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Poster A49, Wednesday, November 8, 10:30 – 11:45 am, Harborview and Loch Raven Ballrooms

MrAnats: Magnetic Resonance-based Adaptive NeuroAnatomy Teaching Software

Paul Fillmore1, Matthew Parham1;1Baylor University

The current work describes the creation of a software program (MrAnats: Magnetic Resonance-based Adaptive NeuroAnatomy Teaching Software) for teaching introductory neuroanatomy. With the advent of neuroimaging techniques such as magnetic resonance imaging (MRI), much has been learned about neuroanatomy and brain structure. However, many of these advances have occurred primarily in the realms of scientific research and clinical care, often without significant effect on the ways in which students learn about the brain. For example, most textbooks offer fairly simple two-dimensional views of neuroanatomy and do not make use of modern three-dimensional visualization methods common in scientific applications. Additionally, in learning about the brain, there are many different sets of terminology and labels used, making it especially difficult for the new learner to see how the different organizational systems fit together. There is no widespread framework in use for comparing and contrasting these systems. Lastly, current research in learning theory has highlighted the inefficiency of some of the most popular methods of studying (e.g. highlighting, re-reading), and has suggested specific learning methods (e.g. iterative self-testing) that are the most effective use of students’ time. The availability of tools to make use of these insights, however, is still lacking. Thus, we describe a program which: 1) Leverages high-resolution MRI scans to visualize neuroanatomy interactively in three dimensions, 2) Presents the common labeling systems for human brain structure and allows for explorative comparing and contrasting, and 3) Uses current best-practices in learning theory to help students learn about the brain efficiently.

Topic Area: Methods

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