State dependent properties of epileptic brain networks: Comparative graph–theoretical analyses of simultaneously recorded EEG and MEG

Horstmann, Marie-Therese; Bialonski, Stephan; Noennig, Nina; Mai, Heinke; Prusseit, Jens; Wellmer, Jörg; Hinrichs, Hermann; Lehnertz, Klaus

doi:10.1016/j.clinph.2009.10.013

Cited by 187 publications

(189 citation statements)

References 87 publications

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“…In contrast, when being able to eliminate all influencing factors, findings of the special (and thus possibly rare) topologies of random or lattice interaction networks would be surprising. Besides deciding upon smallworld characteristics in an absolute sense, more recent studies 27,43,44 aim at studying relative changes of C and L only. In view of the wide regime of small-world networks, such an approach appears more promising.…”

Section: Discussionmentioning

confidence: 99%

“…Fig. 1A)) from a healthy subject with eyes closed 27 (the subject had signed informed consent that the data might be used and published for research purposes, and the study was approved by the local medical ethics committee). Here we restrict ourselves to N = 130 sensors in order to minimize contaminations with muscle activity in the lowermost ring of sensors.…”

Section: An Illustrative Example From Field Data Analysismentioning

confidence: 99%

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From brain to earth and climate systems: Small-world interaction networks or not?

Bialonski

Horstmann

Lehnertz

2010

Chaos: An Interdisciplinary Journal of Nonlinear Science

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106

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We consider recent reports on small-world topologies of interaction networks derived from the dynamics of spatially extended systems that are investigated in diverse scientific fields such as neurosciences, geophysics, or meteorology. With numerical simulations that mimic typical experimental situations we have identified an important constraint when characterizing such networks: indications of a small-world topology can be expected solely due to the spatial sampling of the system along with commonly used time series analysis based approaches to network characterization. The complex dynamics of spatially extended natural systems such as the human brain or the climate and the earth system is notoriously difficult to understand and has repeatedly stimulated a variety of scientific efforts, among them the development of sophisticated data analysis methods. Concepts from network theory promise to improve our understanding of such complex systems. These approaches consider a system as being composed of dynamically interacting subsystems whose functional interdependencies are reflected as links in an interaction network. Interaction networks derived from field data of different dynamical systems have been consistently reported to possess small-world characteristicshigh local clustering and small average shortest path length -which is considered as an indication of a common organization principle of natural dynamical systems. However, little attention has so far been paid to the conditions and assumptions underlying such analysis approaches. We here demonstrate that indications of smallworld characteristics of interaction networks can solely be expected due to the spatial sampling of dynamical systems together with commonly used time series analysis techniques for the characterization of network links. Our findings not only call for the development and use of methods taking into account the spatial sampling of the studied systems but also for a careful interpretation and re-consideration of analysis results obtained so far.

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Section: Discussionmentioning

confidence: 99%

Section: An Illustrative Example From Field Data Analysismentioning

confidence: 99%

From brain to earth and climate systems: Small-world interaction networks or not?

Bialonski

Horstmann

Lehnertz

2010

Chaos: An Interdisciplinary Journal of Nonlinear Science

Self Cite

106

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“…33 Most studies comparing people with epilepsy in the interictal state with healthy controls reported higher functional connectivity in epilepsy. 19,20,34,35 An increase in theta band functional connectivity was associated with seizure frequency in tumor-related epilepsy. 36 One could speculate that increased functional connectivity in epilepsy is somehow a protective mechanism against seizure generation, which fails in a stress-sensitive subset of subjects as cortisol increases.…”

