# Using network-guided TMS to ameliorate memory deficits in early Alzheimer's disease

> **NIH NIH R21** · DUKE UNIVERSITY · 2020 · $431,216

## Abstract

Project Summary
Dementia due to Alzheimer’s disease (AD) is a leading public health concern in the US with tremendous care
costs and no effective pharmacotherapy despite multiple clinical trials. Numerous studies have shown mild
cognitive impairment (MCI) to be a precursor to AD and potentially amenable to nonpharmacological intervention.
Transcranial magnetic stimulation (TMS) is a promising non-invasive therapeutic approach that has been shown
to increase brain plasticity and enhance cognitive functions that are impaired across the AD spectrum. Yet, while
TMS has shown benefits in normative populations, there is still a need to show efficacy in AD-related populations.
 Most previous neurostimulation research on AD and MCI has focused on effects of stimulation at one brain
region, however the cognitive processes underlying successful memory are mediated by a complex whole-brain
network. Neurostimulation affects multiple sites within a cortical network, but these more global effects have not
been used as targets for stimulation because of limited knowledge about what influence of a single site on more
widespread cortical changes. The novelty of the current proposal is that we use information about the network
control structure of the affected brain areas by considering the influence of neuromodulation on global changes
in brain state or connectivity and the underlying vascular changes mediating long-term consequences for
behavior. This network-based TMS is informed by functional connectivity and neurovascular as mediators of the
behavioral response as a means to specifically tailor the TMS treatment to the neuropathology of each MCI
patient, thus individualizing the treatment to achieve better therapeutic effects.
 To address this problem, we will use multimodal neuroimaging and network modeling during a titrated memory
task to demonstrate how focal neurostimulation evokes changes in neural function and behavior in MCI. These
goals will be addressed in two specific aims. First, we will use network-based TMS to optimize the activation of
a memory success network (MSN) in a group of MCI patients, targeting a TMS site that focused on the
controllability of a stimulation site to provide the maximum benefit to memory performance. Second, we will
assess longitudinal change in structural and neurovascular factors affecting the efficacy of individualized
network-based TMS across multiple sessions of concurrent TMS-fMRI. By creating a multimodal model of these
neurovascular deficits related to MCI, we will systematically adjust network-based TMS to demonstrate how the
MCI brain might compensate for these neural deficits. The proposed work will be the first of its kind to estimate
the utility of network controllability as a TMS target for memory enhancement in AD-related syndromes, and the
first to assess the short-term neuroplastic effects of neuromodulation in such rich detail. The knowledge gained
by this project may therefore lead to novel and innovati...

## Key facts

- **NIH application ID:** 10128788
- **Project number:** 1R21AG058161-01A1
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Simon W Davis
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $431,216
- **Award type:** 1
- **Project period:** 2020-09-15 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10128788

## Citation

> US National Institutes of Health, RePORTER application 10128788, Using network-guided TMS to ameliorate memory deficits in early Alzheimer's disease (1R21AG058161-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10128788. Licensed CC0.

---

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