234 research outputs found
Relating Alpha Power and Phase to Population Firing and Hemodynamic Activity Using a Thalamo-cortical Neural Mass Model
Oscillations are ubiquitous phenomena in the animal and human brain. Among
them, the alpha rhythm in human EEG is one of the most prominent examples.
However, its precise mechanisms of generation are still poorly understood. It
was mainly this lack of knowledge that motivated a number of simultaneous
electroencephalography (EEG) – functional magnetic resonance imaging (fMRI)
studies. This approach revealed how oscillatory neuronal signatures such as
the alpha rhythm are paralleled by changes of the blood oxygenation level
dependent (BOLD) signal. Several such studies revealed a negative correlation
between the alpha rhythm and the hemodynamic BOLD signal in visual cortex and
a positive correlation in the thalamus. In this study we explore the potential
generative mechanisms that lead to those observations. We use a bursting
capable Stefanescu-Jirsa 3D (SJ3D) neural-mass model that reproduces a wide
repertoire of prominent features of local neuronal-population dynamics. We
construct a thalamo-cortical network of coupled SJ3D nodes considering
excitatory and inhibitory directed connections. The model suggests that an
inverse correlation between cortical multi-unit activity, i.e. the firing of
neuronal populations, and narrow band local field potential oscillations in
the alpha band underlies the empirically observed negative correlation between
alpha-rhythm power and fMRI signal in visual cortex. Furthermore the model
suggests that the interplay between tonic and bursting mode in thalamus and
cortex is critical for this relation. This demonstrates how biophysically
meaningful modelling can generate precise and testable hypotheses about the
underpinnings of large-scale neuroimaging signals
Modeling brain dynamics in brain tumor patients using the virtual brain
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
Relating Alpha Power and Phase to Population Firing and Hemodynamic Activity Using a Thalamo-cortical Neural Mass Model
International audienceOscillations are ubiquitous phenomena in the animal and human brain. Among them, the alpha rhythm in human EEG is one of the most prominent examples. However, its precise mechanisms of generation are still poorly understood. It was mainly this lack of knowledge that motivated a number of simultaneous electroencephalography (EEG) – functional magnetic resonance imaging (fMRI) studies. This approach revealed how oscillatory neuronal signatures such as the alpha rhythm are paralleled by changes of the blood oxygenation level dependent (BOLD) signal. Several such studies revealed a negative correlation between the alpha rhythm and the hemodynamic BOLD signal in visual cortex and a positive correlation in the thalamus. In this study we explore the potential generative mechanisms that lead to those observations. We use a bursting capable Stefanescu-Jirsa 3D (SJ3D) neural-mass model that reproduces a wide repertoire of prominent features of local neuronal-population dynamics. We construct a thalamo-cortical network of coupled SJ3D nodes considering excitatory and inhibitory directed connections. The model suggests that an inverse correlation between cortical multi-unit activity, i.e. the firing of neuronal populations , and narrow band local field potential oscillations in the alpha band underlies the empirically observed negative correlation between alpha-rhythm power and fMRI signal in visual cortex. Furthermore the model suggests that the interplay between tonic and bursting mode in thalamus and cortex is critical for this relation. This demonstrates how biophysically meaningful modelling can generate precise and testable hypotheses about the underpinnings of large-scale neuroimaging signals
Editorial: State-dependent brain computation
International audienceThe brain is a self-organizing system, which has evolved such that neuronal responses and related behavior are continuously adapted with respect to the external and internal context. This powerful capability is achieved through the modulation of neuronal interactions depending on the history of previously processed information. In particular, the brain updates its connections as it learns successful versus unsuccessful strategies. The resulting connectivity changes, together with stochastic processes (i.e., noise) influence ongoing neuronal dynamics. The role of such state-dependent fluctuations may be one of the fundamental computational properties of the brain, being pervasively present in human behavior and leaving a distinctive fingerprint in neuroscience data. This development is captured by the present Frontiers Research Topic, " State-Dependent Brain Computation
A deep learning approach to estimating initial conditions of Brain Network Models in reference to measured fMRI data
