An MRI Fingerprint of Synapse Loss in Alzheimer’s Disease

Terrance Kummer, MD, PhD Washington University School of Medicine


ShiNung Ching, PhD Washington University
Joong-Hee Kim, PhD Washington University School of Medicine


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.

Project Details

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.