# Mechanisms, response heterogeneity and dosing from MRI-derived electric field models in tDCS augmented cognitive training: a secondary data analysis of the ACT study

> **NIH NIH R01** · UNIVERSITY OF FLORIDA · 2024 · $724,859

## Abstract

ABSTRACT
There is a pressing need for effective interventions to remediate age-related cognitive decline and alter the
trajectory toward Alzheimer’s disease. The NIA Alzheimer’s Disease Initiative funded Phase III Augmenting
Cognitive Training in Older Adults (ACT) trial aimed to demonstrate that transcranial direct current stimulation
(tDCS) paired with cognitive training could achieve this goal. The present study proposes a state of the art
secondary data analysis of ACT trial data that will further this aim by 1) elucidate mechanism of action underlying
response to tDCS treatment with CT, 2) address heterogeneity of response in tDCS augmented CT by determining
how individual variation in the dose of electrical current delivered to the brain interacts with individual brain
anatomical characteristics; and 3) refine the intervention strategy of tDCS paired with CT by evaluating methods
for precision delivery targeted dosing characteristics to facilitate tDCS augmented outcomes. tDCS intervention to
date, including ACT, apply a fixed dosing approach whereby a single stimulation intensity (e.g., 2mA) and set of
electrode positions on the scalp (e.g., F3/F4) is applied to all participants/patients. However, our recent work has
demonstrated that age-related changes in neuroanatomy as well as individual variability in head/brain structures
(e.g., skull thickness) significantly impacts the distribution and intensity of electrical current induced in the brain
from tDCS. This project will use person-specific MRI-derived finite element computational models of electric current
characteristics (current intensity and direction of current flow) and new methods for enhancing the precision and
accuracy of derived models to precisely quantify the heterogeneity of current delivery in older adults. We will
leverage these individualized precision models with state-of-the-art support vector machine learning methods to
determine the relationship between current characteristics and treatment response to tDCS and CT. We will
leverage the inherent heterogeneity of neuroanatomy and fixed current delivery to provide insight in the not only
which dosing parameters were associated with treatment response, but also brain region specific information to
facilitate targeted delivery of stimulation in future trials. Further still, the current study will also pioneer new methods
for calculation of precision dosing parameters for tDCS delivery to potentially optimize treatment response, as well
as identify clinical and demographic characteristics that are associated with response to tDCS and CT in older
adults. Leveraging a robust and comprehensive behavioral and multimodal neuroimaging data set for ACT with
advanced computational methods, the proposed study will provide critical information for mechanism,
heterogeneity of treatment response and a pathway to refined precision dosing approaches for remediating age-
related cognitive decline and altering the trajectory of older adul...

## Key facts

- **NIH application ID:** 10892337
- **Project number:** 4R01AG071469-02
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Ruogu Fang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $724,859
- **Award type:** 4N
- **Project period:** 2021-06-01 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10892337

## Citation

> US National Institutes of Health, RePORTER application 10892337, Mechanisms, response heterogeneity and dosing from MRI-derived electric field models in tDCS augmented cognitive training: a secondary data analysis of the ACT study (4R01AG071469-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10892337. Licensed CC0.

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