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IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 1
Analysis of fMRI Time Series with Mixtures of Gaussians
Como, Italy
July 24-July 27
ISBN: 0-7695-0619-4
ASCII Text
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Vittorio Sanguineti, Claudio Parodi, Sergio Perissinotto, Francesco Frisone, Paolo Vitali, Pietro Morasso, Guido Rodriguez, "Analysis of fMRI Time Series with Mixtures of Gaussians," Neural Networks, IEEE - INNS - ENNS International Joint Conference on, vol. 1, pp. 1331, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 1, 2000.
 
 
BibTex
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@article{ 10.1109/IJCNN.2000.857857,
author = {Vittorio Sanguineti and Claudio Parodi and Sergio Perissinotto and Francesco Frisone and Paolo Vitali and Pietro Morasso and Guido Rodriguez},
title = {Analysis of fMRI Time Series with Mixtures of Gaussians},
journal ={Neural Networks, IEEE - INNS - ENNS International Joint Conference on},
volume = {1},
year = {2000},
issn = {1098-7576},
pages = {1331},
doi = {http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.857857},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Neural Networks, IEEE - INNS - ENNS International Joint Conference on
TI - Analysis of fMRI Time Series with Mixtures of Gaussians
SN - 1098-7576
SP
EP
A1 - Vittorio Sanguineti,
A1 - Claudio Parodi,
A1 - Sergio Perissinotto,
A1 - Francesco Frisone,
A1 - Paolo Vitali,
A1 - Pietro Morasso,
A1 - Guido Rodriguez,
PY - 2000
VL - 1
JA - Neural Networks, IEEE - INNS - ENNS International Joint Conference on
ER -
 
Vittorio Sanguineti, University of Genova
Claudio Parodi, University of Genova
Sergio Perissinotto, University of Genova
Francesco Frisone, University of Genova
Paolo Vitali, University of Genova
Pietro Morasso, University of Genova
Guido Rodriguez, University of Genova
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.
Citation:
Vittorio Sanguineti, Claudio Parodi, Sergio Perissinotto, Francesco Frisone, Paolo Vitali, Pietro Morasso, Guido Rodriguez, "Analysis of fMRI Time Series with Mixtures of Gaussians," ijcnn, vol. 1, pp.1331, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 1, 2000
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