Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography

Sip, Viktor; Hashemi, Meysam; Vattikonda, Anirudh N.; Woodman, Michael; Wang, Hong; Scholly, Julia; Villalon, Samuel Médina; Guye, Maxime; Bartolomei, Fabricē; Jirsa, Viktor K.

doi:10.1101/2020.07.30.20165183

Cited by 8 publications

(22 citation statements)

References 84 publications

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“…In fact, the SI model as it was used here corresponds to the highly-super critical case in which seizures always spread. Thus, in order to avoid more assumptions that would hinder the interpretation of the outcomes of such a model, the SIR parameters would need to be tuned with clinical data, using for instance high-resolution spatio-temporal seizure-propagation patterns [36,44,89].…”

Section: Modelling Resectionsmentioning

confidence: 99%

“…However, in order to do this realistically and in a systematic manner, more detailed spatio-temporal patterns of seizure propagation are needed for each patient. These could be obtained from the SEEG recordings directly [36,44,89].…”

Section: Limitationsmentioning

confidence: 99%

“…Such computational models can be used to identify epileptogenic areas [33,34] or analyze different resection strategies [26,32,[35][36][37][38], such that patient-specific resection strategies, that may lead to a better outcome or fewer side-effects than the standard surgery, can be tested [33,[39][40][41]. Validation of the models is usually attempted by looking for differences in the model predictions between seizure free and non-seizure free patients [42,43] or by correlating seizure propagation on the model with the empirical data [44].…”

Section: Introductionmentioning

confidence: 99%

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Epidemic models characterize seizure propagation and the effects of epilepsy surgery in individualized brain networks based on MEG and invasive EEG recordings

Millán

Straaten

Stam

et al. 2021

Preprint

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Background Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients. However, seizure-freedom is currently achieved in only 2/3 of the patients after surgery. In this study we have developed an individualized computational model based on functional brain networks to explore seizure propagation and the efficacy of different virtual resections. Eventually, the goal is to obtain individualized models to optimize resection strategy and outcome. Methods We have modelled seizure propagation as an epidemic process using the susceptible-infected (SI) model on individual functional networks derived from presurgical MEG. We included 10 patients who had received epilepsy surgery and for whom the surgery outcome at least one year after surgery was known. The model parameters were tuned in order to reproduce the patient-specific seizure propagation patterns as recorded with invasive EEG. We defined a personalized search algorithm that combined structural and dynamical information to find resections that maximally decreased seizure propagation for a given resection size. The optimal resection for each patient was defined as the smallest resection leading to at least a $90\%$ reduction in seizure propagation. Results The individualized model reproduced the basic aspects of seizure propagation for 9 out of 10 patients when using the resection area as the origin of epidemic spreading, and for 10 out of 10 patients with an alternative definition of the seed region. We found that, for 7 patients, the optimal resection was smaller than the resection area, and for 4 patients we also found that a resection smaller than the resection area could lead to a 100% decrease in propagation. Moreover, for two cases these alternative resections included nodes outside the resection area. Conclusion Epidemic spreading models fitted with patient specific data can capture the fundamental aspects of clinically observed seizure propagation, and can be used to test virtual resections in silico. Combined with optimization algorithms, smaller or alternative resection strategies, that are individually targeted for each patient, can be determined with the ultimate goal to improve surgery outcome.

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Section: Modelling Resectionsmentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Epidemic models characterize seizure propagation and the effects of epilepsy surgery in individualized brain networks based on MEG and invasive EEG recordings

Millán

Straaten

Stam

et al. 2021

Preprint

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“…Even though these coupling definitions are different, they have been used for similar purposes in the literature. For example, in the epilepsy literature, the additive coupling has been used in studies to distinguish functional networks from healthy people and people with epilepsy [12], to simulate epilepsy surgery [6,13,14], and to model seizure propagation [15]. On the other hand, the diffusive coupling has also been used in November 25, 2021 2/33 studies to differentiate controls from people with epilepsy [11], to investigate epilepsy surgery [10,16,17], and to understand patterns of seizure emergence [18].…”

