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.

A time domain frequency-selective multivariate Granger causality approach.

The investigation of effective connectivity is one of the major topics in computational neuroscience to understand the interaction between spatially distributed neuronal units of the brain. Thus, a wide variety of methods has been developed during the last decades to investigate functional and effective connectivity in multivariate systems. Their spectrum ranges from model-based to model-free approaches with a clear separation into time and frequency range methods.

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.

Rod Driven Frequency Entrainment and Resonance Phenomena.

A controversy exists on photic driving in the human visual cortex evoked by intermittent photic stimulation. Frequency entrainment and resonance phenomena are reported for frequencies higher than 12 Hz in some studies while missing in others. We hypothesized that this might be due to different experimental conditions, since both high and low intensity light stimulation were used.

Discussion of "Computational Electrocardiography: Revisiting Holter ECG Monitoring".

This article is part of a For-Discussion-Section of Methods of Information in Medicine about the paper "Computational Electrocardiography: Revisiting Holter ECG Monitoring" written by Thomas M. Deserno and Nikolaus Marx. It is introduced by an editorial.

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.

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.

Matching Pursuit-Based Time-Variant Bispectral Analysis and its Application to Biomedical Signals.

Objective: Principle aim of this study is to investigate the performance of a matching pursuit (MP)-based bispectral analysis in the detection and quantification of quadratic phase couplings (QPC) in biomedical signals. Nonlinear approaches such as time-variant bispectral analysis are able to provide information about phase relations between oscillatory signal components.

Methods: Time-variant QPC analysis is commonly performed using Gabor transform (GT) or Morlet wavelet transform (MWT), and is affected by either constant or frequency-dependent time-frequency resolution (TFR).

Advantages of signal-adaptive approaches for the nonlinear, time-variant analysis of heart rate variability of children with temporal lobe epilepsy.

Major aim of our study is to demonstrate that signal-adaptive approaches improve the nonlinear and time-variant analysis of heart rate variability (HRV) of children with temporal lobe epilepsy (TLE). Nonlinear HRV analyses are frequently applied in epileptic patients. As HRV is characterized by components with oscillatory properties frequency-selective methods are in the focus, whereby application of nonlinear analysis to linear filtered signals seems to be doubtful.

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.

Time-variant, frequency-selective, linear and nonlinear analysis of heart rate variability in children with temporal lobe epilepsy.

The major aim of our study is to demonstrate that a concerted combination of time-variant, frequency-selective, linear and nonlinear analysis approaches can be beneficially used for the analysis of heart rate variability (HRV) in epileptic patients to reveal premonitory information regarding an imminent seizure and to provide more information on the mechanisms leading to changes of the autonomic nervous system. The quest is to demonstrate that the combined approach gains new insights into specific short-term patterns in HRV during preictal, ictal, and postictal periods in epileptic children. The continuous Morlet-wavelet transform was used to explore the time-frequency characteristics of the HRV using spectrogram, phase-locking, band-power and quadratic phase coupling analyses.

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.

Entrainment of spontaneous cerebral hemodynamic oscillations to behavioral responses.

Entrainment in physiological systems can be manifest in cases where phase-coupling (synchronization) between slow intrinsic oscillations and periodic motor responses, or vice versa, takes place. To test whether voluntary movement has something in common with entrainment of slow hemodynamic oscillations to motor responses, we studied blood pressure (BP), heart rate beat-to-beat intervals (RRI) and prefrontal (de)oxyhemoglobin (Hb/HbO2) during 5min of rest, 10min of self-paced, voluntary movements and 10min of stimulus-paced movements at 10s intervals in 9 subjects. Subjects were divided into 2 groups according to the timing of voluntary finger movements.

Extracting labeled topological patterns from samples of networks.

An advanced graph theoretical approach is introduced that enables a higher level of functional interpretation of samples of directed networks with identical fixed pairwise different vertex labels that are drawn from a particular population. Compared to the analysis of single networks, their investigation promises to yield more detailed information about the represented system. Often patterns of directed edges in sample element networks are too intractable for a direct evaluation and interpretation.

Network redundancy analysis of effective brain networks: a comparison of healthy controls and patients with major depression.

This study investigated electroencephalographic correlates in chronically depressed patients compared to healthy controls using intracutaneously applied electrical pain stimulus, to better understand the interaction between pain processing and depression. A close interaction between pain and depression is generally recognized although the precise mechanisms are not yet fully understood. The present study focuses on the hypothesis that effective brain connectivity in major depression patients is altered.

A processing scheme for time-variant phase analysis in EEG burst activity of premature and full-term newborns in quiet sleep: a methodological study.

A processing scheme for the investigation of neonatal electroencephalographic burst oscillations that is composed of time-variant methods for linear and nonlinear phase analysis is introduced. Starting from a time-frequency analysis of oscillations' amplitudes, time-variant approaches for quantification of phase locking, n:m phase synchronization, and quadratic phase coupling are applied. Tracé discontinue patterns from premature newborns and tracé alternant patterns from full-term newborns were investigated using bipolar EEG recordings.