A Novel Approach to Large Scale Brain Network Models: An Algorithmic Model for Place Cell Emergence With Robotic Sensor Input

2004 ◽  
Author(s):  
John L. Baker ◽  
James L. Olds ◽  
Joel L. Davis
Keyword(s):  
Large Scale ◽  
Network Models ◽  
Place Cell ◽  
Brain Network ◽  
Novel Approach ◽  
Robotic Sensor ◽  
Algorithmic Model

scholarly journals The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core

2016 ◽  
Author(s):  
Gustavo Deco ◽  
Morten L. Kringelbach ◽  
Viktor K. Jirsa ◽  
Petra Ritter
Keyword(s):  
Human Brain ◽  
Resting State ◽  
Large Scale ◽  
Functional Neuroimaging ◽  
Spectral Characteristics ◽  
Network Models ◽  
Brain Network ◽  
Network Modelling ◽  
Novel Approach ◽  
Underlying Mechanisms

AbstractIn 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.

scholarly journals Self-regulation of stress-related large-scale brain network balance using real-time fMRI Neurofeedback

2021 ◽  
Author(s):  
Florian Krause ◽  
Nikolaos Kogias ◽  
Martin Krentz ◽  
Michael Luehrs ◽  
Rainer Goebel ◽  
...  
Keyword(s):  
Real Time ◽  
Executive Control ◽  
Large Scale ◽  
Acute Stress ◽  
Self Regulation ◽  
Real Life ◽  
Brain Network ◽  
Large Scale Network ◽  
Novel Approach ◽  
Scale Network

It has recently been shown that acute stress affects the allocation of neural resources between large-scale brain networks, and the balance between the executive control network and the salience network in particular. Maladaptation of this dynamic resource reallocation process is thought to play a major role in stress-related psychopathology, suggesting that stress resilience may be determined by the retained ability to adaptively reallocate neural resources between these two networks. Actively training this ability could hence be a potentially promising way to increase resilience in individuals at risk for developing stress-related symptomatology. Using real-time functional Magnetic Resonance Imaging, the current study investigated whether individuals can learn to self-regulate stress-related large-scale network balance. Participants were engaged in a bidirectional and implicit real-time fMRI neurofeedback paradigm in which they were intermittently provided with a visual representation of the difference signal between the average activation of the salience and executive control networks, and tasked with attempting to self-regulate this signal. Our results show that, given feedback about their performance over three training sessions, participants were able to (1) learn strategies to differentially control the balance between SN and ECN activation on demand, as well as (2) successfully transfer this newly learned skill to a situation where they (a) did not receive any feedback anymore, and (b) were exposed to an acute stressor in form of the prospect of a mild electric stimulation. The current study hence constitutes an important first successful demonstration of neurofeedback training based on stress-related large-scale network balance - a novel approach that has the potential to train control over the central response to stressors in real-life and could build the foundation for future clinical interventions that aim at increasing resilience.

scholarly journals Automatic Traits Extraction and Fitting for Field High-throughput Phenotyping Systems

2020 ◽  
Author(s):  
Xingche Guo ◽  
Yumou Qiu ◽  
Dan Nettleton ◽  
Cheng-Ting Yeh ◽  
Zihao Zheng ◽  
...  
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Plant Traits ◽  
Network Models ◽  
Modern Technology ◽  
Neural Network Models ◽  
Novel Approach ◽  
Field Phenotyping ◽  
High Throughput Phenotyping ◽  
Rich Data

ABSTRACTHigh-throughput phenotyping is a modern technology to measure plant traits efficiently and in large scale by imaging systems over the whole growth season. Those images provide rich data for statistical analysis of plant phenotypes. We propose a pipeline to extract and analyze the plant traits for field phenotyping systems. The proposed pipeline include the following main steps: plant segmentation from field images, automatic calculation of plant traits from the segmented images, and functional curve fitting for the extracted traits. To deal with the challenging problem of plant segmentation for field images, we propose a novel approach on image pixel classification by transform domain neural network models, which utilizes plant pixels from greenhouse images to train a segmentation model for field images. Our results show the proposed procedure is able to accurately extract plant heights and is more stable than results from Amazon Turks, who manually measure plant heights from original images.

scholarly journals Intracranial brain stimulation modulates fMRI-based network switching

2021 ◽  
Author(s):  
Mangor Pedersen ◽  
Andrew Zalesky
Keyword(s):  
Clinical Efficacy ◽  
Brain Stimulation ◽  
Large Scale ◽  
Brain Networks ◽  
Network Models ◽  
Brain Regions ◽  
Brain Network ◽  
Intracranial Stimulation ◽  
List Type ◽  
Network Modelling

