Functional brain network studies using the Blood Oxygen-Level Dependent (BOLD) signal
September 23, 2017
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. AutoRegressive (MVAR) model from the time series of the selected voxels, and LBH589 (Panobinostat) manufacture 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. Author Summary Modern cognitive neuroscience views cognition in terms of brain network function. A network is a physical system of nodes connected to each other by edges. From the network perspective, cognitive function depends on activity patterns involving the nodes and edges of functional brain networks. It is important then, to appropriately define the nodes and edges of functional brain networks in order to understand cognition. In this study we consider the nodes of functional brain networks to be brain regions, and demonstrate a method that effectively measures the edge pattern between regions with a technique called Granger Causality. Our method is made possible by the utilization of recent advances from the field of statistics. Our approach is generally applicable to functional brain network analysis and contributes to the understanding of network properties of the brain. Introduction The modern understanding of human cognition relies heavily on the concept of large-scale functional brain networks, and large-scale functional network analysis of Blood-Oxygenation-Level-Dependent (BOLD) signals from functional Magnetic Resonance Imaging (fMRI) is playing an increasingly important role in cognitive neuroscience . From this perspective, knowledge of cognition may be obtained from BOLD signals by identification of the nodes and edges of large-scale functional brain networks. An important unresolved question remaining in the field, however, is how best to define the nodes and edges of large-scale functional brain networks. 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 C. 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) LBH589 (Panobinostat) manufacture that GRS the functional interactions (connectivity) between the voxels of an ROI with those in other ROIs is also homogeneous. If these homogeneity assumptions are not true, edge measurements computed from ROI-averaged BOLD signals may be erroneous since averaging may distort the time series information. Here we present a novel procedure for the analysis of directed interregional functional interactions that is based on the BOLD activity of the individual voxels of ROIs and the Granger Causality (GC) measure of directed interaction between voxels. GC tests whether the prediction of the present value of one time series by its own past values can be significantly improved by including past values of another time series in the prediction. If so, the second time series is said to Granger cause the first, and the degree of significance of the improvement may be taken as the strength of GC . The GC measure is typically implemented by AutoRegressive (AR) modeling  and has been shown to be a powerful and flexible tool for measuring the predictability of one neural time series from another C. 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 be made about functional interactions than does simple correlation. The use of GC to measure directed interactions in the brain from fMRI BOLD data has received intense scrutiny in recent years, with some arguing in its favor C and others opposed to it C. In the present work, we focus on improving the application of GC analysis to LBH589 (Panobinostat) manufacture fMRI BOLD data in order to LBH589 (Panobinostat) manufacture better understand the role of top-down influences in visuospatial.