Neural Connectivity in Epilepsy as Measured by Granger Causality

Coben, Robert; Mohammad-Rezazadeh, Iman

doi:10.3389/fnhum.2015.00194

Cited by 45 publications

(31 citation statements)

References 37 publications

(47 reference statements)

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“…It has been tested in previous studied specifically in estimating epilepsy network nodes [31], [32], [84], [85]. Other networks like the pain network have also been studied using combined ESI and ADTF analysis [86].…”

Section: Discussionmentioning

confidence: 99%

Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach

Sohrabpour

Worrell

et al. 2016

IEEE Trans. Biomed. Eng.

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Objective Combined source imaging techniques and directional connectivity analysis can provide useful information about the underlying brain networks in a non-invasive fashion. Source imaging techniques have been used successfully to either determine the source of activity or to extract source time-courses for Granger causality analysis, previously. In this work, we utilize source imaging algorithms to both find the network nodes (regions of interest) and then extract the activation time series for further Granger causality analysis. The aim of this work is to find network nodes objectively from noninvasive electromagnetic signals, extract activation time-courses and apply Granger analysis on the extracted series to study brain networks under realistic conditions. Methods Source imaging methods are used to identify network nodes and extract time-courses and then Granger causality analysis is applied to delineate the directional functional connectivity of underlying brain networks. Computer simulations studies where the underlying network (nodes and connectivity pattern) is known were performed; additionally, this approach has been evaluated in partial epilepsy patients to study epilepsy networks from inter-ictal and ictal signals recorded by EEG and/or MEG. Results Localization errors of network nodes are less than 5 mm and normalized connectivity errors of ~20% in estimating underlying brain networks in simulation studies. Additionally, two focal epilepsy patients were studied and the identified nodes driving the epileptic network were concordant with clinical findings from intracranial recordings or surgical resection. Conclusion Our study indicates that combined source imaging algorithms with Granger causality analysis can identify underlying networks precisely (both in terms of network nodes location and internodal connectivity). Significance The combined source imaging and Granger analysis technique is an effective tool for studying normal or pathological brain conditions.

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Section: Discussionmentioning

confidence: 99%

Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach

Sohrabpour

Worrell

et al. 2016

IEEE Trans. Biomed. Eng.

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“…Specifically, Granger Causality analysis (GC) was used to determine the directed causal dependencies of brain regions from the EEG signal. GC is a method for investigating whether one time-series correctly predicts another and allows us to analyze brain circuit connections and how they change over the course of a cognitive process (Coben and Mohammad-Rezazadeh, 2015). We hypothesized that, similar to Scharinger et al (2015), an increase in updating load would affect both behavioral and neurophysiological measures and these metrics would be modulated by individual working memory capacity (i.e., OSPAN score).…”

Section: Introductionmentioning

confidence: 99%

Behavioral and Neural Correlates of Executive Function: Interplay between Inhibition and Updating Processes

2017

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This study investigated the interaction between two executive function processes, inhibition and updating, through analyses of behavioral, neurophysiological, and effective connectivity metrics. Although, many studies have focused on behavioral effects of executive function processes individually, few studies have examined the dynamic causal interactions between these two functions. A total of twenty participants from a local university performed a dual task combing flanker and n-back experimental paradigms, and completed the Operation Span Task designed to measure working memory capacity. We found that both behavioral (accuracy and reaction time) and neurophysiological (P300 amplitude and alpha band power) metrics on the inhibition task (i.e., flanker task) were influenced by the updating load (n-back level) and modulated by working memory capacity. Using independent component analysis, source localization (DIPFIT), and Granger Causality analysis of the EEG time-series data, the present study demonstrated that manipulation of cognitive demand in a dual executive function task influenced the causal neural network. We compared connectivity across three updating loads (n-back levels) and found that experimental manipulation of working memory load enhanced causal connectivity of a large-scale neurocognitive network. This network contains the prefrontal and parietal cortices, which are associated with inhibition and updating executive function processes. This study has potential applications in human performance modeling and assessment of mental workload, such as the design of training materials and interfaces for those performing complex multitasking under stress.

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“…26 In this study, we used effective connectivity analysis and found a statistically significant effective connectivity of the limbic network in TLE patients with HS, especially through the thalamus, in contrast to TLE patients without HS and normal controls. Effective connectivity analyzes the direction, interaction, and causality between two regions, and it can be used to understand causal relationships between entities in the brain network.…”

Section: Discussionmentioning

confidence: 94%