scholarly journals Bridging multiple scales in the human brain using computational modelling

2016 ◽  
Author(s):  
Michael Schirner ◽  
Anthony Randal McIntosh ◽  
Viktor K. Jirsa ◽  
Gustavo Deco ◽  
Petra Ritter
Keyword(s):  
Resting State ◽  
Time Scale ◽  
Large Scale ◽  
Multiple Scales ◽  
Feedback Inhibition ◽  
Computational Modelling ◽  
Imaging Data ◽  
Spatial And Temporal Scales ◽  
Brain Connectome ◽  
Subject Specific

Brain dynamics span multiple spatial and temporal scales, from fast spiking neurons to slow fluctuations over distributed areas. No single experimental method links data across scales. Here, we bridge this gap using The Virtual Brain connectome-based modelling platform to integrate multimodal data with biophysical models and support neurophysiological inference. Simulated cell populations were linked with subject-specific white-matter connectivity estimates and driven by electroencephalography-derived electric source activity. The models were fit to subject-specific resting-state functional magnetic resonance imaging data, and overfitting was excluded using 5-fold cross-validation. Further evaluation of the models show how balancing excitation with feedback inhibition generates an inverse relationship between α-rhythms and population firing on a faster time scale and resting-state network oscillations on a slower time scale. Lastly, large-scale interactions in the model lead to the emergence of scale-free power-law spectra. Our novel findings underscore the integrative role for computational modelling to complement empirical studies.

scholarly journals Multiscale dynamics of COVID-19 and model-based recommendations for 105 countries

2020 ◽  
Author(s):  
Jithender J. Timothy ◽  
Vijaya Holla ◽  
Günther Meschke
Keyword(s):  
Large Scale ◽  
Multiple Scales ◽  
Computational Modelling ◽  
Real Data ◽  
Mitigation Measures ◽  
Agent Based ◽  
Multiscale Dynamics ◽  
Large Scale Analysis ◽  
Disease Spreading ◽  
Country Specific

We analyse the dynamics of COVID-19 using computational modelling at multiple scales. For large scale analysis, we propose a 2-scale lattice extension of the classical SIR-type compartmental model with spatial interactions called the Lattice-SIRQL model. Computational simulations show that global quantifiers are not completely representative of the actual dynamics of the disease especially when mitigation measures such as quarantine and lockdown are applied. Furthermore, using real data of confirmed COVID-19 cases, we calibrate the Lattice-SIRQL model for 105 countries. The calibrated model is used to make country specific recommendations for lockdown relaxation and lockdown continuation. Finally, using an agent-based model we analyse the influence of cluster level relaxation rate and lockdown duration on disease spreading.

scholarly journals Bout-associated intrinsic functional network changes in cluster headache: A longitudinal resting-state functional MRI study

Cephalalgia ◽  
2016 ◽  
Vol 37 (12) ◽  
pp. 1152-1163 ◽  
Author(s):  
Kun-Hsien Chou ◽  
Fu-Chi Yang ◽  
Jong-Ling Fuh ◽  
Chen-Yuan Kuo ◽  
Yi-Hsin Wang ◽  
...  
Keyword(s):  
Cluster Headache ◽  
Resting State ◽  
Large Scale ◽  
Brain Networks ◽  
Functional Network ◽  
Imaging Data ◽  
Dynamic Changes ◽  
Attention Networks ◽  
Clinical Observations ◽  
Mri Study

Background Previous imaging studies on the pathogenesis of cluster headache (CH) have implicated the hypothalamus and multiple brain networks. However, very little is known regarding dynamic bout-associated, large-scale resting state functional network changes related to CH. Methods Resting-state functional magnetic resonance imaging data were obtained from CH patients and matched controls. Data were analyzed using independent component analysis for exploratory assessment of the changes in intrinsic brain networks and their relationship between in-bout and out-of-bout periods, as well as correlations with clinical observations. Results Compared to healthy controls, CH patients had functional connectivity (FC) changes in the temporal, frontal, salience, default mode, somatosensory, dorsal attention, and visual networks, independent of bout period. Compared to out-of-bout scans, in-bout scans showed altered FC in the frontal and dorsal attention networks. Lower frontal network FC correlated with longer duration of CH. Conclusions The present findings suggest that episodic CH with dynamic bout period shifts may involve bout-associated FC changes in multiple discrete cortical areas within networks outside traditional pain processing areas. Dynamic changes in FC in frontal and dorsal attention networks between bout periods could be important for understanding episodic CH pathophysiology.

scholarly journals MaPPeRTrac: A Massively Parallel, Portable, and Reproducible Tractography Pipeline

