Epilepsy as a dynamical system, a most needed paradigm shift in epileptology

Moraes, Márcio Flávio Dutra; Medeiros, Daniel de Castro; Mourão, Flávio Afonso Gonçalves; Cançado, Sérgio Augusto Vieira; Cota, Vinícius Rosa

doi:10.1016/j.yebeh.2019.106838

Cited by 18 publications

(17 citation statements)

References 112 publications

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“…For 49% of seizures, the occurrence of a very fast, sharp transition in brain activity may be missed by the EEG 48 or by the EEG features (e.g., due to the size of the window under analysis 26 ), and, therefore, there are no seizure precursors. For the remaining 51%, we found distinguishable clusters that might reflect either a still sharp, but not so fast, transition or a gradual (possibly multistate) preictal transition 45,49 .…”

Section: Preictal Changes In Light Of the Nonlinear Nature Of Brain D...mentioning

confidence: 87%

“…Supervised learning methods are typically successful when this scenario occurs as it allows for a binary classification of the data into interictal state and subsequent preictal state. In the second scenario, we hypothesise that even though regulatory mechanisms may have been triggered in the brain towards seizure suppression (hence the decrease in features value before the seizure onset), the seizure threshold may have been crossed, which deemed the seizure inevitable 8,49 . Interestingly, some studies on seizure risk forecasting analysing long-term EEG data show a similar behaviour preceding seizures.…”

Section: Preictal Changes In Light Of the Nonlinear Nature Of Brain D...mentioning

confidence: 99%

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Unsupervised EEG Preictal Interval Identification in Patients with Drug-resistant Epilepsy

Leal

Curty

Lopes

et al. 2022

Preprint

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Typical seizure prediction models aim at discriminating interictal brain activity from pre-seizure electrographic patterns. Given the lack of a preictal clinical definition, a fixed interval is widely used to develop these models. Recent studies reporting preictal interval selection among a range of fixed intervals show inter- and intra-patient preictal interval variability, possibly reflecting the heterogeneity of the seizure generation process. Obtaining accurate labels of the preictal interval can be used to train supervised prediction models and, hence, avoid setting a fixed preictal interval for all seizures within the same patient. Unsupervised learning methods hold great promise for exploring preictal alterations on a seizure-specific scale. Multivariate and univariate linear and nonlinear features were extracted from scalp electroencephalography (EEG) signals collected from 41 patients with drug-resistant epilepsy undergoing presurgical monitoring. Nonlinear dimensionality reduction was performed for each group of features and each of the 226 seizures. We applied different clustering methods in searching for preictal clusters located until two hours before the seizure onset. We identified preictal patterns in 90% of patients and 51% of the visually inspected seizures. The preictal clusters manifested a seizure-specific profile with varying duration (22.9±21.0 minutes) and starting time before seizure onset (47.6±27.3 minutes). Searching for preictal patterns on the EEG trace using unsupervised methods showed that it is possible to identify seizure-speciﬁc preictal signatures for some patients and some seizures within the same patient.

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Section: Preictal Changes In Light Of the Nonlinear Nature Of Brain D...mentioning

confidence: 87%

Section: Preictal Changes In Light Of the Nonlinear Nature Of Brain D...mentioning

confidence: 99%

Unsupervised EEG Preictal Interval Identification in Patients with Drug-resistant Epilepsy

Leal

Curty

Lopes

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…Furthermore, drug-resistant epileptic patients are often poor candidates for surgical treatment due to the difficulty in identifying the seizure focus (Rincon et al, 2021 ). From a system-wide standpoint, epilepsy emerges as a hyper synchronization phenomenon based on a modern concept stating that the brain is a complex system and that synchronization is an emergent property resulting from a dynamical coupling of neural oscillators (Moraes et al, 2019 ; Stojanović et al, 2020 ). Thereby, hypersynchrony of neural tissues is an essential feature of neurological disorders such as epilepsy and Parkinson's disease (Khaledi-Nasab et al, 2020 ; Boaretto et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, understanding the dynamic evolution of epilepsy can assist in further elucidating its neurobiological mechanisms (Nelson and Bonner, 2021 ), which may lead to the development of novel, safer and more efficacious treatments (Moraes et al, 2019 ). For such a purpose, different analysis have been proposed such as phase Amplitude Coupling (Damborská et al, 2021 ), Granger Causality (He et al, 2019 ) or Partial Directed Coherence (Ciaramidaro et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Network Dynamical Model of Rat Brains During Acute Ictogenesis

