Cyclicity in multivariate time series and applications to functional MRI data

Yuliy Baryshnikov, Emily Schlafly

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

Abstract

One of the challenging problems in fMRI data analysis is the absence of natural time scale at which to consider the traces of the resting state, the aggregate name for the processes in the brain happening in the absence of task of strong external stimuli. This necessitates development of the tools that are able to extract from the time series information invariant with respect to reparametrization of the time coordinate. A recently developed notion of cyclicity, based on the hierarchy of iterated path integrals and corresponding algorithms were applied to several hundreds of the fMRI records, to reveal potential presence of an external auditory stimulus.

Original languageEnglish (US)
Title of host publication2016 IEEE 55th Conference on Decision and Control, CDC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1625-1630
Number of pages6
ISBN (Electronic)9781509018376
DOIs
StatePublished - Dec 27 2016
Event55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, United States
Duration: Dec 12 2016Dec 14 2016

Publication series

Name2016 IEEE 55th Conference on Decision and Control, CDC 2016

Other

Other55th IEEE Conference on Decision and Control, CDC 2016
CountryUnited States
CityLas Vegas
Period12/12/1612/14/16

ASJC Scopus subject areas

  • Artificial Intelligence
  • Decision Sciences (miscellaneous)
  • Control and Optimization

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  • Cite this

    Baryshnikov, Y., & Schlafly, E. (2016). Cyclicity in multivariate time series and applications to functional MRI data. In 2016 IEEE 55th Conference on Decision and Control, CDC 2016 (pp. 1625-1630). [7798498] (2016 IEEE 55th Conference on Decision and Control, CDC 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2016.7798498