In This Article
- Patients with similar types of stroke can have drastically different outcomes, which can partially be explained by brain resiliency and susceptibility
- MRI-GENIE has provided a computational framework and machine learning approach to characterizing the pre-existing burden of cerebrovascular disease for patients with stroke
- Extracted information from MRI images to model genetic determinants of stroke will be instrumental for analysis of the large-scale, multicenter study of post-stroke cognitive impairment and dementia
Natalia S. Rost, MD, MPH, is chief of the Stroke Division in the Department of Neurology at Massachusetts General Hospital. She is the principal investigator of the MRI-GENetics Interface Exploration (MRI-GENIE) study as part of the International Stroke Genetics Consortium, and principal investigator of the Determinants of Incident Stroke Cognitive Outcomes and Vascular Effects on Recovery (DISCOVERY) study, a $39 million National Institute of Health-funded national network. In this Q&A, Dr. Rost discusses her work on developing neuroimaging markers of cerebrovascular disease, stroke genetics and big data science for outcome prediction in patients with acute stroke.
Q: What led you to your current research?
Rost: From the early days of my practice as a vascular neurologist, I found it fascinating how vastly variable the severity and outcomes of acute stroke patients can be. All factors being equal, two patients with very similar types of stroke can have drastically different outcomes, ranging from full recovery to severe and permanent disability or death. Furthermore, some factors that we traditionally thought as key elements of vulnerability to poor outcomes—like advanced age—were not always the culprits, while other factors were unexpectedly impactful. One of our earlier observations indicated that stroke patients whose brain MRI at the time of acute stroke showed signs of an extensive pre-existing injury, such as diffuse white matter changes, had a propensity for poor functional outcome after stroke.
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Q: What were some of your early key findings?
Rost: In a series of systematic investigations, we demonstrated that the pre-existing burden of white matter disease on brain MRI was an important marker of acute stroke severity and post-stroke disability. As a result of our research, we are able to assess the extent of lifelong injury to the brain due to common vascular risk factors, such as hypertension and age, on a microstructural level. Using MRI, we can now visualize and quantify diffuse microvascular changes to white matter tracts and the blood-brain barrier to assess their clinical significance in the prediction of post-stroke recovery. We also discovered that when we account for common risk factors and aging, a large part of the variability in the severity of brain injury and clinical outcomes after stroke is still unexplained. A significant portion of this unexplained variability is driven by genetic factors. These factors contribute to whether those brains can withstand cerebrovascular insult, which we refer to as brain resiliency or susceptibility. Understanding the mechanisms became a central focus of the Rost Research Program.
Q: How are you using MRI scans to explore genetic determinants of acute and chronic stroke?
Rost: We explore clinical brain MRI scans of patients admitted acutely for their stroke symptoms. These types of scans carry countless amounts of information about an individual's health, but have been understudied due to significant technical challenges in effectively using that information.
Due to the severity and urgency of the medical conditions, we have no control over the specifics of the scan acquisition, no access to a high-resolution scanner and little time to acquire top-notch quality scans. Instead of discarding the "not-so-perfect" data, we set to develop a methodology that will address the technical challenges of clinical stroke MRI analysis. For example, we addressed the heterogeneity of image acquisition for this patient population across multiple hospitals in the United States and around the world. To find genetic variation related to the brain characteristics of stroke patients requires thousands of scans. In collaboration with the Mass General Athinoula A. Martinos Center at Mass General and MIT, MRI-GENIE has delivered a number of technological advances including computational framework and machine learning approaches to characterization of the pre-existing burden of cerebrovascular diseases such as white matter hyperintensity (WMH) and acute lesions for patients with stroke.
Our next step is to use information extracted from these images in modeling genetic determinants of stroke. This will be instrumental for big-data clinical analysis of the large-scale, multicenter study of post-stroke cognitive impairment and dementia.
Q: Where is there room for improvement in post-stroke treatments?
Rost: The lack of reliable outcome prediction models limits the ability to individualize stroke management strategies by clinical teams, undermining the very idea of personalized medicine. This is especially true in the critical early hours of acute ischemic stroke, leading to an "all-size-fits-all" approach to diagnosis and management, which is often ineffective, frequently wasteful and at times, harmful. Hence, understanding the mechanisms of post-stroke outcomes is of utmost clinical importance, an urgent public health priority and the key to delivering equitable health care.
Q: In 2019, you were named a Samana Cay MGH Research Scholar. What has that meant for you and your work?
Rost: This has been one of the greatest honors of my professional life. As clinician-scientists, we dedicate countless hours to research that is often inspired by the human suffering and a selfless quest for discovery to alleviate this suffering. In my clinical work, witnessing the devastating effect of stroke on patients and their families has been the greatest driver behind my work. I am forever thankful to our patients for contributing to our research efforts and helping the future of stroke care. As the 2019 MGH Research Scholar, I am infinitely grateful to have the appreciation of our research approach and the vote of confidence in the scientific vision and promise of our research program. An endorsement like this by my colleagues and peers is a deeply cherished accomplishment, which will serve as fuel for our future discoveries.
Q: What is the potential impact of your research?
Rost: We are hopeful that with our research, and by bringing together novel diagnostic and prognostic tools, personalized approaches to brain health will be a realistic part of patient care and management. While the biological meaning of brain resilience remains unknown, our work is directly involved in developing a battery of clinical, neuroimaging and genetic tests that focuses on finding the mechanisms of brain resilience and related cerebrovascular pathology. Now, with the new DISCOVERY study, there is even greater potential for our research to make a clinical impact on stroke recovery and personalized stroke care.
Learn more about the Department of Neurology
Learn more about the Athinoula A. Martinos Center for Biomedical Imaging