Adaptive windowing and windowless approaches to estimate dynamic functional brain connectivity

Maziar Yaesoubi, Vince Daniel Calhoun

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


In this work, we discuss estimation of dynamic dependence of a multi-variate signal. Commonly used approaches are often based on a locality assumption (e.g. sliding-window) which can miss spontaneous changes due to blurring with local but unrelated changes. We discuss recent approaches to overcome this limitation including 1) a wavelet-space approach, essentially adapting the window to the underlying frequency content and 2) a sparse signal-representation which removes any locality assumption. The latter is especially useful when there is no prior knowledge of the validity of such assumption as in brain-analysis. Results on several large resting-fMRI data sets highlight the potential of these approaches.

Original languageEnglish (US)
Title of host publicationWavelets and Sparsity XVII
ISBN (Electronic)9781510612457
StatePublished - 2017
Externally publishedYes
EventWavelets and Sparsity XVII 2017 - San Diego, United States
Duration: Aug 6 2017Aug 9 2017


OtherWavelets and Sparsity XVII 2017
Country/TerritoryUnited States
CitySan Diego

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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