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

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

doi:10.1051/epjnbp/2017002

Cited by 9 publications

(6 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is noteworthy that our approach is essentially different by combing EMD with existing causality methods, such as assessing Granger’s causality between paired IMFs of economic time series 26 , applying CCM to detect the nonlinear coupling of decomposed brain wave data 27 , or measuring time dependency between IMFs decomposed from stock market data 28 . The decomposition of time series with EMD alone may improve the separability of intrinsic components embedded in the time series data, but does not avoid the constraints inherited from the existing prediction-based causality methods.…”

Section: Discussionmentioning

confidence: 99%

Causal decomposition in the mutual causation system

Yang

Huang

2018

Nat Commun

View full text Add to dashboard Cite

Inference of causality in time series has been principally based on the prediction paradigm. Nonetheless, the predictive causality approach may underestimate the simultaneous and reciprocal nature of causal interactions observed in real-world phenomena. Here, we present a causal-decomposition approach that is not based on prediction, but based on the covariation of cause and effect: cause is that which put, the effect follows; and removed, the effect is removed. Using empirical mode decomposition, we show that causal interaction is encoded in instantaneous phase dependency at a specific time scale, and this phase dependency is diminished when the causal-related intrinsic component is removed from the effect. Furthermore, we demonstrate the generic applicability of our method to both stochastic and deterministic systems, and show the consistency of causal-decomposition method compared to existing methods, and finally uncover the key mode of causal interactions in both modelled and actual predator–prey systems.

show abstract

Section: Discussionmentioning

confidence: 99%

Causal decomposition in the mutual causation system

Yang

Huang

2018

Nat Commun

View full text Add to dashboard Cite

show abstract

“…It should be noted though that a verification of this hypothesis requires identification of causal relationships. We expect further insights from recent developments that aim at characterising weighted and directed interactions in complex systems such as evolving large-scale epileptic brain networks 17,26,52 .…”

Section: Discussionmentioning

confidence: 99%

Precursors of seizures due to specific spatial-temporal modifications of evolving large-scale epileptic brain networks

Rings

Wrede

Lehnertz

2019

Sci Rep

View full text Add to dashboard Cite

Knowing when, where, and how seizures are initiated in large-scale epileptic brain networks remains a widely unsolved problem. Seizure precursors – changes in brain dynamics predictive of an impending seizure – can now be identified well ahead of clinical manifestations, but either the seizure onset zone or remote brain areas are reported as network nodes from which seizure precursors emerge. We aimed to shed more light on the role of constituents of evolving epileptic networks that recurrently transit into and out of seizures. We constructed such networks from more than 3200 hours of continuous intracranial electroencephalograms recorded in 38 patients with medication refractory epilepsy. We succeeded in singling out predictive edges and predictive nodes. Their particular characteristics, namely edge weight respectively node centrality (a fundamental concept of network theory), from the pre-ictal periods of 78 out of 97 seizures differed significantly from the characteristics seen during inter-ictal periods. The vast majority of predictive nodes were connected by most of the predictive edges, but these nodes never played a central role in the evolving epileptic networks. Interestingly, predictive nodes were entirely associated with brain regions deemed unaffected by the focal epileptic process. We propose a network mechanism for a transition into the pre-seizure state, which puts into perspective the role of the seizure onset zone in this transition and highlights the necessity to reassess current concepts for seizure generation and seizure prevention.

show abstract

“…Statistically significant pre-ictal increases and post-ictal drops in brain-heart connections were detected in children with temporal lobe epilepsy (TLE) through non-linear information measures from simultaneously recorded EEG and ECG. Employing empirical mode decomposition (EMD) on EEG and ECG in 10-and 20-minute peri-ictal recordings, significant drops in the brain-heart interactions were noticed immediately prior to and during seizures [19]- [21]. However, despite significant research in this area, the neuro-cardio-respiratory system as a unified and directed network in epilepsy has been largely ignored and unexplored.…”

Section: Introductionmentioning

confidence: 99%

Directed Connectivity Analysis of the Neuro-Cardio- and Respiratory Systems Reveals Novel Biomarkers of Susceptibility to SUDEP

Hutson

Rezaei

Gautier

et al. 2020

IEEE Open J. Eng. Med. Biol.

View full text Add to dashboard Cite

The paper describes the results from a unique experimental and analytical study in a well-controlled animal model of Sudden Unexpected Death in Epilepsy (SUDEP). As the name implies, SUDEP is difficult to impossible to study in humans due to the uncertainty in the timing of death occurrence over years. It is much easier to do this with animals as they also have much shorter life span. The animal model we employed is one of two most prominent animal models of SUDEP. Our aim was to elucidate potential impairments in the functional connectivity between the brain, heart and lungs that could contribute to our better understanding of the underlying causes of SUDEP. This is the first study of its kind in SUDEP and, as far as we know, the first multivariate study between the heart, brain and lungs, part of the emerging field of organomics. In this contribution, we report for the first time results from tri-organ analysis of concurrent electroencephalographic, electrocardiographic and plethysmographic recordings in a control group (wild-type healthy mice) and a group of mice genetically predisposed to SUDEP and epileptic seizures. We were able to produce strong statistical evidence (p<0.001) that SUDEP-prone animals exhibit statistically significant inter-organ (brain, heart and lungs) abnormalities in specific functional afferent and efferent interactions. The graphic abstract is a schematic representation of the statistically significant (a) decreased and (b) increased neuro-cardio-respiratory network interactions of the KO compared to WT mice averaged over frequencies of 1-200 Hz. The thickness of arrows corresponds to the magnitude, and the (+) or (-) sign above the arrows to the signs of the difference of the ssGPDC values of KO from the ones of WT animals for each interaction. We also show the impact of epileptic seizures on the dynamics of these functional connectivities. These results suggest that the novel measures of neuro-cardio-respiratory connectivity we developed from network analysis in the frequency domain do shed light on potential pathophysiological mechanisms of SUDEP and ictogenesis, could be utilized as biomarkers of susceptibility to SUDEP and seizures, as well as in the assessment and improvement of the efficacy of current and future intervention strategies for treatment of seizures and risk to SUDEP.

show abstract

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

Cited by 9 publications

References 42 publications

Causal decomposition in the mutual causation system

Causal decomposition in the mutual causation system

Precursors of seizures due to specific spatial-temporal modifications of evolving large-scale epileptic brain networks

Directed Connectivity Analysis of the Neuro-Cardio- and Respiratory Systems Reveals Novel Biomarkers of Susceptibility to SUDEP

Contact Info

Product

Resources

About