Neural networks for fMRI spatio-temporal analysis

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Most of the analysis techniques applied to functional magnetic resonance imaging (fMRI) consider only the temporal information of the data. In this paper, a new method combining temporal and spatial information is proposed for the fMRI data analysis. The nonlinear autoregressive with exogenous inputs (NARX) model realized by radial basis function (RBF) neural network is used to model the fMRI data. This new approach models the fMRI waveform in each voxel as a regression model that combines the time series of neighboring voxels together with its own. Both simulated as well as real fMRI data were tested using the proposed algorithm. Results show that this new approach can model the fMRI data very well and as a result, can detect the activated areas of human brain successfully and accurately.
Original languageEnglish
Title of host publicationNeural Information Processing
Publication date2004
Pages1292-1297
ISBN (Print)3-540-23931-6
DOIs
Publication statusPublished - 2004
Externally publishedYes
Event11th International Conference on Neural Information Processing (ICONIP) - Calcutta, India
Duration: 22 Jan 200425 Nov 2004
Conference number: 11

Conference

Conference11th International Conference on Neural Information Processing (ICONIP)
Number11
CountryIndia
CityCalcutta
Period22/01/200425/11/2004
SeriesLecture Notes in Computer Science
Volume3316
ISSN0302-9743

Fingerprint Dive into the research topics of 'Neural networks for fMRI spatio-temporal analysis'. Together they form a unique fingerprint.

Cite this