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Proceedings Paper

Adaptive windowing and windowless approaches to estimate dynamic functional brain connectivity
Author(s): Maziar Yaesoubi; Vince D. Calhoun
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Paper Abstract

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.

Paper Details

Date Published: 24 August 2017
PDF: 11 pages
Proc. SPIE 10394, Wavelets and Sparsity XVII, 1039411 (24 August 2017); doi: 10.1117/12.2274425
Show Author Affiliations
Maziar Yaesoubi, The Mind Research Network (United States)
Vince D. Calhoun, The Mind Research Network (United States)
Univ. of New Mexico (United States)

Published in SPIE Proceedings Vol. 10394:
Wavelets and Sparsity XVII
Yue M. Lu; Dimitri Van De Ville; Manos Papadakis, Editor(s)

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