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

François Deloche; Fabrizio De Vico Fallani; Stéphanie Alassoniѐre

doi:10.1117/12.2189640

24 August 2015 Innovation-based sparse estimation of functional connectivity from multivariate autoregressive models

François Deloche, Fabrizio De Vico Fallani, Stéphanie Alassoniѐre

Proceedings Volume 9597, Wavelets and Sparsity XVI; 95971I (2015) https://doi.org/10.1117/12.2189640
Event: SPIE Optical Engineering + Applications, 2015, San Diego, California, United States

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.

Citation Download Citation

François Deloche, Fabrizio De Vico Fallani, and Stéphanie Alassoniѐre "Innovation-based sparse estimation of functional connectivity from multivariate autoregressive models", Proc. SPIE 9597, Wavelets and Sparsity XVI, 95971I (24 August 2015); https://doi.org/10.1117/12.2189640

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