Innovation-based sparse estimation of functional connectivity from multivariate autoregressive models

Abstract : One of the main limitations of functional connectivity estimators of brain networks is that they can suffer from statistical reliability when the number of areas is large and the available time series are short. To estimate directed functional connectivity with multivariate autoregressive (MVAR) model on sparse connectivity assumption, we propose a modified Group Lasso procedure with an adapted penalty. Our procedure includes the innovation estimates as explaining variables. This approach is inspired by two criteria that are used to interpret the coefficients of the MVAR model, the Directed Transfer Function (DTF) and the Partial Directed Coherence (PDC). A causality measure can be deduced from the output coefficients which can be understood as a synthesis of PDC and DTF. We demonstrate the potential of our method and compare our results with the standard Group Lasso on simulated data. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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https://hal.inria.fr/hal-01260223
Contributor : Fabrizio de Vico Fallani <>
Submitted on : Thursday, January 21, 2016 - 4:51:33 PM
Last modification on : Tuesday, April 30, 2019 - 3:43:46 PM

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François Deloche, Fabrizio de Vico Fallani, Stéphanie Allassonniere. Innovation-based sparse estimation of functional connectivity from multivariate autoregressive models. SPIE, Wavelets and Sparsity XVI, Aug 2015, San Diego, United States. ⟨10.1117/12.2189640⟩. ⟨hal-01260223⟩

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