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.
Analysis of fMRI time series with mixtures of Gaussians / V. Sanguineti, C. Parodi, S. Perissinotto, F. Frisone, P. Vitali, P. Morasso, G. Rodriguez - In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium[s.l] : IEEE, 2000. - ISBN 0-7695-0619-4. - pp. 331-335 (( convegno International Joint Conference on Neural Networks (IJCNN'2000) tenutosi a Como nel 2000 [10.1109/ijcnn.2000.857857].
Analysis of fMRI time series with mixtures of Gaussians
P. Vitali;
2000
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.
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