2020 ◽  
Author(s):  
Joseph Moon ◽  
Peer-Timo Bremer ◽  
Pratik Mukherjee ◽  
Amy J Markowitz ◽  
Eva M Palacios ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
High Performance ◽  
Large Scale ◽  
Computational Cost ◽  
Edge Density ◽  
Imaging Data ◽  
Brain Connectome ◽  
Wide Range ◽  
Fair Principles ◽  
Brain Imaging Data

Large scale diffusion MRI tractography remains a significant challenge. Users must orchestrate a complex sequence of instructions that require many software packages with complex dependencies and high computational cost. We developed MaPPeRTrac, a diffusion MRI tractography pipeline that simplifies and vastly accelerates this process on a wide range of high performance computing environments. It fully automates the entire tractography workflow, from management of raw MRI machine data to edge-density visualization of the connectome. Data and dependencies, handled by the Brain Imaging Data Structure (BIDS) and Containerization using Docker and Singularity, are de-coupled from code to enable rapid prototyping and modification. Data artifacts are designed to be findable, accessible, interoperable, and reusable in accordance with FAIR principles. The pipeline takes full advantage of HPC resources using the Parsl parallel programming framework, resulting in creation of connectome datasets of unprecedented size. MaPPeRTrac is publicly available and tested on commercial and scientific hardware, so that it may accelerate brain connectome research for a broader user community.

scholarly journals A macaque connectome for large-scale network simulations in TheVirtualBrain

2018 ◽  
Author(s):  
Kelly Shen ◽  
Gleb Bezgin ◽  
Michael Schirner ◽  
Petra Ritter ◽  
Stefan Everling ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Function ◽  
Large Scale ◽  
Spatial And Temporal Scales ◽  
Multi Scale ◽  
Large Scale Network ◽  
Scale Network ◽  
Network Simulations ◽  
Dynamical Function ◽  
The Brain

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.

scholarly journals Characterisation of Haemodynamic Activity in Resting State Networks by Fractal Analysis

2020 ◽  
Vol 30 (12) ◽  
pp. 2050061 ◽  
Author(s):  
Camillo Porcaro ◽  
Stephen D. Mayhew ◽  
Marco Marino ◽  
Dante Mantini ◽  
Andrew P. Bagshaw
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Brain Activity ◽  
Blood Oxygen Level Dependent ◽  
Resting State Networks ◽  
Blood Oxygen Level ◽  
Spatial And Temporal Scales ◽  
Functional Roles ◽  
Large Scale Networks ◽  
Haemodynamic Activity

Intrinsic brain activity is organized into large-scale networks displaying specific structural–functional architecture, known as resting-state networks (RSNs). RSNs reflect complex neurophysiological processes and interactions, and have a central role in distinct sensory and cognitive functions, making it crucial to understand and quantify their anatomical and functional properties. Fractal dimension (FD) provides a parsimonious way of summarizing self-similarity over different spatial and temporal scales but despite its suitability for functional magnetic resonance imaging (fMRI) signal analysis its ability to characterize and investigate RSNs is poorly understood. We used FD in a large sample of healthy participants to differentiate fMRI RSNs and examine how the FD property of RSNs is linked with their functional roles. We identified two clusters of RSNs, one mainly consisting of sensory networks (C1, including auditory, sensorimotor and visual networks) and the other more related to higher cognitive (HCN) functions (C2, including dorsal default mode network and fronto-parietal networks). These clusters were defined in a completely data-driven manner using hierarchical clustering, suggesting that quantification of Blood Oxygen Level Dependent (BOLD) signal complexity with FD is able to characterize meaningful physiological and functional variability. Understanding the mechanisms underlying functional RSNs, and developing tools to study their signal properties, is essential for assessing specific brain alterations and FD could potentially be used for the early detection and treatment of neurological disorders.

scholarly journals An open MRI dataset for multiscale neuroscience

2021 ◽  
Author(s):  
Jessica Royer ◽  
Raul Rodriguez-Cruces ◽  
Shahin Tavakol ◽  
Sara Lariviere ◽  
Peer Herholz ◽  
...  
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Multiple Scales ◽  
Future Research ◽  
Brain Organization ◽  
Integrative Framework ◽  
Data Portal ◽  
Multimodal Mri ◽  
Brain Microstructure ◽  
And Function

