# Real-time control of memory encoding

> **NIH NIH F32** · UNIVERSITY OF CHICAGO · 2020 · $69,306

## Abstract

Project Summary
Fluctuations of neural activity impact memory. Subsequent memory analyses have demonstrated that
particular neural states predict better working memory and long-term memory behavior. These analyses are
typically conducted after data collection, but monitoring neural fluctuations in real time would enable more
direct and timely interventions. We will use real-time electroencephalography (EEG) to track moment-to-
moment fluctuations of neural activity in order to more directly link brain signals with behavior, and to enhance
memory performance. In this proposal, we focus on two key moments for memories: pre-stimulus (Aim 1) and
active maintenance during a retention interval (Aim 2). In Aim 1, we will test the hypothesis that pre-stimulus
neural signals (oscillatory alpha and theta) predict memory encoding success. In Experiment 1, we will vary the
point of time when the stimuli appear based on real-time calculations of alpha and theta power. We will use
neural activity as the independent variable to “trigger” stimulus presentation when the brain is in either
advantageous states (low alpha, high theta) or disadvantageous states (high alpha, low theta). We predict that
better brain states will predict better working memory and long-term memory precision in a sensitive
continuous report task. In Experiment 2, we will provide neurofeedback to reward advantageous pre-stimulus
brain states (low alpha, high theta). We predict that up-regulating these advantageous states will lead to
enhanced memory performance (more precise memories). In Aim 2, we will test the hypothesis that sustained
activity tracks active maintenance of information. Sustained activity is a key signature of working memory, but
recent evidence has questioned its role through the demonstration of activity-silent working memory. In
Experiment 3, we will use real-time measures of sustained activity (contralateral delay activity, multivariate
alpha topography) to adjust the duration of a retention interval and the identity of working memory probes. We
predict that performance will be better (quicker reaction times, more precise memories) when memory probes
are triggered based on higher sustained activity. In Experiment 4, we will provide neurofeedback during the
retention interval to reward greater sustained activity. We predict that up-regulating these advantageous states
will lead to greater memory precision. Across these experiments, we will explore memory encoding via the lens
of real-time EEG to trigger information (Experiments 1 & 3) and provide feedback (Experiments 2 & 4). The
proposed research will characterize the fate of mnemonic representations by tracking and driving neural
activity both pre-encoding (Aim 1) and post-encoding (Aim 2), in order to understand how neural fluctuations
give rise to what we remember.

## Key facts

- **NIH application ID:** 9977812
- **Project number:** 5F32MH115597-03
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Megan Teresa deBettencourt
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $69,306
- **Award type:** 5
- **Project period:** 2018-08-01 → 2021-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9977812

## Citation

> US National Institutes of Health, RePORTER application 9977812, Real-time control of memory encoding (5F32MH115597-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9977812. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*