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

Schiecke, Karin; Pester, Britta; Piper, Diana; Benninger, Franz; Feucht, Martha; Leistritz, Lutz; Witte, Herbert

doi:10.1109/tbme.2016.2579021

Cited by 36 publications

(71 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a former study [16], we have shown that CCM is able to replicate defined directed interactions in simulated deterministic system and systems with a certain level of additive noise. The introduced moving window-based timevarying implementation of CCM is able to replicate correct directionality and (relative) strength of interactions also by using rather short window lengths.…”

Section: Results Of Investigations Of Bivariate Ccm Are Given In Figumentioning

confidence: 96%

“…Practically, CCM X!Y and CCM Y!X are examined using data sets X = x(t) and Y = x(t) of increasing data length L (the so-called "library length"). Information about the basic algorithm for cross mapping of X by using M Y (CCM X!Y ) for each library length L (and CCM Y!X respectively) can be found in [16] and in the supplementary material of [13]. The following are the main steps: -generate "shadow manifold" M Y ; -find defined number of nearest neighbors at each time point t in M Y ;…”

Section: Convergent Cross Mapping (Ccm)mentioning

confidence: 99%

“…Furthermore, system noise is strongly influencing the estimation of CCM. Simulations concerning those influences were performed in [16].…”

Section: Convergent Cross Mapping (Ccm)mentioning

confidence: 99%

“…In [16] we provided an advanced time-variant investigation of CCM to analyze interaction between the heart rate variability (HRV) and specific EEG components of the same group children with TLE. We could prove that ARbased gPDC analysis failed for our data, mainly due to the fact that our data seems to not fit in the concept of (linear) AR-modeling.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Advanced nonlinear approach to quantify directed interactions within EEG activity of children with temporal lobe epilepsy in their time course

Schiecke

Pester

Piper

et al. 2017

EPJ Nonlinear Biomed. Phys.

Self Cite

View full text Add to dashboard Cite

Abstract. Background. The quantification of directed interactions within the brain and in particular their time courses are of highest interest for the investigation of epilepsy. The underlying coordinated neuronal mass activities span functionally diverse and structurally widely distributed cortical and subcortical brain regions, i.e. dynamic, distributed epileptic network can be assumed possibly not fitting in the concept of linearity. Consequently, nonlinear, time-variant, and directed connectivity and synchronization analysis could be helpful to understand processes contributing to the seizure onset and propagation. Methods. The nonlinear convergent cross mapping (CCM) quantifies directed interactions between time series by using nonlinear state space reconstruction. CCM is applied to the EEG of 18 children with temporal lobe epilepsy (TLE), i.e. directed interactions within EEG activity and within specific components of EEG activity (d-activity and a-activity) are investigated. Linear time-variant multivariate AR modeling was performed for these data to test for subsequent applications of linear AR-based connectivity measures. Results. Linear MVAR models proved to be inappropriate for our data. Time-varying application of CCM revealed that statistically significant nonlinear interactions within the EEG activity and within specific components of the EEG exist in the preictal, ictal, and postictal periods. Distinct time courses of such interactions and differences in the time pattern of interactions occurring in the different components of EEG activity that we investigated discovered the high complexity of the underlying processes. No distinct results could be found concerning the presumed directionalities of interactions. Statistical relevant interactions were quantified by bootstrapping and surrogate data approach. Conclusion. Advanced nonlinear CCM approach was able to uncover time pattern of nonlinear interactions thereby possibly contributing to the further understanding of complex behavior of the brain during TLE. Our investigation may provide deeper insight into physiological state of complex networks, e.g. during the development of an epileptic seizure or the recovery in the postictal state.Keywords: convergent cross mapping / directed interactions / electroencephalogram / empirical mode decomposition / nonlinear time-variant analysis / temporal lobe epilepsy BackgroundThe detection and quantification of directed (causal) interactions within the brain and their time courses are of highest interest in neuroscience in general and in particular for the investigation of epilepsy. Temporal lobe epilepsy (TLE) is the most common medically intractable epilepsy in adults, which is frequently associated with mesial hippocampal sclerosis (mesial TLE), characterized by severe neuronal cell loss and change of glial cells in the hippocampus. The zone concept of epileptic seizure generation implies that the hippocampus and other areas of the temporal cortex can act as epileptic focus for seizureonset generation (fo...

