149 research outputs found
Fast-slow bursters in the unfolding of a high codimension singularity and the ultra-slow transitions of classes
Bursting is a phenomenon found in a variety of physical and biological
systems. For example, in neuroscience, bursting is believed to play a key role
in the way information is transferred in the nervous system. In this work, we
propose a model that, appropriately tuned, can display several types of
bursting behaviors. The model contains two subsystems acting at different
timescales. For the fast subsystem we use the planar unfolding of a high
codimension singularity. In its bifurcation diagram, we locate paths that
underly the right sequence of bifurcations necessary for bursting. The slow
subsystem steers the fast one back and forth along these paths leading to
bursting behavior. The model is able to produce almost all the classes of
bursting predicted for systems with a planar fast subsystems. Transitions
between classes can be obtained through an ultra-slow modulation of the model's
parameters. A detailed exploration of the parameter space allows predicting
possible transitions. This provides a single framework to understand the
coexistence of diverse bursting patterns in physical and biological systems or
in models.Comment: 22 pages, 15 figure
Phase-lags in large scale brain synchronization : Methodological considerations and in-silico analysis
Architecture of phase relationships among neural oscillations is central for their functional significance but has remained theoretically poorly understood. We use phenomenological model of delay-coupled oscillators with increasing degree of topological complexity to identify underlying principles by which the spatio-temporal structure of the brain governs the phase lags between oscillatory activity at distant regions. Phase relations and their regions of stability are derived and numerically confirmed for two oscillators and for networks with randomly distributed or clustered bimodal delays, as a first approximation for the brain structural connectivity. Besides in-phase, clustered delays can induce anti-phase synchronization for certain frequencies, while the sign of the lags is determined by the natural frequencies and by the inhomogeneous network interactions. For in-phase synchronization faster oscillators always phase lead, while stronger connected nodes lag behind the weaker during frequency depression, which consistently arises for in-silico results. If nodes are in antiphase regime, then a distance Pi is added to the in-phase trends. The statistics of the phases is calculated from the phase locking values (PLV), as in many empirical studies, and we scrutinize the method's impact. The choice of surrogates do not affects the mean of the observed phase lags, but higher significance levels that are generated by some surrogates, cause decreased variance and might fail to detect the generally weaker coherence of the interhemispheric links. These links are also affected by the non-stationary and intermittent synchronization, which causes multimodal phase lags that can be misleading if averaged. Taken together, the results describe quantitatively the impact of the spatio-temporal connectivity of the brain to the synchronization patterns between brain regions, and to uncover mechanisms through which the spatio-temporal structure of the brain renders phases to be distributed around 0 and Pi.Peer reviewe
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
Predicting the spatiotemporal diversity of seizure propagation and termination in human focal epilepsy
Recent studies have shown that seizures can spread and terminate across brain
areas via a rich diversity of spatiotemporal patterns. In particular, while the
location of the seizure onset area is usually in-variant across seizures in a
same patient, the source of traveling (2-3 Hz) spike-and-wave discharges (SWDs)
during seizures can either move with the slower propagating ictal wavefront or
remain stationary at the seizure onset area. In addition, although most focal
seizures terminate quasi-synchronously across brain areas, some evolve into
distinct ictal clusters and terminate asynchronously. To provide a unifying
perspective on the observed diversity of spatiotemporal dynamics for seizure
spread and termination, we introduce here the Epileptor neural field model. Two
mechanisms play an essential role. First, while the slow ictal wavefront
propagates as a front in excitable neural media, the faster SWDs propagation
results from coupled-oscillator dynamics. Second, multiple time scales interact
during seizure spread, allowing for low-voltage fast-activity (>10 Hz) to
hamper seizure spread and for SWD propagation to affect the way a seizure
terminates. These dynamics, together with variations in short and long-range
connectivity strength, play a central role on seizure spread, maintenance and
termination. We demonstrate how Epileptor field models incorporating the above
mechanisms predict the previously reported diversity in seizure spread
patterns. Furthermore, we confirm the predictions for synchronous or
asynchronous (clustered) seizure termination in human seizures recorded via
stereotactic EEG. Our new insights into seizure spatiotemporal dynamics may
also contribute to the development of new closed-loop neuromodulation therapies
for focal epilepsy.Comment: 10 pages + 9 pages Supporting Information (SI), 7 figures, 1 SI
table, 7 SI figure
The Dynamic Brain: From Spiking Neurons to Neural Masses and Cortical Fields
The cortex is a complex system, characterized by its dynamics and architecture,
which underlie many functions such as action, perception, learning, language,
and cognition. Its structural architecture has been studied for more than a
hundred years; however, its dynamics have been addressed much less thoroughly.
In this paper, we review and integrate, in a unifying framework, a variety of
computational approaches that have been used to characterize the dynamics of the
cortex, as evidenced at different levels of measurement. Computational models at
different space–time scales help us understand the fundamental
mechanisms that underpin neural processes and relate these processes to
neuroscience data. Modeling at the single neuron level is necessary because this
is the level at which information is exchanged between the computing elements of
the brain; the neurons. Mesoscopic models tell us how neural elements interact
to yield emergent behavior at the level of microcolumns and cortical columns.
Macroscopic models can inform us about whole brain dynamics and interactions
between large-scale neural systems such as cortical regions, the thalamus, and
brain stem. Each level of description relates uniquely to neuroscience data,
from single-unit recordings, through local field potentials to functional
magnetic resonance imaging (fMRI), electroencephalogram (EEG), and
magnetoencephalogram (MEG). Models of the cortex can establish which types of
large-scale neuronal networks can perform computations and characterize their
emergent properties. Mean-field and related formulations of dynamics also play
an essential and complementary role as forward models that can be inverted given
empirical data. This makes dynamic models critical in integrating theory and
experiments. We argue that elaborating principled and informed models is a
prerequisite for grounding empirical neuroscience in a cogent theoretical
framework, commensurate with the achievements in the physical sciences
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
Selective activation of resting state networks following focal stimulation in a connectome- based network model of the human brain
Imaging studies suggest that the functional connectivity patterns of resting
state networks (RS-networks) reflect underlying structural connectivity (SC).
If the connectome constrains how brain areas are functionally connected, the
stimulation of specific brain areas should produce a characteristic wave of
activity ultimately resolving into RS-networks. To systematically test this
hypothesis, we use a connectome-based network model of the human brain with
detailed realistic SC. We systematically activate all possible thalamic and
cortical areas with focal stimulation patterns and confirm that the stimulation
of specific areas evokes network patterns that closely resemble RS-networks.
For some sites, one or no RS-network is engaged, whereas for other sites more
than one RS-network may evolve. Our results confirm that the brain is operating
at the edge of criticality, wherein stimulation produces a cascade of
functional network recruitments, collapsing onto a smaller subspace that is
constrained in part by the anatomical local and long-range SCs. We suggest that
information flow, and subsequent cognitive processing, follows specific routes
imposed by connectome features, and that these routes explain the emergence of
RS-networks. Since brain stimulation can be used to diagnose/treat neurological
disorders, we provide a look-up table showing which areas need to be stimulated
to activate specific RS-networks.Comment: 25 pages (in total), 7 figures, 2 table
audiology 2012
<p>All other parameters as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000092#pcbi-1000092-g010" target="_blank">Figure 10</a>. (A) Individual mean synaptic currents of all nonsensory nodes. (B) Total synaptic currents averaged across the nonsensory sheet. The injection of the externally evoked sensory currents into the prior activity actually has a slightly desynchronizing effect.</p
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