Personalised virtual brain models in epilepsy

Jirsa, Viktor; Wang, Hong; Triebkorn, Paul; Hashemi, Meysam; Jha, Jayant; González-Martínez, Jorge; Guye, Maxime; Makhalova, Julia; Bartolomei, Fabricē

doi:10.1016/s1474-4422(23)00008-x

Cited by 47 publications

(35 citation statements)

References 77 publications

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“…At its initial phase, it quickly fell apart for various reasons. Currently one of the most successful outcomes of the HBP is The Virtual Brain (TVB) project led by our colleague Viktor Jirsa [4], who I worked with 17 years ago. In the TVB, a mean field model was developed to simulate the whole human brain activity: a set of equations is used to simulate each brain region.…”

Section: Figurementioning

confidence: 99%

“…In the TVB, a mean field model was developed to simulate the whole human brain activity: a set of equations is used to simulate each brain region. In this project, the participants went on to run a clinical trial in many hospitals in France and some very positive results arose, for the treatment of epilepsy [4]. The eBrain, an EU organization with Viktor Jirsa as its chief scientist, is the successor of the HBP.…”

Section: Figurementioning

confidence: 99%

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Simulating the whole brain as an alternative way to achieve AGI

Feng

2023

Quant. Biol.

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Section: Figurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Simulating the whole brain as an alternative way to achieve AGI

Feng

2023

Quant. Biol.

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“…A critical element of the concept of digital twin is the inclusion of detail found in the individual patient. For example, Jirsa et al [28] describe a method to obtain digital virtual brains by mapping the brain network of a subject with epilepsy, using data derived from magnetic resonance imaging (MRI) images of the subject. The digital twin model can be used clinically to estimate the extent, localization and organization of the epileptogenic regions related to seizure.…”

Section: Personalized Models or Digital Twinsmentioning

confidence: 99%

The stochastic digital human is now enrolling for in silico imaging trials—methods and tools for generating digital cohorts

Badano,

Lago,

Sizikova

et al. 2023

Prog. Biomed. Eng.

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Randomized clinical trials, while often viewed as the highest evidentiary bar by which to judge the quality of a medical intervention, are far from perfect. In silico imaging trials are computational studies that seek to ascertain the performance of a medical device by collecting this information entirely via computer simulations. The benefits of in silico trials for evaluating new technology include significant resource and time savings, minimization of subject risk, the ability to study devices that are not achievable in the physical world, allow for the rapid and effective investigation of new technologies and ensure representation from all relevant subgroups. To conduct in silico trials, digital representations of humans are needed. We review the latest developments in methods and tools for obtaining digital humans for in silico imaging studies. First, we introduce terminology and a classification of digital human models. Second, we survey available methodologies for generating digital humans with healthy status and for generating diseased cases and discuss briefly the role of augmentation methods. Finally, we discuss approaches for sampling digital cohorts and understanding the trade-offs and potential for study bias associated with selecting specific patient distributions.

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“…It emphasizes the incorporation of domain knowledge (Gelman et al, 2013), hypothesis testing (MacKay, 2003), interpretability (Hastie et al, 2009), generalizability (Vapnik, 1999), and causality (Pearl, 2009). It also often provides superior performance over purely data-driven methods, particularly in the context of brain disorders, such as epilepsy (Hashemi et al, 2020;Wang et al, 2023;Jirsa et al, 2023), and Alzheimer's disease (Triebkorn et al, 2022;Yalcinkaya et al, 2023). One class of computational network models commonly used to analyze functional neuroimaging modalities, such as fMRI, MEG, and EEG, is the class of connectome-based models (Ghosh et al, 2008;Sanz-Leon et al, 2015;Bassett and Sporns, 2017), also known as Virtual Brain Models (VBMs; Sanz Leon et al (2013); Jirsa et al (2023)).…”

Section: Introductionmentioning

confidence: 99%

“…It also often provides superior performance over purely data-driven methods, particularly in the context of brain disorders, such as epilepsy (Hashemi et al, 2020;Wang et al, 2023;Jirsa et al, 2023), and Alzheimer's disease (Triebkorn et al, 2022;Yalcinkaya et al, 2023). One class of computational network models commonly used to analyze functional neuroimaging modalities, such as fMRI, MEG, and EEG, is the class of connectome-based models (Ghosh et al, 2008;Sanz-Leon et al, 2015;Bassett and Sporns, 2017), also known as Virtual Brain Models (VBMs; Sanz Leon et al (2013); Jirsa et al (2023)). Each node in these models corresponds to a brain region (Bullmore and Sporns, 2009;Sporns, 2016).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Inference on Virtual Brain Models of Disorders

Hashemi,

Ziaeemehr,

Woodman

et al. 2024

Preprint

Self Cite

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Connectome-based models, also known as Virtual Brain Models (VBMs), have been well established in network neuroscience to investigate pathophysiological causes underlying a large range of brain diseases. The integration of an individual’s brain imaging data in VBMs has improved patient-specific predictivity, although Bayesian estimation of spatially distributed parameters remains challenging even with state-of-the-art Monte Carlo sampling. VBMs imply latent nonlinear state space models driven by noise and network input, necessitating advanced probabilistic machine learning techniques for widely applicable Bayesian estimation. Here we present Simulation-Based Inference on Virtual Brain Models (SBI-VBMs), and demonstrate that training deep neural networks on both spatio-temporal and functional features allows for accurate estimation of generative parameters in brain disorders. The systematic use of brain stimulation provides an effective remedy for the non-identifiability issue in estimating the degradation of intra-hemispheric connections. By prioritizing model structure over data, we show that the hierarchical structure in SBI-VBMs renders the inference more effective, precise and biologically plausible. This approach could broadly advance precision medicine by enabling fast and reliable prediction of patient-specific brain disorders.

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Personalised virtual brain models in epilepsy

Cited by 47 publications

References 77 publications

Simulating the whole brain as an alternative way to achieve AGI

Simulating the whole brain as an alternative way to achieve AGI

The stochastic digital human is now enrolling for in silico imaging trials—methods and tools for generating digital cohorts

Probabilistic Inference on Virtual Brain Models of Disorders

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