Multiple Subject Barycentric Discriminant Analysis (MUSUBADA): How to Assign Scans to Categories without Using Spatial Normalization

dc.contributor.ISNI0000 0001 1648 3631 (Abdi, H.)
dc.contributor.LCNA94060324‏ (Abdi)
dc.contributor.authorAbdi, Hervéen_US
dc.contributor.authorWilliams, Lynne J.en_US
dc.contributor.authorConnolly, Andrew C.en_US
dc.contributor.authorGobbini, M. Idaen_US
dc.contributor.authorDunlop, Joseph P.en_US
dc.contributor.authorHaxby, James V.en_US
dc.date.accessioned2013-10-04T19:40:44Z
dc.date.available2013-10-04T19:40:44Z
dc.date.created2011-12-21
dc.description.abstractWe present a new discriminant analysis (DA) method called Multiple Subject Barycentric Discriminant Analysis (MUSUBADA) suited for analyzing fMRI data because it handles datasets with multiple participants that each provides different number of variables (i.e., voxels) that are themselves grouped into regions of interest (ROIs). Like DA, MUSUBADA (1) assigns observations to predefined categories, (2) gives factorial maps displaying observations and categories, and (3) optimally assigns observations to categories. MUSUBADA handles cases with more variables than observations and can project portions of the data table (e.g., subtables, which can represent participants or ROIs) on the factorial maps. Therefore MUSUBADA can analyze datasets with different voxel numbers per participant and, so does not require spatial normalization. MUSUBADA statistical inferences are implemented with cross-validation techniques (e.g., jackknife and bootstrap), its performance is evaluated with confusion matrices (for fixed and random models) and represented with prediction, tolerance, and confidence intervals. We present an example where we predict the image categories (houses, shoes, chairs, and human, monkey, dog, faces, ) of images watched by participants whose brains were scanned. This example corresponds to a DA question in which the data table is made of subtables (one per subject) and with more variables than observations.en_US
dc.identifier.bibliographicCitationAbdi, Hervé, Lynne J. Williams, Andrew C. Connolly, M. Ida Gobbini, et al. 2012. "Multiple subject barycentric discriminant analysis (MUSUBADA): How to assign scans to categories without using spatial normalization." Computational and Mathematical Methods in Medicine: 634165-1 to 634165-15.en_US
dc.identifier.issn1748-670Xen_US
dc.identifier.startpage634165-1en_US
dc.identifier.urihttp://hdl.handle.net/10735.1/2865
dc.relation.urihttp://dx.doi.org/10.1155/2012/634165en_US
dc.rightsCC BY 3.0 (Attribution)en_US
dc.rights© 2012 The Authors.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en_US
dc.source.journalComputational and Mathematical Methods in Medicineen_US
dc.subjectCerebral cortexen_US
dc.subjectMagnetic Resonance Imagingen_US
dc.subjectMultiple Subject Barycentric Discriminant Analysis (MUSUBADA)en_US
dc.titleMultiple Subject Barycentric Discriminant Analysis (MUSUBADA): How to Assign Scans to Categories without Using Spatial Normalizationen_US
dc.typeTexten_US
dc.type.genrearticleen_US

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