UW Alzheimer’s Researchers Receive 2021 Garvey Institute Awards for Technology-Driven Solutions

June 01, 2021

Science Updates, Care & Treatment , News

In its second round of funding, the UW Medicine Garvey Institute for Brain Health Solutions is awarding $1.3 million for technology-driven solutions that aim to improve brain health. The technology focus was driven by the need to develop novel approaches to treat large numbers of people affected by brain disorders, as well the depth of innovation and expertise found locally. Among the 13 winning projects of 2021 are several that involve members of the UW Memory and Brain Wellness Center and UW Alzheimer's Disease Research Center and will benefit from our Center resources. Take a look at these projects:

Using Neurocomputational Modeling to Track Memory Decline

Project Lead Andrea Stocco, PhD (Department of Psychology, UW College of Arts & Sciences; UW Institute for Learning and Brain Sciences (I-Labs)) will collaborate with Thomas Grabowski, MD (Departments of Radiology and Neurology, UW School of Medicine; UW Medicine Memory and Brain Wellness Center) and  Hedderik van Rijn, PhD (Department of Experimental Psychology, University of Groningen).

Project Description 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.

Improving Patient-Focused, Population-Informed Care in Clinical Neurosciences 

Project Lead Sean Mooney, PhD, FACMI (Department of Biomedical Informatics and Medical Education, UW School of Medicine), who is also a Co-Investigator of the UW ADRC Imaging and Biomarker Core, will collaborate with UW MBWC's Thomas Grabowski, MD (Departments of Radiology and Neurology, UW School of Medicine; UW Medicine Memory and Brain Wellness Center) and Michael J. Persenaire, MD (Department of Neurology, UW School of Medicine; UW Medicine Memory and Brain Wellness Center).

Project Description 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.

Using Deep Learning to Diagnose Alzheimer's Disease and Predict its Progression 

Project Lead Linda Shapiro, PhD (Paul G. Allen School of Computer Science & Engineering and Department of Electrical and Computer Engineering, UW College of Engineering) will collaborate with Thomas Grabowski, MD (Departments of Radiology and Neurology, School of Medicine; UW Medicine Memory and Brain Wellness Center) and Sheng Wang, PhD (Paul G. Allen School of Computer Science & Engineering, UW College of Engineering)

Project Description 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.


The UW Medicine Garvey Institute for Brain Health Solutions institute was founded last October with a $50 million donation by local philanthropists Lynn and Mike Garvey. Their goal is to fast-track treatments for patients with mental health, addiction and other brain health problems. The new awards join 11 projects already underway to reduce the burden of cognitive aging, trauma and addictions.

“We now have a portfolio of 24 exciting projects that have the potential to make a significant difference in brain health over the next five years,” said Dr. Jürgen Unützer, director of the institute and professor and chair of the department of Psychiatry and Behavioral Sciences at the University of Washington School of Medicine. “These projects are significant investments in new ideas and collaborations to improve the lives of individuals and families living with mental health and brain health problems.”

Faculty and staff involved in the 24 active Innovation Grants represent nine UW schools and colleges, 20 departments and divisions, and all three UW campuses. The work is taking place in multiple locations including the VA Puget Sound Healthcare System, Harborview Medical Center and UW Medical Center. Find a full list of projects and descriptions.  A list of partners and collaborators shows the Garvey Institute’s commitment to advance brain health locally, regionally and nationally.

- Adapted from UW Medicine Newsroom/ UW Medicine Garvey Institute