A macaque connectome for large-scale network simulations in TheVirtualBrain

AbstractModels 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 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.

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Opto-E-Dura: a Soft, Stretchable ECoG Array for Multimodal, Multi-scale Neuroscience

10.1101/2020.06.10.139493 ◽

2020 ◽

Author(s):

Aline F. Renz ◽

Jihyun Lee ◽

Klas Tybrandt ◽

Maciej Brzezinski ◽

Dayra A. Lorenzo ◽

...

Keyword(s):

Brain Function ◽

Large Scale ◽

Spatial Scales ◽

Biological Tissues ◽

Great Promise ◽

Wide Field ◽

Electrode Arrays ◽

Spatial And Temporal Scales ◽

Multi Scale ◽

The Brain

AbstractSoft, stretchable materials hold great promise for the fabrication of biomedical devices due to their capacity to integrate gracefully with and conform to biological tissues. Conformal devices are of particular interest in the development of brain interfaces where rigid structures can lead to tissue damage and loss of signal quality over the lifetime of the implant. Interfaces to study brain function and dysfunction increasingly require multimodal access in order to facilitate measurement of diverse physiological signals that span the disparate temporal and spatial scales of brain dynamics. Here we present the Opto-e-Dura, a soft, stretchable, 16-channel electrocorticography array that is optically transparent. We demonstrate its compatibility with diverse optical and electrical readouts enabling multimodal studies that bridge spatial and temporal scales. The device is chronically stable for weeks, compatible with wide-field and 2-photon calcium imaging and permits the repeated insertion of penetrating multi-electrode arrays. As the variety of sensors and effectors realizable on soft, stretchable substrates expands, similar devices that provide large-scale, multimodal access to the brain will continue to improve fundamental understanding of brain function.

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PeerFlow: Secure Load Balancing in Tor

Proceedings on Privacy Enhancing Technologies ◽

10.1515/popets-2017-0017 ◽

2017 ◽

Vol 2017 (2) ◽

pp. 74-94 ◽

Cited By ~ 7

Author(s):

Aaron Johnson ◽

Rob Jansen ◽

Nicholas Hopper ◽

Aaron Segal ◽

Paul Syverson

Keyword(s):

Load Balancing ◽

Load Balance ◽

Large Scale ◽

Scanning System ◽

Large Scale Network ◽

Improve Accuracy ◽

Scale Network ◽

Network Simulations ◽

Improved Design ◽

Speed And Accuracy

Abstract We present PeerFlow, a system to securely load balance client traffic in Tor. Security in Tor requires that no adversary handle too much traffic. However, Tor relays are run by volunteers who cannot be trusted to report the relay bandwidths, which Tor clients use for load balancing. We show that existing methods to determine the bandwidths of Tor relays allow an adversary with little bandwidth to attack large amounts of client traffic. These methods include Tor’s current bandwidth-scanning system, TorFlow, and the peer-measurement system EigenSpeed. We present an improved design called PeerFlow that uses a peer-measurement process both to limit an adversary’s ability to increase his measured bandwidth and to improve accuracy. We show our system to be secure, fast, and efficient. We implement PeerFlow in Tor and demonstrate its speed and accuracy in large-scale network simulations.

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Graph slepians to probe into large-scale network organization of resting-state functional connectivity

2017 51st Asilomar Conference on Signals, Systems, and Computers ◽

10.1109/acssc.2017.8335615 ◽

2017 ◽

Author(s):

Maria Giulia Preti ◽

Dimitri Van De Ville

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Large Scale ◽

Network Organization ◽

Resting State Functional Connectivity ◽

Large Scale Network ◽

Scale Network

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Plasticity of Sensorimotor Networks

The Neuroscientist ◽

10.1177/1073858416638641 ◽

2016 ◽

Vol 23 (2) ◽

pp. 185-196 ◽

Cited By ~ 6

Author(s):

Ethan R. Buch ◽

Sook-Lei Liew ◽

Leonardo G. Cohen

Keyword(s):

Motor System ◽

Degrees Of Freedom ◽

Large Scale ◽

System Organization ◽

Basic Feature ◽

Intrinsic Variability ◽

Spatial And Temporal Scales ◽

Large Scale Network ◽

Scale Network

Redundancy is an important feature of the motor system, as abundant degrees of freedom are prominent at every level of organization across the central and peripheral nervous systems, and musculoskeletal system. This basic feature results in a system that is both flexible and robust, and which can be sustainably adapted through plasticity mechanisms in response to intrinsic organismal changes and dynamic environments. While much early work of motor system organization has focused on synaptic-based plasticity processes that are driven via experience, recent investigations of neuron–glia interactions, epigenetic mechanisms and large-scale network dynamics have revealed a plethora of plasticity mechanisms that support motor system organization across multiple, overlapping spatial and temporal scales. Furthermore, an important role of these mechanisms is the regulation of intrinsic variability. Here, we review several of these mechanisms and discuss their potential role in neurorehabilitation.

