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


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
Number of pages5
Publication statusPublished - 2000
EventInternational Joint Conference on Neural Networks (IJCNN'2000) - Como, Italy
Duration: Jul 24 2000Jul 27 2000


OtherInternational Joint Conference on Neural Networks (IJCNN'2000)
CityComo, Italy

ASJC Scopus subject areas

  • Software

Fingerprint Dive into the research topics of 'Analysis of fMRI time series with mixtures of Gaussians'. Together they form a unique fingerprint.

Cite this