Over 2.5 million US adolescents struggle with mental health
challenges, and multiracial adolescents are at greatest risk due to limited
access to mental health programs. As roughly half of lifetime mental disorders
have their first onset by mid-adolescence, it is vital to promote help-seeking
for prevention and early intervention during this important developmental
stage.
This project will test the implementation of an evidence-based mental illness prevention program — teen Mental Health First Aid (tMHFA) — in a diverse and underserved school district to facilitate help seeking among teens aged 16-18. While tMHFA has a proven track record of effectively enhancing knowledge of mental health problems, reducing stigma and promoting help-seeking behaviors, its efficacy across dimensions of race and ethnicity is underexamined in the US.
Academic (UW & SMART Center), education (Tacoma Public Schools) and behavioral health organization (MultiCare) stakeholders will address this gap by conducting a mixed-methods study with 1) focus groups to obtain diverse teens aged 15-18 opinions about facilitators and barriers in help-seeking; and 2) longitudinal data collection to examine the impact of the innovative tMHFA’s potential to address help-seeking barriers across dimensions of race and ethnicity. The findings of this project will guide both the revisions to the program to improve its efficacy and the scaling of this program to support government legislation to expand service delivery to other schools and to rural areas across the state.
Suicide is a leading cause of death among 10- to 24-year-olds in the US, and half of youth who die by suicide contact a primary care provider within one month prior to suicide. Suicide risk screening and access to brief and effective suicide prevention interventions remain an important step in reducing suicide, yet comprehensive suicide prevention pathways focused on youth have not been widely implemented or evaluated in primary care settings, in part due to lack of trained clinicians and time to provide services.
This project aims to address these challenges by developing clinician training and adapting and optimizing a brief, evidence-based suicide intervention, SAFETY- Acute(A), for use in primary care to support the development of an effective and sustainable primary care-based suicide prevention pathway for youth with low to moderate suicide risk.
The
ages of 18-25 years are ‘peak onset’ times of major depression and bipolar
disorder. These disorders have different courses and treatments, but diagnosing
bipolar disorder is difficult because manic symptoms occur less often than
depressive symptoms and many individuals do not recall manic symptoms. A
‘misdiagnosis lag’ of 8-10 years can contribute to prolonged periods of
potentially ineffective treatments and suboptimal outcomes such as high symptom
burden, relationship problems, educational attainment and occupational
functioning.
This project will use remote prospective assessment and monitoring of depressive and manic symptoms in at-risk patients in-between patient visits to increase the ‘data points’ clinicians have when assessing a bipolar disorder diagnosis. This is especially important for people at risk for bipolar disorder (for example those with a family history of bipolar disorder) because manic symptoms can be provoked by first-line medication treatments for major depression. The project will use a new manic symptom measure (the Patient Mania Questionnaire-9) and a commonly used depressive symptom measure (the Patient Health Questionnaire-9) to monitor symptoms, and learn how clinicians and patients use this information clinically.
Most young adults with mental health (e.g., depression, anxiety) or substance use disorders do not receive treatment. Untreated mental health and substance use can be associated with impairments in social relationships, overall functioning and suicide. National data indicate that almost half of young adults with symptoms of a mental health disorder reported they needed mental health care in the past year but did not access those services. Barriers to accessing mental health care include stigma, not knowing where to go, lack of transportation and cost.
This project aims to develop a personalized web-based program for young adults to reduce self-reported barriers and increase motivation to access mental health and substance use services. Investigators will work with clinicians and young adults to develop strategies and solutions to address the identified barriers. The team will work with a community advisory board to develop program content that will be further refined through focus groups and individual interviews with young adults and clinicians. From this, the team will develop the web-based program which will serve as the first step needed to establish a larger program of research focused on reducing barriers and increasing access to mental health care to improve young adult well-being.
Through a community-partnered approach we will develop and deliver a parent-teacher relationship intervention at local preschools that serve under-represented minority families.
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.
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.
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.
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.
