Aprinda I Queen

Aprinda I Queen, Ph.D.

Assistant Professor

Department: Department of Clinical and Health Psychology
Business Phone: (352) 294-8990

About Aprinda I Queen

My long-term research goal is to develop personalized medicine within aging research using person-specific models paired with multi-modal data approaches such as behavioral intervention, neuroimaging (structural and functional), and AI methods. For the past eight years, I have been involved in computational-based research, specifically using finite element method (FEM) to construct person-specific models and predict the effects of biomedical devices. In my early graduate study and while pursuing a master’s degree, I performed FEM modeling to investigate the fluid dynamics properties of embolic coils used to treat brain aneurysms. As a predoctoral student, I continued to use FEM and apply it to a type of noninvasive brain stimulation technique called transcranial electrical current stimulation (tES). I gained initial exposure to neuromodulation research while building, testing, and validating tES computational models against in-vivo current density images in humans acquired using an objective electrical current measurement called MREIT. During my postdoctoral training, I was cross-trained in cognitive neuroscience methods and involved in intervention study visits as part of ongoing clinical trials. I was actively involved in phase 2 and phase 3 clinical trials of tDCS administration paired with cognitive training in healthy older adults to remediate cognitive aging. I further expanded my computational expertise to develop a novel method to compute the accuracy and consistency of electrode location as quality control metrics in tES clinical studies. I managed a modeling project to perform the largest tES computational modeling study to date that investigates age-related effects, such as brain atrophy and white matter hyperintensities, on delivered tES current dose in 587 unique older adult brains. White matter hyperintensities are highly prevalent in older adults over the age of 60. These initial research findings are crucial in constructing a robust platform to use computational models as means of predicting tES treatment effects. Further use of these computational models by pairing them with artificial intelligence methods such as machine learning and deep learning algorithms will enable us to predict treatment outcomes. This powerful combination can also give us insight into how to formulate precision dosing that is tailored to the aging population to optimize intervention outcomes. These methods can be translated in the future to investigate other domains of brain function and explore other promising non-invasive/non-pharmacological intervention strategies beyond tES/tDCS. I look forward to future collaborations with experts in the aging field. Together we can formulate a tailored dosing mechanism that will work for everyone, toward achieving precision health and medicine.


Best Poster Award
2021 · The 4th International Brain Stimulation Conference
Young Investigator Award
2018 · NYC Neuromodulation Conference & NANS Summer Series

Teaching Profile

Courses Taught
CLP6971 Research for Master’s Thesis
PHC3793 Higher Thinking for Healthy Humans: AI in Healthcare and Public Health
CLP6905 Individual Work

Research Profile

Transcranial electrical stimulation (tES) is a promising non-invasive neuromodulation technique to improve brain functions. While useful, observed tES outcomes have largely varied across individuals, and thus poses a concern in reliability and reproducibility of tES application. Using multimodal neuroimaging and computational models, Dr. Indahlastari’s research goals are to improve tES reliability/reproducibility, with specific focus in cognition and aging by: predicting tES current dose in stimulated brain regions, identifying/reducing possible sources of individual variability in tES outcomes, and investigating possible mechanisms of action that contribute to physiological changes caused by tES. General research projects include building a workflow that integrates all tES data analysis (behavior, neuroimaging, and computational models) and developing new tools for quality control in tES to ensure reliable tES application across studies. Specific ongoing studies include: building detailed current flow models in 367 cognitively healthy older adults, preparation in constructing tDCS models in 104 older adults with mild cognitive impairment (MCI), conducting a pilot study to mechanistically investigate in-scanner tDCS effects on working memory performance in older adults with and without MCI. Analysis plan include the usage of multi-modal neuroimaging (structural and functional MRI), FEM, and supervised machine learning methods.

Open Researcher and Contributor ID (ORCID)


Areas of Interest
  • Aging
  • Artificial Intelligence
  • Computational Neuroscience
  • Neuromodulation