show abstract

Section: Results Of Investigations Of Bivariate Ccm Are Given In Figumentioning

confidence: 96%

Section: Convergent Cross Mapping (Ccm)mentioning

confidence: 99%

“…Furthermore, system noise is strongly influencing the estimation of CCM. Simulations concerning those influences were performed in [16].…”

Section: Convergent Cross Mapping (Ccm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Advanced nonlinear approach to quantify directed interactions within EEG activity of children with temporal lobe epilepsy in their time course

Schiecke

Pester

Piper

et al. 2017

EPJ Nonlinear Biomed. Phys.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In literature CCM has been successful in revealing the existence of causality in non-linear physiological signals [8,11,13]; its performance has also been superior in comparison to linear Granger causality when applied on non-linear physiological signals [8]. With its advantage, it is expected to unearth complex directional interaction between SBP and EMG.…”

Section: Convergent Cross Mappingmentioning

confidence: 99%

Increased Systolic Blood Pressure driven Skeletal Muscle activation Following Stroke: A causality analysis

Verma

Goswami

et al. 2016

2016 Computing in Cardiology Conference (CinC)

View full text Add to dashboard Cite

show abstract

Intracortical brain‐heart interplay: An EEG model source study of sympathovagal changes

Catrambone,

Candia‐Rivera,

Valenza

2024

Human Brain Mapping

View full text Add to dashboard Cite

The interplay between cerebral and cardiovascular activity, known as the functional brain‐heart interplay (BHI), and its temporal dynamics, have been linked to a plethora of physiological and pathological processes. Various computational models of the brain‐heart axis have been proposed to estimate BHI non‐invasively by taking advantage of the time resolution offered by electroencephalograph (EEG) signals. However, investigations into the specific intracortical sources responsible for this interplay have been limited, which significantly hampers existing BHI studies. This study proposes an analytical modeling framework for estimating the BHI at the source‐brain level. This analysis relies on the low‐resolution electromagnetic tomography sources localization from scalp electrophysiological recordings. BHI is then quantified as the functional correlation between the intracortical sources and cardiovascular dynamics. Using this approach, we aimed to evaluate the reliability of BHI estimates derived from source‐localized EEG signals as compared with prior findings from neuroimaging methods. The proposed approach is validated using an experimental dataset gathered from 32 healthy individuals who underwent standard sympathovagal elicitation using a cold pressor test. Additional resting state data from 34 healthy individuals has been analysed to assess robustness and reproducibility of the methodology. Experimental results not only confirmed previous findings on activation of brain structures affecting cardiac dynamics (e.g., insula, amygdala, hippocampus, and anterior and mid‐cingulate cortices) but also provided insights into the anatomical bases of brain‐heart axis. In particular, we show that the bidirectional activity of electrophysiological pathways of functional brain‐heart communication increases during cold pressure with respect to resting state, mainly targeting neural oscillations in the , , and bands. The proposed approach offers new perspectives for the investigation of functional BHI that could also shed light on various pathophysiological conditions.

show abstract

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

Abstract: In contrast to AR-based estimation of PDC CCM was able to reveal time-courses and frequency-selective views of nonlinear interactions for the further understanding of complex interactions between the epileptic network and the ANS in children with TLE.

Cited by 36 publications

References 36 publications

Advanced nonlinear approach to quantify directed interactions within EEG activity of children with temporal lobe epilepsy in their time course

Advanced nonlinear approach to quantify directed interactions within EEG activity of children with temporal lobe epilepsy in their time course

Increased Systolic Blood Pressure driven Skeletal Muscle activation Following Stroke: A causality analysis

Intracortical brain‐heart interplay: An EEG model source study of sympathovagal changes

Contact Info

Product

Resources

About