An MRI Fingerprint of Synapse Loss in Alzheimer’s Disease
The incredibly complex circuitry of the brain is the structural foundation of normal cognition. In Alzheimer’s disease (AD), these neural circuits begin to break down, leading to the hallmark cognitive decline of AD. This happens at many sites throughout the brain over many years, yet remains invisible to clinical imaging techniques like MRI until so advanced that it is likely irreversible. Our goal is to develop new MRI approaches that can reveal these microscopic circuit injuries in model systems and in patients suffering from AD. With such a technique in-hand, we will be far better positioned to understand and ultimately disrupt the pathways that lead to neural circuit injury in AD.
The aim of our project is to shed light on the breakdown of circuits in the brain during AD by developing new tools to measure these events and connect them to outcomes in animal models and in humans. Our first goal is to develop new MRI sequences that can report on the key changes in brain structures that underlie cognitive decline in AD. We are working to modulate these structures in the MRI scanner using model systems, so that we can learn how circuit breakdown is translated into changes in MRI parameters. This knowledge can then be used to reconstruct an MRI sequence with the desired properties for tracking neurodegeneration. We are separately focused on developing super-resolution, quantitative histological approaches to monitoring synapse loss. The loss of synapses is among the strongest predictors of cognitive decline in AD. These paired approaches will allow us to monitor neural circuits repeatedly and ask how damage in specific regions of the brain is connected to cognitive decline in model systems. Lastly, we will take advantage of the translational potential of MRI to perform these experiments in patients suffering from AD. This will allow us to connect forms of neurodegeneration caused by AD, never before measured in living patients, to cognitive decline.
We are focused on MRI because of its sensitivity to microscopic structural changes in the brain and its tremendous translational potential for patients. Our project takes advantage of artificial intelligence approaches—the same as those used by Google, Facebook, and others to mine user data for meaningful patterns—to find MRI parameters from large data sets that report on key neural circuit elements. In this way, we will avoid building imaging sequences based on speculative models, but rather apply machine learning to recognize meaningful data patterns. We are also utilizing sophisticated super-resolution microscopy techniques that finally bring key elements of neurodegeneration, such as synapses, within the resolution of the light microscope. If we are successful, these approaches could form a model for the development of new MRI techniques to image clinically-important brain microstructure. They could furthermore enable important advancements in the understanding and eventual therapeutic targeting of AD and other conditions characterized by neural circuit breakdown.
About the Researcher
: Dr. Terrance T. Kummer began his research career at the University of Minnesota, where he earned a Beckman Scholarship for his studies of complex carbohydrates. He completed medical training at Washington University School of Medicine, where he also received his PhD studying mechanisms of synapse formation and maintenance. During his PhD training, Dr. Kummer discovered a unique form of synaptic patterning imposed at the neuromuscular junction. Dr. Kummer completed his residency in neurology at Harvard University, and received subspecialty training in neurocritical care at Harvard. He then joined the faculty at Washington University School of Medicine, where he is currently an assistant professor in the Department of Neurology. Dr. Kummer’s research focuses on circuit injury in multiple disease conditions, including traumatic brain injury, brain hemorrhage, and Alzheimer’s disease. His particular interest is in developing and applying techniques in model systems that simultaneously reveal new features of disease and provide endpoints that can be translated to the human patients he cares for in the hospital. To this end, his studies involve the use of animal models of disease and state-of-the-art microscopic and MRI-based imaging approaches to both reveal novel forms of circuit injury and establish translational biomarkers that can track these injuries in humans.
Foundation support is the lifeblood of scientist’s best ideas. I believe that the greatest future advancements will come from approaching questions from the interfaces of not just science and medicine, but also technology and culture. Unfortunately, such approaches can be challenging to fund through traditional mechanisms. Thanks to the generous support of BrightFocus donors, we are able to bring together expertise from multiple disciplines at Washington University and beyond to build infrastructure, invest in long-term goals, and attack our most challenging, but often also most pressing questions about neurodegeneration’s impact on brain function in Alzheimer’s and related diseases.
First published on: July 28, 2017
Last modified on: June 30, 2020