Facilitation of working memory capacity by transcranial direct current stimulation: a secondary analysis from the augmenting cognitive training in older adults (ACT) study
GeroScience. [DOI] 10.1007/s11357-024-01205-0.
Precise and Rapid Whole-Head Segmentation from Magnetic Resonance Images of Older Adults using Deep Learning.
Imaging neuroscience (Cambridge, Mass.). 2 [DOI] 10.1162/imag_a_00090. [PMID] 38465203.
The impact of a tDCS and cognitive training intervention on task-based functional connectivity.
GeroScience. 46(3):3325-3339 [DOI] 10.1007/s11357-024-01077-4. [PMID] 38265579.
DOMINO: Domain-aware loss for deep learning calibration.
Software impacts. 15 [DOI] 10.1016/j.simpa.2023.100478. [PMID] 37091721.
Impact of electrode selection on modeling tDCS in the aging brain.
Frontiers in human neuroscience. 17 [DOI] 10.3389/fnhum.2023.1274114. [PMID] 38077189.
Machine-learning defined precision tDCS for improving cognitive function
Brain Stimulation. 16(3):969-974 [DOI] 10.1016/j.brs.2023.05.020. [PMID] 37279860.
The importance of accurately representing electrode position in transcranial direct current stimulation computational models
Brain Stimulation. 16(3):930-932 [DOI] 10.1016/j.brs.2023.05.010. [PMID] 37209869.
DOMINO: Domain-Aware Model Calibration in Medical Image Segmentation
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. 13435 [DOI] https://doi.org/10.1007/978-3-031-16443-9_44.
Effects of inter-individual variation on transcranial electrical stimulation in the aging brain
Brain Stimulation. 16(1) [DOI] https://doi.org/10.1016/j.brs.2023.01.267.
Electric Field Strength From Prefrontal Transcranial Direct Current Stimulation Determines Degree of Working Memory Response: A Potential Application of Reverse-Calculation Modeling?
Neuromodulation : journal of the International Neuromodulation Society. 25(4):578-587 [DOI] 10.1111/ner.13342. [PMID] 35670064.
A Systematic Review and Meta-Analysis of Transcranial Direct Current Stimulation to Remediate Age-Related Cognitive Decline in Healthy Older Adults
Neuropsychiatric Disease and Treatment. Volume 17:971-990 [DOI] 10.2147/ndt.s259499.
Impact of Transcranial Direct Current Stimulation and Cognitive Training on Frontal Lobe Neurotransmitter Concentrations.
Frontiers in aging neuroscience. 13 [DOI] 10.3389/fnagi.2021.761348. [PMID] 34744698.
Individualized tDCS modeling predicts functional connectivity changes within the working memory network in older adults
Brain Stimulation. 14(5):1205-1215 [DOI] 10.1016/j.brs.2021.08.003. [PMID] 34371212.
White matter hyperintensities affect transcranial electrical stimulation in the aging brain
Brain Stimulation. 14(1):69-73 [DOI] 10.1016/j.brs.2020.11.009. [PMID] 33217610.
Machine learning and individual variability in electric field characteristics predict tDCS treatment response
Brain Stimulation. 13(6):1753-1764 [DOI] 10.1016/j.brs.2020.10.001. [PMID] 33049412.
Modeling transcranial electrical stimulation in the aging brain
Brain Stimulation. 13(3):664-674 [DOI] 10.1016/j.brs.2020.02.007. [PMID] 32289695.
Benchmarking transcranial electrical stimulation finite element models: a comparison study
Journal of Neural Engineering. 16(2) [DOI] 10.1088/1741-2552/aafbbd. [PMID] 30605892.
Effects of in-Scanner Bilateral Frontal tDCS on Functional Connectivity of the Working Memory Network in Older Adults.
Frontiers in aging neuroscience. 11 [DOI] 10.3389/fnagi.2019.00051. [PMID] 30930766.
Effects of Transcranial Direct Current Stimulation Paired With Cognitive Training on Functional Connectivity of the Working Memory Network in Older Adults.
Frontiers in aging neuroscience. 11 [DOI] 10.3389/fnagi.2019.00340. [PMID] 31998111.
Methods to monitor accurate and consistent electrode placements in conventional transcranial electrical stimulation
Brain Stimulation. 12(2):267-274 [DOI] 10.1016/j.brs.2018.10.016. [PMID] 30420198.
Methods to Compare Predicted and Observed Phosphene Experience in tACS Subjects
Neural Plasticity. 2018:1-10 [DOI] 10.1155/2018/8525706. [PMID] 30627150.
Non-invasive Brain Stimulation: Probing Intracortical Circuits and Improving Cognition in the Aging Brain.
Frontiers in aging neuroscience. 10 [DOI] 10.3389/fnagi.2018.00177. [PMID] 29950986.
Imaging of current flow in the human head during transcranial electrical therapy.
Brain stimulation. 10(4):764-772 [DOI] 10.1016/j.brs.2017.04.125. [PMID] 28457836.


Jun 2024 ACTIVE
Mechanisms, response heterogeneity and dosing from MRI-derived electric field models in tDCS augmented cognitive training: a secondary data analysis of the ACT study
Role: Project Manager
May 2023 ACTIVE
Cognitively engaging walking exercise and neuromodulation to enhance brain function in older adults
Role: Co-Investigator
Jun 2021 – May 2024
Mechanisms, response heterogeneity and dosing from MRI-derived electric field models in tDCS augmented cognitive training: a secondary data analysis of the ACT study
Role: Project Manager


Doctor of Philosophy in Biomedical Engineering
2017 · Arizona State University
Master of Science in Biomedical Engineering
2013 · Arizona State University
Bachelor of Science in Bioengineering
2011 · University of California, San Diego
Associate of Science in Mathematics
2008 · Grossmont Community College

Contact Details

(352) 294-8990
Business Mailing:
PO Box 100165
Business Street: