The Experts below are selected from a list of 279 Experts worldwide ranked by ideXlab platform

Petra Ritter - One of the best experts on this subject based on the ideXlab platform.

  • differentiation of alzheimer s disease based on local and global parameters in personalized virtual Brain Models
    NeuroImage: Clinical, 2018
    Co-Authors: Joelle Zimmermann, Michael Schirner, Alistair Perry, Michael Breakspear, Perminder S Sachdev, Wei Wen, Nicole A Kochan, Michael Mapstone, Petra Ritter
    Abstract:

    Alzheimer's disease (AD) is marked by cognitive dysfunction emerging from neuropathological processes impacting Brain function. AD affects Brain dynamics at the local level, such as changes in the balance of inhibitory and excitatory neuronal populations, as well as long-range changes to the global network. Individual differences in these changes as they relate to behaviour are poorly understood. Here, we use a multi-scale neurophysiological model, "The Virtual Brain (TVB)", based on empirical multi-modal neuroimaging data, to study how local and global dynamics correlate with individual differences in cognition. In particular, we modeled individual resting-state functional activity of 124 individuals across the behavioural spectrum from healthy aging, to amnesic Mild Cognitive Impairment (MCI), to AD. The model parameters required to accurately simulate empirical functional Brain imaging data correlated significantly with cognition, and exceeded the predictive capacity of empirical connectomes.

  • the dynamics of resting fluctuations in the Brain metastability and its dynamical cortical core
    Scientific Reports, 2017
    Co-Authors: Gustavo Deco, Viktor K. Jirsa, Morten L Kringelbach, Petra Ritter
    Abstract:

    In the human Brain, spontaneous activity during resting state consists of rapid transitions between functional network states over time but the underlying mechanisms are not understood. We use connectome based computational Brain network modeling to reveal fundamental principles of how the human Brain generates large-scale activity observable by noninvasive neuroimaging. We used structural and functional neuroimaging data to construct whole- Brain Models. With this novel approach, we reveal that the human Brain during resting state operates at maximum metastability, i.e. in a state of maximum network switching. In addition, we investigate cortical heterogeneity across areas. Optimization of the spectral characteristics of each local Brain region revealed the dynamical cortical core of the human Brain, which is driving the activity of the rest of the whole Brain. Brain network modelling goes beyond correlational neuroimaging analysis and reveals non-trivial network mechanisms underlying non-invasive observations. Our novel findings significantly pertain to the important role of computational connectomics in understanding principles of Brain function.

  • the dynamics of resting fluctuations in the Brain metastability and its dynamical cortical core
    bioRxiv, 2016
    Co-Authors: Gustavo Deco, Viktor K. Jirsa, Morten L Kringelbach, Petra Ritter
    Abstract:

    In the human Brain, spontaneous activity during resting state consists of rapid transitions between functional network states over time but the underlying mechanisms are not understood. We use connectome based computational Brain network modeling to reveal fundamental principles of how the human Brain generates large-scale activity observable by noninvasive neuroimaging. By including individual structural and functional neuroimaging data into Brain network Models we construct personalized Brain Models. With this novel approach, we reveal that the human Brain during resting state operates at maximum metastability, i.e. in a state of maximum network switching. In addition, we investigate cortical heterogeneity across areas. Optimization of the spectral characteristics of each local Brain region revealed the dynamical cortical core of the human Brain, which is driving the activity of the rest of the whole Brain. Personalized Brain network modelling goes beyond correlational neuroimaging analysis and reveals non-trivial network mechanisms underlying non-invasive observations. Our novel findings significantly pertain to the important role of computational connectomics in understanding principles of Brain function.

  • how do parcellation size and short range connectivity affect dynamics in large scale Brain network Models
    NeuroImage, 2016
    Co-Authors: Timothée Proix, Andreas Spiegler, Michael Schirner, Simon Rothmeier, Petra Ritter, Viktor K. Jirsa
    Abstract:

    Abstract Recent efforts to model human Brain activity on the scale of the whole Brain rest on connectivity estimates of large-scale networks derived from diffusion magnetic resonance imaging (dMRI). This type of connectivity describes white matter fiber tracts. The number of short-range cortico-cortical white-matter connections is, however, underrepresented in such large-scale Brain Models. It is still unclear on the one hand, which scale of representation of white matter fibers is optimal to describe Brain activity on a large-scale such as recorded with magneto- or electroencephalography (M/EEG) or functional magnetic resonance imaging (fMRI), and on the other hand, to which extent short-range connections that are typically local should be taken into account. In this article we quantified the effect of connectivity upon large-scale Brain network dynamics by (i) systematically varying the number of Brain regions before computing the connectivity matrix, and by (ii) adding generic short-range connections. We used dMRI data from the Human Connectome Project. We developed a suite of preprocessing modules called SCRIPTS to prepare these imaging data for The Virtual Brain, a neuroinformatics platform for large-scale Brain modeling and simulations. We performed simulations under different connectivity conditions and quantified the spatiotemporal dynamics in terms of Shannon Entropy, dwell time and Principal Component Analysis. For the reconstructed connectivity, our results show that the major white matter fiber bundles play an important role in shaping slow dynamics in large-scale Brain networks (e.g. in fMRI). Faster dynamics such as gamma oscillations (around 40  Hz) are sensitive to the short-range connectivity if transmission delays are considered.

  • an automated pipeline for constructing personalized virtual Brains from multimodal neuroimaging data
    NeuroImage, 2015
    Co-Authors: Michael Schirner, Simon Rothmeier, Viktor K. Jirsa, Anthony R Mcintosh, Petra Ritter
    Abstract:

    Large amounts of multimodal neuroimaging data are acquired every year worldwide. In order to extract high-dimensional information for computational neuroscience applications standardized data fusion and efficient reduction into integrative data structures are required. Such self-consistent multimodal data sets can be used for computational Brain modeling to constrain Models with individual measurable features of the Brain, such as done with The Virtual Brain (TVB). TVB is a simulation platform that uses empirical structural and functional data to build full Brain Models of individual humans. For convenient model construction, we developed a processing pipeline for structural, functional and diffusion-weighted magnetic resonance imaging (MRI) and optionally electroencephalography (EEG) data. The pipeline combines several state-of-the-art neuroinformatics tools to generate subject-specific cortical and subcortical parcellations, surface-tessellations, structural and functional connectomes, lead field matrices, electrical source activity estimates and region-wise aggregated blood oxygen level dependent (BOLD) functional MRI (fMRI) time-series. The output files of the pipeline can be directly uploaded to TVB to create and simulate individualized large-scale network Models that incorporate intra- and intercortical interaction on the basis of cortical surface triangulations and white matter tractograpy. We detail the pitfalls of the individual processing streams and discuss ways of validation. With the pipeline we also introduce novel ways of estimating the transmission strengths of fiber tracts in whole-Brain structural connectivity (SC) networks and compare the outcomes of different tractography or parcellation approaches. We tested the functionality of the pipeline on 50 multimodal data sets. In order to quantify the robustness of the connectome extraction part of the pipeline we computed several metrics that quantify its rescan reliability and compared them to other tractography approaches. Together with the pipeline we present several principles to guide future efforts to standardize Brain model construction. The code of the pipeline and the fully processed data sets are made available to the public via The Virtual Brain website (thevirtualBrain.org) and via github (https://github.com/BrainModes/TVB-empirical-data-pipeline). Furthermore, the pipeline can be directly used with High Performance Computing (HPC) resources on the Neuroscience Gateway Portal (http://www.nsgportal.org) through a convenient web-interface.

Morten L Kringelbach - One of the best experts on this subject based on the ideXlab platform.

  • loss of consciousness reduces the stability of Brain hubs and the heterogeneity of Brain dynamics
    bioRxiv, 2020
    Co-Authors: Ane Lopezgonzalez, Morten L Kringelbach, Rajanikant Panda, Adrian Poncealvarez, Gorka Zamoralopez, Anira Escrichs, Charlotte Martial, Aurore Thibaut, Olivia Gosseries
    Abstract:

    Abstract Low-level states of consciousness are characterised by disruptions of Brain dynamics that sustain arousal and awareness. Yet, how structural, dynamical, local and network Brain properties interplay in the different levels of consciousness is unknown. Here, we studied the fMRI Brain dynamics from patients that suffered Brain injuries leading to a disorder of consciousness and from subjects undergoing propofol-induced anaesthesia. We showed that pathological and pharmacological low-level states of consciousness displayed less recurrent, less diverse, less connected, and more segregated synchronization patterns than conscious states. We interpreted these effects using whole-Brain Models built on healthy and injured connectomes. We showed that altered dynamics arise from a global reduction of network interactions, together with more homogeneous and more structurally constrained local dynamics. These effects were accentuated using injured connectomes. Notably, these changes lead the hub regions to lose their stability during low-level states of consciousness, thus attenuating the core-periphery structure of Brain dynamics.

