scholarly journals neurolib: A Simulation Framework for Whole-Brain Neural Mass Modeling

2021 ◽  
Author(s):  
Caglar Cakan ◽  
Nikola Jajcay ◽  
Klaus Obermayer
Keyword(s):  
Empirical Data ◽  
Brain Region ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Whole Brain ◽  
Neural Mass ◽  
Set Up ◽  
Function Relationship ◽  
Relationship Of

Abstractneurolib is a computational framework for whole-brain modeling written in Python. It provides a set of neural mass models that represent the average activity of a brain region on a mesoscopic scale. In a whole-brain network model, brain regions are connected with each other based on biologically informed structural connectivity, i.e., the connectome of the brain. neurolib can load structural and functional datasets, set up a whole-brain model, manage its parameters, simulate it, and organize its outputs for later analysis. The activity of each brain region can be converted into a simulated BOLD signal in order to calibrate the model against empirical data from functional magnetic resonance imaging (fMRI). Extensive model analysis is made possible using a parameter exploration module, which allows one to characterize a model’s behavior as a function of changing parameters. An optimization module is provided for fitting models to multimodal empirical data using evolutionary algorithms. neurolib is designed to be extendable and allows for easy implementation of custom neural mass models, offering a versatile platform for computational neuroscientists for prototyping models, managing large numerical experiments, studying the structure–function relationship of brain networks, and for performing in-silico optimization of whole-brain models.

scholarly journals neurolib: a simulation framework for whole-brain neural mass modeling

2021 ◽  
Author(s):  
Caglar Cakan ◽  
Nikola Jajcay ◽  
Klaus Obermayer
Keyword(s):  
Empirical Data ◽  
Brain Region ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Whole Brain ◽  
Neural Mass ◽  
Set Up ◽  
Function Relationship ◽  
Relationship Of

ABSTRACTneurolib is a computational framework for whole-brain modeling written in Python. It provides a set of neural mass models that represent the average activity of a brain region on a mesoscopic scale. In a whole-brain network model, brain regions are connected with each other based on biologically informed structural connectivity, i.e. the connectome of the brain. neurolib can load structural and functional datasets, set up a whole-brain model, manage its parameters, simulate it, and organize its outputs for later analysis. The activity of each brain region can be converted into a simulated BOLD signal in order to calibrate the model against empirical data from functional magnetic resonance imaging (fMRI). Extensive model analysis is possible using a parameter exploration module, which allows one to characterize the model’s behavior given a set of changing parameters. An optimization module can fit a model to multimodal empirical data using an evolutionary algorithm. neurolib is designed to be extendable such that custom neural mass models can be implemented easily, offering a versatile platform for computational neuroscientists for prototyping models, managing large numerical experiments, studying the structure-function relationship of brain networks, and for performing in-silico optimization of whole-brain models.

scholarly journals Dynamic properties of simulated brain network models and empirical resting-state data

2019 ◽  
Vol 3 (2) ◽  
pp. 405-426 ◽  
Author(s):  
Amrit Kashyap ◽  
Shella Keilholz
Keyword(s):  
Resting State ◽  
Dynamic Properties ◽  
Brain Activity ◽  
Structural Connectivity ◽  
Simulated Data ◽  
Network Models ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Brain Network ◽  
Whole Brain

Brain network models (BNMs) have become a promising theoretical framework for simulating signals that are representative of whole-brain activity such as resting-state fMRI. However, it has been difficult to compare the complex brain activity obtained from simulations to empirical data. Previous studies have used simple metrics to characterize coordination between regions such as functional connectivity. We extend this by applying various different dynamic analysis tools that are currently used to understand empirical resting-state fMRI (rs-fMRI) to the simulated data. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the brain network model. We conclude that the dynamic properties that explicitly examine patterns of signal as a function of time rather than spatial coordination between different brain regions in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole-brain activity.

scholarly journals Synchronization dependent on spatial structures of a mesoscopic whole-brain network

2018 ◽  
Author(s):  
Hannah Choi ◽  
Stefan Mihalas
Keyword(s):  
Power Law ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Mammalian Brain ◽  
Phase Oscillators ◽  
Whole Brain ◽  
Mapping Algorithm ◽  
Complex Structural

We study how the spatial structure of connectivity shapes synchronization in a system of coupled phase oscillators on a mammalian whole-brain network at the mesoscopic level. Complex structural connectivity of the mammalian brain is believed to underlie the versatility of neural computations. The Allen Mouse Brain Connectivity Atlas constructed from viral tracing experiments together with a new mapping algorithm reveals that the connectivity has a significant spatial dependence: the connection strength decreases with distance between the regions, following a power law. However, there are a number of residuals above the power-law fit, predominantly for long-range connections. We show how these strong connections between distal brain regions promote rapid transitions between highly localized synchronization and more global synchronization as the amount of dispersion in the frequency distribution changes. This may explain the brain’s ability to switch rapidly between global and modularized computations.

scholarly journals Dynamic Properties of Simulated Brain Network Models and Empirical Resting State Data

