About Aprinda Indahlastari
My long-term research goal is to develop personalized medicine using cutting-edge technology such as the state-of-the-art neuroimaging modalities and individualized computational models. For the past eight years, I have been involved in computational-based research, specifically using finite element modeling to predict the effects of biomedical devices. In my early graduate study and while pursuing a master’s degree, I performed finite element method (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 different medical device which is a type of a noninvasive brain stimulation 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 clinical trials. I was actively involved in phase 2 and phase 3 clinical trials of tDCS administration paired with cognitive training in 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 it with artificial intelligence methods such as machine learning and deep learning algorithms will enable us to predict treatment outcomes and formulate precision dosing that is tailored to individuals to optimize gains in cognitive performance resulted from tES application, specifically in older adults. These methods can be translated in the future to investigate other domains of brain function as well as exploring other intervention methods beyond tES/tDCS. I look forward to future collaborations with experts in the 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 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. Dr. Indahlastari is part of the Woods Neuromodulation Laboratory in the Department of Clinical and Health Psychology. In this lab, her current role involves data analysis in tES participants collected from clinical trials. Specific 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.
- Computational Neuroscience
- Precision Health