Abstract
In this paper, we discuss the application of the mixtures of Gaussians model for density estimation to the analysis of fMRI time series. We show that, in a classical sensorimotor paradigm (finger-tapping), the performance of the proposed method (in terms of number and location of the detected activity-related voxels) is very similar to that of voxel-by-voxel linear regression, but does not require an explicit model of the activation pattern and/or of the hemodynamic response. In addition, if the number of mixture elements is increased, our method is capable of detecting additional activity-related areas.
Original language | English |
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Title of host publication | Proceedings of the International Joint Conference on Neural Networks |
Publisher | IEEE |
Pages | 331-335 |
Number of pages | 5 |
Volume | 1 |
Publication status | Published - 2000 |
Event | International Joint Conference on Neural Networks (IJCNN'2000) - Como, Italy Duration: Jul 24 2000 → Jul 27 2000 |
Other
Other | International Joint Conference on Neural Networks (IJCNN'2000) |
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City | Como, Italy |
Period | 7/24/00 → 7/27/00 |
ASJC Scopus subject areas
- Software