2018 ◽  
Author(s):  
Amrit Kashyap ◽  
Shella Keilholz
Keyword(s):  
Resting State ◽  
Dynamic Properties ◽  
Brain Activity ◽  
Temporal Structure ◽  
Structural Connectivity ◽  
Network Models ◽  
Resting State Fmri ◽  
Brain Network ◽  
Dynamical Analysis ◽  
Whole Brain

AbstractBrain Network Models have become a promising theoretical framework in simulating signals that are representative of whole brain activity such as resting state fMRI. However, it has been difficult to compare the complex brain activity between simulated and empirical data. Previous studies have used simple metrics that surmise coordination between regions such as functional connectivity, and we extend on this by using various different dynamical analysis tools that are currently used to understand resting state fMRI. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the Brain Network Model. We conclude that the dynamic properties that gauge more temporal structure rather than spatial coordination in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole brain activity.

scholarly journals Integrative Structural Brain Network Analysis In Diffusion Tensor Imaging

2017 ◽  
Author(s):  
Moo K. Chung ◽  
Jamie L. Hanson ◽  
Nagesh Adluru ◽  
Andrew L. Alexander ◽  
Richard J. Davidson ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Node Degree ◽  
Fiber Tracts ◽  
White Matter Fiber Tracts ◽  
Structural Brain Network ◽  
Structural Networks

AbstractIn diffusion tensor imaging, structural connectivity between brain regions is often measured by the number of white matter fiber tracts connecting them. Other features such as the length of tracts or fractional anisotropy (FA) are also used in measuring the strength of connectivity. In this study, we investigated the effects of incorporating the number of tracts, the tract length and FA-values into the connectivity model. Using various node-degree based graph theory features, the three connectivity models are compared. The methods are applied in characterizing structural networks between normal controls and maltreated children, who experienced maltreatment while living in post-institutional settings before being adopted by families in the US.

scholarly journals The specific absorption rate in different brain regions of rats exposed to electromagnetic plane waves

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Hao-Yu Wang ◽  
Chun-Fang Li ◽  
Chao Yu ◽  
Ji Dong ◽  
Yong Zou ◽  
...  
Keyword(s):  
Brain Region ◽  
Absorption Rate ◽  
Specific Absorption Rate ◽  
Brain Regions ◽  
Dose Effect ◽  
Whole Body ◽  
Polarization Direction ◽  
Whole Brain ◽  
Specific Absorption ◽  
Effect Relationship

Abstract Accurate dosimetry of a specific brain region in rats exposed to an electromagnetic field (EMF) is essential for studies focusing on dose-effect relationship of the region. However, only dosimetry of whole brain or whole body were evaluated in most of previous studies. In this study, a numerical voxel rat model with 10 segmented brain regions was constructed. Then, the effects of frequency, incidence direction, and E-polarization direction of plane wave EMF on brain region averaged specific absorption rate (BRSAR) of rats were investigated. At last, the reliability of using whole-body averaged SAR (WBDSAR) and whole-brain averaged SAR (WBRSAR) as estimations of BRSAR were also evaluated. Our results demonstrated that the BRSAR depended on the frequency, incidence direction, and E-polarization direction of the EMF. Besides, the largest deviation could be up to 13.1 dB between BRSAR and WBDSAR and 9.59 dB between BRSAR and WBRSAR. The results suggested that to establish an accurate dose-effect relationship, the variance of the BRSAR induced by alteration of frequency, incidence direction, and E-polarization direction of EMF should be avoided or carefully evaluated. Furthermore, the use of WBDSAR and WBRSAR as estimations of BRSAR should be restricted to certain conditions such that the deviations are not too large.

scholarly journals The dynamic connectome of speech control

2021 ◽  
Vol 376 (1836) ◽  
Author(s):  
Davide Valeriani ◽  
Kristina Simonyan
Keyword(s):  
Neural Network ◽  
Effective Connectivity ◽  
Vocal Learning ◽  
Brain Regions ◽  
Brain Network ◽  
Motor Output ◽  
Network Activity ◽  
Imaging Data ◽  
Whole Brain ◽  
Cost Efficient

Speech production relies on the orchestrated control of multiple brain regions. The specific, directional influences within these networks remain poorly understood. We used regression dynamic causal modelling to infer the whole-brain directed (effective) connectivity from functional magnetic resonance imaging data of 36 healthy individuals during the production of meaningful English sentences and meaningless syllables. We identified that the two dynamic connectomes have distinct architectures that are dependent on the complexity of task production. The speech was regulated by a dynamic neural network, the most influential nodes of which were centred around superior and inferior parietal areas and influenced the whole-brain network activity via long-ranging coupling with primary sensorimotor, prefrontal, temporal and insular regions. By contrast, syllable production was controlled by a more compressed, cost-efficient network structure, involving sensorimotor cortico-subcortical integration via superior parietal and cerebellar network hubs. These data demonstrate the mechanisms by which the neural network reorganizes the connectivity of its influential regions, from supporting the fundamental aspects of simple syllabic vocal motor output to multimodal information processing of speech motor output. This article is part of the theme issue ‘Vocal learning in animals and humans’.

