Epileptic-network-based prediction and control of seizures in humans

Lehnertz, Klaus; Bröhl, Timo; Wrede, Randi von

doi:10.1016/j.nbd.2023.106098

Cited by 20 publications

(18 citation statements)

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“…Despite the well-known observation that vulnerability to seizure activity in any one part of the network is influenced by activity everywhere else in the network, and that the network as a whole is responsible for the clinical and electrographic phenomena that we associate with human seizures ( Spencer, 2002 ), we still lack a sufficient quantitative assessment of the time-evolving epileptic brain network’s metrics (from the local to the global network scale) that would help to improve understanding of how the network generates seizures ( Kuhlmann et al, 2018 ; Lehnertz, 2021 ; Lehnertz et al, 2023 ) as well as other pathophysiological phenomena ( Weiss et al, 2022 ).…”

Section: The Time-evolving Epileptic Brain Network: What Have We Lear...mentioning

confidence: 99%

“…Considering the recent promising developments of centrality concepts and metrics to characterize properties of edges as well as the edges’ time-varying role in the larger network ( Bröhl and Lehnertz, 2019 ; Bröhl and Lehnertz, 2022 ; Contisciani et al, 2022 ; Altafini et al, 2023 ), adopting an edge-centric perspective (cf. Faskowitz et al, 2022 ; Novelli and Razi, 2022 ) could lead to a further improved understanding of the time-evolving epileptic brain network and its control ( Sinha et al, 2022 ; Lehnertz et al, 2023 ; Frauscher et al, 2023 ).…”

Section: Current Limitations and Potential Prospectsmentioning

confidence: 99%

“…Choosing treatment: Currently, clinical decisions regarding treatment options are primarily guided by the epilepsy syndrome and its burden. Despite several options [e.g., pharmacological treatment ( Kwan and Brodie, 2000 ; Wandschneider and Koepp, 2016 ; Höller et al, 2018 ; Höller and Nardone, 2021 ), surgical treatment ( Téllez-Zenteno et al, 2007 ), nutritional treatment ( Pizzo et al, 2022 ), neurostimulation ( Schulze-Bonhage, 2019 ; Piper et al, 2022 ); see Lehnertz et al (2023) for an overview of network-based treatment concepts], the treatment is successful in only about half of the cases. Moreover, from a clinical point of view, the goal of epilepsy treatment is seizure freedom and if this is not possible reduction of seizure frequency and burden of the disease.…”

Section: Current Limitations and Potential Prospectsmentioning

confidence: 99%

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The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives

Bröhl,

Rings,

Pukropski

et al. 2024

Front. Netw. Physiol.

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Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus—a discrete cortical area from which seizures originate—to a widespread epileptic network—spanning lobes and hemispheres—considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.

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Section: The Time-evolving Epileptic Brain Network: What Have We Lear...mentioning

confidence: 99%

Section: Current Limitations and Potential Prospectsmentioning

confidence: 99%

Section: Current Limitations and Potential Prospectsmentioning

confidence: 99%

See 1 more Smart Citation

The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives

Bröhl,

Rings,

Pukropski

et al. 2024

Front. Netw. Physiol.

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“…In addition to seizures, the quality of life for people with epilepsy (PWE) can be markedly affected by psychiatric and neurologic comorbidities 3 . Epilepsy is a network circuit disorder with dysregulation of specific brain networks underlying the sporadic seizures and chronic comorbidites 3–5 . Anti-seizure medications are the mainstay of epilepsy therapy, but over 1/3 of PWE are drug resistant 6 and continue to have seizures despite taking daily medications.…”

Section: Introductionmentioning

confidence: 99%

A platform for brain network sensing and stimulation with quantitative behavioral tracking: Application to limbic circuit epilepsy

Kremen,

Sladky,

Mivalt

et al. 2024

Preprint

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Temporal lobe epilepsy (TLE) is a common neurological disease characterized by recurrent focal seizures. These seizures often originate from the mesial temporal limbic networks and the parahippocampal neocortex. People with TLE frequently experience comorbidities related to memory, mood, and sleep (MMS). Deep brain stimulation targeting the anterior nucleus of the thalamus (ANT-DBS) is a proven therapy for reducing TLE seizures, but the optimal stimulation parameters for improving seizures and MMS comorbidities remains unclear. We developed a neurotechnology platform for tracking seizures and MMS during ANT-DBS to address this clinical gap. The platform enables bidirectional data streaming between a brain sensing-stimulation implant, compact mobile devices, and cloud-based data storage, viewing, and computing environment. Machine learning algorithms provided accurate, unbiased catalogs of seizures, interictal epileptiform spikes (IES), and wake-sleep brain states to inform ANT-DBS. Remotely administered memory and mood assessments were used to objectively and densely sample cognitive and behavioral response to ANT-DBS. In participants with mesial TLE, we evaluated the efficacy of low-frequency versus high-frequency ANT-DBS. Low-frequency and high-frequency ANT-DBS both reduced reported seizures. But continuous low-frequency ANT-DBS showed greater reductions in electrographic seizures and IES, as well as better sleep and verbal memory compared to high-frequency ANT-DBS. These results highlight the potential of synchronized brain sensing and behavioral tracking for optimizing neuromodulation therapies.

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“…Soon, physicians could consult web-based practical algorithms or deep machine learning (ML) models to aid individualized ASM selection and identify patients with a high risk of drug resistance or adverse events. Besides, the recognition of epilepsy as a network disorder and the integration of personalized altered network structure and function 68 , 69 in future ML platforms could help predict ASM response. These platforms promise personalized pharmacotherapy in epilepsy, an elusive goal for decades.…”

Section: Introductionmentioning

confidence: 99%

The evolution of antiseizure medication therapy selection in adults: Is artificial intelligence -assisted antiseizure medication selection ready for prime time?

Gunasekera,

Sirven,

Feyissa

2023

J Cent Nerv Syst Dis

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Antiseizure medications (ASMs) are the mainstay of symptomatic epilepsy treatment. The primary goal of pharmacotherapy with ASMs in epilepsy is to achieve complete seizure remission while minimizing therapy-related adverse events. Over the years, more ASMs have been introduced, with approximately 30 now in everyday use. With such a wide variety, much guidance is needed in choosing ASMs for initial therapy, subsequent replacement monotherapy, or adjunctive therapy. The specific ASMs are typically tailored by the patient’s related factors, including epilepsy syndrome, age, sex, comorbidities, and ASM characteristics, including the spectrum of efficacy, pharmacokinetic properties, safety, and tolerability. Weighing these key clinical variables requires experience and expertise that may be limited. Furthermore, with this approach, patients may endure multiple trials of ineffective treatments before the most appropriate ASM is found. A more reliable way to predict response to different ASMs is needed so that the most effective and tolerated ASM can be selected. Soon, alternative approaches, such as deep machine learning (ML), could aid the individualized selection of the first and subsequent ASMs. The recognition of epilepsy as a network disorder and the integration of personalized epilepsy networks in future ML platforms can also facilitate the prediction of ASM response. Augmenting the conventional approach with artificial intelligence (AI) opens the door to personalized pharmacotherapy in epilepsy. However, more work is needed before these models are ready for primetime clinical practice.

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Epileptic-network-based prediction and control of seizures in humans

Cited by 20 publications

References 162 publications

The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives

The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives

A platform for brain network sensing and stimulation with quantitative behavioral tracking: Application to limbic circuit epilepsy

The evolution of antiseizure medication therapy selection in adults: Is artificial intelligence -assisted antiseizure medication selection ready for prime time?

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