# Learning novel structure across time and sleep

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2024 · $120,812

## Abstract

Abstract
Extracting information that allows us to understand our experiences and predict the future is crucial for interacting
adaptively with our environment. This information takes on many forms, however, and we cannot always know
in advance the optimal way to encode something new. We know that the brain solves this problem in part by
encoding new information in multiple formats, but it must then have a way of retrieving the right format at the
right moments. Prior literature suggests that the medial prefrontal cortex (mPFC) and hippocampus are likely to
be the key regions involved in extracting new information and deciding how to deploy it, and this supplement has
the potential to provide significant new insight into how these regions communicate to accomplish these feats,
through both empirical and computational modeling investigations. The literature suggests that the monosynaptic
pathway (MSP) of the hippocampus is especially well suited to representing commonalities, whereas the
trisynaptic pathway (TSP) excels at the distinctions, and we propose that the mPFC is well positioned to help
guide the flow of information through these pathways of the hippocampus. Using univariate and multivariate
functional connectivity analyses, we will evaluate how subregions of the hippocampus and mPFC work together
to retrieve commonalities vs. distinctions as a function of the task. This work would be the first to use multivariate
connectivity techniques to investigate how the mPFC and hippocampus interact in deploying different forms of
task-dependent representations. In tandem with the functional connectivity analyses, we propose a
computational model that will provide a mechanistic account of how the mPFC influences the representational
content and information flow in the hippocampus. Our work will tackle basic brain mechanisms in a healthy
population but will set the stage for better understanding of how these networks and processes may break down
in the context of psychiatric and neurological disorders impacting the hippocampus and PFC.

## Key facts

- **NIH application ID:** 10992253
- **Project number:** 3R01MH129436-02S1
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Anna C Schapiro
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $120,812
- **Award type:** 3
- **Project period:** 2023-03-15 → 2025-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10992253

## Citation

> US National Institutes of Health, RePORTER application 10992253, Learning novel structure across time and sleep (3R01MH129436-02S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10992253. Licensed CC0.

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