Multivariate Model Specification for fMRI Data

• Published in: NeuroImage (Orlando, Fla.) Vol. 16; no. 4; pp. 1068 - 1083
• Main Authors: Kherif, Ferath, Poline, Jean-Baptiste, Flandin, Guillaume, Benali, Habib, Simon, Olivier, Dehaene, Stanislas, Worsley, Keith J.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.08.2002; Elsevier Limited; Elsevier
• Subjects: Bioengineering; Brain - physiology; brain imaging method; Cognition - physiology; Computer Science; Engineering Sciences; fMRI; Humans; Imaging; Life Sciences; Linear Models; Magnetic Resonance Imaging; Mathematics; Medical Imaging; Mental Processes - physiology; model selection; model specification; Modeling and Simulation; Models, Neurological; Multivariate Analysis; Signal and Image processing; statistical analysis; brain imaging method; model selection; fMRI; model specification; statistical analysis; multivariate analysis
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1006/nimg.2002.1094

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.