Seizure Forecasting in Epilepsy: From Computation to Clinical Practice

Brinkmann, Benjamin H.; Gregg, Nicholas M.; Worrell, Gregory A.

doi:10.1002/9781119431893.ch26

“…From investigating the impact of spiking timing stimulation on frequency-specific oscillations (Quinarez et al, 2023 ) to exploring the potential of linear delayed feedback control in thalamocortical models (Zhou et al, 2020 ), these studies collectively contribute to an expanding body of knowledge that spans from the cellular level (Lu et al, 2017 ) to network dynamics (Depannemaecker et al, 2021 ). Moreover, the translation of computational findings into clinical practice has been deliberated upon by Brinkmann et al ( 2021 ), emphasizing the promising trajectory of computational seizure forecasting. As the field continues to grow, collaboration between computational neuroscientists and medical practitioners holds the potential to revolutionize our ability to suppress seizures and improve the quality of life for individuals living with epilepsy.…”

Section: Discussionmentioning

confidence: 99%

An exploratory computational analysis in mice brain networks of widespread epileptic seizure onset locations along with potential strategies for effective intervention and propagation control

Courson,

Quoy,

Timofeeva

et al. 2024

Front. Comput. Neurosci.

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Mean-field models have been developed to replicate key features of epileptic seizure dynamics. However, the precise mechanisms and the role of the brain area responsible for seizure onset and propagation remain incompletely understood. In this study, we employ computational methods within The Virtual Brain framework and the Epileptor model to explore how the location and connectivity of an Epileptogenic Zone (EZ) in a mouse brain are related to focal seizures (seizures that start in one brain area and may or may not remain localized), with a specific focus on the hippocampal region known for its association with epileptic seizures. We then devise computational strategies to confine seizures (prevent widespread propagation), simulating medical-like treatments such as tissue resection and the application of an anti-seizure drugs or neurostimulation to suppress hyperexcitability. Through selectively removing (blocking) specific connections informed by the structural connectome and graph network measurements or by locally reducing outgoing connection weights of EZ areas, we demonstrate that seizures can be kept constrained around the EZ region. We successfully identified the minimal connections necessary to prevent widespread seizures, with a particular focus on minimizing surgical or medical intervention while simultaneously preserving the original structural connectivity and maximizing brain functionality.

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“…From investigating the impact of spiking timing stimulation on frequency-specific oscillations (Quinarez et al, 2023) to exploring the potential of linear delayed feedback control in thalamocortical models (Zhou et al, 2020), these studies collectively contribute to an expanding body of knowledge that spans from the cellular level (Lu et al, 2017) to network dynamics (Depannemaecker et al, 2021). Moreover, the translation of computational findings into clinical practice has been deliberated upon by Brinkmann et al (2021), emphasizing the promising trajectory of computational seizure forecasting. As the field continues to grow, collaboration between computational neuroscientists and medical practitioners holds the potential to revolutionize our ability to suppress seizures and improve the quality of life for individuals living with epilepsy.…”

Section: Discussionmentioning

confidence: 99%

An exploratory computational analysis in mice brain networks of widespread epileptic seizure onset locations along with potential strategies for effective intervention and propagation control

Courson,

Quoy,

Timofeeva

et al. 2023

Preprint

0

View full text Add to dashboard Cite

Mean-field models have been developed to replicate key features of epileptic seizure dynamics. However, the precise mechanisms and the role of the brain area responsible for seizure onset and propagation remain incompletely understood. In this study, we employ computational methods within The Virtual Brain framework and the Epileptor model to explore how the location and connectivity of an Epileptogenic Zone (EZ) in a mouse brain are related to focal seizures (seizures that start in one brain area and may or may not remain localized), with a specific focus on the hippocampal region known for its association with epileptic seizures. We then devise computational strategies to confine seizures (prevent widespread propagation), simulating medical-like treatments such as tissue resection and the application of an anti-seizure drugs or neurostimulation to suppress hyperexcitability. Through selectively removing (blocking) specific connections informed by the structural connectome and graph network measurements or by locally reducing outgoing connection weights of EZ areas, we demonstrate that seizures can be kept constrained around the EZ region. We successfully identified the minimal connections necessary to prevent widespread seizures, with a particular focus on minimizing surgical or medical intervention while simultaneously preserving the original structural connectivity and maximizing brain functionality.

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“…A continuous seizure propensity biomarker is distinct from seizure forecasting. 10,11 Seizure forecasting provides a unidimensional quantification of seizure likelihood and assumes that the functional state of the brain (i.e. "brain-state") is always on a linear progression toward a seizure.…”

Section: Introductionmentioning

confidence: 99%

Brain-state modeling using electroencephalography: Application to adaptive closed-loop neuromodulation for epilepsy

Johnson,

Doss,

Makhoul

et al. 2024

Preprint

2

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The progress of developing an effective closed-loop neuromodulation system for many neurological pathologies is hindered by the difficulties in accurately capturing a useful representation of a brain’s instantaneous functional state. Existing approaches rely on expert labeling of electroencephalography data to develop biomarkers of neurophysiological pathology. These techniques do not capture the highly complex functional states of the brain that are presumed to exist between labeled states or allow for the likely possibility of variation among identically labeled states. Thus, we propose BrainState, a self-supervised technique to model an arbitrarily complex instantaneous functional state of a brain using neural multivariate timeseries data. Application of BrainState to intracranial electroencephalography data from patients with epilepsy was able to capture diverse pre-seizure states and quantify nuanced effects of neuromodulation. We anticipate that BrainState will enable the development of sophisticated closed-loop neuromodulation systems for a diverse array of neurological pathologies.

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Seizure Forecasting in Epilepsy: From Computation to Clinical Practice

Cited by 5 publications

References 49 publications

An exploratory computational analysis in mice brain networks of widespread epileptic seizure onset locations along with potential strategies for effective intervention and propagation control

An exploratory computational analysis in mice brain networks of widespread epileptic seizure onset locations along with potential strategies for effective intervention and propagation control

An exploratory computational analysis in mice brain networks of widespread epileptic seizure onset locations along with potential strategies for effective intervention and propagation control

Brain-state modeling using electroencephalography: Application to adaptive closed-loop neuromodulation for epilepsy

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