  • human Brain connectivity clinical applications for clinical neurophysiology
    Clinical Neurophysiology, 2020
    Co-Authors: Mark Hallett, Gustavo Deco, Willem De Haan, R Dengler, Riccardo Di Iorio, Cecile Gallea, Christian Gerloff, Christian Grefkes, Rick C Helmich, Morten L Kringelbach
    Abstract:

    This manuscript is the second part of a two-part description of the current status of understanding of the network function of the Brain in health and disease. We start with the concept that Brain function can be understood only by understanding its networks, how and why information flows in the Brain. The first manuscript dealt with methods for network analysis, and the current manuscript focuses on the use of these methods to understand a wide variety of neurological and psychiatric disorders. Disorders considered are neurodegenerative disorders, such as Alzheimer disease and amyotrophic lateral sclerosis, stroke, movement disorders, including essential tremor, Parkinson disease, dystonia and apraxia, epilepsy, psychiatric disorders such as schizophrenia, and phantom limb pain. This state-of-the-art review makes clear the value of networks and Brain Models for understanding symptoms and signs of disease and can serve as a foundation for further work.

  • novel intrinsic ignition method measuring local global integration characterizes wakefulness and deep sleep
    eNeuro, 2017
    Co-Authors: Gustavo Deco, Morten L Kringelbach, Enzo Tagliazucchi, Helmut Laufs, Ana Sanjuan
    Abstract:

    Abstract A precise definition of a Brain state has proven elusive. Here, we introduce the novel local-global concept of intrinsic ignition characterizing the dynamical complexity of different Brain states. Naturally occurring intrinsic ignition events reflect the capability of a given Brain area to propagate neuronal activity to other regions, giving rise to different levels of integration. The ignitory capability of Brain regions is computed by the elicited level of integration for each intrinsic ignition event in each Brain region, averaged over all events. This intrinsic ignition method is shown to clearly distinguish human neuroimaging data of two fundamental Brain states (wakefulness and deep sleep). Importantly, whole-Brain computational modelling of this data shows that at the optimal working point is found where there is maximal variability of the intrinsic ignition across Brain regions. Thus, combining whole Brain Models with intrinsic ignition can provide novel insights into underlying mechanisms of Brain states.

  • the dynamics of resting fluctuations in the Brain metastability and its dynamical cortical core
    Scientific Reports, 2017
    Co-Authors: Gustavo Deco, Viktor K. Jirsa, Morten L Kringelbach, Petra Ritter
    Abstract:

    In the human Brain, spontaneous activity during resting state consists of rapid transitions between functional network states over time but the underlying mechanisms are not understood. We use connectome based computational Brain network modeling to reveal fundamental principles of how the human Brain generates large-scale activity observable by noninvasive neuroimaging. We used structural and functional neuroimaging data to construct whole- Brain Models. With this novel approach, we reveal that the human Brain during resting state operates at maximum metastability, i.e. in a state of maximum network switching. In addition, we investigate cortical heterogeneity across areas. Optimization of the spectral characteristics of each local Brain region revealed the dynamical cortical core of the human Brain, which is driving the activity of the rest of the whole Brain. Brain network modelling goes beyond correlational neuroimaging analysis and reveals non-trivial network mechanisms underlying non-invasive observations. Our novel findings significantly pertain to the important role of computational connectomics in understanding principles of Brain function.

  • the dynamics of resting fluctuations in the Brain metastability and its dynamical cortical core
    bioRxiv, 2016
    Co-Authors: Gustavo Deco, Viktor K. Jirsa, Morten L Kringelbach, Petra Ritter
    Abstract:

    In the human Brain, spontaneous activity during resting state consists of rapid transitions between functional network states over time but the underlying mechanisms are not understood. We use connectome based computational Brain network modeling to reveal fundamental principles of how the human Brain generates large-scale activity observable by noninvasive neuroimaging. By including individual structural and functional neuroimaging data into Brain network Models we construct personalized Brain Models. With this novel approach, we reveal that the human Brain during resting state operates at maximum metastability, i.e. in a state of maximum network switching. In addition, we investigate cortical heterogeneity across areas. Optimization of the spectral characteristics of each local Brain region revealed the dynamical cortical core of the human Brain, which is driving the activity of the rest of the whole Brain. Personalized Brain network modelling goes beyond correlational neuroimaging analysis and reveals non-trivial network mechanisms underlying non-invasive observations. Our novel findings significantly pertain to the important role of computational connectomics in understanding principles of Brain function.

