Analysis of fMRI time series with mixtures of Gaussians

Author

Sanguineti, Vittorio ; Parodi, Claudio ; Perissinotto, Sergio ; Frisone, Francesco ; Vitali, Paolo ; Morasso, Pietro ; Rodriguez, Guido

Author_Institution

Dept. of Inf., Syst. & Telematics, Genova Univ., Italy

Volume

1

fYear

2000

fDate

2000

Firstpage

331

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

Keywords

Gaussian distribution; biomedical MRI; time series; Gaussian mixture model; activity-related voxels; density estimation; fMRI time series analysis; finger-tapping; functional MRI; sensorimotor paradigm; voxel-by-voxel linear regression; Biomedical imaging; Biomedical informatics; Brain modeling; Electronic mail; Gaussian processes; Hemodynamics; Linear regression; Probability density function; Time series analysis; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on

Conference_Location

Como

ISSN

1098-7576

Print_ISBN

0-7695-0619-4

Type

conf

DOI

10.1109/IJCNN.2000.857857

Filename

857857