Multi-kernel change detection for dynamic functional connectivity graphs

Georgios Vasileios Karanikolas, Olaf Sporns, Georgios B Giannakis

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

1 Scopus citations

Abstract

Dynamic functional connectivity (dFC) analyses of fMRI time-courses are typically performed using sliding-window based schemes. Such approaches not only inherently confine analysis to a single time-scale, but also do not generally lend themselves to accurate change-time estimates of the dynamically evolving graph topology. Change point detection methods on the other hand, offer the potential to overcome both limitations. However, the approaches employed so far in the dFC context are limited to detecting changes in linear relationships among time-courses corresponding to distinct regions of the brain. The present work puts forth a novel multi-kernel change point detection approach with the goal of capturing changes in the generally nonlinear relationships among time-courses, and thus in the topologies of the corresponding dynamically evolving FC graphs. The approach is tested on dynamic causal model (DCM) based synthetic resting-state fMRI data.

Original languageEnglish (US)
Title of host publicationConference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
EditorsMichael B. Matthews
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1555-1559
Number of pages5
ISBN (Electronic)9781538618233
DOIs
StatePublished - Apr 10 2018
Event51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 - Pacific Grove, United States
Duration: Oct 29 2017Nov 1 2017

Publication series

NameConference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Volume2017-October

Other

Other51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
CountryUnited States
CityPacific Grove
Period10/29/1711/1/17

Keywords

  • change detection
  • fMRI
  • kernel-based regression
  • multiple kernel learning

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