TVB-EduPack—An Interactive Learning and Scripting Platform for The Virtual Brain

Matzke, Henrik; Schirner, Michael; Vollbrecht, Daniel; Rothmeier, Simon; Llarena, Adalberto; Rojas, Raul Fernandez; Triebkorn, Paul; Domide, Lia; Mersmann, Jochen; Solodkin, Ana; Jirsa, Viktor K.; McIntosh, Anthony R.; Ritter, Petra

doi:10.3389/fninf.2015.00027

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…The micro-and meso-scale model consists of neuronal mass models or assembles of neuronal mass models, generating the so-called mass field. This simulation environment enables the model-based inference of neurophysiological mechanisms across different brain scales that underlie the generation of macroscopic neuroimaging signals including EEG and MEG [426][427][428][429]. The Virtual Brain has been used for many applications to investigate different diseases, such as neurodegenerative disorders [430], PD [431], dementia [432], TBI [433], AD [434][435][436], epilepsy [437][438][439][440], brain tumor [441], chronic stroke [442], or physiological brain dynamics [431,443,444].…”

Section: Multi-scale Models Represent the Ultimate Translational Appr...mentioning

confidence: 99%

The potential of in vitro neuronal networks cultured on micro electrode arrays for biomedical research

2023

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In vitro neuronal models have become an important tool to study healthy and diseased neuronal circuits. The growing interest of neuroscientists to explore the dynamics of neuronal systems and the increasing need to observe, measure and manipulate not only single neurons but populations of cells pushed for technological advancement. In this sense, Micro-Electrode Arrays (MEAs) emerged as a promising technique, made of cell culture dishes with embedded micro-electrodes allowing non-invasive and relatively simple measurement of the activity of neuronal cultures at the network level.In the past decade, MEAs popularity has rapidly grown. MEAs devices have been extensively used to measure the activity of neuronal cultures mainly derived from rodents. Rodent neuronal cultures on MEAs have been employed to investigate physiological mechanisms, study the effect of chemicals in neurotoxicity screenings, and model the electrophysiological phenotype of neuronal networks in different pathological conditions. With the advancements in human induced pluripotent stem cells (hiPSCs) technology, the differentiation of human neurons from the cells of adult donors became possible. hiPSCs-derived neuronal networks on MEAs have been employed to develop patient-specific in vitro platforms to characterize the pathophysiological phenotype and to test drugs, paving the way towards personalized medicine.In this review, we first describe MEAs technology and the information that can be obtained from MEAs recordings. Then, we give an overview of studies in which MEAs have been used in combination with different neuronal systems (i.e., rodent 2D and 3D neuronal cultures, organotypic brain slices, hiPSCs-derived 2D and 3D neuronal cultures, and brain organoids) for biomedical research, including physiology studies, neurotoxicity screenings, disease modeling, and drug testing. We end by discussing potential, challenges and future perspectives of MEAs technology, and providing some guidance for the choice of theneuronal model and MEAs device, experimental design, data analysis and reporting for scientific publications.

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Section: Multi-scale Models Represent the Ultimate Translational Appr...mentioning

confidence: 99%

The potential of in vitro neuronal networks cultured on micro electrode arrays for biomedical research

2023

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“…With the present work, we aim to provide guidance for personalized brain network modelling using SJHMR3D nodes. Example data sets, code and didactic video tutorials for published TVB studies are being made available as didactic use cases (84). A systematic exploration of other population models and comparisons of their respective parameter landscapes will help to further put to test and to validate the biological interpretability of parameters and variables of those models (85).…”

Section: Mechanisms Underlying Alpha Bimodalitymentioning

confidence: 99%

Fifty shades of The Virtual Brain: Converging optimal working points yield biologically plausible electrophysiological and imaging features

