Showing papers by "Bertrand Thirion published in 2004"
TL;DR: A method for automatically and accurately extracting surface meshes of several tissues of the head from anatomical magnetic resonance (MR) images and a new theory of random shapes that may prove useful in building anatomical and functional atlases are discussed.
42 citations
15 Apr 2004
TL;DR: Using a Laplacian embedding approach, the use of nonlinear dimension reduction for the analysis of functional neuroimaging datasets is introduced, showing the power of this method to detect significant structures within the noisy and complex dynamics of fMRI datasets.
Abstract: In this paper, we introduce the use of nonlinear dimension reduction for the analysis of functional neuroimaging datasets. Using a Laplacian embedding approach, we show the power of this method to detect significant structures within the noisy and complex dynamics of fMRI datasets; it outperforms classical linear techniques in the discrimination of structures of interest. Moreover, it can also be used in a more constrained framework, allowing for an exploration of the manifold of the hemodynamic responses of interest. A solution is proposed for the issue of dimension selection, which is not yet completely satisfactory. However, our studies show the power of the method for data exploration, visualization and understanding.
27 citations
TL;DR: The IB quantization provides a consistent representation of the data, allowing for an easy interpretation and comparison of datasets, and is principled through an information theoretic formulation, which is relevant in many situations.
25 citations