effects (safety biomarkers), as well as to find the “sweet spot” of ECT dosing for optimal antidepressant benefit and cognitive safety. This proposal will result in a paradigm shift from “trial and error” approaches of ECT parameter selection to individualized, precision dosing to improve patient outcomes.
Community resilience to late life depression among first generation Asian Indian immigrants in the greater Seattle area (The CREED Seattle Study)
Asian Indians, are one of the fastest growing ethnic groups in the country, growing from 1.9 million (2000) to 4.6 million (2020). With a median household income of $119,000, Asian Indians are highly educated (43% have a postgraduate degree), are proficient English speakers (82%) and are often touted as a “model minority”. While these data create an impression of general well-being and success, there is limited information on the mental health of this community, as most research tends to aggregate results of the Asian population. Aging and age related mental health issues, especially late life depression in the community, in particular, has been under-studied. As culture exerts a significant influence on psychiatric morbidity, it is likely that this population has unique drivers to late life syndromic and subsyndromal depression beyond what is known from typical studies. Additionally, migration related, as well as acculturative stress, may provide unique influences. However, immigrant Indian communities are known for community engagement, providing large social networks and support which may reduce risk for depression. As a result, it is possible that higher risk resulting from immigration related stress might be mitigated by social engagement. This project will study older first-generation Asian Indians in the Greater Seattle Area to study the association between community engagement and depression symptoms.
Family and Caregiver Training and Support Program (FACTS) pilot
We know from decades of research that caregiver involvement, including family and non‐family members, in a patient’s mental health treatment can make a tremendous difference in the trajectory of their loved one’s life by supporting recovery, reducing relapse, and decreasing mental health crises. Family and caregiver involvement also decreases provider stress, improves caregiver well-being, and can lead to lower patient healthcare utilization and costs. But despite their importance, many family members and caregivers struggle to engage in the kind of support that can benefit the patient and themselves. They often lack access to education, resources, or skills to step into this critical role despite a desire to help. Our initiative intends to develop a pilot Family and Caregiver Training and Support Program (FACTS) program that aims to decrease barriers to caregiver involvement and improve caregiver support.
Our team will develop online training that will include an orientation to having a loved one who is psychiatrically hospitalized and will teach families and caregivers practical communication skills while their loved one is in our care. These topics would be relevant regardless of a patient’s diagnosis and will be adapted from existing evidence‐based models. The pilot will be tested with caregivers of patients hospitalized at the new Center for Behavioral Health and Learning and we will proactively integrate input and feedback from participants to inform program improvements along the way.
We will also build a public-facing website to host FACTS training materials as well as mental health information and resources that we will curate for accuracy and reliability. We expect the FACTS pilot content will serve as a foundation for additional offerings that will include diagnosis specific skills trainings as well opportunities for in-person sessions and Family Peer Support programming.
Immune changes with neuropsychiatric symptoms in dementia
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.
Spanish-language lay-delivered Behavioral Activation in senior centers
This supplement seeks to expand the Collaborative R01 on Lay-delivered Behavioral Activation in Senior Centers for clients whose preferred language is Spanish. The aims are to translate DMFB intervention materials and and test the effect of Spanish DMFB in comparison to professionally-delivered BA (Clinician BA) among older senior center clients on increased activity level and decreased depressive symptoms.
Using neurocomputational modeling to track memory decline
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.
Using deep learning to diagnose Alzheimer’s disease and predict its progression
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.
Synthesizing position emission tomography (PET) data from MRI using deep learning
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.
WITH (Whole person Integrated Texting for Health)
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.
