Determining How Alzheimer's Disease Alters Hippocampal Network Signatures of Memory in Mouse Models

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Project Summary As the U.S. population ages, diseases that affect older adults are becoming a growing concern. The most prominent of these diseases is Alzheimer's disease (AD), a form of dementia characterized by progressive cognitive decline which affects 11% of the US population over age of 65. In the US, AD is the sixth leading cause of mortality and costs $236 billion annually. The leading genetic risk factor for AD is the ε4 variant of the APOE gene (apoE4), which is found in 65-80% of all AD patients. Despite this prevalence, there is no successful preventative therapy, treatment, or cure for AD. The research proposed for this fellowship will help fill two critical gaps by dissecting how AD alters hippocampal network activity. One important gap in our understanding of AD pathophysiology is how to predict future cognitive decline in healthy older adults. This would allow testing of potential preventative therapies before the neurodegeneration advances beyond repair. The research proposed in this application will determine if aberrant hippocampal network activity can be used to predict future learning and memory impairment. This tool could be harnessed in mouse models to monitor progression of AD deficits over aging and to measure the efficacy of compound administration before behavioral symptom onset. A second significant gap in our understanding of AD is how specific types of neurons uniquely contribute to cognitive deficits. Narrowing the focus of AD research towards a small cell population would allow therapies to specifically target these cells in in vitro and in vivo screens. The research proposed in this application will attempt to induce or rescue spatial learning and memory and hippocampal network activity deficits by manipulating a very small population of neurons, specifically GABAergic interneurons. In summary, the research proposed herein will accomplish three aims using mice which express apoE4 as a model: (1) measure the degeneration of hippocampal network activity across aging and how this degeneration predicts spatial learning and memory at later ages, (2) determine the role of specific GABAergic interneuron subclasses in spatial learning and memory and hippocampal network signatures thereof, and (3) dissect how apoE4 expression specifically in these interneuron subclasses contributes to deficits in spatial learning and memory and hippocampal network activity. The proposed research will assess a crucial biomarker for cognitive decline and elucidate potential disease mechanisms and cellular targets for future therapies.