SummaryThe extent to which resting-state fMRI (rsfMRI) reflects direct neuronal changes remains unknown. Using 160 simultaneous rsfMRI and intracranial brain stimulation recordings acquired in 26 individuals with epilepsy (with varying electrode locations), we tested whether brain networks dynamically change during intracranial brain stimulation, aiming to establish whether switching between brain networks is reduced during intracranial brain stimulation. As the brain spontaneously switches between a repertoire of intrinsic functional network configurations and the rate of switching is typically increased in brain disorders, we hypothesised that intracranial stimulation would reduce the brain’s switching rate, thus potentially normalising aberrant brain network dynamics. To test this hypothesis, we quantified the rate that brain regions changed networks over time in response to brain stimulation, using network switching applied to multilayer modularity analysis of time-resolved rsfMRI connectivity. Network switching was significantly decreased during epochs with brain stimulation compared to epochs with no brain stimulation. The initial stimulation onset of brain stimulation was associated with the greatest decrease in network switching, followed by a more consistent reduction in network switching throughout the scans. These changes were most commonly observed in cortical networks spatially distant from the stimulation targets. Our results suggest that neuronal perturbation is likely to modulate large-scale brain networks, and multilayer network modelling may be used to inform the clinical efficacy of brain stimulation in neurological disease.HighlightsrsfMRI network switching is attenuated during intracranial brain stimulationStimulation-induced switching is observed distant from electrode targetsOur results are validated across a range of network parametersNetwork models may inform clinical efficacy of brain stimulation

Novel Large Scale Brain Network Models for EEG Epileptic Pattern Generations

2022 ◽  
pp. 116477
Author(s):  
Auhood Al-Hossenat ◽  
Bo Song ◽  
Peng Wen ◽  
Yan Li
Keyword(s):  
Large Scale ◽  
Network Models ◽  
Brain Network

scholarly journals The Virtual Brain: a neuroinformatics platform for simulating large-scale brain network models

2013 ◽  
Vol 14 (S1) ◽  
Author(s):  
Paula Sanz Leon ◽  
Marmaduke Woodman ◽  
Randy McIntosh ◽  
Viktor Jirsa
Keyword(s):  
Large Scale ◽  
Network Models ◽  
Brain Network

scholarly journals Distance-dependent consistency thresholds for generating group-representative structural brain networks

2018 ◽  
Author(s):  
Richard F. Betzel ◽  
Alessandra Griffa ◽  
Patric Hagmann ◽  
Bratislav Mišić
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Length Distribution ◽  
Brain Network ◽  
Maladaptive Behavior ◽  
Neurocognitive Deficits ◽  
Simple Modification ◽  
Novel Approach ◽  
Wide Range ◽  
Structural Brain

Large-scale structural brain networks encode white-matter connectivity patterns among distributed brain areas. These connection patterns are believed to support cognitive processes and, when compromised, can lead to neurocognitive deficits and maladaptive behavior. A powerful approach for studying the organizing principles of brain networks is to construct group-representative networks from multi-subject cohorts. Doing so amplifies signal to noise ratios and provides a clearer picture of brain network organization. Here, we show that current approaches for generating grouprepresentative networks over-estimate the proportion of short-range connections present in a network and, as a result, fail to match subject-level networks along a wide range of network statistics. We present an alternative approach that preserves the connection-length distribution of individual subjects. Due to this simple modification, the networks generated using this novel approach successfully recapitulate subject-level properties, outperforming all existing approaches by better preserving features that promote integrative brain function rather than segregative. The method developed here holds promise for future studies investigating basic organizational principles and features of largescale structural brain networks.

scholarly journals Investigating spatial heterogeneity within fracture networks using hierarchical clustering and graph distance metrics

Solid Earth ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 2159-2209
Author(s):  
Rahul Prabhakaran ◽  
Giovanni Bertotti ◽  
Janos Urai ◽  
David Smeulders
Keyword(s):  
Spatial Variation ◽  
Hierarchical Clustering ◽  
Large Scale ◽  
Similarity Measures ◽  
Network Models ◽  
Fracture Network ◽  
Fracture Networks ◽  
Network Modelling ◽  
Graph Similarity ◽  
Novel Approach