Tsukahara

Júnior

Barth

et al. 2022

Front. Neural Circuits

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Epilepsy is one of the most common neurological disorders worldwide. Recent findings suggest that the brain is a complex system composed of a network of neurons, and seizure is considered an emergent property resulting from its interactions. Based on this perspective, network physiology has emerged as a promising approach to explore how brain areas coordinate, synchronize and integrate their dynamics, both under perfect health and critical illness conditions. Therefore, the objective of this paper is to present an application of (Dynamic) Bayesian Networks (DBN) to model Local Field Potentials (LFP) data on rats induced to epileptic seizures based on the number of arcs found using threshold analytics. Results showed that DBN analysis captured the dynamic nature of brain connectivity across ictogenesis and a significant correlation with neurobiology derived from pioneering studies employing techniques of pharmacological manipulation, lesion, and modern optogenetics. The arcs evaluated under the proposed approach achieved consistent results based on previous literature, in addition to demonstrating robustness regarding functional connectivity analysis. Moreover, it provided fascinating and novel insights, such as discontinuity between forelimb clonus and generalized tonic-clonic seizure (GTCS) dynamics. Thus, DBN coupled with threshold analytics may be an excellent tool for investigating brain circuitry and their dynamical interplay, both in homeostasis and dysfunction conditions.

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“…In this context, a novel method has been developed which involves a non-standard form of ES with randomized inter-pulse intervals called non-periodic stimulation (NPS), initially proposed by COTA et al (2009). OLIVEIRA et al (2019), MORAES et al (2019) and COTA et al (2019) discuss it in literature, however, a comparative analysis to evaluate the time to develop an epileptic seizure (using the NPS therapy or not) has never been performed. Thus, the Survival Analysis should be applied due to its extensive use in medicine to evaluate treatments.…”

Section: List Of Tablesmentioning

confidence: 99%

Dynamic evaluation of induced epilepsy in rats: a bayesian network perspective

Tsukahara¹

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A epilepsia é uma das doenças neurológicas mais comuns em todo o mundo. Considerando o cérebro um sistema complexo, estudos tem utilizado esta abordagem para realizar análise de conectividade funcional para indivíduos saudáveis, bem como acometidos pela patologia. A tese apresenta a aplicação das Redes Bayesianas (Dinâmicas) (DBN) para modelar os registros dos Potenciais de Campo Locais (LFP) de ratos induzidos a convulsões epilépticas, avaliando a influência da variável tempo para as análises. Os resultados mostraram que a análise DBN captou a natureza dinâmica da conectividade cerebral através da ictogênese com uma correlação significativa com a neurobiologia derivada de estudos pioneiros que empregavam técnicas de manipulação farmacológica, lesão e optogênese moderna. A avaliação dos arcos sob a abordagem proposta foi consistente com a literatura anterior, propôs novos entendimentos, como a descontinuidade entre o mioclonia de membros inferiores e a dinâmica generalizada da convulsão tônico-clônica (GTCS). Após a incorporação de atrasos entre os registros eletroencefalográficos, houve a indicação de melhor aderência do conjunto de sinais ao modelo DBN.

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Epilepsy as a dynamical system, a most needed paradigm shift in epileptology

Cited by 18 publications

References 112 publications

Unsupervised EEG Preictal Interval Identification in Patients with Drug-resistant Epilepsy

Unsupervised EEG Preictal Interval Identification in Patients with Drug-resistant Epilepsy

Data-Driven Network Dynamical Model of Rat Brains During Acute Ictogenesis

Dynamic evaluation of induced epilepsy in rats: a bayesian network perspective

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