The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread

Jirsa, Viktor K.; Proix, Timothée; Perdikis, D.; Woodman, Michael; Wang, Huifang; González-Martínez, Jorge; Bernard, Christophe; Bénar, Christian; Guye, Maxime; Chauvel, Patrick; Bartolomei, Fabricē

doi:10.1016/j.neuroimage.2016.04.049

Cited by 363 publications

(411 citation statements)

References 59 publications

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“…Results showed that individual optimization of the modeling parameters significantly increased the prediction accuracy of individual functional connectivity. However, whether model parameters were tuned on the individual SC matrices or on the average SC matrix across all control subjects did not yield a significant difference, corroborating findings in previous research (e.g., Jirsa et al, 2017). Future computational modeling studies could use finer and more elaborated parcellation schemes, for example based on multimodal data (Glasser et al, 2016), to investigate whether individual traits can be captured through tractography that have relevance for the prediction of individual functional connectivity.…”

Section: Discussionsupporting

confidence: 86%

Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain

Aerts

Schirner

Jeurissen

et al. 2018

eNeuro

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Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, noninvasive neuroimaging techniques such as functional MRI and diffusion-weighted imaging fiber tracking are currently employed. However, taking into account this information is often still insufficient, as the complex nonlinear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention. Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics. As a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong–Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed. Results showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters that can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance.

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Section: Discussionsupporting

confidence: 86%

Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain

Aerts

Schirner

Jeurissen

et al. 2018

eNeuro

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“…While different models of effective connectivity have been proposed (for a comprehensive summary, see Valdes-Sosa et al, 2011), to our knowledge, none has so far enabled estimates of connection-specific strengths for networks derived from typical whole-brain parcellation schemes with more than 100 nodes (Glasser et al, 2016;Tzourio-Mazoyer et al, 2002). For instance, biophysical network models (BNMs) combine mean-field models of local neuronal dynamics with anatomical data on long-range connections, capturing many structural and physiological details of whole-brain networks (Deco et al, 2013a;Jirsa et al, 2016). However, the complexity of these models has made parameter estimation computationally extremely challenging.…”

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confidence: 99%

A generative model of whole-brain effective connectivity

et al. 2018

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A B S T R A C TThe development of whole-brain models that can infer effective (directed) connection strengths from fMRI data represents a central challenge for computational neuroimaging. A recently introduced generative model of fMRI data, regression dynamic causal modeling (rDCM), moves towards this goal as it scales gracefully to very large networks. However, large-scale networks with thousands of connections are difficult to interpret; additionally, one typically lacks information (data points per free parameter) for precise estimation of all model parameters.This paper introduces sparsity constraints to the variational Bayesian framework of rDCM as a solution to these problems in the domain of task-based fMRI. This sparse rDCM approach enables highly efficient effective connectivity analyses in whole-brain networks and does not require a priori assumptions about the network's connectivity structure but prunes fully (all-to-all) connected networks as part of model inversion. Following the derivation of the variational Bayesian update equations for sparse rDCM, we use both simulated and empirical data to assess the face validity of the model. In particular, we show that it is feasible to infer effective connection strengths from fMRI data using a network with more than 100 regions and 10,000 connections. This demonstrates the feasibility of whole-brain inference on effective connectivity from fMRI data -in single subjects and with a run-time below 1 min when using parallelized code. We anticipate that sparse rDCM may find useful application in connectomics and clinical neuromodeling -for example, for phenotyping individual patients in terms of wholebrain network structure. IntroductionThe human brain comprises multiple levels of organization, with cognitive functions arising from the interplay of functional specialization and integration (Sporns, 2013;Tononi et al., 1994). With the advent of non-invasive neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), researchers have begun to systematically study these fundamental properties of human brain organization. While early neuroimaging studies focused on localizing cognitive processes to specific brain regions, contemporary studies are commonly concerned with functional integration of these regions; this requires analyzing brain connectivity (Smith, 2012). In addition to analyses of structural (anatomical) connections, assessments of functional connectivity (statistical dependencies between network nodes) play a major role. Particularly, functional connectivity has been used frequently for studying the functional organization of large (whole-brain) networks both in tasks and in the "resting state" (i.e., unconstrained cognition in the absence of external perturbations). Various methods have been proposed (for a comprehensive review, see Karahanoglu and Van De Ville, 2017), ranging from conventional correlation or coherence analyses which assume stationarity (Fox et al., 2005) to sliding-window correlation analyses that can capture dyna...

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“…A comprehensive presur-gical evaluation is necessary to pinpoint the epileptogenic zone along with the identification of the risks of postoperative neurologic morbidity. Based on structural anomalies as identified by MRI and the assessment of functional data such as EEG, estimation of TVB parameters (epileptogenicity, anomalies) are set in the network model to predict the propagation pattern of seizures in order to explore brain intervention strategies [41]. …”

Section: The Virtual Brain-derived Models Are Sensitive To Disruptionmentioning

confidence: 99%

“…The network model is composed of two neuronal populations, characterized by fast excitatory bursting neurons and regular spiking inhibitory neurons, embedded in a common extracellular environment represented by a slow variable. By systematically analyzing the parameter landscape via the simulation, it is possible to reproduce typical sequences of neural activity observed during status epilepticus [41,42]. …”

Section: The Virtual Brain-derived Models Are Sensitive To Disruptionmentioning

confidence: 99%

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A new neuroinformatics approach to personalized medicine in neurology: The Virtual Brain

Falcon¹,

Jirsa

Solodkin

2016

Current Opinion in Neurology

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Purpose of review An exciting advance in the field of neuroimaging is the acquisition and processing of very large data sets (so called ‘big data’), permitting large-scale inferences that foster a greater understanding of brain function in health and disease. Yet what we are clearly lacking are quantitative integrative tools to translate this understanding to the individual level to lay the basis for personalized medicine. Recent findings Here we address this challenge through a review on how the relatively new field of neuroinformatics modeling has the capacity to track brain network function at different levels of inquiry, from microscopic to macroscopic and from the localized to the distributed. In this context, we introduce a new and unique multiscale approach, The Virtual Brain (TVB), that effectively models individualized brain activity, linking large-scale (macroscopic) brain dynamics with biophysical parameters at the microscopic level. We also show how TVB modeling provides unique biological interpretable data in epilepsy and stroke. Summary These results establish the basis for a deliberate integration of computational biology and neuroscience into clinical approaches for elucidating cellular mechanisms of disease. In the future, this can provide the means to create a collection of disease-specific models that can be applied on the individual level to personalize therapeutic interventions. Video abstract http://links.lww.com/CONR/A41

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The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread

Cited by 363 publications

References 59 publications

Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain

Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain

A generative model of whole-brain effective connectivity

A new neuroinformatics approach to personalized medicine in neurology: The Virtual Brain

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