The sensitivity of network statistics to incomplete electrode                     sampling on intracranial EEG

Conrad, Erin C.; Bernabei, John M.; Kini, Lohith G.; Shah, Palak; Mikhail, Fadi; Kheder, Ammar; Shinohara, Russell T.; Davis, Kathryn A.; Bassett, Danielle S.; Litt, Brian

doi:10.1162/netn_a_00131

Cited by 22 publications

(19 citation statements)

References 89 publications

(114 reference statements)

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“…This flexibility, although proven advantageous, hinders our ability to generalize neuronal network findings. Indeed, the topology is sensitive to different spatial parcellations as well as the density of the sampling approach (Bonzanni et al, 2020; Conrad et al, 2020; Giacopelli et al, 2020); which means that the network descriptors change based on the spatial granulation chosen, preventing the comparison of network data using different recording methods. Because the activity of nodes in higher spatial granulations (a cluster of neurons at mesoscale) originates from the activity of lower‐level nodes (single neurons at microscale), the following question naturally arose: given the same functional network represented at microscale and mesoscale, is the microscale topology conserved at mesoscale?…”

Section: Discussionmentioning

confidence: 99%

“…Synchronizability ( Sync ) quantifies the stability of the fully synchronous network state (Conrad et al, 2020). It is computed as follows:

italicSync = \frac{λ_{2}}{λ_{italicmax}}

where λ 2 is the second smallest eigenvalue of the weighted Laplacian matrix (computed as the difference between the node strength matrix D and the adjacent matrix) and λ max is the largest eigenvalue.…”

Section: Methodsmentioning

confidence: 99%

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On the prediction of neuronal microscale topology descriptors based on mesoscale recordings

Bonzanni

Kaplan

2021

Eur J of Neuroscience

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The brain possesses structural and functional hierarchical architectures organized over multiple scales. Considering that functional recordings commonly focused on a single spatial level, and because multiple scales interact with one another, we explored the behaviour of in silico neuronal networks across different scales. We established ad hoc relations of several topological descriptors (average clustering coefficient, average path length, small‐world propensity, modularity, network degree, synchronizability and fraction of long‐term connections) between different scales upon application and empirical validation of a Euclidian renormalization approach. We tested a simple network (distance‐dependent model) as well as an artificial cortical network (Vertex; undirected and directed networks) finding the same qualitative power law relations of the parameters across levels: their quantitative nature is model dependent. Those findings were then organized in a workflow that can be used to predict, with approximation, microscale topologies from mesoscale recordings. The present manuscript not only presents a theoretical framework for the renormalization of biological neuronal network and their study across scales in light of the spatial features of the recording method but also proposes an applicable workflow to compare real functional networks across scales.

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Section: Discussionmentioning

confidence: 99%

“…Synchronizability ( Sync ) quantifies the stability of the fully synchronous network state (Conrad et al, 2020). It is computed as follows:

italicSync = \frac{λ_{2}}{λ_{italicmax}}

Section: Methodsmentioning

confidence: 99%

On the prediction of neuronal microscale topology descriptors based on mesoscale recordings

Bonzanni

Kaplan

2021

Eur J of Neuroscience

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“…However, given that node strength is among the least sensitive network metrics to sampling bias 22 , we felt that it was a reasonable choice to compare these approaches. Another key limitation is that we could not balance SEEG and ECoG groups for surgical type due to clinical practice at our center.…”

Section: Discussionmentioning

confidence: 99%

“…One is that while the graph metric we chose for localization, node strength, is frequently studied in patients with epilepsy, other metrics and models might have revealed a different relationship between findings in ECoG and SEEG. However, given that node strength is among the least sensitive network metrics to sampling bias 22 , we felt that it was a reasonable choice to compare these approaches. Another key limitation is that we could not balance SEEG and ECoG groups for surgical type due to clinical practice at our center.…”

Section: Discussionmentioning

confidence: 99%

Electrocorticography and stereo EEG provide distinct measures of brain connectivity: Implications for network models

Bernabei

Arnold

Shah

et al. 2020

Preprint

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Brain network models derived from graph theory have the potential to guide functional neurosurgery, and to improve rates of post-operative seizure freedom for patients with epilepsy. A barrier to applying these models clinically is that intracranial EEG electrode implantation strategies vary by center, region and country, from cortical grid & strip electrodes, to purely stereotactic depth electrodes, to a mixture of both. To determine whether models derived from one type of study are broadly applicable to others, we investigate the differences in brain networks mapped by electrocortiography (ECoG) and stereoelectroencephalography (SEEG) in a matched cohort of patients who underwent epilepsy surgery. We show that ECoG and SEEG map broad network structure differently, and demonstrate substantial disparity in the ability of node strength to localize the epileptogenic zone in SEEG compared to ECoG. We demonstrate that eliminating white matter contacts and reducing network nodes to anatomical regions of interest rather than individual contacts improves the ability of these models to distinguish between epileptogenic and non-epileptogenic regions in SEEG. Our findings suggest that effectively applying computational models to localize epileptic networks requires accounting for the effects of spatial sampling, particularly when analyzing both ECoG and SEEG recordings in the same cohort. Finally, we share all code and data in this study, aiming for our findings to accelerate research in brain network connectivity in epilepsy and beyond.

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“…We wrote custom MATLAB scripts to create null distributions for our energy analysis by randomly sampling with replacement 5,000 sets of nodes containing the same number of nodes as our set of interest. Our random resampling method is an extension of similar methods used to compute subject-specific confidence intervals for nodal metric values ( 52 ).…”

Section: Methodsmentioning

confidence: 99%

Time-evolving controllability of effective connectivity networks during seizure progression

Scheid

Ashourvan

Stiso

et al. 2021

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

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Over one third of the estimated 3 million people with epilepsy in the United States are medication resistant. Responsive neurostimulation from chronically implanted electrodes provides a promising treatment alternative to resective surgery. However, determining optimal personalized stimulation parameters, including when and where to intervene to guarantee a positive patient outcome, is a major open challenge. Network neuroscience and control theory offer useful tools that may guide improvements in parameter selection for control of anomalous neural activity. Here we use a method to characterize dynamic controllability across consecutive effective connectivity (EC) networks based on regularized partial correlations between implanted electrodes during the onset, propagation, and termination regimes of 34 seizures. We estimate regularized partial correlation adjacency matrices from 1-s time windows of intracranial electrocorticography recordings using the Graphical Least Absolute Shrinkage and Selection Operator (GLASSO). Average and modal controllability metrics calculated from each resulting EC network track the time-varying controllability of the brain on an evolving landscape of conditionally dependent network interactions. We show that average controllability increases throughout a seizure and is negatively correlated with modal controllability throughout. Our results support the hypothesis that the energy required to drive the brain to a seizure-free state from an ictal state is smallest during seizure onset, yet we find that applying control energy at electrodes in the seizure onset zone may not always be energetically favorable. Our work suggests that a low-complexity model of time-evolving controllability may offer insights for developing and improving control strategies targeting seizure suppression.

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The sensitivity of network statistics to incomplete electrode sampling on intracranial EEG

Cited by 22 publications

References 89 publications

On the prediction of neuronal microscale topology descriptors based on mesoscale recordings

On the prediction of neuronal microscale topology descriptors based on mesoscale recordings

Electrocorticography and stereo EEG provide distinct measures of brain connectivity: Implications for network models

Time-evolving controllability of effective connectivity networks during seizure progression

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