Viktor K. Jirsa - One of the best experts on this subject based on the ideXlab platform.

  • the dynamics of resting fluctuations in the Brain metastability and its dynamical cortical core
    Scientific Reports, 2017
    Co-Authors: Gustavo Deco, Viktor K. Jirsa, Morten L Kringelbach, Petra Ritter
    Abstract:

    In the human Brain, spontaneous activity during resting state consists of rapid transitions between functional network states over time but the underlying mechanisms are not understood. We use connectome based computational Brain network modeling to reveal fundamental principles of how the human Brain generates large-scale activity observable by noninvasive neuroimaging. We used structural and functional neuroimaging data to construct whole- Brain Models. With this novel approach, we reveal that the human Brain during resting state operates at maximum metastability, i.e. in a state of maximum network switching. In addition, we investigate cortical heterogeneity across areas. Optimization of the spectral characteristics of each local Brain region revealed the dynamical cortical core of the human Brain, which is driving the activity of the rest of the whole Brain. Brain network modelling goes beyond correlational neuroimaging analysis and reveals non-trivial network mechanisms underlying non-invasive observations. Our novel findings significantly pertain to the important role of computational connectomics in understanding principles of Brain function.

  • Individual Brain structure and modelling predict seizure propagation.
    Brain : a journal of neurology, 2017
    Co-Authors: Timothée Proix, Fabrice Bartolomei, Maxime Guye, Viktor K. Jirsa
    Abstract:

    See Lytton (doi:10.1093/awx018) for a scientific commentary on this article.Neural network oscillations are a fundamental mechanism for cognition, perception and consciousness. Consequently, perturbations of network activity play an important role in the pathophysiology of Brain disorders. When structural information from non-invasive Brain imaging is merged with mathematical modelling, then generative Brain network Models constitute personalized in silico platforms for the exploration of causal mechanisms of Brain function and clinical hypothesis testing. We here demonstrate with the example of drug-resistant epilepsy that patient-specific virtual Brain Models derived from diffusion magnetic resonance imaging have sufficient predictive power to improve diagnosis and surgery outcome. In partial epilepsy, seizures originate in a local network, the so-called epileptogenic zone, before recruiting other close or distant Brain regions. We create personalized large-scale Brain networks for 15 patients and simulate the individual seizure propagation patterns. Model validation is performed against the presurgical stereotactic electroencephalography data and the standard-of-care clinical evaluation. We demonstrate that the individual Brain Models account for the patient seizure propagation patterns, explain the variability in postsurgical success, but do not reliably augment with the use of patient-specific connectivity. Our results show that connectome-based Brain network Models have the capacity to explain changes in the organization of Brain activity as observed in some Brain disorders, thus opening up avenues towards discovery of novel clinical interventions.

  • the virtual epileptic patient individualized whole Brain Models of epilepsy spread
    NeuroImage, 2017
    Co-Authors: Viktor K. Jirsa, Timothée Proix, Maxime Guye, Dionysios Perdikis, Michael Marmaduke Woodman, Huifang Wang, Jorge Gonzalezmartinez, Christophe Bernard, Christian G Benar, Patrick Chauvel
    Abstract:

    Individual variability has clear effects upon the outcome of therapies and treatment approaches. The customization of healthcare options to the individual patient should accordingly improve treatment results. We propose a novel approach to Brain interventions based on personalized Brain network Models derived from non-invasive structural data of individual patients. Along the example of a patient with bitemporal epilepsy, we show step by step how to develop a Virtual Epileptic Patient (VEP) Brain model and integrate patient-specific information such as Brain connectivity, epileptogenic zone and MRI lesions. Using high-performance computing, we systematically carry out parameter space explorations, fit and validate the Brain model against the patient's empirical stereotactic EEG (SEEG) data and demonstrate how to develop novel personalized strategies towards therapy and intervention.

