# Adaptive Neuromodulation of Working Memory Networks in Aging and Dementia

> **NIH NIH R01** · TRUSTEES OF INDIANA UNIVERSITY · 2024 · $520,523

## 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 dorsolateral prefrontal cortex (DLPFC) 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 dose–response relationship between TMS
intensity and brain activity in the site associated with memory function (e.g., DLPFC); such a relationship is a
fundamental means of titrating individualized responses 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 target brain state, it should be
possible to manipulate DLPFC 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 control brain states during 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, net...

## Key facts

- **NIH application ID:** 11467666
- **Project number:** 7R01AG075417-04
- **Recipient organization:** TRUSTEES OF INDIANA UNIVERSITY
- **Principal Investigator:** Simon W Davis
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $520,523
- **Award type:** 7
- **Project period:** 2022-09-15 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11467666

## Citation

> US National Institutes of Health, RePORTER application 11467666, Adaptive Neuromodulation of Working Memory Networks in Aging and Dementia (7R01AG075417-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/11467666. Licensed CC0.

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