Current Projects

The Garvey Institute for Brain Health Solutions is currently funding 24 projects led by UW teams that have the potential to make a big difference in brain health over the next five years. The work spans 9 schools and colleges and 20 departments across all three UW campuses as well as numerous local and regional organizations. The first round of funding focused on cognitive aging, trauma and addictions. The second round of funding focused on innovative uses of technology that have the potential to improve access to behavioral health care. The list of Garvey Institute partners and collaborators shows the Institute’s commitment to further brain health across the University of Washington as well as locally, regionally and nationally.

2021 projects: innovative uses of technology

Project Lead
Andrea Stocco, PhD (Department of Psychology, UW College of Arts & Sciences; UW Institute for Learning and Brain Sciences (I-Labs))

Collaborators
Thomas Grabowski, MD (Departments of Radiology and Neurology, UW School of Medicine; UW Medicine Memory and Brain Wellness Center)

Hedderik van Rijn, PhD (Department of Experimental Psychology, University of Groningen)

Project Summary
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.

Project Leads
Rebecca Hendrickson, MD, PhD (Department of Psychiatry and Behavioral Sciences, UW School of Medicine; VA Puget Sound Healthcare System)

John Oakley, MD, PhD (Department of Neurology, UW School of Medicine)

Collaborators
Aaron Bunnell, MD (Department of Rehabilitation Medicine, UW School of Medicine)

Catherine McCall, MD (Department of Psychiatry and Behavioral Sciences, UW School of Medicine; VA Puget Sound Healthcare System)

Kathleen Pagulayan, PhD (Department of Psychiatry and Behavioral Sciences, UW School of Medicine; VA Puget Sound Healthcare System)

Abigail Schindler, PhD (Department of Psychiatry and Behavioral Sciences, UW School of Medicine; VA Puget Sound Healthcare System)

Project Summary
After COVID infection, 10-50% of people experience persistent symptoms such as fatigue, palpitations, insomnia, cognitive problems, and headache – often with significant associated distress and functional impairment. The exact combination of symptoms varies from person to person, and it is expected that the specific causes vary from person to person as well.

Because of this variability, the current recommendation is for an evaluation by a multidisciplinary team. This creates a demand on our medical system that far outstrips current resources, and risks exposing patients to long, complex medical evaluations whose results are hard to interpret. In addition, clinical treatment trials that mix patients with similar symptoms but different underlying causes have high failure rates.

To address these challenges, we are testing an online platform to identify patients whose pattern of symptoms suggest a particular underlying cause that is common after certain physical and psychological stressors: increased adrenergic (adrenaline/noradrenaline) signaling in the brain and peripheral nervous system. We will pair this with a smaller number of detailed in-person assessments to validate our symptom-based measures and characterize associated biomarkers.

Our results will provide a detailed assessment of the patterns of symptoms caused by high amounts of adrenergic that are seen in persistent post-COVID syndrome, how they change over time and their association with objective measures of cognition and physiology. The project will provide the information needed to begin clinical treatment trials using existing, well-tolerated treatments that modulate adrenergic signaling. We hope the results will also have strong relevance to other stress- and trauma-related disorders such as chronic fatigue syndrome and fibromyalgia.

Project Lead
Caleb Stokes, MD, PhD (Department of Pediatrics, UW School of Medicine)

Collaborators
Michael Gale, Jr., PhD (Department of Immunology, UW School of Medicine)

Juliane Gust, MD, PhD (Department of Neurology, UW School of Medicine; Seattle Children’s Research Institute, Center for Integrative Brain Research)

Francisco Perez, MD, PhD (Department of Radiology, UW School of Medicine)

Project Summary
Infection by West Nile Virus can lead to encephalitis, or harmful inflammation of the brain. The immune system is critical for controlling viral replication and spread early in West Nile Virus infection, but persistent immune activation causes encephalitis that can result in brain damage even after the virus has been cleared. Recent pharmacologic advances have produced drugs that modulate the body’s immune response and can control inflammation, but these drugs have not yet been tested in conditions of viral encephalitis. In order for patients to benefit from these therapies, clinicians need tools that help identify when excessive immune activity is causing encephalitis.

