Leading memory theories emphasize that new learning occurs on the background of existing knowledge. Retrieving prior knowledge during new experiences allows new information to be integrated into existing memories, resulting in the formation of rich, cohesive memory networks that relate discrete events. This integration process is proposed to facilitate new learning and enable memories to extend beyond direct experience to anticipate the relationships among events. However, memory for evidence neurobiological inability to directly measure the contents of reactivated memories during new experiences. To address this critical gap, the proposed studies employ a new experimental paradigm that uses highly sensitive pattern classifier algorithms applied to functional neuroimaging data to quantify incidental memory reactivation during new event encoding. Quantifying memory reactivation allows us to test mechanistic predictions about how past memories influence learning in the present.
Aim 1 will use this paradigm to test the hypothesis that hippocampus and ventromedial prefrontal cortex (VMPFC) work in concert to support memory integration during new learning. We propose that by linking new information with well-established memories, this hippocampal-VMPFC mediated encoding process improves new learning and enables novel judgments about relationships among distinct events.
Aim 2 will examine how temporal context and memory strength influence the formation of integrated memory traces for related events. We propose that learning overlapping events within the same temporal context facilitates memory integration by enhancing memory reactivation and recruiting hippocampal-VMPFC encoding processes. We will also adjudicate between opposing theoretical perspectives of learning that make competing predictions for whether strong or weak memories lead to enhanced memory integration.
Aim 3 will use high- resolution fMRI focused on the medial temporal lobe to determine the precise hippocampal computations and coding strategies that underlie memory integration. We will determine the relationship between memory reactivation and hippocampal mismatch responses that signal differences between current events and existing memories to test the hypothesis that mismatch responses trigger memory integration. We will also use pattern- information analysis to test the hypothesis that the hippocampus creates integrated memories by forming overlapping neural codes for related events. Collectively, this work will determine how internally generated content influences new learning and will isolate the precise neural networks, computations, and coding strategies that underlie memory integration. Understanding how the brain uses prior experience to make sense of new information will lay the foundation for translational work onintegration and its functional significance is sorely lacking due primarily to an effective for interventions therapeutic psychiatric and neurological disorders that require acquisition and maintenance of new behaviors.

Public Health Relevance

This research will improve our understanding of how memories of the past influence the ability to learn new things in the present. By detecting (based on neural activity) incidental reactivation of existing memories during new learning experiences, we will investigate how new information is integrated with existing knowledge to enhance new learning and promote the flexibility of memory. These advancements will aid in the development of effective treatment interventions for psychiatric and acquisition require that disorders neurological and maintenance of new behaviors.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
4R01MH100121-04
Application #
9050708
Study Section
Cognition and Perception Study Section (CP)
Program Officer
Buhring, Bettina D
Project Start
2013-04-17
Project End
2018-03-31
Budget Start
2016-04-01
Budget End
2017-03-31
Support Year
4
Fiscal Year
2016
Total Cost
Indirect Cost
Name
University of Texas Austin
Department
Psychology
Type
Schools of Arts and Sciences
DUNS #
170230239
City
Austin
State
TX
Country
United States
Zip Code
78712
Spalding, Kelsey N; Schlichting, Margaret L; Zeithamova, Dagmar et al. (2018) Ventromedial Prefrontal Cortex Is Necessary for Normal Associative Inference and Memory Integration. J Neurosci 38:3767-3775
Zeithamova, Dagmar; Gelman, Bernard D; Frank, Lea et al. (2018) Abstract Representation of Prospective Reward in the Hippocampus. J Neurosci 38:10093-10101
Mack, Michael L; Love, Bradley C; Preston, Alison R (2018) Building concepts one episode at a time: The hippocampus and concept formation. Neurosci Lett 680:31-38
Zeithamova, Dagmar; Preston, Alison R (2017) Temporal Proximity Promotes Integration of Overlapping Events. J Cogn Neurosci 29:1311-1323
Liang, Jackson C; Preston, Alison R (2017) Medial temporal lobe reinstatement of content-specific details predicts source memory. Cortex 91:67-78
Morton, Neal W; Sherrill, Katherine R; Preston, Alison R (2017) Memory integration constructs maps of space, time, and concepts. Curr Opin Behav Sci 17:161-168
Schlichting, Margaret L; Guarino, Katharine F; Schapiro, Anna C et al. (2017) Hippocampal Structure Predicts Statistical Learning and Associative Inference Abilities during Development. J Cogn Neurosci 29:37-51
Schlichting, Margaret L; Preston, Alison R (2016) Hippocampal-medial prefrontal circuit supports memory updating during learning and post-encoding rest. Neurobiol Learn Mem 134 Pt A:91-106
Zeithamova, Dagmar; Manthuruthil, Christine; Preston, Alison R (2016) Repetition suppression in the medial temporal lobe and midbrain is altered by event overlap. Hippocampus 26:1464-1477
Mack, Michael L; Preston, Alison R (2016) Decisions about the past are guided by reinstatement of specific memories in the hippocampus and perirhinal cortex. Neuroimage 127:144-157

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