PMID- 31592214
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20201016
IS  - 1053-587X (Print)
IS  - 1053-587X (Linking)
VI  - 67
IP  - 20
DP  - 2019 Oct 15
TI  - Nonlinear Structural Vector Autoregressive Models with Application to Directed 
      Brain Networks.
PG  - 5325-5339
LID - 10.1109/TSP.2019.2940122 [doi]
AB  - Structural equation models (SEMs) and vector autoregressive models (VARMs) are 
      two broad families of approaches that have been shown useful in effective brain 
      connectivity studies. While VARMs postulate that a given region of interest in 
      the brain is directionally connected to another one by virtue of time-lagged 
      influences, SEMs assert that directed dependencies arise due to instantaneous 
      effects, and may even be adopted when nodal measurements are not necessarily 
      multivariate time series. To unify these complementary perspectives, linear 
      structural vector autoregressive models (SVARMs) that leverage both instantaneous 
      and time-lagged nodal data have recently been put forth. Albeit simple and 
      tractable, linear SVARMs are quite limited since they are incapable of modeling 
      nonlinear dependencies between neuronal time series. To this end, the overarching 
      goal of the present paper is to considerably broaden the span of linear SVARMs by 
      capturing nonlinearities through kernels, which have recently emerged as a 
      powerful nonlinear modeling framework in canonical machine learning tasks, e.g., 
      regression, classification, and dimensionality reduction. The merits of 
      kernel-based methods are extended here to the task of learning the effective 
      brain connectivity, and an efficient regularized estimator is put forth to 
      leverage the edge sparsity inherent to real-world complex networks. Judicious 
      kernel choice from a preselected dictionary of kernels is also addressed using a 
      data-driven approach. Numerical tests on ECoG data captured through a study on 
      epileptic seizures demonstrate that it is possible to unveil previously unknown 
      directed links between brain regions of interest.
FAU - Shen, Yanning
AU  - Shen Y
AD  - Dept. of EECS and the Center for Pervasive Communications and Computing at the 
      University of California, Irvine, CA 92697.
FAU - Giannakis, Georgios B
AU  - Giannakis GB
AD  - Dept. of ECE and the Digital Technology Center, University of Minnesota.
FAU - Baingana, Brian
AU  - Baingana B
AD  - NextEra Analytics.
LA  - eng
GR  - R01 GM104975/GM/NIGMS NIH HHS/United States
PT  - Journal Article
DEP - 20190911
PL  - United States
TA  - IEEE Trans Signal Process
JT  - IEEE transactions on signal processing : a publication of the IEEE Signal 
      Processing Society
JID - 9885223
PMC - PMC6779157
MID - NIHMS1540485
OTO - NOTNLM
OT  - Network topology inference
OT  - nonlinear models
OT  - structural vector autoregressive models
EDAT- 2019/10/09 06:00
MHDA- 2019/10/09 06:01
PMCR- 2020/10/15
CRDT- 2019/10/09 06:00
PHST- 2019/10/09 06:00 [entrez]
PHST- 2019/10/09 06:00 [pubmed]
PHST- 2019/10/09 06:01 [medline]
PHST- 2020/10/15 00:00 [pmc-release]
AID - 10.1109/TSP.2019.2940122 [doi]
PST - ppublish
SO  - IEEE Trans Signal Process. 2019 Oct 15;67(20):5325-5339. doi: 
      10.1109/TSP.2019.2940122. Epub 2019 Sep 11.