Investigating time-varying brain connectivity with functional magnetic resonance imaging using sequential Monte Carlo

Pierfrancesco Ambrosi, Mauro Costagli, Ercan E. Kuruoglu, Laura Biagi, Guido Buonincontri, Michela Tosetti

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

Abstract

There is a rising interest in studying the degree of connection and the causal relationships between brain regions, as a growing body of evidence suggests that features of these interactions could play a role as markers in a host of neurological diseases. The vast majority of brain connectivity studies treats the brain network as stationary. New insights on the temporal behaviour of these connections could significantly improve our understanding of brain networking in both physiology and pathology. In this paper, we propose the application of a computational methodology, named Particle Filter (PF), to functional Magnetic Resonance Imaging (fMRI) data. The PF algorithm aims to estimate time-varying hidden variables of a given observational model through a Sequential Monte Carlo approach. The fMRI data are represented as a first-order linear time-varying Vector Autoregression model (VAR). On simulated time series, the PF approach effectively detected and enabled to follow time-varying hidden parameters and it captured causal relationships among signals. The method was also applied to real fMRI data and provided similar results to those obtained by using a different proxy measure of causal dependency, that is, correlation between delayed time series. Interestingly, the PF approach also enabled to detect statistically significant changes in the cause-effect relationships between areas, which correlated with the underlying stimulation pattern delivered to subjects during the fMRI acquisition.

Original languageEnglish
Title of host publicationEUSIPCO 2019 - 27th European Signal Processing Conference
PublisherEuropean Signal Processing Conference, EUSIPCO
ISBN (Electronic)9789082797039
DOIs
Publication statusPublished - Sep 2019
Event27th European Signal Processing Conference, EUSIPCO 2019 - A Coruna, Spain
Duration: Sep 2 2019Sep 6 2019

Publication series

NameEuropean Signal Processing Conference
Volume2019-September
ISSN (Print)2219-5491

Conference

Conference27th European Signal Processing Conference, EUSIPCO 2019
CountrySpain
CityA Coruna
Period9/2/199/6/19

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Keywords

  • Brain connectivity
  • FMRI
  • Particle Filtering
  • Sequential Monte Carlo
  • VAR model

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Ambrosi, P., Costagli, M., Kuruoglu, E. E., Biagi, L., Buonincontri, G., & Tosetti, M. (2019). Investigating time-varying brain connectivity with functional magnetic resonance imaging using sequential Monte Carlo. In EUSIPCO 2019 - 27th European Signal Processing Conference (European Signal Processing Conference; Vol. 2019-September). European Signal Processing Conference, EUSIPCO. https://doi.org/10.23919/EUSIPCO.2019.8902503