Emergence of Persistent Networks in Long-Term Intracranial EEG Recordings

Kramer, Mark A.; Eden, Uri T.; Lepage, Kyle Q.; Kolaczyk, Eric D.; Bianchi, Matt T.; Cash, Sydney S.

doi:10.1523/jneurosci.2287-11.2011

Cited by 113 publications

(131 citation statements)

References 83 publications

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“…This reduced state space of brain dynamics accounts for mutual interactions and network effects among different brain regions, and can be accessed through a network-based approach as proposed here. In particular, the existence of a finite set of states confirms that the transient neural activity of the brain has metastable properties (34,35) and suggests that such activity changes during seizure events, whereas it is not apparently affected by cognitive processes or sleep/wake transitions (36). Moreover, because we uncovered these states by using ECoG recordings and we tracked the evolution of the brain network from one state to one another, our results suggest that a finite-state, data-driven model (e.g., hidden Markov model) could capture the brain dynamics, thus expanding the arrays of tools currently available to study brain activity and perform seizure onset time detection (37)(38)(39)(40).…”

Section: Discussionmentioning

confidence: 84%

“…Recent evidence has indicated that the epileptic brain network may experience topological and functional alterations both during seizures and interictal stages, which can be captured by using network-based analyses (9,11,21,26,28,35,36,41,42). In particular, it has been shown both with local field potentials and ECoG recordings that during seizures the brain network evolves through a multifarious pattern of distinct topological structures, with the emergence of large subnetworks both at seizure onset and termination (9, 28).…”

Section: Discussionmentioning

confidence: 99%

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Network dynamics of the brain and influence of the epileptic seizure onset zone

Burns¹,

Santaniello²,

Yaffe

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

259

241

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The human brain is a dynamic networked system. Patients with partial epileptic seizures have focal regions that periodically diverge from normal brain network dynamics during seizures. We studied the evolution of brain connectivity before, during, and after seizures with graph-theoretic techniques on continuous electrocorticographic (ECoG) recordings (5.4 ± 1.7 d per patient, mean ± SD) from 12 patients with temporal, occipital, or frontal lobe partial onset seizures. Each electrode was considered a node in a graph, and edges between pairs of nodes were weighted by their coherence within a frequency band. The leading eigenvector of the connectivity matrix, which captures network structure, was tracked over time and clustered to uncover a finite set of brain network states. Across patients, we found that (i) the network connectivity is structured and defines a finite set of brain states, (ii) seizures are characterized by a consistent sequence of states, (iii) a subset of nodes is isolated from the network at seizure onset and becomes more connected with the network toward seizure termination, and (iv) the isolated nodes may identify the seizure onset zone with high specificity and sensitivity. To localize a seizure, clinicians visually inspect seizures recorded from multiple intracranial electrode contacts, a time-consuming process that may not always result in definitive localization. We show that network metrics computed from all ECoG channels capture the dynamics of the seizure onset zone as it diverges from normal overall network structure. This suggests that a state space model can be used to help localize the seizure onset zone in ECoG recordings.focal epilepsy | seizure localization | network analysis | eigenvector centrality | ECoG signals E pilepsy affects over 60 million people worldwide, and approximately 40% of patients have drug-resistant epilepsy (DRE) with recurrent seizures that are not controlled by available medications (1-3). It is now routine to consider drugresistant partial epilepsy patients, who represent the largest cohort of patients with uncontrolled seizures, for possible resective seizure surgery (4). Successful seizure surgery is predicated upon the ability to localize the seizure onset zone. Although some patients (e.g., those with mesial temporal sclerosis or lesional epilepsy) can proceed to surgery following scalp recordings of seizures delineating a seizure onset zone (5), a significant number of patients have seizures that are challenging to localize with scalp ictal (i.e., seizure) recordings. In this case, ictal recordings using intracranial electrodes (e.g., subdural strips, grids, or depth electrode arrays) are necessary. The purpose of these intracranial recording arrays is to provide information about seizure onset and propagation, representing spatiotemporal changes in cerebral function.Using intracranial electrocorticographic (ECoG) recordings taken over several days to capture ictal events, clinicians visually inspect the ECoG recordings at the onset of the seizures an...

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Section: Discussionmentioning

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Network dynamics of the brain and influence of the epileptic seizure onset zone

Burns¹,

Santaniello²,

Yaffe

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

259

241

View full text Add to dashboard Cite

show abstract

“…A study using typical rsMRI parameters in humans found that correlation began to plateau at around 4 min (Van Dijk et al, 2010). In comparison, an intracranial electrophysiology study found that stable, frequency-dependent patterns of correlation emerge after *100 sec (Kramer et al, 2011). These findings do not include the effect of ongoing tasks, which can affect the spontaneous oscillations even after the task is completed.…”

Section: Time Scales Of Stationaritymentioning

confidence: 96%

The Neural Basis of Time-Varying Resting-State Functional Connectivity

2014

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Dynamic network analysis based on resting-state magnetic resonance imaging (rsMRI) is a fairly new and potentially powerful tool for neuroscience and clinical research. Dynamic analysis can be sensitive to changes that occur in psychiatric or neurologic disorders and can detect variations related to performance on individual trials in healthy subjects. However, the appearance of time-varying connectivity can also arise in signals that share no temporal information, complicating the interpretation of dynamic functional connectivity studies. Researchers have begun utilizing simultaneous imaging and electrophysiological recording to elucidate the neural basis of the networks and their variability in animals and in humans. In this article, we review findings that link changes in electrically recorded brain states to changes in the networks obtained with rsMRI and discuss some of the challenges inherent in interpretation of these studies. The literature suggests that multiple brain processes may contribute to the dynamics observed, and we speculate that it may be possible to separate particular aspects of the rsMRI signal to enhance sensitivity to certain types of neural activity, providing new tools for basic neuroscience and clinical research.

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“…Matrices of functional connectivity as an alternative representation are also popular [1,19,34] and are occasionally used in the form of small multiples to illustrate trends across different connectivity datasets [5,20,28]. To support direct comparisons, correlation coefficients from multiple scan states can be shown within nested quadrants of a matrix cell [39].…”

Section: Use Of Visualizations In Brain Connectivity Analysismentioning

confidence: 99%

Weighted graph comparison techniques for brain connectivity analysis

Alper

Bach

Riche

et al. 2013

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

154

149

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The analysis of brain connectivity is a vast field in neuroscience with a frequent use of visual representations and an increasing need for visual analysis tools. Based on an in-depth literature review and interviews with neuroscientists, we explore high-level brain connectivity analysis tasks that need to be supported by dedicated visual analysis tools. A significant example of such a task is the comparison of different connectivity data in the form of weighted graphs. Several approaches have been suggested for graph comparison within information visualization, but the comparison of weighted graphs has not been addressed. We explored the design space of applicable visual representations and present augmented adjacency matrix and node-link visualizations. To assess which representation best support weighted graph comparison tasks, we performed a controlled experiment. Our findings suggest that matrices support these tasks well, outperforming node-link diagrams. These results have significant implications for the design of brain connectivity analysis tools that require weighted graph comparisons. They can also inform the design of visual analysis tools in other domains, e.g. comparison of weighted social networks or biological pathways.

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Emergence of Persistent Networks in Long-Term Intracranial EEG Recordings

Cited by 113 publications

References 83 publications

Network dynamics of the brain and influence of the epileptic seizure onset zone

Network dynamics of the brain and influence of the epileptic seizure onset zone

The Neural Basis of Time-Varying Resting-State Functional Connectivity

Weighted graph comparison techniques for brain connectivity analysis

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