Filters








121,869 Hits in 4.8 sec

Estimating effective connectivity in linear brain network models [article]

Giulia Prando, Mattia Zorzi, Alessandra Bertoldo, Alessandro Chiuso
2017 arXiv   pre-print
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

Farras Abdelnour, Henning U. Voss, Ashish Raj
2014 NeuroImage  
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

Giulia Prando, Mattia Zorzi, Alessandra Bertoldo, Maurizio Corbetta, Marco Zorzi, Alessandro Chiuso
2019 NeuroImage  
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?

Diego C. Nascimento, Marco A. Pinto-Orellana, Joao P. Leite, Dylan J. Edwards, Francisco Louzada, Taiza E. G. Santos
2020 Frontiers in Systems Neuroscience  
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]

Marius Buibas, Gabriel A. Silva
2015 arXiv   pre-print
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

Wouter De Baene, Martijn J. Jansma, Irena T. Schouwenaars, Geert-Jan M. Rutten, Margriet M. Sitskoorn
2019 Brain Imaging and Behavior  
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

Luca Faes, Ralph G. Andrzejak, Mingzhou Ding, Dimitris Kugiumtzis
2012 Computational and Mathematical Methods in Medicine  
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]

Manjari Narayan, Genevera I Allen
2015 bioRxiv   pre-print
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

Manjari Narayan, Genevera I. Allen
2016 Frontiers in Neuroscience  
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

Hyekyoung Lee, Dong Soo Lee, Hyejin Kang, Boong-Nyun Kim, Moo K. Chung, John B. Weaver, Robert C. Molthen
2011 Medical Imaging 2011: Biomedical Applications in Molecular, Structural, and Functional Imaging  
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

G. de Marco, A. le Pellec
2010 Wireless Sensor Network  
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]

Shahab Kadkhodaeian Bakhtiari, Gholam-Ali Hossein-Zadeh
2011 arXiv   pre-print
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]

Pierfrancesco Ambrosi, Mauro Costagli, Ercan E. Kuruoglu, Laura Biagi, Guido Buonincontri, Michela Tosetti
2021 bioRxiv   pre-print
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

Ka Hamid, An Yusoff, Mza Rahman, M Mohamad, Aia Hamid
2012 Biomedical Imaging and Intervention Journal  
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

Yitian Zhao, Daniel J. Blackburn, Ptolemaios G. Sarrigiannis, Pholpat Durongbhan, Liangyu Chen, Jiang Liu, S. A. Billings, Panagiotis Zis, Zoe C. Unwin, Matteo De Marco, Annalena Venneri
2019 IEEE Transactions on Medical Imaging  
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
« Previous Showing results 1 — 15 out of 121,869 results