  • the dynamics of resting fluctuations in the Brain metastability and its dynamical cortical core
    bioRxiv, 2016
    Co-Authors: Gustavo Deco, Viktor K. Jirsa, Morten L Kringelbach, Petra Ritter
    Abstract:

    In the human Brain, spontaneous activity during resting state consists of rapid transitions between functional network states over time but the underlying mechanisms are not understood. We use connectome based computational Brain network modeling to reveal fundamental principles of how the human Brain generates large-scale activity observable by noninvasive neuroimaging. By including individual structural and functional neuroimaging data into Brain network Models we construct personalized Brain Models. With this novel approach, we reveal that the human Brain during resting state operates at maximum metastability, i.e. in a state of maximum network switching. In addition, we investigate cortical heterogeneity across areas. Optimization of the spectral characteristics of each local Brain region revealed the dynamical cortical core of the human Brain, which is driving the activity of the rest of the whole Brain. Personalized Brain network modelling goes beyond correlational neuroimaging analysis and reveals non-trivial network mechanisms underlying non-invasive observations. Our novel findings significantly pertain to the important role of computational connectomics in understanding principles of Brain function.

  • how do parcellation size and short range connectivity affect dynamics in large scale Brain network Models
    NeuroImage, 2016
    Co-Authors: Timothée Proix, Andreas Spiegler, Michael Schirner, Simon Rothmeier, Petra Ritter, Viktor K. Jirsa
    Abstract:

    Abstract Recent efforts to model human Brain activity on the scale of the whole Brain rest on connectivity estimates of large-scale networks derived from diffusion magnetic resonance imaging (dMRI). This type of connectivity describes white matter fiber tracts. The number of short-range cortico-cortical white-matter connections is, however, underrepresented in such large-scale Brain Models. It is still unclear on the one hand, which scale of representation of white matter fibers is optimal to describe Brain activity on a large-scale such as recorded with magneto- or electroencephalography (M/EEG) or functional magnetic resonance imaging (fMRI), and on the other hand, to which extent short-range connections that are typically local should be taken into account. In this article we quantified the effect of connectivity upon large-scale Brain network dynamics by (i) systematically varying the number of Brain regions before computing the connectivity matrix, and by (ii) adding generic short-range connections. We used dMRI data from the Human Connectome Project. We developed a suite of preprocessing modules called SCRIPTS to prepare these imaging data for The Virtual Brain, a neuroinformatics platform for large-scale Brain modeling and simulations. We performed simulations under different connectivity conditions and quantified the spatiotemporal dynamics in terms of Shannon Entropy, dwell time and Principal Component Analysis. For the reconstructed connectivity, our results show that the major white matter fiber bundles play an important role in shaping slow dynamics in large-scale Brain networks (e.g. in fMRI). Faster dynamics such as gamma oscillations (around 40  Hz) are sensitive to the short-range connectivity if transmission delays are considered.

Gustavo Deco - One of the best experts on this subject based on the ideXlab platform.

  • human Brain connectivity clinical applications for clinical neurophysiology
    Clinical Neurophysiology, 2020
    Co-Authors: Mark Hallett, Gustavo Deco, Willem De Haan, R Dengler, Riccardo Di Iorio, Cecile Gallea, Christian Gerloff, Christian Grefkes, Rick C Helmich, Morten L Kringelbach
    Abstract:

    This manuscript is the second part of a two-part description of the current status of understanding of the network function of the Brain in health and disease. We start with the concept that Brain function can be understood only by understanding its networks, how and why information flows in the Brain. The first manuscript dealt with methods for network analysis, and the current manuscript focuses on the use of these methods to understand a wide variety of neurological and psychiatric disorders. Disorders considered are neurodegenerative disorders, such as Alzheimer disease and amyotrophic lateral sclerosis, stroke, movement disorders, including essential tremor, Parkinson disease, dystonia and apraxia, epilepsy, psychiatric disorders such as schizophrenia, and phantom limb pain. This state-of-the-art review makes clear the value of networks and Brain Models for understanding symptoms and signs of disease and can serve as a foundation for further work.