The key innovation of this project is the combination of noninvasive imaging with novel immune modulating drugs to improve the diagnosis and treatment of encephalitis. Our central hypothesis is that specialized immune cells known as macrophages are key drivers of encephalitis in West Nile Virus infection, and that preventing their activation will preserve memory and other cognitive functions. Our studies will explore and develop noninvasive positron emission tomography (PET) imaging as a tool for diagnosing brain inflammation. We will test our hypothesis utilizing West Nile Virus infection of mice, which captures the key elements of human disease including encephalitis. This model allows us to evaluate existing diagnostic and therapeutic tools currently used in humans for other purposes, from which we will define new clinical applications. We will thus be poised to translate our findings to human studies defining and treating viral encephalitis.

Project Lead
Trevor Cohen, MBChB, PhD, FACMI (Department of Biomedical Informatics and Medical Education, UW School of Medicine)

Collaborators
Ellen Bradley, MD (UCSF Weill Institute for Neurosciences)

Benjamin Buck, PhD (Department of Psychiatry and Behavioral Sciences, UW School of Medicine)

Project Description
Schizophrenia is a debilitating mental health condition with high societal and personal costs, due largely to chronic difficulties with social and occupational functioning. While classical symptoms of schizophrenia – such as hearing voices – are often responsive to medication, people with schizophrenia also experience difficulties in social cognition, or understanding and interpreting the intentions and emotions of others. Social cognition affects the ability to function in society, and is a key determinant of real-world outcomes in schizophrenia.

Despite its importance, we lack objective and easy-to-deploy instruments to assess social cognition. This measurement gap presents a critical stumbling block for development of interventions to improve social cognition, because the effects of potential treatments cannot be assessed efficiently and at high resolution. Better measurements are also needed to identify individuals likely to benefit from such treatments and monitor treatment effects over time.

This project will develop innovative automated methods to measure a key component of social cognition – the ability to recognize the intentions and emotions of others. The underlying idea is to present a participant with a cue – such as a short video clip intended to be amusing – and then apply computational methods to their spoken response to see if it aligns with the intention behind the cue. The result will be a set of validated measurement tools to facilitate objective, repeatable, and scalable assessment of social cognition. These tools will accelerate our ability to rigorously test new treatments targeting these key deficits impacting people living with schizophrenia.

Project Lead
Trevor Cohen, MBChB, PhD, FACMI (Department of Biomedical Informatics and Medical Education, UW School of Medicine)

Collaborator
Dror Ben-Zeev, PhD (Department of Psychiatry and Behavioral Sciences, UW School of Medicine)

Project Description
Cognitive therapies help patients by providing ways to modify habitual but unproductive thought patterns, known as maladaptive thinking styles. Cognitive therapies are effective in treating depression, amongst other conditions, and are increasingly delivered remotely as text-based interventions. This trend toward digital delivery has accelerated on account of physical isolation and psychological stressors during the global pandemic. While this means cognitive therapy can potentially reach more patients, the effectiveness of this therapy depends on the ability of a skilled practitioner to recognize types of maladaptive thinking, and there is a critical shortage of mental health practitioners with this expertise.

In radiology, computer-aided diagnosis systems driven by artificial intelligence are used to help physicians detect signs of illness they may otherwise miss. In this project, we will develop a computer-aided detection system to support text-based cognitive therapy. To do so, we will identify indicators of maladaptive thinking styles within a set of text messages exchanged between clients and their therapists, and train neural networks to detect these indicators automatically. The resulting tools will provide a basis for an artificial intelligence-based decision support system to help clinicians recognize and manage maladaptive thinking styles that will enhance the quality and effectiveness of text-based cognitive therapy.

Project Lead
Sean Mooney, PhD, FACMI (Department of Biomedical Informatics and Medical Education, UW School of Medicine)

Collaborators
Thomas Grabowski, MD (Departments of Radiology and Neurology, UW School of Medicine; UW Medicine Memory and Brain Wellness Center)

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.

