High-resolution memory-circuit biomarkers for identifying risk for Alzheimer’s disease
It is well known that patients suffering from Alzheimer’s disease present with an impairment in memory function. It is also well known that individuals suffering from Alzheimer’s disease also suffer from progressive atrophy of their hippocampus (one of the main centres for memory formation in the brain). The hippocampus and it’s size can be measured using magnetic resonance imaging (MRI). However, all too often, the hippocampus is treated as a single structure. In reality it is composed of several sub-components as subfields, each with their own independent role in the formation and maintenance of memories. Previously, visualization of these individual subfields have been impossible using standard MRI acquisition techniques. However, our group has recently innovated a novel technique that allows for high-resolution imaging of whole brain, in a clinically tolerable time. These new scans allow us to analyze the hippocampus’ subfields in unprecedented details. To better understand, and potentially use this information to predict onset of Alzheimer’s disease, we will study these subfields in healthy controls, healthy individuals family history of Alzheimers disease (therefore elevating their risk), individuals suffering from the early preclinical phases of Alzheimer’s disease, and Alzheimer’s patients as well (280 total study participants).
Relevance to the acceleration of therapeutics for neurodegenerative diseases of aging
In order to develop robust indicators for treatment response an improved understanding of the memory circuitry and in healthy and pathological ageing is required. Our advanced preliminary data demonstrates that while the hippocampus volume decreases as a function of age, the rate of deterioration is not constant across the subfields. The Cornu Ammonis 1, one of the larger subfields, appears to be preferentially preserved over the span of the adult lifespan. However, individuals holding a copy of a risk gene demonstrate shrinking in this region. These findings hold large potential for use as a biomarker for Alzheimer’s disease progression.
We believe that we can deliver a fast and efficient magnetic resonance imaging (MRI) and image analysis technique that may be informative of treatment response and risk for Alzheimer’s disease. One of the important parts of this technique is that this work can be performed on regular clinical-grade MRI technology and does not require any other skill. Further, it will provide improved specificity with respect to how the memory networks degenerates and how this may result in the initiation of Alzheimer’s disease.