  • personalization of hybrid Brain Models from neuroimaging and electrophysiology data
    bioRxiv, 2018
    Co-Authors: Roser Sancheztodo, Gustavo Deco, Ricardo Salvador, Emiliano Santarnecchi, Fabrice Wendling, Giulio Ruffini
    Abstract:

    Personalization is rapidly becoming standard practice in medical diagnosis and treatment. This study is part of an ambitious program towards computational personalization of neuromodulatory interventions in neuropsychiatry. We propose to model the individual human Brain as a network of neural masses embedded in a realistic physical matrix capable of representing measurable electrical Brain activity. We call this a hybrid Brain model (HBM) to highlight that it encodes both biophysical and physiological characteristics of an individual Brain. Although the framework is general, we provide here a pipeline for the integration of anatomical, structural and functional connectivity data obtained from magnetic resonance imaging (MRI), diffuse tensor imaging (DTI connectome) and electroencephalography (EEG). We personalize model parameters through a comparison of simulated cortical functional connectivity with functional connectivity profiles derived from cortically-mapped, subject-specific EEG. We show that individual information can be represented in model space through the proper adjustment of two parameters (global coupling strength and conduction velocity), and that the underlying structural information has a strong impact on the functional outcome of the model. These findings provide a proof of concept and open the door for further advances, including the model-driven design of non-invasive Brain-stimulation protocols.

  • novel intrinsic ignition method measuring local global integration characterizes wakefulness and deep sleep
    eNeuro, 2017
    Co-Authors: Gustavo Deco, Morten L Kringelbach, Enzo Tagliazucchi, Helmut Laufs, Ana Sanjuan
    Abstract:

    Abstract A precise definition of a Brain state has proven elusive. Here, we introduce the novel local-global concept of intrinsic ignition characterizing the dynamical complexity of different Brain states. Naturally occurring intrinsic ignition events reflect the capability of a given Brain area to propagate neuronal activity to other regions, giving rise to different levels of integration. The ignitory capability of Brain regions is computed by the elicited level of integration for each intrinsic ignition event in each Brain region, averaged over all events. This intrinsic ignition method is shown to clearly distinguish human neuroimaging data of two fundamental Brain states (wakefulness and deep sleep). Importantly, whole-Brain computational modelling of this data shows that at the optimal working point is found where there is maximal variability of the intrinsic ignition across Brain regions. Thus, combining whole Brain Models with intrinsic ignition can provide novel insights into underlying mechanisms of Brain states.

  • the dynamics of resting fluctuations in the Brain metastability and its dynamical cortical core
    Scientific Reports, 2017
    Co-Authors: Gustavo Deco, Viktor K. Jirsa, Morten L Kringelbach, Petra Ritter
    Abstract:

    In the human Brain, spontaneous activity during resting state consists of rapid transitions between functional network states over time but the underlying mechanisms are not understood. We use connectome based computational Brain network modeling to reveal fundamental principles of how the human Brain generates large-scale activity observable by noninvasive neuroimaging. We used structural and functional neuroimaging data to construct whole- Brain Models. With this novel approach, we reveal that the human Brain during resting state operates at maximum metastability, i.e. in a state of maximum network switching. In addition, we investigate cortical heterogeneity across areas. Optimization of the spectral characteristics of each local Brain region revealed the dynamical cortical core of the human Brain, which is driving the activity of the rest of the whole Brain. Brain network modelling goes beyond correlational neuroimaging analysis and reveals non-trivial network mechanisms underlying non-invasive observations. Our novel findings significantly pertain to the important role of computational connectomics in understanding principles of Brain function.

  • the dynamics of resting fluctuations in the Brain metastability and its dynamical cortical core
    bioRxiv, 2016
    Co-Authors: Gustavo Deco, Viktor K. Jirsa, Morten L Kringelbach, Petra Ritter
    Abstract:

    In the human Brain, spontaneous activity during resting state consists of rapid transitions between functional network states over time but the underlying mechanisms are not understood. We use connectome based computational Brain network modeling to reveal fundamental principles of how the human Brain generates large-scale activity observable by noninvasive neuroimaging. By including individual structural and functional neuroimaging data into Brain network Models we construct personalized Brain Models. With this novel approach, we reveal that the human Brain during resting state operates at maximum metastability, i.e. in a state of maximum network switching. In addition, we investigate cortical heterogeneity across areas. Optimization of the spectral characteristics of each local Brain region revealed the dynamical cortical core of the human Brain, which is driving the activity of the rest of the whole Brain. Personalized Brain network modelling goes beyond correlational neuroimaging analysis and reveals non-trivial network mechanisms underlying non-invasive observations. Our novel findings significantly pertain to the important role of computational connectomics in understanding principles of Brain function.

Lucas C. Parra - One of the best experts on this subject based on the ideXlab platform.