Multimodal neuroimaging grants a powerful window into the structure and function of the human brain at multiple scales. Recent methodological and conceptual advances have enabled investigations of the interplay between large-scale spatial trends (also referred to as gradients) in brain microstructure and connectivity, offering an integrative framework to study multiscale brain organization. Here, we share a multimodal MRI dataset for Microstructure-Informed Connectomics (MICA-MICs) acquired in 50 healthy adults (23 women; 29.54±5.62 years) who underwent high-resolution T1-weighted MRI, myelin-sensitive quantitative T1 relaxometry, diffusion-weighted MRI, and resting-state functional MRI at 3 Tesla. In addition to raw anonymized MRI data, this release includes brain-wide connectomes derived from i) resting-state functional imaging, ii) diffusion tractography, iii) microstructure covariance analysis, and iv) geodesic cortical distance, gathered across multiple parcellation scales. Alongside, we share large-scale gradients estimated from each modality and parcellation scale. Our dataset will facilitate future research examining the coupling between brain microstructure, connectivity, and macroscale function. MICA-MICs is available on the Canadian Open Neuroscience Platform data portal (https://portal.conp.ca).

scholarly journals Brain network dynamics fingerprints are resilient to data heterogeneity

2020 ◽  
Author(s):  
Tommaso Menara ◽  
Giuseppe Lisi ◽  
Fabio Pasqualetti ◽  
Aurelio Cortese
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Brain Activity ◽  
Network Dynamics ◽  
Brain Network ◽  
Fmri Data ◽  
Dynamical Models ◽  
Subject Specific ◽  
Data Heterogeneity ◽  
Data Collections

AbstractLarge multi-site neuroimaging datasets have significantly advanced our quest to understand brain-behaviour relationships and to develop biomarkers of psychiatric and neurodegenerative disorders. Yet, such data collections come at a cost, as the inevitable differences across samples may lead to biased or erroneous conclusions. Previous work has investigated this critical issue in resting-state functional magnetic resonance imaging (rs-fMRI) data in terms of effects on static measures, such as functional connectivity and brain parcellations. Here, we depart from prior approaches and utilize dynamical models to examine how diverse scanning factors in multi-site fMRI recordings affect our ability to infer the brain’s spatiotemporal wandering between large-scale networks of activity. Building upon this premise, we first confirm the emergence of robust subject-specific dynamical patterns of brain activity. Next, we exploit these individual fingerprints to show that scanning sessions belonging to different sites and days tend to induce high variability, while other factors, such as the scanner manufacturer or the number of coils, affect the same metrics to a lesser extent. These results concurrently indicate that we can recover the unique trajectories of brain activity changes in each individual, but also that our ability to infer such patterns is affected by how, where and when we try to do so.Author summaryWe investigate the important issue of data heterogeneity in large multi-site data collections of brain activity recordings. At a time in which appraising the source of variability in large datasets is gaining increasing attention, this study provides a novel point of view based on data-driven dynamical models. By employing subject-specific signatures of brain network dynamics, we find that certain scanning factors significantly affect the quality of resting-state fMRI data. More specifically, we first validate the existence of subject-specific brain dynamics fingerprints. As a proof of concept, we show that dynamical states can be estimated reliably, even across different datasets. Finally, we assess which scanning factors, and to what extent, influence the variability of such fingerprints.

Large-scale Motion of Solar Filaments

2000 ◽  
Vol 179 ◽  
pp. 205-208
Author(s):  
Pavel Ambrož ◽  
Alfred Schroll
Keyword(s):  
Magnetic Flux ◽  
Proper Motion ◽  
Time Scale ◽  
Large Scale ◽  
Accuracy Of Measurements ◽  
Solar Filaments ◽  
General Movement ◽  
Scale Motion ◽  
Velocity Scatter

AbstractPrecise measurements of heliographic position of solar filaments were used for determination of the proper motion of solar filaments on the time-scale of days. The filaments have a tendency to make a shaking or waving of the external structure and to make a general movement of whole filament body, coinciding with the transport of the magnetic flux in the photosphere. The velocity scatter of individual measured points is about one order higher than the accuracy of measurements.

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

scholarly journals Fractional ridge regression: a fast, interpretable reparameterization of ridge regression

GigaScience ◽  
2020 ◽  
Vol 9 (12) ◽  
Author(s):  
Ariel Rokem ◽  
Kendrick Kay
Keyword(s):  
Ridge Regression ◽  
Large Scale ◽  
Imaging Data ◽  
Regularization Technique ◽  
Large Scale Data ◽  
Novel Approach ◽  
Manual Exploration ◽  
L2 Norm ◽  
Software Implementations ◽  
Brain Imaging Data

Abstract Background Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable. Results The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and matlab (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets. Conclusion Fractional ridge regression has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations; and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets.

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