Multi-kernel based nonlinear models for connectivity identification of brain networks

G. V. Karanikolas, Georgios B Giannakis, K. Slavakis, R. M. Leahy

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

34 Scopus citations

Abstract

Partial correlations (PCs) of functional magnetic resonance imaging (fMRI) time series play a principal role in revealing connectivity of brain networks. To explore nonlinear behavior of the blood-oxygen-level dependent signal, the present work postulates a kernel-based nonlinear connectivity model based on which it obtains topology revealing PCs. Instead of relying on a single predefined kernel, a data-driven approach is advocated to learn the combination of multiple kernel functions that optimizes the data fit. Synthetically generated data based on both a dynamic causal and a linear model are used to validate the proposed approach in resting-state fMRI scenarios, highlighting the gains in edge detection performance when compared with the popular linear PC method. Tests on real fMRI data demonstrate that connectivity patterns revealed by linear and nonlinear models are different.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6315-6319
Number of pages5
ISBN (Electronic)9781479999880
DOIs
StatePublished - May 18 2016
Event41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China
Duration: Mar 20 2016Mar 25 2016

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2016-May
ISSN (Print)1520-6149

Other

Other41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Country/TerritoryChina
CityShanghai
Period3/20/163/25/16

Bibliographical note

Funding Information:
This work was supported by MURI AFOSR FA9550-10-1-0567, NIH 1R01GM104975-01, NSF grants 1343860, 1500713, 1514056. This research has been co-financed by the European Union (European Social Fund - ESF), and Greek national funds through the Operational Program Education and Lifelong Learning of the National Strategic Reference Framework (NSRF) - Research Funding Program: THALES. Investing in knowledge society through the ESF

Publisher Copyright:
© 2016 IEEE.

Keywords

  • fMRI
  • kernel-based regression
  • multiple kernel learning
  • partial correlation

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