Open AccessCombining independent component analysis and Granger causality to investigate brain network dynamics with fNIRS measurements
Author(s) -
Zhen Yuan
Publication year - 2013
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.4.002629
Subject(s) - independent component analysis , granger causality , computer science , functional near infrared spectroscopy , artificial intelligence , voxel , pattern recognition (psychology) , component (thermodynamics) , artificial neural network , neuroimaging , machine learning , cognition , neuroscience , psychology , physics , thermodynamics , prefrontal cortex
In this study a new strategy that combines Granger causality mapping (GCM) and independent component analysis (ICA) is proposed to reveal complex neural network dynamics underlying cognitive processes using functional near infrared spectroscopy (fNIRS) measurements. The GCM-ICA algorithm implements the following two procedures: (i) extraction of the region of interests (ROIs) of cortical activations by ICA, and (ii) estimation of the direct causal influences in local brain networks using Granger causality among voxels of ROIs. Our results show that the use of GCM in conjunction with ICA is able to effectively identify the directional brain network dynamics in time-frequency domain based on fNIRS recordings.