Abstract. Rock fractures organize as networks, exhibiting natural variation in their spatial arrangements. Therefore, identifying, quantifying, and comparing variations in spatial arrangements within network geometries are of interest when explicit fracture representations or discrete fracture network models are chosen to capture the influence of fractures on bulk rock behaviour. Treating fracture networks as spatial graphs, we introduce a novel approach to quantify spatial variation. The method combines graph similarity measures with hierarchical clustering and is applied to investigate the spatial variation within large-scale 2-D fracture networks digitized from the well-known Lilstock limestone pavements, Bristol Channel, UK. We consider three large, fractured regions, comprising nearly 300 000 fractures spread over 14 200 m2 from the Lilstock pavements. Using a moving-window sampling approach, we first subsample the large networks into subgraphs. Four graph similarity measures – fingerprint distance, D-measure, Network Laplacian spectral descriptor (NetLSD), and portrait divergence – that encapsulate topological relationships and geometry of fracture networks are then used to compute pair-wise subgraph distances serving as input for the statistical hierarchical clustering technique. In the form of hierarchical dendrograms and derived spatial variation maps, the results indicate spatial autocorrelation with localized spatial clusters that gradually vary over distances of tens of metres with visually discernable and quantifiable boundaries. Fractures within the identified clusters exhibit differences in fracture orientations and topology. The comparison of graph similarity-derived clusters with fracture persistence measures indicates an intra-network spatial variation that is not immediately obvious from the ubiquitous fracture intensity and density maps. The proposed method provides a quantitative way to identify spatial variations in fracture networks, guiding stochastic and geostatistical approaches to fracture network modelling.

scholarly journals A mathematical model of ephaptic interactions in neuronal fiber pathways: Could there be more than transmission along the tracts?

2020 ◽  
Vol 4 (3) ◽  
pp. 595-610
Author(s):  
Hiba Sheheitli ◽  
Viktor K. Jirsa
Keyword(s):  
Large Scale ◽  
Numerical Solutions ◽  
Phase Locking ◽  
Finite Size ◽  
Network Models ◽  
Brain Regions ◽  
Brain Network ◽  
Fiber Tracts ◽  
Proposed Model ◽  
Ephaptic Coupling

While numerous studies of ephaptic interactions have focused on either axons of peripheral nerves or on cortical structures, no attention has been given to the possibility of ephaptic interactions in white matter tracts. Inspired by the highly organized, tightly packed geometry of axons in fiber pathways, we aim to investigate the potential effects of ephaptic interactions along these structures that are resilient to experimental probing. We use axonal cable theory to derive a minimal model of a sheet of N ephaptically coupled axons. Numerical solutions of the proposed model are explored as ephaptic coupling is varied. We demonstrate that ephaptic interactions can lead to local phase locking between adjacent traveling impulses and that, as coupling is increased, traveling impulses trigger new impulses along adjacent axons, resulting in finite size traveling fronts. For strong enough coupling, impulses propagate laterally and backwards, resulting in complex spatiotemporal patterns. While common large-scale brain network models often model fiber pathways as simple relays of signals between different brain regions, our work calls for a closer reexamination of the validity of such a view. The results suggest that in the presence of significant ephaptic interactions, the brain fiber tracts can act as a dynamic active medium.

scholarly journals DNN-assisted statistical analysis of a model of local cortical circuits

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yaoyu Zhang ◽  
Lai-Sang Young
Keyword(s):  
Biological Networks ◽  
Large Scale ◽  
Network Models ◽  
Medium Size ◽  
Test Case ◽  
Cortical Circuits ◽  
Dimensional Parameter ◽  
Theoretical Understanding ◽  
Novel Approach ◽  
Data Driven Approach

AbstractIn neuroscience, computational modeling is an effective way to gain insight into cortical mechanisms, yet the construction and analysis of large-scale network models—not to mention the extraction of underlying principles—are themselves challenging tasks, due to the absence of suitable analytical tools and the prohibitive costs of systematic numerical exploration of high-dimensional parameter spaces. In this paper, we propose a data-driven approach assisted by deep neural networks (DNN). The idea is to first discover certain input-output relations, and then to leverage this information and the superior computation speeds of the well-trained DNN to guide parameter searches and to deduce theoretical understanding. To illustrate this novel approach, we used as a test case a medium-size network of integrate-and-fire neurons intended to model local cortical circuits. With the help of an accurate yet extremely efficient DNN surrogate, we revealed the statistics of model responses, providing a detailed picture of model behavior. The information obtained is both general and of a fundamental nature, with direct application to neuroscience. Our results suggest that the methodology proposed can be scaled up to larger and more complex biological networks when used in conjunction with other techniques of biological modeling.

Sign in / Sign up

Export Citation Format

Share Document