Imaging Electrical Current Flow in the Brain

Transcranial electrical stimulation involves the weak application of electrical current to the scalp through two or more electrodes. Current penetrates scalp, skull and other intervening tissue to stimulate underlying brain regions. However, direct quantification of the amount and location of current flow in the brain has been limited to computational models of “predicted” current flow or, invasively, using intracranial electrodes implanted for various medical conditions. While computational models provide important insight into where the current may flow, objective quantification of current could shed light on which element of current flow in the brain is important for functional gains (e.g., spread of current across a network, focal intensity of current within a region of interest, etc.). This information would help the field of neuromodulation better understand how to design clinical interventions for maximal efficacy. With this information, objective current flow measurement could provide a method for calibrating transcranial electrical current dose on an individual basis taking into account person specific differences in brain structure. Further still, this information can be used to enhance the ¬†accuracy of predictive computational models, improving available tools for personalizing neuromodulation medicine. This NIH Brain Initiative funded study (Sadleir, PI) involves a collaborative team across the University of Florida and Arizona State University. Using Magnetic Resonance Electrical Impedance Topography (MREIT), the team will not only optimize a whole brain sequence across multiple scanner platforms, but also investigate the relationship between objective current flow and change in functional magnetic resonance imaging measures from transcranial direct current stimulation (tDCS). At completion, this study will provide critical insight into the relationship between tDCS current flow and behavioral gains from stimulation, in addition to enhancing the accuracy of predictive models common in the field.

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Funding Source:

National Institute on Mental Health RF1MH114290