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Computational models provide additional tools for studying the brain, however, many techniques are cur-rently disconnected from each other. hTere is a need for new computational approaches that span the range of physics operating in the brain. In this review paper, we offer some new perspectives on how the embedded element method can ifll this gap and has the potential to connect a myriad of modeling genre. hTe embedded element method is a mesh superposition technique used within ifnite element analysis. hTis method allows for the incorporation of axonal ifber tracts to be explicitly represented. Here, we explore the use of the approach beyond its original goal of predicting axonal strain in brain injury. We explore the potential application of the embedded element method in areas of electrophysiology, neurodegeneration, neuropharmacology and mech-anobiology. We conclude that this method has the potential to provide us with an integrated computational framework that can assist in developing improved diagnostic tools and regeneration technologies.