Analysis of fMRI time series with mixtures of Gaussians

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherIEEE
Pages331-335
Number of pages5
Volume1
Publication statusPublished - 2000
EventInternational Joint Conference on Neural Networks (IJCNN'2000) - Como, Italy
Duration: Jul 24 2000Jul 27 2000

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'2000)
CityComo, Italy
Period7/24/007/27/00

ASJC Scopus subject areas

  • Software

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