Aprinda I Queen, Ph.D.

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.

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.

• Aging
• Artificial Intelligence
• Computational Neuroscience
• Neuromodulation