Project Lead
Amritha Bhat, MBBS, MD, MPH (Department of Psychiatry and Behavioral Sciences, UW School of Medicine)

Collaborators
Committee for Children, Nurture Seattle

Douglass Russell, MD (Department of Psychiatry and Behavioral Sciences, UW School of Medicine)

Joy Zia, MD (UW Medical Center – Northwest)

Project Description
Perinatal mood and anxiety disorders affect one in seven pregnant and postpartum women nationwide, making them the most common complication of pregnancy. Unfortunately, only one in 20 women who need treatment for these conditions actually receives it. This translates to a multigenerational issue, which can negatively affect the mother and child’s long-term physical, emotional and developmental health. It also means an estimated $14.2 billion annually in societal costs in the U.S. alone. While not every perinatal individual with mental health concerns has access to a mental health provider, cell phones and text messaging are ubiquitous. Nonjudgmental support delivered through text messaging may be a low cost approach to reaching women who need emotional support in the perinatal period.

Our project aims to evaluate a text-based mentoring program, the Nurture Program, and assess whether it is possible to support mothers through their third trimester of pregnancy and nine months postpartum and enhance their emotional well-being. The Nurture Program combines the convenience of secure text messaging with the personalization of having a trained peer mentor with whom the mother can develop a trusting relationship. This program also provides resources on child development, connections to local support agencies and suggestions for parent-child bonding and parental wellness activities. Surveyed participants of the Nurture Program consistently report their mentor helped them feel less stressed and more confident in their role as a parent. This study will allow us to measure the impact of this cost-effective approach to promoting perinatal emotional well-being.

Project Lead
Katherine Anne (Kate) Comtois, PhD, MPH (Department of Psychiatry and Behavioral Sciences, UW School of Medicine)

Collaborators

Trevor Cohen, MBChB, PhD, FACMI (Department of Biomedical Informatics and Medical Education, UW School of Medicine)

Andrea Hartzler, PhD (Department of Biomedical Informatics and Medical Education, UW School of Medicine)

Bill Lober, MD, MS (Department of Biobehavioral Nursing and Heath Informatics, UW School of Nursing; Department of Biomedical Informatics and Medical Education, UW School of Medicine; Department of Global Health, UW School of Public Health)

Project Description
On top of climate change, political divisiveness and cultural turbulence, we have faced the most devastating pandemic since global influenza 100 years ago. The resulting social and economic stresses have manifested as widespread anxiety, a worsening opioid epidemic and the highest suicide rates in decades.

Proven behavioral health strategies like Caring Contacts offer hope. Caring Contacts is a program where suicidal individuals receive periodic letters or text messages from a behavioral health practitioner, creating a connection and showing someone cares. Caring Contacts have reduced suicide deaths, attempts and thoughts of suicide and offer an easy re-connection to healthcare, but behavioral health practitioners are in high demand and short supply and often struggle with prioritizing messages and sending timely replies. By analyzing a patient’s text messages, computerized algorithms can identify indicators of risk and other important information to help behavioral health practitioners with the nature and timing of their responses, allowing one behavioral health practitioner to reach hundreds of suicidal patients.

This project brings together behavioral health care, mobile technologies that people now expect and innovative methods to identify critical signs of suicide risk that busy practitioners may miss. Our team consists of experts in behavioral health, usability and design, artificial intelligence/natural language processing, software engineering, health care information systems and emergency medicine. Our goal is simple: to use technology to provide critical support for those in crisis, and to save lives.

Project Lead
Jennifer Erickson, DO, FAPA (Department of Psychiatry and Behavioral Sciences, UW School of Medicine)

Collaborators
Charles Bombardier, PhD (Department of Rehabilitation Medicine, UW School of Medicine)

Jesse Fann, MD, MPH (Department of Psychiatry and Behavioral Sciences, UW School of Medicine)

Cherry Junn, MD (Department of Rehabilitation Medicine, UW School of Medicine)

Cara Towle, RN, MSN (Department of Psychiatry and Behavioral Sciences, UW School of Medicine)

Project Description
Traumatic brain injury (TBI) is a major cause of disability in Washington state and throughout the US. TBI increases the risk and complexity of multiple behavioral health conditions including post traumatic stress disorder, depression, anxiety, irritability, anger/aggression, substance misuse and cognitive impairment. In addition, TBI impairs a person’s ability to manage their health care and increases the risk of unemployment, long-term functional impairment, and caregiver burnout. Successful TBI recovery can depend in large part on access to and engagement in behavioral health treatment. Unfortunately, TBI-focused community resources are scarce and fragmented. Treatment of post-TBI symptoms often falls to community providers who have little support and are under-prepared to manage these complexities. This burden disproportionally affects rural providers who have little access to specialist care at academic centers.

