Time-Variant Modeling of Brain Processes

Leistritz, Lutz; Schiecke, Karin; Astolfi, Laura; Witte, Herbert

doi:10.1109/jproc.2015.2497144

Cited by 15 publications

(9 citation statements)

References 95 publications

(127 reference statements)

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“…In this work, we propose a novel methodological concept, where spatially high-dimensional data are incorporated into connectivity analysis. These data originate from a (possibly large) system of connected and interacting elements [ 8 ] and thus, this system may be considered as a network [ 9 ], which represents the functional connectivity structure by linking a set of vertices (recording sites) by edges (interactions). The consideration of spatially high-dimensional data contributes to a much better preservation of the functional attribution of a huge set of network vertices to topological features given by the measuring modality.…”

Section: Introductionmentioning

confidence: 99%

A Multivariate Granger Causality Concept towards Full Brain Functional Connectivity

et al. 2016

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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. The resulting functional connectivity networks may consist of several thousand vertices and thus contain more detailed information compared to connectivity networks obtained from approaches based on particular regions of interest. Our large scale Granger Causality approach is applied to synthetic and resting state fMRI data with a focus on how well network community structure, which represents a functional segmentation of the network, is preserved. It is demonstrated that a number of different community detection algorithms, which utilize a variety of algorithmic strategies and exploit topological features differently, reveal meaningful information on the underlying network module structure.

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Section: Introductionmentioning

confidence: 99%

A Multivariate Granger Causality Concept towards Full Brain Functional Connectivity

et al. 2016

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“…Traditional definitions of GC, DTF and PDC all rely on the hypothesis of wide-sense stationarity of the data, needed to build the MVAR model on which the estimators are computed. However, this can be an important limitation when the stationarity is not verified and when one is interested in the dynamic behavior of the brain in terms of connectivity (for a review, see [23]). To overcome this limitation, a number of approaches were developed to provide a time-varying extension of all MVARand GC-based connectivity estimators.…”

Section: E Adaptive Dtf and Pdcmentioning

confidence: 99%

Electrophysiological Brain Connectivity: Theory and Implementation

Astolfi

Valdés‐Sosa

et al. 2019

IEEE Trans. Biomed. Eng.

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We review the theory and algorithms of electrophysiological brain connectivity analysis. This tutorial is aimed at providing an introduction to brain functional connectivity from electrophysiological signals, including electroencephalography, magnetoencephalography, electrocorticography, and stereoelectroencephalography. Various connectivity estimators are discussed, and algorithms introduced. Important issues for estimating and mapping brain functional connectivity with electrophysiology are discussed.Index Terms-Brain functional connectivity, electrophysiological connectivity, effective connectivity, EEG, MEG, intracranial EEG, electrophysiological connectome.

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“…Electrophysiological measures such as electroencephalography (EEG) and magnetoencephalography (MEG) can provide unique insight into the dynamic and directed interactions between anatomical regions, thanks to their high temporal resolution (Leistritz et al. 2016 ; Lopes da Silva 2013 ). This relies on the validity of methods and strategies used to derive time-varying directed connectivity from EEG and MEG when cortical sources are estimated (Siebenhü et al.…”

Section: Introductionmentioning

confidence: 99%

“…Once the time series are extracted in selected dipoles, directed and time-varying connectivity between these sources can be studied to determine information processing in the human brain (Leistritz et al. 2016 ; Lie and Mierlo 2017 ; Liu et al. 2016 ; Mao et al.…”

Section: Introductionmentioning

confidence: 99%

A Time-Varying Connectivity Analysis from Distributed EEG Sources: A Simulation Study

et al. 2018

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Time-varying connectivity analysis based on sources reconstructed using inverse modeling of electroencephalographic (EEG) data is important to understand the dynamic behaviour of the brain. We simulated cortical data from a visual spatial attention network with a time-varying connectivity structure, and then simulated the propagation to the scalp to obtain EEG data. Distributed EEG source modeling using sLORETA was applied. We compared different dipole (representing a source) selection strategies based on their time series in a region of interest. Next, we estimated multivariate autoregressive (MVAR) parameters using classical Kalman filter and general linear Kalman filter approaches followed by the calculation of partial directed coherence (PDC). MVAR parameters and PDC values for the selected sources were compared with the ground-truth. We found that the best strategy to extract the time series of a region of interest was to select a dipole with time series showing the highest correlation with the average time series in the region of interest. Dipole selection based on power or based on the largest singular value offer comparable alternatives. Among the different Kalman filter approaches, the use of a general linear Kalman filter was preferred to estimate PDC based connectivity except when only a small number of trials are available. In the latter case, the classical Kalman filter can be an alternative.

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Time-Variant Modeling of Brain Processes

Cited by 15 publications

References 95 publications

A Multivariate Granger Causality Concept towards Full Brain Functional Connectivity

A Multivariate Granger Causality Concept towards Full Brain Functional Connectivity

Electrophysiological Brain Connectivity: Theory and Implementation

A Time-Varying Connectivity Analysis from Distributed EEG Sources: A Simulation Study

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