Triebkorn

Zimmermann

Stefanovski

et al. 2020

Preprint

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Using The Virtual Brain (TVB, thevirtualbrian.org) simulation platform, we explored for 50 individual adult human brains (ages 18-80), how personalized connectome based brain network modelling captures various empirical observations as measured by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). We compare simulated activity based on individual structural connectomes (SC) inferred from diffusion weighted imaging with fMRI and EEG in the resting state. We systematically explore the role of the following model parameters:conduction velocity, global coupling and graph theoretical features of individual SC. First, a subspace of the parameter space is identified for each subject that results in realistic brain activity, i.e. reproducing the following prominent features of empirical EEG-fMRI activity: topology of resting-state fMRI functional connectivity (FC), functional connectivity dynamics (FCD), electrophysiological oscillations in the delta (3-4 Hz) and alpha (8-12 Hz) frequency range and their bimodality, i.e. low and high energy modes. Interestingly, FCD fit, bimodality and static FC fit are highly correlated. They all show their optimum in the same range of global coupling. In other words, only when our local model is in a bistable regime we are able to generate switching of modes in our global network. Second, our simulations reveal the explicit network mechanisms that lead to electrophysiological oscillations, their bimodal behaviour and inter-regional differences. Third, we discuss biological interpretability of the Stefanescu-Jirsa-Hindmarsh-Rose-3D model when embedded inside the large-scale brain network and mechanisms underlying the emergence of bimodality of the neural signal.With the present study, we set the cornerstone for a systematic catalogue of spatiotemporal brain activity regimes generated with the connectome-based brain simulation platform The Virtual Brain. Author SummaryIn order to understand brain dynamics we use numerical simulations of brain network models. Combining the structural backbone of the brain, that is the white matter fibres connecting distinct regions in the grey matter, with dynamical systems describing the activity of neural populations we are able to simulate brain function on a large scale. In order to make accurate prediction with this network, it is crucial to determine optimal model parameters. We here use an explorative approach to adjust model parameters to individual brain activity, showing that subjects have their own optimal point in the parameter space, depending on their brain structure and function. At the same time, we investigate the relation between bistable phenomena on the scale of neural populations and the changed in functional connectivity on the brain network scale. Our results are important for future modelling approaches trying to make accurate predictions of brain function.Brain network models allow us to investigate how realistic neural behaviour arises as a function of variations in specific neural parameters. Large-sc...

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“…The simulated brain connectivity was the TVB built in 76 region connectivity consists of the right and left hemisphere. The region covers some prefrontal cortex, some auditory, motor, visual, association areas, amygdala, and the full region list can be found in the TVB study [20]. The region stimulus signal was made using a pulse train function with onset of 10.0, fire period (T) of 100.0 ms, fire duration (tau) of 10.0 ms, and amplitude of 1.0.…”

Section: Brain Region Simulationmentioning

confidence: 99%

The development of cognitive workload management framework based on neuronal dynamics principle to maintain train driver’s health and railway safety

et al. 2023

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Fatigue increases the tendency of poor train driving strategy decision. Decision making in cognitive overload and cognitive underload situation mostly outputs bad decisions. Accordingly, train driver’s cognitive function is required to be sTable during travel so that they can give correct response at a given situation. This study constructs a conceptual framework for cognitive workload management (CWM) of train driver by taking the energy expenses from cognition into the account. This study combines objective and subjective cognitive workload analysis to evaluate train driver duty readiness. The objective load analysis was performed through energy level approximation based on neuronal dynamics simulation from 76 brain regions. The cognitive energy expenditure (CEE) calculated from neuron action potential (NAP) and the ion-membrane current (IMC) from the simulation results. The cognitive load (CL) approximated by converts the continuous time-based CEE to discrete frequency-based CL using Fourier series. The subjective cognitive workload obtained from train simulation results followed by 27 participants. The participants fill the questionnaire based on their simulated journey experience. The results of the evaluation used to build readiness evaluation classifier based on control chart. The control chart evaluation helps the management to determine weekly rest period and daily short rest period treatment base on each train driver workload. The CWM framework allows different recovery treatment to be applied to each train driver. The impact of the CWM application is the performance of train drivers are kept stable. Thus, the CWM framework based on CEE is useful to prevent physical and mental fatigue

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TVB-EduPack—An Interactive Learning and Scripting Platform for The Virtual Brain

Cited by 7 publications

References 16 publications

The potential of in vitro neuronal networks cultured on micro electrode arrays for biomedical research

The potential of in vitro neuronal networks cultured on micro electrode arrays for biomedical research

Fifty shades of The Virtual Brain: Converging optimal working points yield biologically plausible electrophysiological and imaging features

The development of cognitive workload management framework based on neuronal dynamics principle to maintain train driver’s health and railway safety

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