The purpose of this project is to create and assess the use of the ECHO (Extension for Community Healthcare Outcomes) model to provide education and support by experienced TBI experts to community providers who treat persons with TBI. The ECHO model uses both a virtual educational lecture series and patient case discussion to improve provider preparedness to treat patients and improve patient outcomes. We will launch a monthly to bi-monthly program that will train providers from a variety of disciplines and settings in identification and evidence-based behavioral health treatments, web technologies and mobile technologies, and provide detailed case consultation. We will assess the success, reach and impact of our TBI ECHO by collecting and comparing attendee experiences, clinical information and patient outcomes.

Project Leads
Chieh (Sunny) Cheng, RN, PhD (Nursing and Healthcare Leadership, School of Nursing, UW Tacoma)

Sarah Kopelovich, PhD (Department of Psychiatry and Behavioral Sciences, UW School of Medicine)

Collaborator
Dong Si, PhD (Division of Computing & Software Systems, School of Science, Technology, Engineering & Mathematics, UW Bothell)

Project Description
The World Health Organization ranks psychotic disorders as the third most disabling health condition worldwide. Eleven million Americans will experience psychosis during their lifetime, and roughly 60 million Americans have a loved one affected by psychosis. Research affirms that psychotherapeutic interventions can help family caregivers develop skills to better connect and communicate with their loved one, which corresponds to better treatment engagement, symptom improvement, fewer hospitalizations, improved functioning, reduced substance use, reduced mortality and overall improvement in quality of life for the individual with psychosis. Family interventions are therefore critical to a holistic and effective clinical response to a psychotic disorder. Nevertheless, a recent federal investigation found that fewer than 2% of US families caring for someone with psychosis had received a family intervention for psychosis.

Psychosis REACH (Recovery by Enabling Adult Carers at Home) is a family intervention for psychosis co-developed by faculty in the UW Department of Psychiatry and Behavioral Sciences that delivers psychoeducation and illness management skills training to family caregivers in the community. To enhance broad and equitable access to tens of millions of families and caregivers, this project will develop “Psychosis iREACH,” a digital platform that uses Artificial Intelligence (AI) technology to deliver Psychosis REACH to diverse families navigating psychosis. A virtual coach will assist families to access self-management skills practice, automated self-assessment, tailored training goals and individualized learning trajectories whenever and wherever families need the support. Psychosis iREACH represents a multidisciplinary collaboration among faculty in the School of Medicine, School of Nursing and School of Science, Technology, Engineering & Mathematics.

Project Lead
Dror Ben-Zeev, PhD (Department of Psychiatry and Behavioral Sciences, UW School of Medicine)

Collaborators
Allie Franklin, LICSW (UW Medicine Behavioral Health Institute at Harborview Medical Center)

Jessica Maura, PhD (Department of Psychiatry and Behavioral Sciences, UW School of Medicine)

Nami Bhatt, LMHC (Specialized Treatment for Early Psychosis (STEP) Program, Harborview Medical Center)

Project Description
Psychotic disorders are brain illnesses that increase individuals’ risk for hospitalizations, substance use, homelessness, victimization and suicide. Significant declines in functioning occur during the first years of psychotic illness, a phase called early psychosis. Time-sensitive treatment can mitigate the negative outcomes of these conditions, in some cases leading to partial or full recovery. However, young adults are particularly reluctant to seek services or engage in traditional clinic-based care. Novel approaches are needed to successfully reach this critically vulnerable population, in time.

The vast majority of young adults with early psychosis own mobile phones, identify texting as their preferred communication modality, and report an interest in messaging-based treatments. We developed a texting intervention for people with psychosis called the Mobile Interventionist. Treatment is conducted via daily recovery-oriented text conversations between patients and a trained messaging practitioner. This novel form of engagement produces an asynchronous but continuous form of treatment and combines the advantages of digital health (i.e., accessibility, reach beyond the brick-and-mortar clinic, low intensity), with the flexibility, personal tone and sensitivity of a clinician. Several studies have demonstrated that our texting intervention approach is feasible, acceptable, engaging and effective. This initiative will help translate this promising research into real-world clinical practice by implementing the Mobile Interventionist texting model at the University of Washington’s Specialized Treatment Program for Early Psychosis (STEP).