IntroductionBrain Network Models (BNMs) are mathematical models that simulate the activity of the entire brain. These models use neural mass models to represent local activity in different brain regions that interact with each other via a global structural network. Researchers have been interested in using these models to explain measured brain activity, particularly resting state functional magnetic resonance imaging (rs-fMRI). BNMs have shown to produce similar properties as measured data computed over longer periods of time such as average functional connectivity (FC), but it is unclear how well simulated trajectories compare to empirical trajectories on a timepoint-by-timepoint basis. During task fMRI, the relevant processes pertaining to task occur over the time frame of the hemodynamic response function, and thus it is important to understand how BNMs capture these dynamics over these short periods.MethodsTo test the nature of BNMs’ short-term trajectories, we used a deep learning technique called Neural ODE to simulate short trajectories from estimated initial conditions based on observed fMRI measurements. To compare to previous methods, we solved for the parameterization of a specific BNM, the Firing Rate Model, using these short-term trajectories as a metric.ResultsOur results show an agreement between parameterization of using previous long-term metrics with the novel short term metrics exists if also considering other factors such as the sensitivity in accuracy with relative to changes in structural connectivity, and the presence of noise.DiscussionTherefore, we conclude that there is evidence that by using Neural ODE, BNMs can be simulated in a meaningful way when comparing against measured data trajectories, although future studies are necessary to establish how BNM activity relate to behavioral variables or to faster neural processes during this time period
metastability and its dynamical cortical core
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
Collective Neurofeedback in an Immersive Art Environment
While human brains are specialized for complex and variable real world tasks,
most neuroscience studies reduce environmental complexity, which limits the
range of behaviours that can be explored. Motivated to overcome this
limitation, we conducted a large-scale experiment with electroencephalography
(EEG) based brain-computer interface (BCI) technology as part of an immersive
multi-media science-art installation. Data from 523 participants were
collected in a single night. The exploratory experiment was designed as a
collective computer game where players manipulated mental states of relaxation
and concentration with neurofeedback targeting modulation of relative spectral
power in alpha and beta frequency ranges. Besides validating robust time-of-
night effects, gender differences and distinct spectral power patterns for the
two mental states, our results also show differences in neurofeedback learning
outcome. The unusually large sample size allowed us to detect unprecedented
speed of learning changes in the power spectrum (~ 1 min). Moreover, we found
that participants' baseline brain activity predicted subsequent neurofeedback
beta training, indicating state-dependent learning. Besides revealing these
training effects, which are relevant for BCI applications, our results
validate a novel platform engaging art and science and fostering the
understanding of brains under natural conditions
An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data
AbstractLarge 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
A macaque connectome for large-scale network simulations in TheVirtualBrain
© 2019, The Author(s). Models of large-scale brain networks that are informed by the underlying anatomical connectivity contribute to our understanding of the mapping between the structure of the brain and its dynamical function. Connectome-based modelling is a promising approach to a more comprehensive understanding of brain function across spatial and temporal scales, but it must be constrained by multi-scale empirical data from animal models. Here we describe the construction of a macaque (Macaca mulatta and Macaca fascicularis) connectome for whole-cortex simulations in TheVirtualBrain, an open-source simulation platform. We take advantage of available axonal tract-tracing datasets and enhance the existing connectome data using diffusion-based tractography in macaques. We illustrate the utility of the connectome as an extension of TheVirtualBrain by simulating resting-state BOLD-fMRI data and fitting it to empirical resting-state data
Event-related potential correlates of spatiotemporal regularities in vision
Spatiotemporal regularities in stimulus structure have been shown to influence visual target detection and discrimination. Here we investigate whether the influence of spatiotemporal regularity is associated with the modulation of early components (P1/N1) in Event-Related Potentials (ERP). Stimuli consisted of five horizontal bars (predictors) appearing successively towards the fovea followed by a target bar at fixation, and participants performed a key-press on target detection. Results showed that compared to the condition where five predictors were presented in a temporally regular but spatially randomised order, target detection-times were faster and contralateral N1 peak latencies were shorter when the predictors and the target were presented with spatial and temporal regularity. Both measures were most prolonged when only the target was presented. In this latter condition, an additional latency prolongation was observed for the P1 peak compared to the conditions where the target was preceded by the predictors. The latency shifts associated with early ERP components provides additional support for involvement of early visual processing stages in the coding of spatiotemporal regularities in humans
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