Section: Introductionmentioning

confidence: 99%

The interaction between neural populations: Additive versus diffusive coupling

Lopes

Hamandi

Zhang

et al. 2021

Preprint

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Models of networks of populations of neurons commonly assume that the interactions between neural populations are via additive or diffusive coupling. When using the additive coupling, a population’s activity is affected by the sum of the activities of neighbouring populations. In contrast, when using the diffusive coupling a neural population is affected by the sum of the differences between its activity and the activity of its neighbours. These two coupling functions have been used interchangeably for similar applications. Here, we show that the choice of coupling can lead to strikingly different brain network dynamics. We focus on a model of seizure transitions that has been used both with additive and diffusive coupling in the literature. We consider networks with two and three nodes, and large random and scale-free networks with 64 nodes. We further assess functional networks inferred from magnetoencephalography (MEG) from people with epilepsy and healthy controls. To characterize the seizure dynamics on these networks, we use the escape time, the brain network ictogenicity (BNI) and the node ictogenicity (NI), which are measures of the network’s global and local ability to generate seizures. Our main result is that the level of ictogenicity of a network is strongly dependent on the coupling function. We find that people with epilepsy have higher additive BNI than controls, as hypothesized, while the diffusive BNI provides the opposite result. Moreover, individual nodes that are more likely to drive seizures with one type of coupling are more likely to prevent seizures with the other coupling function. Our results on the MEG networks and evidence from the literature suggest that the additive coupling may be a better modelling choice than the diffusive coupling, at least for BNI and NI studies. Thus, we highlight the need to motivate and validate the choice of coupling in future studies.Author summaryMost models of brain dynamics assume that distinct brain regions interact in either an additive or a diffusive way. With additive coupling, each brain region sums incoming signals. In contrast, with diffusive coupling, each region sums the differences between its own signal and incoming signals. Although they are different, these two couplings have been used for very similar applications, particularly within models of epilepsy. Here we assessed the effect of this choice on seizure behaviour. Using a model of seizures and both artificial and real brain networks, we showed that the coupling choice can lead to very different seizure dynamics. We found that networks that are more prone to seizures using one coupling, are less prone to them using the other. Likewise, individual brain regions that are more likely to drive seizures when using additive coupling, are more likely to prevent them when using diffusive coupling. Using real brain networks, we found that the additive coupling predicted higher seizure propensity in people with epilepsy compared to healthy controls, whereas the diffusive coupling did not. Our results highlight the need to justify the choice of coupling used and show that the additive coupling may be a better option in some applications.

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“…Inferences can be further improved by integrating prior knowledge about the spatial distribution of the EZN, such as that obtained from the spatial extent of lesions in MRI data or seizure semiology, to reduce uncertainty and to bias the non-identifiable parameter estimation. After the first proof-of-concept of the VEP 11 , we studied and tested different modeling methods [12][13][14][15] , model inversion methods [16][17][18][19] , and surgery strategies 20,21 . Here, we present a complete and extensively tested VEP workflow that can be used in clinical practice.…”

mentioning

confidence: 99%

Virtual Epileptic Patient (VEP): Data-driven probabilistic personalized brain modeling in drug-resistant epilepsy

Wang

Woodman

Triebkorn

et al. 2022

Preprint

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One-third of 50 million epilepsy patients worldwide suffer from drug resistant epilepsy and are candidates for surgery. Precise estimates of the epileptogenic zone networks (EZNs) are crucial for planning intervention strategies. Here, we present the Virtual Epileptic Patient (VEP), a multimodal probabilistic modeling framework for personalized end-to-end analysis of brain imaging data of drug resistant epilepsy patients. The VEP uses data-driven, personalized virtual brain models derived from patient-specific anatomical (such as T1-MRI, DW-MRI, and CT scan) and functional data (such as stereo-EEG). It employs Markov Chain Monte Carlo (MCMC) and optimization methods from Bayesian inference to estimate a patient&'s EZN while considering robustness, convergence, sensor sensitivity, and identifiability diagnostics. We describe both high-resolution neural field simulations and a low-resolution neural mass model inversion. The VEP workflow was evaluated retrospectively with 53 epilepsy patients and is now being used in an ongoing clinical trial (EPINOV).

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Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography

Cited by 8 publications

References 84 publications

Epidemic models characterize seizure propagation and the effects of epilepsy surgery in individualized brain networks based on MEG and invasive EEG recordings

Epidemic models characterize seizure propagation and the effects of epilepsy surgery in individualized brain networks based on MEG and invasive EEG recordings

The interaction between neural populations: Additive versus diffusive coupling

Virtual Epileptic Patient (VEP): Data-driven probabilistic personalized brain modeling in drug-resistant epilepsy

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