Paper Info
Title
A realistic neuronal network and neurovascular coupling model for the study of multivariate directed connectivity in fMRI data.
Abstract
The use of Multivariate Granger Causality (MVGC) in estimating directed Blood-Oxygen-Level- Dependant (BOLD) connectivity is still controversial. This is mostly due to the short data Ienghts typically available in func- tional MRI (fMRI) acquisitions, to the very nature of the BOLD acquisition strategy (which yields extremely low signal- to-noise-ratio) and importantly to the fact that neuronal activi- ty is convolved with a slow-varying haemodynamic response function (HRF) which therefore generates a temporal confound which is arduous to account for when basing MVGC estimates on vector autoregressive models (VAR). In this paper, we em- ploy realistic complex network models based on Izhikevich neuronal populations, interlinked by realistic neuronal fiber bundles which exert compounded directed influences and cas- cade into Baloon-model-like neurovascular coupling, to explore and validate the MVGC approach to directed connectivity es- timation in realistic fMRI conditions and in a complex directed network setting. In particular, we show in silico that the top 1 percentile of a BOLD connectivity matrix estimated with MVGC from BOLD data similar to the one provided by the Human Connectome Project (HCP) has a Positive Predictive Value very close to 1, hence corroborating the evidence that the "strongest" connections can be safely studied with this method in fMRI.
Year
DOI
Venue
2018
10.1109/EMBC.2018.8513589
EMBC
Field
DocType
Volume
Computer vision,Autoregressive model,Data modeling,Neuroscience,Human Connectome Project,Functional magnetic resonance imaging,Multivariate statistics,Computer science,Granger causality,Artificial intelligence,Complex network,Biological neural network
Conference
2018
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Andrea Duggento1117.37
Luca Passamonti24311.28
maria guerrisi3116.95
nicola toschi43615.57