Impaired spatial decoding and neural population code rescaling in AD mice Project Summary: Considerable evidence exists to support the notion that amyloid beta (A?) and tau pathology impair neuronal circuit integrity and function in Alzheimer?s disease (AD). Unfortunately, few studies have tested the direct influence of AD pathology on spatial computation within affected neuronal populations, resulting in an information gap at the neuronal network level. Moreover, in vivo experiments that examine large scale, neuronal network activity in mouse models of A? and tau pathology are lacking. In this proposal, I test the overarching hypothesis that A? and tau associated neuronal network dysfunction impairs task-relevant, spatial information encoding in large populations of neurons within the EC-HIPP circuit, and that combating this aberrant activity can restore order and improve spatial information processing in AD mice.
In Aim 1, I will test the hypothesis that oligomeric forms of A? and tau disturb spatial information content encoded within large populations of neurons in the entorhinal cortex ? hippocampal (EC-HIPP) circuit. I will also test if these oligomeric peptides alter the number of neurons recruited into the population code responsible for memory encoding in a spatial learning and memory task.
In Aims 2 & 3, I will leverage the predictive power of machine learning to decipher the neural code for spatial information processing in EC-HIPP population activity. Specifically, my goals in Aim 2 will be to examine the individual and combined impact of A? and tau pathologies on features of spatial information encoding in the EC-Tau/hAPP mouse line.
In Aim 3, I will employ chemogenetics using a novel DREADDs ligand to combat aberrant neuronal activity in AD mouse models, with the ultimate goal of improving spatial information processing in neuronal networks burdened with pathology. Excitatory neurons will be specifically targeted in an effort to better understand their contribution to impaired spatial information processing in AD mouse models. The proposed research aims are designed to bridge an information gap between AD-related cognitive impairment and the underlying circuit pathology. This Mentored Research Scientist Development (K01) Award will afford me the opportunity to accomplish this major goal while enriching my technical skillset and expanding my knowledge of AD pathophysiology. In addition, the integrated training and mentorship that I will receive will help me develop additional expertise in machine learning for spatial decoding analyses. Together, the proposed studies and career development plan will ensure that I achieve my long-term career goal of launching a competitive, independent research career at a major research university.
The progressive accumulation of amyloid beta (A?) and tau pathology in Alzheimer?s disease (AD) is associated with dysfunctional neuronal signaling and poor cognitive performance. In this proposal, newly developed machine learning strategies will be used to decode features of task-relevant, spatial information from neural ensemble activity in the entorhinal cortex ? hippocampal (EC-HIPP) circuit of transgenic mice expressing A? and tau pathology, with the overarching goal of understanding how A? and tau interfere with spatial information encoded within neuronal networks. Dysfunctional neuronal populations will then be selectively targeted using chemogenetics to correct their aberrant firing patterns, with the aim of improving the quality of spatial information carried by pathology afflicted neuronal networks.