Clinically, the intervention may improve the illness management of young adults with early psychosis participating in the pilot, improving their long-term trajectories. Programmatically, the pilot bridges the research/practice gap by providing training and guided clinical experience to a real-world clinical team.

Project Lead
Linda Shapiro, PhD (Paul G. Allen School of Computer Science & Engineering and Department of Electrical and Computer Engineering, UW College of Engineering)

Collaborators
Thomas Grabowski, MD (Departments of Radiology and Neurology, School of Medicine; UW Medicine Memory and Brain Wellness Center)

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.

Project Lead
Tim Althoff, PhD (Paul G. Allen School of Computer Science & Engineering, UW College of Engineering)

Collaborators
David Atkins, PhD (Department of Psychiatry and Behavioral Sciences, UW School of Medicine)

Adam Miner, PsyD, MS (Psychiatry and Behavioral Sciences, School of Medicine, Stanford University)

Project Description
Millions of people lack access to mental health treatment due to barriers such as limited therapist availability, long wait times, high cost, and stigma. The COVID-19 pandemic has problematically increased demand for treatment while decreasing access. Because the internet is widely available, many people first turn to the internet for mental health support, giving rise to massive online psychotherapy, counseling and peer-to-peer support platforms such as Ginger and Talklife. However, not all conversations lead to improvement and may miss opportunities to help or even make things worse as platforms struggle to keep up with the increasing demands and lack methods for evaluating and promoting high-quality conversations.

This project seeks to improve the quality and scalability of online mental health support through real-time, evidence-based conversation feedback. We will leverage and analyze datasets of support interactions and associated outcomes across millions of individuals that use the partnering online mental health platforms at Ginger and Talklife. Our goal is to develop and pilot-test artificial intelligence methods that provide supporters on these platforms with practical just-in-time feedback and training. If successful, at least three benefits will follow our work. First, millions of help seekers using partnering mental health platforms Ginger and Talklife will receive higher quality responses through, for example, an expression of higher empathy. Second, those providing help will gain expertise faster and with less distress. Third, platforms and researchers will discover conversational best practices which can then be used to improve helper training and quality evaluation.


2020 projects: cognitive aging, trauma and addictions

Project Lead
Hesamoddin Jahanian, PhD (Department of Radiology, School of Medicine; Department of Bioengineering)

Collaborator
Tom Grabowski, MD (Departments of Radiology and Neurology, School of Medicine; UW Medicine Memory and Brain Wellness Center)

Project Summary
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.

Project Lead
Kathleen Pagulayan, PhD (Department of Psychiatry and Behavioral Sciences, School of Medicine; VA Puget Sound Healthcare System)

Collaborators
Jeanne Hoffman, PhD, ABPP-RP (Department of Rehabilitation Medicine, School of Medicine)

Orli Shulein, MS, CCC-SLP (Department of Rehabilitation Medicine, School of Medicine)

Rhonda Williams, PhD, ABPP-RP (Department of Rehabilitation Medicine, School of Medicine; VA Puget Sound Healthcare System)

Project Summary
More than a million people in the United States sustain a mild traumatic brain injury (mTBI) every year. Although many individuals recover fully, there are also a high number of people who report difficulties with attention, memory and other thinking abilities months and even years following their injury. These cognitive difficulties can affect multiple aspects of day to day functioning such as work, school and relationships. As such, effective and accessible clinical interventions are critically needed.

A promising treatment option is cognitive rehabilitation which has been shown to improve both objective and subjective cognitive functioning in people with mild traumatic brain injury. However, most cognitive rehabilitation research has focused on full-length (20 hours), in-person interventions, which is not feasible for many individuals due to time and financial constraints. This is further complicated by the current risks of in-person care associated with COVID-19.

This study will evaluate a brief (6 hours), virtual cognitive rehabilitation intervention developed specifically for individuals with persisting cognitive difficulties after mTBI. We will evaluate several outcomes related to improving patient care including treatment satisfaction, feasibility of this intervention when using telehealth and preliminary treatment effectiveness. The proposed treatment aims to provide the same clinical impact of traditional cognitive rehabilitation while reducing treatment burden and increasing access to a broader population.

