Multivariate model specification for fMRI data

被引:52
作者
Kherif, F [1 ]
Poline, JB
Flandin, G
Benali, H
Simon, O
Dehaene, S
Worsley, KJ
机构
[1] CEA, Serv Hosp Frederic Joliot, INSERM, U334, F-91406 Orsay, France
[2] Inst Imagerie Neurofonct, IFR 49, Paris, France
[3] INRIA, Epidaure Project, Sophia Antipolis, France
[4] CHU Pitie Salpetriere, INSERM, U494, Paris, France
[5] McGill Univ, Dept Math & Stat, Montreal, PQ, Canada
关键词
model specification; model selection; multivariate analysis; statistical analysis; fMRI; brain imaging method;
D O I
10.1006/nimg.2002.1094
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
We present a general method-denoted MoDef-to help specify (or define) the model used to analyze brain imaging data. This method is based on the use of the multivariate linear model on a training data set. We show that when the a priori knowledge about the expected brain response is not too precise, the method allows for the specification of a model that yields a better sensitivity in the statistical results. This obviously relies on the validity of the a priori information, in our case the representativity of the training set, an issue addressed using a cross-validation technique. We propose a fast implementation that allows the use of the method on large data sets as found with functional Magnetic Resonance Images. An example of application is given on an experimental fMRI data set that includes nine subjects who performed a mental computation task. Results show that the method increases the statistical sensitivity of fMRI analyses. (C) 2002 Elsevier Science (USA).
引用
收藏
页码:1068 / 1083
页数:16
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