Multivariate Model Specification for fMRI Data
By Ferath Kherif, Jean-Baptiste Poline, Guillaume Flandin, Habib Benali, Olivier Simon, Stanislas Dehaene, Keith J. Worsley. 2002
66 citations recorded by the source corpus.
Subfields: Applied Mathematics, Artificial Intelligence, Cognitive Neuroscience
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Applied Mathematics
In Artificial Intelligence
- Christos Davatzikos
- Bilwaj Gaonkar
- Christian Gaser
- Bogdan Draganski
- Russell T. Shinohara
- Richard S. J. Frackowiak
- Gabriel Ziegler
- Robert Dahnke
In Cognitive Neuroscience
- Christian Gaser
- Bilwaj Gaonkar
- Christos Davatzikos
- Robert Dahnke
- John Ashburner
- Bogdan Draganski
- Russell T. Shinohara
- Nikolaus Weiskopf
Citations in the corpus
- Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, methodology): “Related work regarding model selection in multivariate analysis was presented in Kherif et al. (2002).”
- Models of the Aging Brain Structure and Individual Decline (2012, methodology): “, using spatial prior modeling, canonical variates analysis (CVA), etc.(Kherif et al., 2002).”
- Regional specificity of MRI contrast parameter changes in normal ageing revealed by voxel-based quantification (VBQ) (2011, methodology): “To summarise the age-related parameter changes we generated a statistical map based on a conjunction analysis of parameter-specific differential contrasts and explored the covariance of the parameter maps using a multivariate linear model projecting the data in the subspace of the designmatrix over ”
- Where the BOLD signal goes when alpha EEG leaves (2006, methodology): “Singular value decomposition (principal component analysis) of the fMRI time series was performed using the multivariate linear models toolbox as implemented in SPM99 (Kherif et al., 2002) after removing the session mean and the variance explained by the realignment parameters.”
- Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data (2005, methodology): “Kherif et al. (2002) presented a general method for specifying a model to analyze neuroimaging data based on multivariate linear model on a training data set.”