# Adaptive Neuromodulation of Working Memory Networks in Aging and Dementia

> **NIH NIH R01** · DUKE UNIVERSITY · 2022 · $733,471

## Abstract

PROJECT SUMMARY
Dementia due to Alzheimer’s disease (AD) is a leading public health concern in the US with enormous care costs
and no effective pharmacotherapy despite multiple clinical trials. Multiple studies have shown mild cognitive
impairment (MCI) to be a precursor risk for AD and to be more amenable to intervention. While preclinical studies
have shown that directly modulating activity in the prefrontal cortex (PFC) using non-invasive brain stimulation
techniques, such as transcranial magnetic stimulation (TMS), can modulate cognitive function in healthy older
adults, there is little evidence of reliable efficacy in MCI. We posit three reasons for this lack of efficacy. First,
there is no established means of estimating a reliable biomarker as well as a unique dose-response relationship
between TMS intensity and brain activity, which remains a fundamental means of titrating individualized
response to neuromodulation. Second, standard TMS protocols fail to capture the dynamic nature of cognitive
states and the reaction of endogenous brain states to exogenous neuromodulation. By understanding the
dynamic changes associated with a successful brain state, it should be possible to manipulate PFC dynamically
in a manner that enhances cognition. Third, no studies using TMS in AD-related populations have accounted for
the influence of cerebrovascular disease in the response to TMS. We propose to address these shortcomings
by using closed-loop TMS, based on individualized brain networks to establish parameters that can reliably
control brain states during normal memory functioning in healthy aging and MCI.
 To achieve this goal, we will study network activation and neural oscillatory mechanisms underlying the network
that regulates working memory (WM), a cognition function with a reliable PFC-based network characterization.
We will then target this network using closed-loop TMS to the PFC and measure the impact on WM performance
and task-based neural activity. This approach, which builds on our existing K01, U01, and RF1 awards, uses
concurrent TMS-fMRI to identify dose-response relationships in the working memory network, which can be used
to identify neuroplasticity and optimize targeting for TMS (Aim 1). Next, we apply novel closed-loop TMS to
perturb this network using temporally-precise TMS-EEG (Aim 2), optimizing the encoding of memory by
minimizing endogenous alpha oscillations. Lastly, we will integrate information collected via fMRI and EEG into
a single computational framework in order to model spatiotemporal dynamics of the global brain network,
accounting for the influence of both connectivity and cerebrovascular pathology in predicting the success of the
TMS-related response in our MCI cohort (Aim 3). In sum, the project will use cutting-edge brain stimulation and
network modeling techniques to enhance WM in healthy older adults and MCI and will provide a demonstration
of the value of closed-loop, network-guided TMS for future clinical...

## Key facts

- **NIH application ID:** 10526714
- **Project number:** 1R01AG075417-01A1
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Simon W Davis
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $733,471
- **Award type:** 1
- **Project period:** 2022-09-15 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10526714

## Citation

> US National Institutes of Health, RePORTER application 10526714, Adaptive Neuromodulation of Working Memory Networks in Aging and Dementia (1R01AG075417-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10526714. Licensed CC0.

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