From phenomenological to biophysical models of seizures

Depannemaecker, Damien; Ezzati, Aitakin; Wang, Hong; Jirsa, Viktor; Bernard, Christophe

doi:10.1016/j.nbd.2023.106131

Cited by 10 publications

(4 citation statements)

References 97 publications

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“…The first aspect is technical and can be addressed by incorporating additional information such as multimodal imaging data, integrating multiscale models and co-simulation, and introducing reparameterization techniques within the model configuration space. The coexistence of models with different levels of description from the most biophysically detailed to the most phenomenological may also help to address this challenge [ 127 ]. The second aspect is intrinsic to the brain and needs to be conceptually integrated and dealt with as an important brain characteristic when we design personalized brain models and make interpretations in clinical use [ 26 ].…”

Section: Key Challenges and Future Directionsmentioning

confidence: 99%

Virtual brain twins: from basic neuroscience to clinical use

Wang,

Triebkorn,

Breyton

et al. 2024

National Science Review

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Virtual brain twins are personalized, generative and adaptive brain models based on data from an individual’s brain for scientific and clinical use. After description of key elements of virtual brain twins, we present the standard model for personalized whole-brain network models. The personalization is accomplished using a subject’s brain imaging data by three means: 1) assemble cortical and subcortical areas in the subject-specific brain space; 2) directly map connectivity into the brain models, which can be generalised to other parameters; and 3) estimate relevant parameters through model inversion, typically using probabilistic machine learning. We present the use of personalized whole-brain network models in healthy ageing and five clinical uses: epilepsy, Alzheimer’s disease, multiple sclerosis, Parkinson’s disease and psychiatric disorder. Specifically, we introduce spatial masks for relevant parameters and demonstrate their use based on the physiological and pathophysiological hypotheses. Finally, we pinpoint the key challenges and future directions.

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Section: Key Challenges and Future Directionsmentioning

confidence: 99%

Virtual brain twins: from basic neuroscience to clinical use

Wang,

Triebkorn,

Breyton

et al. 2024

National Science Review

Self Cite

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“…Phenomena that span multiple temporal scales are ubiquitous in brain research ( Breakspear, 2017 ; D’Angelo & Jirsa, 2022 ). For instance, an objective of epilepsy research is to understand the genesis of seizures by analysing the itinerancy of cortical circuitry parameters over seconds to minutes ( Courtiol, Guye, Bartolomei, Petkoski, & Jirsa, 2020 ; Depannemaecker, Destexhe, Jirsa, & Bernard, 2021 ; Depannemaecker, Ezzati, Wang, Jirsa, & Bernard, 2023 ; V. Jirsa et al, 2023 ; V. K. Jirsa, Stacey, Quilichini, Ivanov, & Bernard, 2014 ; Lisi, Rivela, Takai, & Morimoto, 2018 ; Lopez-Sola et al, 2022 ; Ponce-Alvarez et al, 2015 ; R. E. Rosch, Hunter, Baldeweg, Friston, & Meyer, 2018 ). In naturalistic experiments, researchers study the dynamics of the freely-behaving brain in exchange with its environment, on the scale of minutes to hours ( Demirtaş et al, 2019 ; Echeverria-Altuna et al, 2022 ; Lee, Aly, & Baldassano, 2021 ; Meer, Breakspear, Chang, Sonkusare, & Cocchi, 2020 ; Shain, Blank, van Schijndel, Schuler, & Fedorenko, 2020 ).…”

Section: Understanding the Brain: A Multiscale Problemmentioning

confidence: 99%

Linking fast and slow: The case for generative models

Medrano,

Friston,

Zeidman

2024

Network Neuroscience

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A pervasive challenge in neuroscience is testing whether neuronal connectivity changes over time due to specific causes, such as stimuli, events, or clinical interventions. Recent hardware innovations and falling data storage costs enable longer, more naturalistic neuronal recordings. The implicit opportunity for understanding the self-organised brain calls for new analysis methods that link temporal scales: from the order of milliseconds over which neuronal dynamics evolve, to the order of minutes, days or even years over which experimental observations unfold. This review article demonstrates how hierarchical generative models and Bayesian inference help to characterise neuronal activity across different time scales. Crucially, these methods go beyond describing statistical associations among observations and enable inference about underlying mechanisms. We offer an overview of fundamental concepts in state-space modeling and suggest a taxonomy for these methods. Additionally, we introduce key mathematical principles that underscore a separation of temporal scales, such as the slaving principle, and review Bayesian methods that are being used to test hypotheses about the brain with multi-scale data. We hope that this review will serve as a useful primer for experimental and computational neuroscientists on the state of the art and current directions of travel in the complex systems modelling literature.

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“…[13,29] This type of model is a simplification of biophysics models, and can suggest the key feature during the seizures process and give exhaustive research on dynamics. [30,31] Based on this, Hebbink et al introduced the oscillator excitability as a slow variable and considered node dynamics as a fast-slow system. [27] Noise or perturbation can induce transition behaviors between healthy and epileptic states, and a number of studies have focused on the seizure generation and effective regulation paradigms.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the optimal target node to reduce seizure-like discharge in networks

Yan 闫,

Zhang 张,

Sun 孙

2024

Chinese Phys. B

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Network approaches have been widely accepted to guide surgical strategy and predict outcome for epilepsy treatment. This study starts with a single oscillator to explore brain activity, using a phenomenological model capable of describing healthy and epileptic states. The ictal number of seizures decreases or remains unchanged with increasing the speed of oscillator excitability and in each seizure, there is an increasing tendency for ictal duration with respect to the speed. The underlying reason is that the strong excitability speed is conducive to reduce transition behaviors between two attractor basins. Moreover, the selection of the optimal removal node is estimated by an indicator proposed in this study. Results show that when the indicator is less than the threshold, removing the driving node is more possible to reduce seizures significantly, while the indicator exceeds the threshold, the epileptic node could be the removal one. Furthermore, the driving node is such a potential target that stimulating it is obviously effective in suppressing seizure- like activity compared to other nodes, and the propensity of seizures can be reduced 60% with the increased stimulus strength. Our results could provide new therapeutic ideas for epilepsy surgery and neuromodulation.

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From phenomenological to biophysical models of seizures

Cited by 10 publications

References 97 publications

Virtual brain twins: from basic neuroscience to clinical use

Virtual brain twins: from basic neuroscience to clinical use

Linking fast and slow: The case for generative models

Analysis of the optimal target node to reduce seizure-like discharge in networks

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