Identifying directional connections in brain networks via multi-kernel granger models

G. V. Karanikolas, Georgios B Giannakis

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

3 Scopus citations

Abstract

Granger causality based approaches are popular in unveiling directed interactions among brain regions. The present work advocates a multi-kernel based nonlinear model for obtaining the effective connectivity between brain regions, by wedding the merits of partial correlation in undirected topology identification with the ability of partial Granger causality (PGC) to estimate edge directionality. The premise is that existing linear PGC approaches may be inadequate for capturing certain dependencies, whereas available nonlinear connectivity models lack data adaptability that multi-kernel learning methods can offer. The proposed approach is tested on both synthetic and real resting-state fMRI data, with the former illustrating the gains in directed edge presence detection performance, as compared to existing PGC methods, and with the latter highlighting differences in the estimated test statistics.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6304-6308
Number of pages5
ISBN (Electronic)9781509041176
DOIs
StatePublished - Jun 16 2017
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: Mar 5 2017Mar 9 2017

Publication series

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

Other

Other2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
CountryUnited States
CityNew Orleans
Period3/5/173/9/17

Bibliographical note

Funding Information:
This work was supported by NSF 1500713, 1514056, and NIH 1R01GM104975-01.

Keywords

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

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