Teaching Statistical Analysis Of Fmri Data
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| Publicat a: | Association for Engineering Education - Engineering Library Division Papers (Jun 22, 2003), p. 8.1081.1 |
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American Society for Engineering Education-ASEE
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| Resum: | Functional magnetic resonance imaging (fMRI) represents a new and important topic in biomedical engineering. Statistical analysis of fMRI data is typically performed using free or commercial software packages that do not facilitate learning about the underlying assumptions and analysis methods; these shortcomings can lead to misinterpretation of the fMRI data and spurious results. We are developing an instructional module for learning the fundamentals of statistical analysis of fMRI data. The goal is to provide a tool for learning about the steps and assumptions underlying standard fMRI data analysis so that students and researchers can develop insights required to use existing analysis methods in an informed fashion and adapt them to their own purposes. The module includes a simulation of fMRI data analysis that provides students with opportunities for hands-on exploration of the key concepts using phantom data as well as sample human fMRI data. The simulation allows students to control relevant parameters and observe intermediate results for each step in the analysis stream (spatial smoothing, motion correction, statistical model parameter selection). It is accompanied by a tutorial that directs students as they use the simulation. The tutorial guides students through the individual processing steps, considering multiple cycles of fMRI data analysis and prompting them to make direct comparisons, with emphasis on how processing choices affect the ultimate interpretation of the fMRI data. I. Introduction While magnetic resonance imaging (MRI) was introduced for clinical use in the 1970s, functional magnetic resonance imaging (fMRI) was discovered in the early 1990s1-4. This relatively new research tool has found widespread use in a variety of applications at the intersection of biomedical engineering and neuroscience, for example, mapping the boundaries between functional regions of the brain, identifying tumor margins prior to surgery, and investigating the pathology underlying diseases such as schizophrenia. fMRI detects activity in the brain by taking advantage of the change in magnetic properties of the blood surrounding Proceedings of the 2003 American Society for Engineering Education Annual Conference & Exposition Copyright 2003, American Society for Engineering Education |
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| Font: | Library Science Database |