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On the Reduction of the Complexity of Realistic Large Scale Network Simulations

AEU - International Journal of Electronics and Communications ◽

10.1078/1434-8411-54100258 ◽

2004 ◽

Vol 58 (6) ◽

pp. 371-381

Author(s):

Kai Below ◽

Ulrich Killat

Keyword(s):

Large Scale ◽

Large Scale Network ◽

Scale Network ◽

Network Simulations

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Robust Modulation of Integrate-and-Fire Models

Neural Computation ◽

10.1162/neco_a_01065 ◽

2018 ◽

Vol 30 (4) ◽

pp. 987-1011 ◽

Cited By ~ 5

Author(s):

Tomas Van Pottelbergh ◽

Guillaume Drion ◽

Rodolphe Sepulchre

Keyword(s):

Large Scale ◽

Neuronal Excitability ◽

Population Studies ◽

Fire Model ◽

Neuronal Populations ◽

Integrate And Fire ◽

Large Scale Network ◽

Channel Expression ◽

Scale Network ◽

Network Simulations

By controlling the state of neuronal populations, neuromodulators ultimately affect behavior. A key neuromodulation mechanism is the alteration of neuronal excitability via the modulation of ion channel expression. This type of neuromodulation is normally studied with conductance-based models, but those models are computationally challenging for large-scale network simulations needed in population studies. This article studies the modulation properties of the multiquadratic integrate-and-fire model, a generalization of the classical quadratic integrate-and-fire model. The model is shown to combine the computational economy of integrate-and-fire modeling and the physiological interpretability of conductance-based modeling. It is therefore a good candidate for affordable computational studies of neuromodulation in large networks.

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Synthesizing Brain-network-inspired Interconnections for Large-scale Network-on-chips

ACM Transactions on Design Automation of Electronic Systems ◽

10.1145/3480961 ◽

2022 ◽

Vol 27 (1) ◽

pp. 1-30

Author(s):

Mengke Ge ◽

Xiaobing Ni ◽

Xu Qi ◽

Song Chen ◽

Jinglei Huang ◽

...

Keyword(s):

Large Scale ◽

Small World ◽

Brain Network ◽

Graph Processing ◽

Hop Count ◽

Scale Free ◽

Large Scale Network ◽

Average Latency ◽

Scale Network ◽

The Brain

Brain network is a large-scale complex network with scale-free, small-world, and modularity properties, which largely supports this high-efficiency massive system. In this article, we propose to synthesize brain-network-inspired interconnections for large-scale network-on-chips. First, we propose a method to generate brain-network-inspired topologies with limited scale-free and power-law small-world properties, which have a low total link length and extremely low average hop count approximately proportional to the logarithm of the network size. In addition, given the large-scale applications, considering the modularity of the brain-network-inspired topologies, we present an application mapping method, including task mapping and deterministic deadlock-free routing, to minimize the power consumption and hop count. Finally, a cycle-accurate simulator BookSim2 is used to validate the architecture performance with different synthetic traffic patterns and large-scale test cases, including real-world communication networks for the graph processing application. Experiments show that, compared with other topologies and methods, the brain-network-inspired network-on-chips (NoCs) generated by the proposed method present significantly lower average hop count and lower average latency. Especially in graph processing applications with a power-law and tightly coupled inter-core communication, the brain-network-inspired NoC has up to 70% lower average hop count and 75% lower average latency than mesh-based NoCs.

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Turbulent-like dynamics in the human brain

10.1101/865923 ◽

2019 ◽

Author(s):

Gustavo Deco ◽

Morten L. Kringelbach

Keyword(s):

Human Brain ◽

Brain Function ◽

Information Transfer ◽

Large Scale ◽

Brain Dynamics ◽

Whole Brain ◽

Large Scale Network ◽

Maximal Sensitivity ◽

Scale Network ◽

Best Fit

SummaryTurbulence facilitates fast energy/information transfer across scales in physical systems. These qualities are important for brain function, but it is currently unknown if the dynamic intrinsic backbone of brain also exhibits turbulence. Using large-scale neuroimaging empirical data from 1003 healthy participants, we demonstrate Kuramoto’s amplitude turbulence in human brain dynamics. Furthermore, we build a whole-brain model with coupled oscillators to demonstrate that the best fit to the data corresponds to a region of maximally developed amplitude turbulence, which also corresponds to maximal sensitivity to the processing of external stimulations (information capability). The model shows the economy of anatomy by following the Exponential Distance Rule of anatomical connections as a cost-of-wiring principle. This establishes a firm link between turbulence and optimal brain function. Overall, our results reveal a way of analysing and modelling whole-brain dynamics that suggests turbulence as the dynamic intrinsic backbone facilitating large scale network communication.

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