Does independent component analysis influence EEG connectivity analyses?

Analysis of electroencephalographic (EEG) data requires cautious consideration of interfering artefacts such as ocular, muscular or cardiac noise. Independent component analysis (ICA) has proven to be a powerful tool for the detection and separation out of these contaminating components from brain activity. Yet thus far thorough investigation is lacking into how this pre-processing step might affect or even distort the information on brain connectivity inherent in the raw signals.

Nonlinear Interaction Analysis of Cardiovascular-Respiratory Data by Means of Convergent Cross Mapping.

Appropriate analyses of directed complex interactions within the cardiovascular-respiratory system are of growing interest for a better understanding of physiological regulatory mechanisms in healthy subjects and diseased persons. There are various concepts to analyze such interactions. Convergent Cross Mapping (CCM) provides the possibility to define directed interactions in terms of nonlinear stability.

Influence of imputation strategies on the identification of brain functional connectivity networks.

Whenever neurophysiological data, such as EEG data are recorded, occurring artifacts pose an essential problem. This study addresses this issue by using imputation methods whereby whole data sets of a trial, or distinct electrodes, are not removed from the analysis of the EEG data but are replaced. We present different imputation strategies but use only two which are optimal for this particular study; predictive mean matching and data augmentation.

Tracking the Reorganization of Module Structure in Time-Varying Weighted Brain Functional Connectivity Networks.

Identification of module structure in brain functional networks is a promising way to obtain novel insights into neural information processing, as modules correspond to delineated brain regions in which interactions are strongly increased. Tracking of network modules in time-varying brain functional networks is not yet commonly considered in neuroscience despite its potential for gaining an understanding of the time evolution of functional interaction patterns and associated changing degrees of functional segregation and integration. We introduce a general computational framework for extracting consensus partitions from defined time windows in sequences of weighted directed edge-complete networks and show how the temporal reorganization of the module structure can be tracked and visualized.

Cortical Correlates of Human Balance Control.

Balance control is a fundamental component of human every day motor activities such as standing or walking, and its impairment is associated with an increased risk of falling. However, in humans the exact neurobiological mechanisms underlying balance control are still unclear. Specifically, although previous studies have identified a number of cortical regions that become significantly activated during real or imagined balancing, the interactions within and between the relevant cortical regions remain to be investigated.

Detecting spatially highly resolved network modules: a multi subject approach.

The connectivity analysis of spatially highly resolved data results in networks comprising an immense number of nodes and edges which makes it hard or even impossible to investigate the high-dimensional (HD) network as a whole. A solution to this problem is offered by a connectivity-based segmentation of the HD networks into subsets of functionally similar nodes (network modules) that exhibit pronounced interaction. However, an investigation of the results at group level is problematic as identified modules are not assigned to each other across different subjects.

Nonlinear Directed Interactions Between HRV and EEG Activity in Children With TLE.

Objective: Epileptic seizure activity influences the autonomic nervous system (ANS) in different ways. Heart rate variability (HRV) is used as indicator for alterations of the ANS. It was shown that linear, nondirected interactions between HRV and EEG activity before, during, and after epileptic seizure occur.

A Multivariate Granger Causality Concept towards Full Brain Functional Connectivity.

Detecting changes of spatially high-resolution functional connectivity patterns in the brain is crucial for improving the fundamental understanding of brain function in both health and disease, yet still poses one of the biggest challenges in computational neuroscience. Currently, classical multivariate Granger Causality analyses of directed interactions between single process components in coupled systems are commonly restricted to spatially low- dimensional data, which requires a pre-selection or aggregation of time series as a preprocessing step. In this paper we propose a new fully multivariate Granger Causality approach with embedded dimension reduction that makes it possible to obtain a representation of functional connectivity for spatially high-dimensional data.

Convergent Cross Mapping: Basic concept, influence of estimation parameters and practical application.

In neuroscience, data are typically generated from neural network activity. Complex interactions between measured time series are involved, and nothing or only little is known about the underlying dynamic system. Convergent Cross Mapping (CCM) provides the possibility to investigate nonlinear causal interactions between time series by using nonlinear state space reconstruction.

Identification of whole-brain network modules based on a large scale Granger Causality approach.

Spatially high resolved neurophysiological data commonly pose a computational and analytical problem for the identification of functional networks in the human brain. We introduce a multivariate linear Granger Causality approach with an embedded dimension reduction that enables the computation of brain networks at the large scale. In order to grasp the information about connectivity patterns contained in the resulting high-dimensional directed networks, we furthermore propose the inclusion of module detection methods from network theory that can help to identify functionally associated brain areas.

Are Structural Changes Induced by Lithium in the HIV Brain Accompanied by Changes in Functional Connectivity?

Lithium therapy has been shown to affect imaging measures of brain function and microstructure in human immunodeficiency virus (HIV)-infected subjects with cognitive impairment. The aim of this proof-of-concept study was to explore whether changes in brain microstructure also entail changes in functional connectivity. Functional MRI data of seven cognitively impaired HIV infected individuals enrolled in an open-label lithium study were included in the connectivity analysis.

Advanced Insights into Functional Brain Connectivity by Combining Tensor Decomposition and Partial Directed Coherence.

Quantification of functional connectivity in physiological networks is frequently performed by means of time-variant partial directed coherence (tvPDC), based on time-variant multivariate autoregressive models. The principle advantage of tvPDC lies in the combination of directionality, time variance and frequency selectivity simultaneously, offering a more differentiated view into complex brain networks. Yet the advantages specific to tvPDC also cause a large number of results, leading to serious problems in interpretability.

Impact of multivariate Granger causality analyses with embedded dimension reduction on network modules.

High dimensional functional MRI data in combination with a low temporal resolution imposes computational limits on classical Granger Causality analyses with respect to a large-scale representations of functional interactions in the brain. To overcome these limitations and exploit information inherent in resulting brain connectivity networks at the large scale, we propose a multivariate Granger Causality approach with embedded dimension reduction. Using this approach, we computed binary connectivity networks from resting state fMRI images and analyzed them with respect to network module structure, which might be linked to distinct brain regions with an increased density of particular interaction patterns as compared to inter-module regions.

Synchronization analysis between heart rate variability and EEG activity before, during, and after epileptic seizure.

Abstract An innovative concept for synchronization analysis between heart rate (HR) components and rhythms in EEG envelopes is represented; it applies time-variant analyses to heart rate variability (HRV) and EEG, and it was tested in children with temporal lobe epilepsy (TLE). After a removal of ocular and movement-related artifacts, EEG band activity was computed by means of the frequency-selective Hilbert transform providing envelopes of frequency bands. Synchronization between HRV and EEG envelopes was quantified by Morlet wavelet coherence.

Pair-wise Clustering of Large Scale Granger Causality Index Matrices for Revealing Communities.

The analysis of large ensembles of time series is a fundamental challenge in different domains of biomedical image processing applications, specifically in the area of functional MRI data processing. An important aspect of such analysis is the ability to reconstruct community network structures based on interactive behavior between different nodes of the network which are captured in such time series. In this study, we start with a previously proposed novel approach that applies the linear Granger Causality concept to very high-dimensional time series.