Linear Functional Connectivity Research Articles

Identifying good practices for detecting inter-regional linear functional connectivity from EEG

Aggregating voxel-level statistical dependencies between multivariate time series is an important intermediate step when characterising functional connectivity (FC) between larger brain regions. However, there are numerous ways in which voxel-level data can be aggregated into inter-regional FC, and the advantages of each of these approaches are currently unclear.In this study we generate ground-truth data and compare the performances of various pipelines that estimate directed and undirected linear phase-to-phase FC between regions. We test the ability of several existing and novel FC analysis pipelines to identify the true regions within which connectivity was simulated. We test various inverse modelling algorithms, strategies to aggregate time series within regions, and connectivity metrics. Furthermore, we investigate the influence of the number of interactions, the signal-to-noise ratio, the noise mix, the interaction time delay, and the number of active sources per region on the ability of detecting phase-to-phase FC.Throughout all simulated scenarios, lowest performance is obtained with pipelines involving the absolute value of coherency. Further, the combination of dynamic imaging of coherent sources (DICS) beamforming with directed FC metrics that aggregate information across multiple frequencies leads to unsatisfactory results. Pipelines that show promising results with our simulated pseudo-EEG data involve the following steps: (1) Source projection using the linearly-constrained minimum variance (LCMV) beamformer. (2) Principal component analysis (PCA) using the same fixed number of components within every region. (3) Calculation of the multivariate interaction measure (MIM) for every region pair to assess undirected phase-to-phase FC, or calculation of time-reversed Granger Causality (TRGC) to assess directed phase-to-phase FC. We formulate recommendations based on these results that may increase the validity of future experimental connectivity studies.We further introduce the free ROIconnect plugin for the EEGLAB toolbox that includes the recommended methods and pipelines that are presented here. We show an exemplary application of the best performing pipeline to the analysis of EEG data recorded during motor imagery.

Activity flow mapping over probabilistic functional connectivity.

Emerging evidence indicates that activity flow over resting-state network topology allows the prediction of task activations. However, previous studies have mainly adopted static, linear functional connectivity (FC) estimates as activity flow routes. It is unclear whether an intrinsic network topology that captures the dynamic nature of FC can be a better representation of activity flow routes. Moreover, the effects of between- versus within-network connections and tight versus loose (using rest baseline) task contrasts on the prediction of task-evoked activity across brain systems remain largely unknown. In this study, we first propose a probabilistic FC estimation derived from a dynamic framework as a new activity flow route. Subsequently, activity flow mapping was tested using between- and within-network connections separately for each region as well as using a set of tight task contrasts. Our results showed that probabilistic FC routes substantially improved individual-level activity flow prediction. Although it provided better group-level prediction, the multiple regression approach was more dependent on the length of data points at the individual-level prediction. Regardless of FC type, we consistently observed that between-network connections showed a relatively higher prediction performance in higher-order cognitive control than in primary sensorimotor systems. Furthermore, cognitive control systems exhibit a remarkable increase in prediction accuracy with tight task contrasts and a decrease in sensorimotor systems. This work demonstrates that probabilistic FC estimates are promising routes for activity flow mapping and also uncovers divergent influences of connectional topology and task contrasts on activity flow prediction across brain systems with different functional hierarchies.

Analysis and Synthesis of Communication Network Structures according to the Determined Stability Indicators

When solving problems related to the analysis and synthesis of communication networks for stability, a special place is simple and easy to understand indicators, weakly linked to the classical concept of exit probability from a state of health. Such deterministic indicators of stability (connectedness, a couple of connections, linear functional connectivity, the number of spanning trees) allow, albeit very approximately, to solve a complex of tasks related to the assessment of the reliability and survivability complex networks. Due to the rather simple analytical form of a linear functional connectivity for the synthesis of structures, it is possible to use the analytical method presented in the work. In this general formulation for the synthesis of connected graphs is formulated as the maximization of the linear functional connec-tivity for all possible graphs with a given number of edges, vertices, and with fixed values of their weighting coefficients.

Neural traffic as voxel-based measure of cerebral functional connectivity in fMRI

To access functional connectivity by in vivo brain imaging voxel-by-voxel, we developed a novel approach named neural traffic (NT). NT depicts the intensity of functional connectivity on a voxel-by-voxel basis in the whole brain. Functional magnetic resonance imaging (fMRI) experiments were carried out on eight individuals during either hearing or viewing words. The blood oxygen level dependant (BOLD) signal was taken as measure of neural activity. For each voxel, functional connectivity with all other brain voxels was determined by calculating Pearson correlation coefficients at two connectivity thresholds ( r = 0.35 and 0.65). Then, NT images were derived by counting the number of suprathreshold connections for each individual voxel. Calculations based on random networks indicate that statistically reliable NT images can be derived in individuals. With regard to group analysis, at r = 0.35 NT images are similar though not identical with the first component of principal component analysis (PCA), displaying a widespread but not ubiquitous pattern of functionally connected cortical areas. At r = 0.65, NT group images display functional connectivity confined to circumscribed cortical regions which reach beyond the corresponding primary sensory areas, their known associated areas and the default network. In conclusion, NT goes beyond the approach of correlating the BOLD signal with the external stimulus-presentation time course by computing linear functional connectivity between all brain voxels based on any BOLD time course. First results demonstrate that the NT approach is likely – on an individual base – to reveal novel cortical and subcortical connectivities involved in stimulus processing.