Ji, Yongnan, Hervé, Pierre-Yves, Aickelin, Uwe and Pitiot, Alain
(2009)
Parcellation of fMRI datasets with ICA and PLS: a data driven approach.
In:
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009: 12th International Conference, London, UK, September 20-24, 2009: proceedings. Part 1.
Lecture notes in computer science
(5761).
Springer, Berlin, pp. 984-991.
ISBN 9783642042683
Full text not available from this repository.
Abstract
Inter-subject parcellation of functional Magnetic Resonance
Imaging (fMRI) data based on a standard General Linear Model (GLM) and spectral clustering was recently proposed as a means to alleviate the issues associated with spatial normalization in fMRI. However, for all its appeal, a GLM-based parcellation approach introduces its own biases,
in the form of a priori knowledge about the shape of Hemodynamic Response Function (HRF) and task-related signal changes, or about the subject behaviour during the task.
In this paper, we introduce a data-driven version of the spectral clustering parcellation, based on Independent Component Analysis (ICA) and Partial Least Squares (PLS) instead of the GLM. First, a number of independent components are automatically selected. Seed voxels are then
obtained from the associated ICA maps and we compute the PLS latent variables between the fMRI signal of the seed voxels (which covers regional variations of the HRF) and the principal components of the signal across all voxels. Finally, we parcellate all subjects data with a spectral
clustering of the PLS latent variables. We present results of the application of the proposed method on both
single-subject and multi-subject fMRI datasets. Preliminary experimental results, evaluated with intra-parcel variance of GLM t-values and PLS derived t-values, indicate that this data-driven approach offers improvement in terms of parcellation accuracy over GLM based techniques.
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