Identifying directional connections in brain networks via multi-kernel granger models

Granger causality based approaches are popular in unveiling directed interactions among brain regions. The present work advocates a multi-kernel based nonlinear model for obtaining the effective connectivity between brain regions, by wedding the merits of partial correlation in undirected topology identification with the ability of partial Granger causality (PGC) to estimate edge directionality. The premise is that existing linear PGC approaches may be inadequate for capturing certain dependencies, whereas available nonlinear connectivity models lack data adaptability that multi-kernel learning methods can offer. The proposed approach is tested on both synthetic and real resting-state fMRI data, with the former illustrating the gains in directed edge presence detection performance, as compared to existing PGC methods, and with the latter highlighting differences in the estimated test statistics.

[1]  Joseph P. Romano,et al.  The stationary bootstrap , 1994 .

[2]  Mark W. Woolrich,et al.  Network modelling methods for FMRI , 2011, NeuroImage.

[3]  Habib Benali,et al.  Partial correlation for functional brain interactivity investigation in functional MRI , 2006, NeuroImage.

[4]  Daoqiang Zhang,et al.  Integration of Network Topological and Connectivity Properties for Neuroimaging Classification , 2014, IEEE Transactions on Biomedical Engineering.

[5]  Gustavo Deco,et al.  Role of local network oscillations in resting-state functional connectivity , 2011, NeuroImage.

[6]  A. Seth,et al.  Multivariate Granger causality and generalized variance. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  D. Long.  Networks of the Brain , 2011 .

[8]  Jeffrey S. Racine,et al.  Nonparametric Econometrics: The np Package , 2008 .

[9]  L. Shah,et al.  Functional magnetic resonance imaging. , 2010, Seminars in roentgenology.

[10]  H. White,et al.  Automatic Block-Length Selection for the Dependent Bootstrap , 2004 .

[11]  Mehryar Mohri,et al.  L2 Regularization for Learning Kernels , 2009, UAI.

[12]  Kaustubh Supekar,et al.  Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty , 2012, NeuroImage.

[13]  Georgios B. Giannakis,et al.  Multi-kernel based nonlinear models for connectivity identification of brain networks , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[14]  O Sporns,et al.  Predicting human resting-state functional connectivity from structural connectivity , 2009, Proceedings of the National Academy of Sciences.

[15]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[16]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[17]  Vince D. Calhoun,et al.  A multiple kernel learning approach to perform classification of groups from complex-valued fMRI data analysis: Application to schizophrenia , 2014, NeuroImage.

[18]  Yul-Wan Sung,et al.  Functional magnetic resonance imaging , 2004, Scholarpedia.

[19]  Bernhard Schölkopf,et al.  A Generalized Representer Theorem , 2001, COLT/EuroCOLT.

[20]  Rainer Goebel,et al.  Mapping directed influence over the brain using Granger causality and fMRI , 2005, NeuroImage.

[21]  Ethem Alpaydin,et al.  Multiple Kernel Learning Algorithms , 2011, J. Mach. Learn. Res..

[22]  E. Bullmore,et al.  Neurophysiological architecture of functional magnetic resonance images of human brain. , 2005, Cerebral cortex.

[23]  K. Kendrick,et al.  Partial Granger causality—Eliminating exogenous inputs and latent variables , 2008, Journal of Neuroscience Methods.

[24]  R. Buxton,et al.  Dynamics of blood flow and oxygenation changes during brain activation: The balloon model , 1998, Magnetic resonance in medicine.

[25]  Karl J. Friston,et al.  Dynamic causal modelling , 2003, NeuroImage.

[26]  C. Granger.  Investigating causal relations by econometric models and cross-spectral methods , 1969 .

[27]  N. Logothetis,et al.  Neurophysiological investigation of the basis of the fMRI signal , 2001, Nature.

[28]  Wei Liao,et al.  Kernel Granger Causality Mapping Effective Connectivity on fMRI Data , 2009, IEEE Transactions on Medical Imaging.

[29]  Daniele Marinazzo,et al.  Kernel method for nonlinear granger causality. , 2007, Physical review letters.