scholarly journals Heritability of hierarchical structural brain network

2017 ◽  
Author(s):  
Moo K. Chung ◽  
Zhan Luo ◽  
Nagesh Adluru ◽  
Andrew L. Alexander ◽  
Davidson J. Richard ◽  
...  
Keyword(s):  
Diffusion Weighted Imaging ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Imaging Study ◽  
Brain Network ◽  
Genetic Contribution ◽  
Whole Brain ◽  
Diffusion Weighted ◽  
Structural Brain Network ◽  
Structural Brain

ABSTRACTWe present a new structural brain network parcellation scheme that can subdivide existing parcellations into smaller subregions in a hierarchically nested fashion. The hierarchical parcellation was used to build multilayer convolutional structural brain networks that preserve topology across different network scales. As an application, we applied the method to diffusion weighted imaging study of 111 twin pairs. The genetic contribution of the whole brain structural connectivity was determined. We showed that the overall heritability is consistent across different network scales.

scholarly journals Heterogeneity of EEG resting-state brain networks in absolute pitch

2020 ◽  
Author(s):  
Marielle Greber ◽  
Carina Klein ◽  
Simon Leipold ◽  
Silvano Sele ◽  
Lutz Jäncke
Keyword(s):  
Functional Connectivity ◽  
Auditory Cortex ◽  
Resting State ◽  
Large Scale ◽  
Absolute Pitch ◽  
Brain Regions ◽  
Brain Network ◽  
Higher Order ◽  
Neural Basis ◽  
Whole Brain

AbstractThe neural basis of absolute pitch (AP), the ability to effortlessly identify a musical tone without an external reference, is poorly understood. One of the key questions is whether perceptual or cognitive processes underlie the phenomenon as both sensory and higher-order brain regions have been associated with AP. One approach to elucidate the neural underpinnings of a specific expertise is the examination of resting-state networks.Thus, in this paper, we report a comprehensive functional network analysis of intracranial resting-state EEG data in a large sample of AP musicians (n = 54) and non-AP musicians (n = 51). We adopted two analysis approaches: First, we applied an ROI-based analysis to examine the connectivity between the auditory cortex and the dorsolateral prefrontal cortex (DLPFC) using several established functional connectivity measures. This analysis is a replication of a previous study which reported increased connectivity between these two regions in AP musicians. Second, we performed a whole-brain network-based analysis on the same functional connectivity measures to gain a more complete picture of the brain regions involved in a possibly large-scale network supporting AP ability.In our sample, the ROI-based analysis did not provide evidence for an AP-specific connectivity increase between the auditory cortex and the DLPFC. In contrast, the whole-brain analysis revealed three networks with increased connectivity in AP musicians comprising nodes in frontal, temporal, subcortical, and occipital areas. Commonalities of the networks were found in both sensory and higher-order brain regions of the perisylvian area. Further research will be needed to confirm these exploratory results.

scholarly journals Connectomic Assessment of Injury Burden and Longitudinal Structural Network Alterations in Moderate-to-severe Traumatic Brain Injury

2021 ◽  
Author(s):  
Yusuf Osmanlioglu ◽  
Drew Parker ◽  
Jacob A Alappatt ◽  
James J Gugger ◽  
Ramon R Diaz-Arrastia ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Structural Connectivity ◽  
Public Health Problem ◽  
Brain Regions ◽  
Brain Network ◽  
Major Public Health Problem ◽  
Post Injury ◽  
Structural Network ◽  
Injury Burden

Traumatic brain injury (TBI) is a major public health problem. Caused by external mechanical forces, a major characteristic of TBI is the shearing of axons across the white matter, which causes structural connectivity disruptions between brain regions. This diffuse injury leads to cognitive deficits, frequently requiring rehabilitation. Heterogeneity is another characteristic of TBI as severity and cognitive sequelae of the disease have a wide variation across patients, posing a big challenge for treatment. Thus, measures assessing network-wide structural connectivity disruptions in TBI are necessary to quantify injury burden of individuals, which would help in achieving personalized treatment, patient monitoring, and rehabilitation planning. Despite TBI being a disconnectivity syndrome, connectomic assessment of structural disconnectivity has been very scarce. In this study, we propose a novel connectomic measure that we call network anomaly score (NAS) to capture the integrity of structural connectivity in TBI patients by leveraging two major characteristics of the disease: diffuseness of axonal injury and heterogeneity of the disease. Over a longitudinal cohort of moderate-to-severe TBI patients, we demonstrate that structural network topology of patients are more heterogeneous and are significantly different than that of healthy controls at 3 months post-injury, where dissimilarity further increases up to 12 months. We also show that NAS captures injury burden as quantified by post-traumatic amnesia and that alterations in the structural brain network is not related to cognitive recovery. Finally we compare NAS to major graph theory measures used in TBI literature and demonstrate the superiority of NAS in characterizing the disease.

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