  • realistic volumetric approach to simulate transcranial electric stimulation roast a fully automated open source pipeline
    Journal of Neural Engineering, 2019
    Co-Authors: Yu Huang, Abhishek Datta, Marom Bikson, Lucas C. Parra
    Abstract:

    Objective Research in the area of transcranial electrical stimulation (TES) often relies on computational Models of current flow in the Brain. Models are built based on magnetic resonance images (MRI) of the human head to capture detailed individual anatomy. To simulate current flow on an individual, the subject's MRI is segmented, virtual electrodes are placed on this anatomical model, the volume is tessellated into a mesh, and a finite element model (FEM) is solved numerically to estimate the current flow. Various software tools are available for each of these steps, as well as processing pipelines that connect these tools for automated or semi-automated processing. The goal of the present tool-realistic volumetric-approach to simulate transcranial electric simulation (ROAST)-is to provide an end-to-end pipeline that can automatically process individual heads with realistic volumetric anatomy leveraging open-source software and custom scripts to improve segmentation and execute electrode placement. Approach ROAST combines the segmentation algorithm of SPM12, a Matlab script for touch-up and automatic electrode placement, the finite element mesher iso2mesh and the solver getDP. We compared its performance with commercial FEM software, and SimNIBS, a well-established open-source modeling pipeline. Main results The electric fields estimated with ROAST differ little from the results obtained with commercial meshing and FEM solving software. We also do not find large differences between the various automated segmentation methods used by ROAST and SimNIBS. We do find bigger differences when volumetric segmentation are converted into surfaces in SimNIBS. However, evaluation on intracranial recordings from human subjects suggests that ROAST and SimNIBS are not significantly different in predicting field distribution, provided that users have detailed knowledge of SimNIBS. Significance We hope that the detailed comparisons presented here of various choices in this modeling pipeline can provide guidance for future tool development. We released ROAST as an open-source, easy-to-install and fully-automated pipeline for individualized TES modeling.

  • ROAST: An Open-Source, Fully-Automated, Realistic Volumetric-Approach-Based Simulator For TES
    2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018
    Co-Authors: Yu Huang, Abhishek Datta, Marom Bikson, Lucas C. Parra
    Abstract:

    Research in the area of transcranial electrical stimulation (TES) often relies on computational Models of current flow in the Brain. Models are built on magnetic resonance images (MRI) of the human head to capture detailed individual anatomy. To simulate current flow, MRIs have to be segmented, virtual electrodes have to be placed on the scalp, the volume is tessellated into a mesh, and the finite element model is solved numerically to estimate the current flow. Various software tools are available for each step, as well as processing pipelines that connect these tools for automated or semi-automated processing. The goal of the present tool - ROAST - is to provide an end-to-end pipeline that can automatically process individual heads with realistic volumetric anatomy leveraging open-source software (SPM8, iso2mesh and getDP) and custom scripts to improve segmentation and execute electrode placement. When we compare the results on a standard head with other major commercial software for finite element modeling (ScanIP, Abaqus), ROAST only leads to a small difference of 9% in the estimated electric field in the Brain. We obtain a larger difference of 47% when comparing results with SimNIBS, an automated pipeline that is based on surface segmentation of the head. We release ROAST as an open-source, fully-automated pipeline at https://www.parralab.org/roast/.

  • realistic volumetric approach to simulate transcranial electric stimulation roast a fully automated open source pipeline
    bioRxiv, 2017
    Co-Authors: Yu Huang, Abhishek Datta, Marom Bikson, Lucas C. Parra
    Abstract:

    Research in the area of transcranial electrical stimulation (TES) often relies on computational Models of current flow in the Brain. Models are built on magnetic resonance images (MRI) of the human head to capture detailed individual anatomy. To simulate current flow, MRIs have to be segmented, virtual electrodes have to be placed on these anatomical Models, the volume is tessellated into a mesh, and the finite element model is solved numerically to estimate the current flow. Various software tools are available for each step, as well as processing pipelines that connect these tools for automated or semi-automated processing. The goal of the present tool -- ROAST -- is to provide an end-to-end pipeline that can automatically process individual heads with realistic volumetric anatomy leveraging open-source software (SPM8, iso2mesh and getDP) and custom scripts to improve segmentation and execute electrode placement. When we compare the results on a standard head with other major commercial software tools for finite element modeling (ScanIP, Abaqus), ROAST only leads to a small difference of 9% in the estimated electric field in the Brain. We obtain a larger difference of 47% when comparing results with SimNIBS, an automated pipeline that is based on surface segmentation of the head. We release ROAST as a fully automated pipeline available online as a open-source tool for TES modeling.