Project Lead
Debby Tsuang, MD, MSc (Departments of Psychiatry and Behavioral Sciences and Medicine-Medical Genetics, School of Medicine; Department of Epidemiology, School of Public Health; VA Puget Sound Healthcare System)

Collaborators
Gail Li, MD, PhD (Department of Psychiatry and Behavioral Sciences, School of Medicine; VA Puget Sound Healthcare System)

Murray Raskind, MD (Department of Psychiatry and Behavioral Sciences, School of Medicine; VA Puget Sound Healthcare System)

Edmund Seto, PhD (Department of Environmental and Occupational Health Sciences, School of Public Health)

Project Summary
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. African Americans are about two times more likely to develop ADRD than European Americans and are more likely to encounter racial disparities in ADRD diagnosis and care. COVID-19 has widened these disparities; in addition to being more susceptible to COVID-19 infection and fatalities, older African Americans are also more likely to experience digital and technical inequities. This has led older African Americans to feel socially isolated from support systems (e.g., community centers and churches) and bereft of mental-health support which puts them at risk for the development/worsening of depression, anxiety, cognitive impairment and sleep disturbances. Many of these risks are likely exacerbated for older African Americans with cognitive impairment or ADRD.

It is essential that we consider ways to bridge the digital divide and to confront racial disparities in ADRD diagnosis and care in the context of COVID-19-related social distancing. 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 these alarming brain health disparities.

Project Leads
Oleg Zaslavsky, PhD, RN (Department of Biobehavioral Nursing and Health Informatics, School of Nursing)
Annie Chen, PhD (Department of Biomedical Informatics and Medical Education, School of Medicine)

Collaborator
Kimiko Domoto-Reilly, MD, MS (Department of Neurology, School of Medicine)

Project Summary
Lewy body dementias (LBD), a term referring to both dementia with Lewy bodies and Parkinson’s disease dementia, are the second most common type of degenerative dementia in older adults. These are complex disorders in which patients may exhibit disruptive behaviors that make caregiving challenging. Compared to other types of dementias, caregivers of people with LBD report higher stress and more severe depressive symptoms. The ongoing COVID-19 pandemic has multiplied the challenges that caregivers of persons with dementia face in providing care for their loved ones. As such, support interventions for caregivers of persons with LBD are urgently needed.

In this study, we will adapt our online intervention for older adults with frailty to target the unique needs of caregivers of people with LBD. We will conduct participatory design sessions with potential users to determine their needs and priorities specific to LBD and deploy the re-designed intervention in a pilot study focused on usability and efficacy. Through this newly tailored support system, we aim to bolster the health of caregivers as well as their ability to assist care partners living with LBD.

This intervention could potentially be used in conjunction with usual care and/or as a stand-alone module in emergent circumstances, such as the current pandemic, when routine professional interventions may not be readily available. By fostering the development of a community-driven online support system, this project will begin to lay the groundwork for promoting resilience within families affected by the behavioral challenges of dementia.

Project Lead
Robert Bonow, MD (Department of Neurological Surgery, School of Medicine)

Collaborators
Randall Chesnut, MD (Departments of Neurological Surgery, Orthopaedics and Sports Medicine, School of Medicine; Department of Global Health, School of Public Health)

Nancy Temkin, PhD (Department of Neurological Surgery, School of Medicine; Department of Biostatistics, School of Public Health)

Anne Moore (Department of Radiology, School of Medicine)

Project Summary
Following severe cases of traumatic brain injury (TBI), the brain can swell, leading to elevations in intracranial pressure (ICP). Patients who develop high ICP following severe TBI are more likely to have poor neurologic recovery from their injury, and control of ICP likely contributes to improved outcomes. ICP detection and management is typically guided by invasive monitors placed through the skull and into the injured brain. These devices are highly accurate and reliable, but they are also expensive and expose the patient to rare but potentially serious risks. This is problematic because as few as one-third of patients are found to have elevated ICP, even when the best available evidence is used to guide their placement.

