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

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

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Title: Multiple Subject Barycentric Discriminant Analysis (MUSUBADA): How to Assign Scans to Categories without Using Spatial Normalization
Author(s):
Abdi, Hervé;
Williams, Lynne J.;
Connolly, Andrew C.;
Gobbini, M. Ida;
Dunlop, Joseph P.;
Haxby, James V.
Date Created: 2011-12-21
Format: text
Item Type: article
Keywords: Cerebral cortex
Magnetic Resonance Imaging
Multiple Subject Barycentric Discriminant Analysis (MUSUBADA)
Abstract: We 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.
ISSN: 1748-670X
Persistent Link: http://dx.doi.org/10.1155/2012/634165
http://hdl.handle.net/10735.1/2865
Terms of Use: © 2012 The Authors. Licensed and distributed under a Creative Commons Attribution 3.0 Unported License.

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  • Abdi, Hervé
    Professor of Cognitive-Neuroscience and Cognitive Psychology

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