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.
The study will evaluate a novel program developed by our team to improve the effective outpatient management of youth with acute suicide risk. This program evaluation examines just-in time intervention to assess suicide risk level, address imminent risk, and begin treatment to address ideographic suicidal drivers over time. The clinic has served over 200 youth and families since 2019. Qualitative and quantitative data from youth, caregivers, and clinicians demonstrate high levels of fidelity, feasibility, appropriateness, and acceptability, however, impact on core health outcomes has not been conducted. This funding will allow for analysis of the treatment program to demonstrate the impact of the intervention on suicidal thoughts and behaviors.
The proposed research addresses three important objectives including: 1) Can OUD screening be effectively incorporated into primary care mental health screening protocols?; 2) Does implementing Collaborative Care for OUD and mental health disorders improve outcomes?; 3) What implementation strategies are effective at sustaining Collaborative Care programs that concurrently manage mental health disorders and OUD?
Prazosin, like many of our most effective treatments for PTSD, seems to be significantly more effective for some individuals than others. We have hypothesized that this is because prazosin works to compensate for increased noradrenergic signaling, which is a primary driver of symptoms in some, but not all, individuals with PTSD. If we could identify individuals where increased or inappropriate noradrenergic signaling is driving PTSD symptoms, it would not only help us match individuals with treatments that will work well for them, but would also help us identify new treatment options.
The PREDICT study is a 5-year clinical trial designed to (1) test whether clinically-relevant biomarkers can predict in advance who is most likely to benefit from prazosin for PTSD; and (2) test a working model of how pre-synaptic and post-synaptic changes in the regulation of noradrenaline may combine together to produce the symptoms of PTSD.
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.
This project received two years of additional funding from the Washington State Department of Social and Health Services.
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, a team of investigators including Rebecca Hendrickson, MD, PhD (Department of Psychiatry and Behavioral Sciences), John Oakley, MD, PhD (Department of Neurology), and Aaron Bunnell, MD (Department of Rehabilitation Medicine) are testing an online platform to identify patients whose pattern of symptoms suggest a particular underlying cause that is common after certain physiologic (i.e. illness or injury) 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 signaling 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 potentially related disorders such as Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and fibromyalgia.
Explore and suggest policy solutions in response to the public health challenges of high tetrahydrocannabinol potency cannabis. ADAI will host stakeholder sessions to gain perspectives, seek common ground, evaluate, and assess potential policy solutions culminating in a final recommendation report.
The major goal of this project is to evaluate the feasibility and effectiveness of an integrated intervention approach for co-users of marijuana and tobacco who are seeking treatment for tobacco addiction.
Deaths related to the opioid overdose epidemic remain at an all-time high
across the country 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.
This research develops and tests digital technology to help clinicians and patients systematically measure and monitor clinical progress during addiction treatment. The technology is being developed based on end-user input and user-centered design methods and will be pilot tested as an add-on to real-world care in an addiction treatment clinic.