Using ultrasound to measure optic nerve sheath diameter (ONSD) could be an inexpensive, noninvasive and reliable means of monitoring ICP. Located behind the eye, the optic nerve sheath surrounds the nerve carrying visual signals to the brain. Increases in intracranial pressure are transmitted into this conduit, causing it to dilate. Ultrasound-measured ONSD has been shown to correlate with ICP in many neurologic conditions, including TBI, but it has not been systematically evaluated as a screening or a monitoring tool.

This study will routinely measure ONSD in patients undergoing invasive ICP monitoring for severe traumatic brain injury at Harborview Medical Center. The goal is to determine whether ONSD measurement with ultrasound can be combined with readily available clinical data to improve the prediction of elevated ICP, and to assess whether it can be used to monitor ICP during a patient’s hospital stay. If successful, ONSD measurement could have a significant impact on TBI care in both high and low resource settings.

 

Project Lead
Amy Starosta, PhD (Department of Rehabilitation Medicine, School of Medicine)

Collaborators
Jeanne Hoffman, PhD (Department of Rehabilitation Medicine, School of Medicine)

Kari Stephens, PhD (Departments of Family Medicine and Biomedical Informatics and Medical Education, School of Medicine)

Project Summary
Traumatic brain injury (TBI) is common in the United States with 2.87 million emergency department visits related to TBI per year. Chronic pain is a frequent complaint following TBI, with more than half of patients reporting pain. Individuals with TBI are often prescribed opioids for pain following their injury, but unfortunately may be especially vulnerable to post-injury alcohol and drug use problems.

Despite increased opioid prescriptions and risk factors for this population, there are no clinical practice guidelines for opioid prescription following TBI and limited published research. The project seeks to address this knowledge gap by using routinely collected clinical data from several different data sources to examine when and how opioids are prescribed following TBI in a community-based population.

This complete picture of opioid prescription following TBI may reveal trends of higher opioid prescription for specific subpopulations or areas of healthcare. Through understanding the trajectory of opioid prescription following TBI, we will be able to identify the scope of the problem and the most appropriate time points for intervention. Ultimately this project will provide the foundation for new approaches to reduce opioid prescription in the clinical management of TBI.

Project Leads
Michele Bedard-Gilligan, PhD (Department of Psychiatry and Behavioral Sciences, School of Medicine)

Kristen Lindgren, PhD (Department of Psychiatry and Behavioral Sciences, School of Medicine)

Collaborators
Emily Dworkin, PhD (Department of Psychiatry and Behavioral Sciences, School of Medicine)

Rebecca Hendrickson, MD, PhD (Department of Psychiatry and Behavioral Sciences, School of Medicine)

Sarah Kopelovich, PhD (Department of Psychiatry and Behavioral Sciences, School of Medicine)

Laura Merchant, LICSW (School of Social Work)

Minu Ranna-Steward, LICSW (School of Social Work)

Project Summary
Most adults in the United States experience a traumatic event at some point in their lives. Trauma exposure is linked to numerous negative outcomes including development of mental health disorders, increased suicidality, work and relationship impairment and increased physical health conditions. Effective treatments that help trauma survivors cope and decrease negative consequences exist, but many people don’t receive these treatments because of a lack of providers who are trained in evidence-based, trauma-focused treatment, especially those in rural or underserved areas on the frontlines in community clinics.

This project aims to build, implement and test an ECHO (Extension for Community Healthcare Outcomes) model for disseminating evidence-based, trauma-focused care, both psychotherapy and pharmacotherapy approaches, to providers working with underserved communities in Washington state. The ECHO approach links academic experts and community providers using virtual technology and relies on collaborative learning and bidirectional sharing of knowledge to improve patient outcomes in real-world settings. This project aims to increase local capacity for and expertise in providing evidence-based care by building curriculum and training 25-35 providers in Washington who work with underserved communities. By capturing outcomes on provider attitudes and knowledge, clinic-level service provision and patient outcomes, the team will evaluate the impact and reach of the training model, with the goal of expanding the ECHO approach to improve trauma-informed mental health care throughout Washington.

