Abstract
Functional brain network studies using the Blood Oxygen-Level Dependent (BOLD) signal from functional Magnetic Resonance Imaging (fMRI) are becoming increasingly prevalent in research on the neural basis of human cognition. An important problem in functional brain network analysis is to understand directed functional interactions between brain regions during cognitive performance. This problem has important implications for understanding top-down influences from frontal and parietal control regions to visual occipital cortex in visuospatial attention, the goal motivating the present study. A common approach to measuring directed functional interactions between two brain regions is to first create nodal signals by averaging the BOLD signals of all the voxels in each region, and to then measure directed functional interactions between the nodal signals. Another approach, that avoids averaging, is to measure directed functional interactions between all pairwise combinations of voxels in the two regions. Here we employ an alternative approach that avoids the drawbacks of both averaging and pairwise voxel measures. In this approach, we first use the Least Absolute Shrinkage Selection Operator (LASSO) to pre-select voxels for analysis, then compute a Multivariate Vector AutoRegressive (MVAR) model from the time series of the selected voxels, and finally compute summary Granger Causality (GC) statistics from the model to represent directed interregional interactions. We demonstrate the effectiveness of this approach on both simulated and empirical fMRI data. We also show that averaging regional BOLD activity to create a nodal signal may lead to biased GC estimation of directed interregional interactions. The approach presented here makes it feasible to compute GC between brain regions without the need for averaging. Our results suggest that in the analysis of functional brain networks, careful consideration must be given to the way that network nodes and edges are defined because those definitions may have important implications for the validity of the analysis.
Highlights
The modern understanding of human cognition relies heavily on the concept of large-scale functional brain networks, and largescale functional network analysis of Blood-Oxygenation-LevelDependent (BOLD) signals from functional Magnetic Resonance Imaging is playing an increasingly important role in cognitive neuroscience [1]
We report the results of both Multivariate Vector AutoRegressive (MVAR) model simulations and the application of MVAR model estimation to an empirical functional Magnetic Resonance Imaging (fMRI) BOLD dataset obtained during a visuospatial attention task [34]
Application to simulated data Simulation MVAR models were created based on Equation 3, and iterated to generate simulated fMRI BOLD time series data for pseudo-voxels in two pseudo-Region Of Interest (ROI) having fixed sizes (30 pseudo-voxels in X and 50 pseudo-voxels in Y)
Summary
The modern understanding of human cognition relies heavily on the concept of large-scale functional brain networks, and largescale functional network analysis of Blood-Oxygenation-LevelDependent (BOLD) signals from functional Magnetic Resonance Imaging (fMRI) is playing an increasingly important role in cognitive neuroscience [1]. A node is typically represented in brain network studies of fMRI BOLD activity as a lumped Region Of Interest (ROI), formed by averaging the BOLD signals of all the ROI’s voxels [2,3,4,5,6] This collapse of the ROI by averaging has the benefit of reducing the dimensionality of analysis, but rests on the twin assumptions: (1) that the BOLD activity of an ROI is homogeneous over all its voxels; and (2) that the functional interactions (connectivity) between the voxels of an ROI with those in other ROIs is homogeneous. It has advantages as an edge measure over the typically utilized correlation: first, it provides the strength of functional interaction between time series in both directions, as opposed to a single non-directional strength; second, its grounding in prediction allows stronger statements to
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.