Aprinda I Queen, Ph.D.

My long-term research goal is to develop personalized aging interventions focusing on cognition using cutting-edge technologies such as multi-modal neuroimaging, artificial intelligence methods, and individualized computational models. Over the past decade, I have been engaged in computational neuroscience research, specifically employing finite element methods (FEM), neuroimaging (MRI and CT), and image processing tools to predict the effects of biomedical devices. During my early graduate studies while pursuing a master’s degree, I used FEM to investigate the fluid dynamic properties of embolic coils used to treat brain aneurysms. As a predoctoral student, I continued leveraging FEM, applying it to a different medical device—non-invasive brain stimulation known as 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 MREIT. During my postdoctoral training, I expanded my computational expertise to cognitive neuroscience by developing a novel method to assess the accuracy and consistency of electrode placement as quality control metrics in tES clinical studies. I also conducted the largest tES computational modeling study to date, which investigated age-related effects, such as brain atrophy and white matter hyperintensities, on delivered tES current doses in 587 unique older adult brains. White matter hyperintensities, highly prevalent in adults over the age of 60, are significant in neuromodulation research and can serve as a biomarker for Alzheimer’s disease and related dementia (ADRD) trajectory. My initial research findings have been instrumental in constructing a robust platform that uses computational models to predict the effects of non-invasive brain stimulation in aging populations. In my early faculty career, I conduct clinical trials for NIH-funded studies investigating non-invasive brain stimulation as an intervention for working memory in patients with mild cognitive impairment (MCI). I employ neuroimaging, computational modeling, and machine learning to support this work. My ultimate goal is to tailor cognitive interventions, predict treatment outcomes, and develop precision dosing strategies customized to individual needs. These personalized treatments have the potential to optimize the benefits of non-invasive neuromodulation methods for aging populations.

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.