Project Leads
Mark Duncan, MD (Department of Psychiatry and Behavioral Sciences, School of Medicine)

Kevin Hallgren, PhD (Department of Psychiatry and Behavioral Sciences, School of Medicine)

Matt Iles-Shih, MD (Department of Psychiatry and Behavioral Sciences, School of Medicine)

Collaborator
Andrew Saxon, MD (Department of Psychiatry and Behavioral Sciences, School of Medicine)

Project Summary
Deaths related to the opioid overdose epidemic remain at an all-time high across the country, including in Washington, despite significant efforts to reduce them. There is a pressing need to support medication treatment for opioid use disorder (OUD) to help people stay in treatment and reduce the risk of overdose death and other serious health consequences of untreated addiction. Smartphone-based apps can facilitate the delivery of an evidence-based approach called contingency management that incentivizes use of medications for OUD, reduces use of non-prescribed opioids and improves retention in OUD treatment.

This study will leverage a commercially available smartphone app that can bring this much-needed behavioral support to patients receiving OUD treatment in a primary care clinic and in a specialty OUD treatment clinic. The approach offers a potentially non-labor intensive, cost-effective and highly scalable means of delivering OUD care. In addition to these advantages, it is also uniquely suited to ensuring ongoing contingency management therapy regardless of COVID-19 precautions and/or other potential disruptions to usual care. If successful, this project could inform policy decisions related to making contingency management more accessible across a spectrum of OUD treatment settings.

Project Leads
Richard Ries, MD (Department of Psychiatry and Behavioral Sciences, School of Medicine)

Collaborators
Alan Gojdics, MEd (Department of Psychiatry and Behavioral Sciences, School of Medicine)

Adam Livengood, MA (Department of Psychiatry and Behavioral Sciences, School of Medicine)

Diane Powers, MBA, MA (Department of Psychiatry and Behavioral Sciences, School of Medicine)

Project Summary
Although suicide is one of the leading causes of death for people with substance use disorders (SUDs), no widespread suicide prevention intervention exists for delivery in community addiction treatment settings. The effectiveness and feasibility of delivering Preventing Addiction Related Suicide (PARS), a group-based psychoeducational program that provides evidence-based suicide prevention and safety strategies to SUD patients, was recently demonstrated in a NIH-funded trial led by the UW Center for Suicide Prevention and Recovery (CSPAR).

To enhance widespread implementation and dissemination of PARS (and to adapt this crucial intervention for addiction treatment amid COVID-19 disruptions to health care access and delivery), this project will develop online training and implementation tools including a state-of-the-art website and Zoom-based training video with testing and certification capacity. The project, called PARS-Web, is a collaboration between UW CSPAR, the Advancing Integrated Mental Health Solutions (AIMS) Center and the University’s CoMotion Center. Currently, Washington and 37 other states mandate suicide training for behavioral health clinicians. PARS-Web will be created in collaboration with key state agencies and suicide prevention professionals to meet the new training requirements for Washington State Chemical Dependency Professionals. The goal is to design a platform for delivering an easily adopted, feasible and evidence-based suicide prevention intervention (PARS) and to integrate the program as a part of standard care in addiction treatment agencies statewide and, eventually, nationwide.

Project Leads
Jeff Iliff, PhD (Department of Psychiatry and Behavioral Sciences and Neurology, School of Medicine; VA Puget Sound Healthcare System)

Deidre Jansson, MSc, PhD (Department of Psychiatry and Behavioral Sciences, School of Medicine; VA Puget Sound Healthcare System)

Project Summary
This project will 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 Alzheimer’s disease‐related dementias.

Project Leads
Rebecca Hendrickson, MD, PhD (Department of Psychiatry and Behavioral Sciences, School of Medicine; VA Puget Sound Healthcare System)

Kathleen Pagulayan, PhD (Department of Psychiatry and Behavioral Sciences, School of Medicine; VA Puget Sound Healthcare System)

Abigail Schindler, PhD (Department of Psychiatry and Behavioral Sciences, School of Medicine; VA Puget Sound Healthcare System)

Project Summary
This project will conduct a preliminary investigation into the potential association between microbiota abundance, hormone levels, and peripheral inflammation and current symptoms (psychiatric and cognitive) in Veterans with and without a history of mild Traumatic Brain Injury (mTBI). This work has the potential to form a new line of research that could ultimately provide new treatment options for individuals who have treatment-resistant emotional and cognitive difficulties post-mTBI. Learn More