Multivariate Frequency Domain Analysis of Causal Interactions in Physiological Time Series

The mission of this chapter is to enhance the theoretical interpretability of the available frequency domain measures of coupling and causality derived from the MVAR representation of multiple time series

Modeling sparse connectivity between underlying brain sources for EEG/MEG

We propose a novel technique to assess functional brain connectivity in EEG/MEG signals. Our method, called Sparsely-Connected Sources Analysis (SCSA), can overcome the problem of volume conduction by modeling neural data innovatively with the following ingredients: (a) the EEG is assumed to be a linear mixture of correlated sources following a multivariate autoregressive (MVAR) model, (b) the demixing is estimated jointly with the source MVAR parameters, (c) overfitting is avoided by using the Group Lasso penalty. This approach allows to extract the appropriate level cross-talk between the extracted sources and in this manner we obtain a sparse data-driven model of functional connectivity. We demonstrate the usefulness of SCSA with simulated data, and compare to a number of existing algorithms with excellent results.Comment: 9 pages, 6 figure

Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks

Assessing the strength of directed influences among neural signals : An approach to noisy data

Acknowledgements This work was supported by the German Science Foundation (Ti315/4-2), the German Federal Ministry of Education and Research (BMBF grant 01GQ0420), and the Excellence Initiative of the German Federal and State Governments. B.S. is indebted to the Kosterlitz Centre for the financial support of this research project.Peer reviewedPreprin

Effect of autonomic blocking agents on the respiratory-related oscillations of ventricular action potential duration in humans

Ventricular action potential duration (APD) is an important component of many physiological functions including arrhythmogenesis. APD oscillations have recently been reported in humans at the respiratory frequency. This study investigates the contribution of the autonomic nervous system to these oscillations. In 10 patients undergoing treatment for supraventricular arrhythmias, activation recovery intervals (ARI; a conventional surrogate for APD) were measured from multiple left and right ventricular (RV) endocardial sites, together with femoral artery pressure. Respiration was voluntarily regulated and heart rate clamped by RV pacing. Sympathetic and parasympathetic blockade was achieved using intravenous metoprolol and atropine, respectively. Metroprolol reduced the rate of pressure development (maximal change in pressure over time): 1,271 (± 646) vs. 930 (± 433) mmHg/s; P < 0.01. Systolic blood pressure (SBP) showed a trend to decrease after metoprolol, 133 (± 21) vs. 128 (± 25) mmHg; P = 0.06, and atropine infusion, 122 (± 26) mmHg; P < 0.05. ARI and SBP exhibited significant cyclical variations (P < 0.05) with respiration in all subjects with peak-to-peak amplitudes ranging between 0.7 and 17.0 mmHg and 1 and 16 ms, respectively. Infusion of metoprolol reduced the mean peak-to-peak amplitude [ARI, 6.2 (± 1.4) vs. 4.4 (± 1.0) ms, P = 0.008; SBP, 8.4 (± 1.6) vs. 6.2 (± 2.0) mmHg, P = 0.002]. The addition of atropine had no significant effect. ARI, SBP, and respiration showed significant coupling (P < 0.05) at the breathing frequency in all subjects. Directed coherence from respiration to ARI was high and reduced after metoprolol infusion [0.70 (± 0.17) vs. 0.50 (± 0.23); P < 0.05]. These results suggest a role of respiration in modulating the electrophysiology of ventricular myocardium in humans, which is partly, but not totally, mediated by β-adrenergic mechanisms

Directed Coupling in Local Field Potentials of Macaque V4 During Visual Short-Term Memory Revealed by Multivariate Autoregressive Models

Processing and storage of sensory information is based on the interaction between different neural populations rather than the isolated activity of single neurons. In order to characterize the dynamic interaction and transient cooperation of sub-circuits within a neural network, multivariate autoregressive (MVAR) models have proven to be an important analysis tool. In this study, we apply directed functional coupling based on MVAR models and describe the temporal and spatial changes of functional coupling between simultaneously recorded local field potentials in extrastriate area V4 during visual memory. Specifically, we compare the strength and directional relations of coupling based on generalized partial directed coherence (GPDC) measures while two rhesus monkeys perform a visual short-term memory task. In both monkeys we find increases in theta power during the memory period that are accompanied by changes in directed coupling. These interactions are most prominent in the low frequency range encompassing the theta band (3–12 Hz) and, more importantly, are asymmetric between pairs of recording sites. Furthermore, we find that the degree of interaction decreases as a function of distance between electrode positions, suggesting that these interactions are a predominantly local phenomenon. Taken together, our results show that directed coupling measures based on MVAR models are able to provide important insights into the spatial and temporal formation of local functionally coupled ensembles during visual memory in V4. Moreover, our findings suggest that visual memory is accompanied not only by a temporary increase of oscillatory activity in the theta band, but by a direction-dependent change in theta coupling, which ultimately represents a change in functional connectivity within the neural circuit