Section: Cortisol and Functional Connectivitymentioning

confidence: 99%

The relation between cortisol and functional connectivity in people with and without stress‐sensitive epilepsy

et al. 2017

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SummaryObjective: The most common reported seizure-precipitant is stress. We recently showed a biologic basis for stress sensitivity of seizures: cortisol levels in people with stress-sensitive epilepsy correlated with focal interictal epileptiform discharges (IEDs) on electroencephalography (EEG). Here we aimed to determine whether the effect of cortisol on the epileptic brain is global or focal, and whether cortisol affects all brains or just those of stress-sensitive people. Because epilepsy is associated with changes in functional brain connectivity, we studied the relationship between cortisol and changes in global and focal (node-centered) functional connectivity measures for individuals with stress-sensitive and non-stresssensitive epilepsy. Methods: Seventeen people with epilepsy underwent long-term (>24 h) EEG recording. During the first 5 h after waking, saliva was collected every 15 min for cortisol measurements. Theta-band functional connectivity was assessed for every 15 min of the recording. We calculated the average phase-lag index (PLI) between all channels as a measure of global functional connectivity. We used network Strength, the averaged PLI per channel, as focal functional connectivity measure. We correlated cortisol, global, and focal functional connectivity (Strength) with IED frequency using linear mixed models. Analyses were split for people with and without stress-sensitivity of seizures. Results: Cortisol was negatively correlated with global functional connectivity in people with stress-sensitive seizures (estimate À0.0020; P < .01), whereas not in those without stress-sensitivity (estimate À0.0003; P = .46). This relationship occurred irrespective of the presence of IEDs on a channel (channels without IEDs and stress-sensitivity: estimate À0.0019; P < .01, non-stress-sensitive À0.0003; P = .41). Global and focal functional connectivity were negatively correlated with IED frequency, irrespective of stress sensitivity of seizures or channel type. Significance: People with stress-sensitive epilepsy have a whole-brain neuronal response to cortisol that is different from that of people with non-stress-sensitive epilepsy. This offers a basis for understanding seizure genesis in stress-sensitive epilepsy, which might require a different treatment approach.

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“…Epilepsy patients show increased EEGfC in the delta band in the interictal state (Horstmann et al, 2010). Recently, increased theta band synchronization was reported in epilepsy (Douw et al, 2010).…”

Section: Changes In the Delta And Theta Bandsmentioning

confidence: 99%

Valproate treatment normalizes EEG functional connectivity in successfully treated idiopathic generalized epilepsy patients

et al. 2014

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Aim. To investigate the effect of chronic VPA treatment of EEG functional connectivity in successfully treated idiopathic generalized epilepsy (IGE) patients. Patients and Methods. 19-channel waking, resting-state EEG records of 26 IGE patients were analyzed before treatment (IGE) and after the 90th day of treatment (VPA), in seizurefree condition. Three minutes of artifact-free EEG background activity (without epileptiform potentials) was analyzed for each patient in both conditions. A group of 26 age-matched healthy normative control persons (NC) was analyzed in the same way. All the EEG samples were processed to LORETA (Low Resolution Electromagnetic Tomography) to localize multiple distributed sources of EEG activity. Current source density time series were generated for 33 regions of interest (ROI) in each hemisphere for four frequency bands. Pearson correlation coefficients (R) were computed between all ROIs in each hemisphere, for four bands across the investigated samples. R values corresponded to intrahemispheric, cortico-cortical functional EEG connectivity (EEGfC). Group and condition differences were analyzed by statistical parametric network method. Main results. (p<0.05, corrected for multiple comparisons). 1. The untreated IGE group showed increased EEGfC in the delta and theta bands, and decreased EEGfC in the alpha band (as compared to the NC group). 2. VPA treatment normalized EEGfC in the delta, theta and alpha bands. 3. Degree of normalization depended on frequency band and cortical region. Conclusions. VPA treatment normalizes EEGfC in IGE patients.

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State dependent properties of epileptic brain networks: Comparative graph–theoretical analyses of simultaneously recorded EEG and MEG

Cited by 187 publications

References 87 publications

From brain to earth and climate systems: Small-world interaction networks or not?

From brain to earth and climate systems: Small-world interaction networks or not?

The relation between cortisol and functional connectivity in people with and without stress‐sensitive epilepsy

Valproate treatment normalizes EEG functional connectivity in successfully treated idiopathic generalized epilepsy patients

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