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Estimating effective connectivity in linear brain network models
[article]
2017
arXiv
pre-print
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functional Magnetic Resonance Imaging (fMRI), the so-called effective connectivity in brain networks, that is the existing interactions among neuronal populations. ...
In this paper, we consider resting state (rs) fMRI data; building upon a linear population model of the BOLD signal and a stochastic linear DCM model, the model parameters are estimated through an EM-type ...
CONCLUSIONS AND FUTURE WORK We have proposed a new approach for the estimation of the effective connectivity in brain networks using resting state fMRI data. ...
arXiv:1703.10363v1
fatcat:dbzvaz2ifbazjlv7rkpr4wczwm
Network diffusion accurately models the relationship between structural and functional brain connectivity networks
2014
NeuroImage
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The success of our model confirms the linearity of ensemble average signals in the brain, and implies that their long-range correlation structure may percolate within the brain via purely mechanistic processes ...
The relationship between anatomic connectivity of large-scale brain networks and their functional connectivity is of immense importance and an area of active research. ...
Acknowledgments Authors would like to thank Olaf Sporns for supplying computer scripts that allowed the simulation of the nonlinear model. AR and FA were supported by NIH grant R01 NS075425. ...
doi:10.1016/j.neuroimage.2013.12.039
pmid:24384152
pmcid:PMC3951650
fatcat:oxldx6if3fgvjmanjoj3qipbge
Sparse DCM for whole-brain effective connectivity from resting-state fMRI data
2019
NeuroImage
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Despite the developments in non-invasive neuroimaging techniques, a full understanding of the directed interactions in whole brain networks, referred to as effective connectivity, as well as their role ...
Building on the dynamic causal modelling framework, the present study offers a novel method for estimating whole-brain effective connectivity from resting-state functional magnetic resonance data. ...
We then developed an EM-like Algorithm to estimate this linear generative model and in particular the effective connectivity matrix. ...
doi:10.1016/j.neuroimage.2019.116367
pmid:31812714
fatcat:qfeo2piy2rhqtbdeenv42igtvm
BrainWave Nets: Are Sparse Dynamic Models Susceptible to Brain Manipulation Experimentation?
2020
Frontiers in Systems Neuroscience
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Sparse time series models have shown promise in estimating contemporaneous and ongoing brain connectivity. ...
These dynamic graphical models were useful in assessing the role of estimating the brain network structure and describing its causal relationship. ...
Thus, a multivariate time series can be translated into a learning probabilistic connection network structure (as a graph model), aiming to estimate brain connectivity networks. ...
doi:10.3389/fnsys.2020.527757
pmid:33324178
pmcid:PMC7726475
fatcat:4jqtrafdnbh7hlggvswp3hj63e
Algebraic identification of the effective connectivity of constrained geometric network models of neural signaling
[article]
2015
arXiv
pre-print
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The proposed method operates on network-level data, makes use of all relevant prior knowledge, such as dynamical models of individual cells in the network and the physical structural connectivity of the ...
Here, we propose and provide a summary of an approach for calculating effective connectivity from experimental observations of neuronal network activity. ...
even entire brain regions), and effective connectivity, which is stronger and assumes casual dynamic connectivity within the network [3, 12, 13] . ...
arXiv:1505.03964v1
fatcat:eynsp5tkyzhetpl74fxoshqfs4
Task-evoked reconfiguration of the fronto-parietal network is associated with cognitive performance in brain tumor patients
2019
Brain Imaging and Behavior
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It is, however, unclear whether the capacity for network reconfiguration also plays a role in cognitive deficits in brain tumor patients. ...
Task-evoked changes in functional connectivity strength (defined as the mean of the absolute values of all connections) and in functional connectivity patterns within and between the FPN and DMN did not ...
The parameter estimates of the linear mixed models with the task-evoked connection strength change for the different networks as predictor are shown in Table 7 . ...
doi:10.1007/s11682-019-00189-2
pmid:31456158
fatcat:zmjrux7zobccrcvvv4lr7t6j7y
Methodological Advances in Brain Connectivity
2012
Computational and Mathematical Methods in Medicine
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statistical dependencies between spatially separated brain regions; effective connectivity refers to models aimed at elucidating driver-response relationships. ...
Common issues to be addressed are estimation problems arising in the presence of noise contamination and nonstationarity, significance assessment, distinguishing direct from indirect causal effects, and ...
statistical dependencies between spatially separated brain regions; effective connectivity refers to models aimed at elucidating driver-response relationships. ...
doi:10.1155/2012/492902
pmid:22991577
pmcid:PMC3443973
fatcat:6ycurevsvjfwdekl7bn274dgpa
Mixed Effects Models for Resampled Network Statistics Improves Statistical Power to Find Differences in Multi-Subject Functional Connectivity
[article]
2015
bioRxiv
pre-print
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We address this problem in the case of Gaussian graphical models of functional connectivity, by proposing novel two-level models that treat both subject level networks and population level covariate effects ...
