Ehren L. Newman - US grants

[unreadable] DESCRIPTION (provided by applicant): Retrieval-induced forgetting is the forgetting of memories as a result of retrieving other, similar memories. Although retrieval-induced forgetting has been well characterized behaviorally, its neural mechanism is poorly understood. The proposed research uses electrophysiology in humans to monitor the brain activity that correlates with retrieval-induced forgetting. Pattern classification methods will be used to track the activation of memories during retrieval. The pattern of memory activation that leads to forgetting will be extracted using a subsequent forgetting analysis. Neural network simulations of retrieval-induced forgetting predict a specific pattern of memory activation relative to theta band oscillations. These predictions will be tested through examination of how the activation of the to-be-forgotten memories depends on theta phase. The proposed research will also refine the neural network model of retrieval-induced forgetting, by applying it to a hippocampal architecture, and by using more realistic forms of inhibition. Understanding the precise neural mechanisms that govern retrieval-induced forgetting will help us to gain better control over when forgetting occurs, which - in turn - should help us devise better treatments for Post-Traumatic Stress Disorder (where more forgetting is desirable) and age-related memory disorders (where less forgetting is desirable). [unreadable] [unreadable] [unreadable]

DESCRIPTION (provided by applicant): The specific aims of the research proposed here are to 1) test predictions, made by computational models, regarding the relationship between theta rhythms and memory retrieval and memory strength;and 2) further refine and develop these models. These models and their predictions, if verified, carry direct relevance for the development of clinical therapies for disorders such as Alzheimer's disease, addiction, and post-traumatic stress disorder (PTSD). Under Specific Aim #1, I will provide rats with extensive experience in two environments. Defining features of these environments will include the enclosure lighting and the behavior-reward contingency. I will record the activity of place cells in the hippocampus and grid cells in the medial entorhinal cortex as the rats perform pure-environment trials and mixed-environment trials. During pure-environment trials, the environmental features will remain fixed throughout the trial;I will use these trials to characterize the spatial tunings of all of the cells (i.e., the spatial map) in the two separate environments. During mixed-environment trials, the environmental features will be flipped between those that define the separate environments multiple times during each trial. I will infer, based on the population activity, which spatial map is activated at each moment during these mixed-environment trials. In Experiment 1, I will test whether the moment at which one spatial map turns off and the other takes its place occurs non-uniformly over the phases of ongoing theta rhythms. In Experiment 2, I will test whether it is possible to induce the partial retrieval of a spatial map and thereby shift the point at which the alternate spatial map activates as was predicted by my previously proposed model. In Experiment 3, I will test the correspondence between the rat's behavior and which spatial map is activated to test whether such manipulations could serve as a clinical therapy to alter behavior. In Experiment 4, I will explore the relative timing of hippocampal and entorhinal remapping. Under Specific Aim #2, I will use computational modeling to support the empirical work described under Specific Aim #1. Specifically, I will use the same models that generated the predictions that I will address under Specific Aim #1 to simulate the task that I will use to test these predictions. From these simulations, I will generate synthetic data on which I will verify the data analysis methods that I will use to infer which spatial map is active at each moment. These simulations will also allow me to develop the stimulus control protocol, for use during Experiment 2 of Specific Aim #1, that will most reliably generate partial retrieval. Most powerfully, they will allow me to generate additional testable predictions regarding the boundary conditions of the predictions described under Specific Aim #1 and thereby motivate my next series of experiments. PUBLIC HEALTH RELEVANCE: The research proposed here will explore how memories are retrieved, and why memories are forgotten. The results of this study will have direct relevance to the generation of clinical therapies for Alzheimer's disease, addiction, and post-traumatic stress disorder (PTSD).

This project seeks to advance what is known about the neural mechanisms of navigation. Our approach is through the development and empirical testing of a computational framework for how neural circuits encode and decode information to form scale invariant memories. This framework is referred to as SIPI, short for Scale Invariant Path Integration framework. Applying the SIPI framework to navigation provides a mechanistic model for how neurons obtain spatial tuning by encoding an animals? movements and for how neural degeneration affects path integration ability. This project will generate new tools to facilitate broader application and testing of SIPI and test strong predictions of the SIPI framework through empirical studies of rodent and human behavior and brain activity. The new tools include a simulation environment for analyzing SIPI function across parameterizations and variants of SIPI that 1) use visual input to guide navigation, 2) address how positional coding interacts with noisy self-motion cues, and 3) perform memory guided navigation. High-density single unit electrophysiology in rodents will test strong predictions of SIPI regarding whether head direction and boundary tuned neurons encode a multiscale history of movements and whether the history encoded by those neurons accounts for navigation ability in healthy and transgenic models of Alzheimer?s disease (AD). Behavioral and ultra-high resolution functional imaging in humans will test predictions from SIPI that 1) path-integration performance has diagnostic value for identifying preclinical AD, 2) that reduced velocity coding and path integration ability in elderly patients are addressed by approved AD treatments, 3) that AD is associated with increased dependence on environmental boundaries for orienting, and 4) that, in healthy volunteers, the proximity of environmental boundaries are encoded in a multiscale fashion in the entorhinal cortex. That is, this project will perform strong tests of the SIPI framework, will advance our understanding of spatial coding in the brain, and test new avenues for insight into the behavioral deficits that accompany Alzheimer?s disease. RELEVANCE (See instructions): Navigation is a core competency that depends upon intact functioning of circuits impacted first in Alzheimer?s disease. This project aims to determine the neurophysiological mechanisms that contribute to path integration and boundary coding impairments in human preclinical Alzheimer?s disease individuals and develop translatable outcome measures for assessing animal models of Alzheimer?s disease.