To understand how brain communication is impaired in such conditions, functional connectivity studies seek to understand individual differences in brain network structure in terms of covariates that measure ...
connectivity at the subject level and investigate covariate effects using linear models for density based network metrics for the population level. ...
doi:10.1101/027516
fatcat:qtviz55jvrhk3e7waxqsdu2viq
Mixed Effects Models for Resampled Network Statistics Improves Statistical Power to Find Differences in Multi-Subject Functional Connectivity
2016
Frontiers in Neuroscience
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We address this problem in the case of Gaussian graphical models of functional connectivity, by proposing novel two-level models that treat both subject level networks and population level covariate effects ...
To understand how brain communication is impaired in such conditions, functional connectivity studies seek to understand individual differences in brain network structure in terms of covariates that measure ...
connectivity at the subject level and investigate covariate effects using linear models for density based network metrics for the population level. ...
doi:10.3389/fnins.2016.00108
pmid:27147940
pmcid:PMC4828454
fatcat:yiuf6jpst5djzlb2wq643us6na
Sparse brain network using penalized linear regression
2011
Medical Imaging 2011: Biomedical Applications in Molecular, Structural, and Functional Imaging
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In this paper, we consider a sparse linear regression model with a l 1 -norm penalty for estimating sparse brain connectivity based on the partial correlation. ...
Sparse partial correlation is a useful connectivity measure for brain networks, especially, when it is hard to compute the exact partial correlation due to the small-n large-p situation. ...
SPARSE BRAIN NETWORK ESTIMATION We formulate the sparse brain connectivity based on partial correlation in the penalized linear regression framework. ...
doi:10.1117/12.877547
fatcat:vq3ntracsjgblm4aqjxzwjknsa
Modeling of Circuits within Networks by fMRI
2010
Wireless Sensor Network
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After defining the concept of functional and effective connectivity, the authors describe various methods of identification and modeling of circuits within networks. ...
The description of specific circuits in networks should allow a more realistic definition of dynamic functioning of the central nervous system which underlies various brain functions. ...
Effective connectivity can be estimated from linear models to test whether a theoretical model seeking to explain a network of relationships can actually fit the relationships estimated from the observed ...
doi:10.4236/wsn.2010.23028
fatcat:uqudzrg23nhmjosonbw6tptx5q
Subspace-based Identification Algorithm for Characterizing Causal Networks in Resting Brain
[article]
2011
arXiv
pre-print
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seeks causal interactions between brain regions, Effective Connectivity (EC), has been little explored in spontaneous brain oscillations. ...
Using extensive simulations, we study the effects of network size and signal to noise ratio (SNR) on the accuracy of our proposed method in EC detection. ...
Furthermore we utilized SIA to estimate effective connectivity among brain regions of dorsal attention and default mode network in restingstate, using fMRI data. ...
arXiv:1108.4644v2
fatcat:g4p3aoa4mzestfw4yesmleeaua
Modeling Brain Connectivity Dynamics in Functional Magnetic Resonance Imaging via Particle Filtering
[article]
2021
bioRxiv
pre-print
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Most studies in this field treat the brain network as a system of connections stationary in time, but dynamic features of brain connectivity can provide useful information, both on physiology and pathological ...
The PF algorithm estimates timevarying hidden parameters of a first-order linear time-varying Vector Autoregressive model (VAR) through a Sequential Monte Carlo strategy. ...
The authors thank Dr Sergiy Ancherbak for having shared with them his particle filtering code for time-dependent gene network modeling. ...
doi:10.1101/2021.01.19.427249
fatcat:wkoytewi2va7vid7t4zhooh6gq
Effective connectivity between superior temporal gyrus and Heschl's gyrus during white noise listening: linear versus non-linear models
2012
Biomedical Imaging and Intervention Journal
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Based on the winning model, six linear and six non-linear causal models were derived and were again estimated, inferred, and compared to obtain a model that best represents the effective connectivity between ...
This fMRI study is about modelling the effective connectivity between Heschl's gyrus (HG) and the superior temporal gyrus (STG) in human primary auditory cortices. ...
The models assume that the effective connectivity in a neuronal network can be explained by non-linear mechanism types. ...
doi:10.2349/biij.8.2.e13
pmid:22970069
pmcid:PMC3432259
fatcat:fzntdowqkfhitdjuudejuobh7u
Imaging of nonlinear and dynamic functional brain connectivity based on EEG recordings with the application on the diagnosis of Alzheimer's disease
2019
IEEE Transactions on Medical Imaging
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This approach, where linear and non-linear interactions and their spatial distribution and dynamics can be estimated independently, offered us the means to dissect the dynamic brain network disruption ...
We describe a parametric method established upon a Nonlinear Finite Impulse Response model, and a revised orthogonal least squares algorithm used to estimate the linear, nonlinear and combined connectivity ...
In terms of effective connection, Kiebel et al. ...
doi:10.1109/tmi.2019.2953584
pmid:31725372
fatcat:nr3juthasnhr3e2rwbywyk3yea
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