scholarly journals Spatial and Temporal Consistency of Brain Networks for different Multi-Echo fMRI Combination Methods

2021 ◽  
Author(s):  
J. Pilmeyer ◽  
G. Hadjigeorgiou ◽  
R. Lamerichs ◽  
M. Breeuwer ◽  
A.P. Aldenkamp ◽  
...  
Keyword(s):  
Time Series ◽  
Resting State ◽  
Spatial Domain ◽  
Optimal Combination ◽  
Combination Method ◽  
Resting State Networks ◽  
Combination Methods ◽  
Temporal And Spatial ◽  
Noise Ratio ◽  
Weighted Combination

AbstractThe application of multi-echo functional magnetic resonance imaging (fMRI) studies has considerably increased in the last decade due to its superior BOLD sensitivity compared to single-echo fMRI. Various methods have been developed that combine the fMRI time-series derived at different echo times to improve the data quality. Here we evaluated three multi-echo combination schemes, i.e. ‘optimal combination’ (T2*-weighted), temporal Signal-to-Noise Ratio (tSNR) weighted, and temporal Contrast-to-Noise Ratio (tCNR) weighted combination. For the first time, the effect of these multi-echo combinations on functional resting-state networks was assessed in the temporal and spatial domain, and compared to networks derived from the second echo (35 ms) functional images. Sixteen healthy volunteers were scanned during a 5 minutes resting-state fMRI session. After obtaining the networks, several temporal and spatial metrics were calculated for their time-series and spatial maps. Our results showed that, compared to the second echo network time-series, the Pearson correlation and root mean square error were the most consistent for the optimal combination time-series and the least with those derived from tSNR-weighted combination. The frequency analysis further suggested that the time-series from the tSNR-weighted combination method reduced hardware- and physiological-related artifacts as reflected by the reduced power for the associated frequencies in almost all networks. Moreover, the spatial stability and extent of the networks significantly increased after multi-echo combination, primarily for the optimal combination, followed by the tSNR-weighted combination. The performance of the tCNR-weighted combination lacked robustness and instead varied remarkedly between resting-state networks in both the temporal and spatial domain. The results highlight the benefits of multi-echo sequences on resting-state networks as well as the importance of adjusting the choice of multi-echo combination method to the research question and domain of interest.

Identification of Brain Functional Networks Using a Model-Based Approach

2019 ◽  
Vol 34 (08) ◽  
pp. 2057004
Author(s):  
Vangelis P. Oikonomou ◽  
Konstantinos Blekas ◽  
Loukas Astrakas
Keyword(s):  
Time Series ◽  
Resting State ◽  
Probabilistic Method ◽  
Resting State Fmri ◽  
Model Parameters ◽  
Resting State Networks ◽  
Model Based ◽  
Functional Brain Networks ◽  
Regression Mixture ◽  
The Brain

Functional MRI (fMRI) is a valuable brain imaging technique. A significant problem, when analyzing fMRI time series, is the finding of functional brain networks when the brain is at rest, i.e. no external stimulus is applied to the subject. In this work, we present a probabilistic method to estimate the Resting State Networks (RSNs) using a model-based approach. More specifically, RSNs are assumed to be the result of a clustering procedure. In order to perform the clustering, a mixture of regression models are used. Furthermore, special care has been given in order to incorporate important functionalities, such as spatial and embedded sparsity enforcing properties, through the use of informative priors over the model parameters. Another interesting feature of the proposed scheme is the flexibility to handle all the brain time series at once, allowing more robust solutions. We provide comparative experimental results, using an artificial fMRI dataset and two real resting state fMRI datasets, that empirically illustrate the efficiency of the proposed regression mixture model.

scholarly journals Visibility graphs for fMRI data: Multiplex temporal graphs and their modulations across resting-state networks

2017 ◽  
Vol 1 (3) ◽  
pp. 208-221 ◽  
Author(s):  
Speranza Sannino ◽  
Sebastiano Stramaglia ◽  
Lucas Lacasa ◽  
Daniele Marinazzo
Keyword(s):  
Time Series ◽  
Resting State ◽  
Brain Activity ◽  
Multivariate Time Series ◽  
Global Dynamics ◽  
Resting State Networks ◽  
Visibility Graphs ◽  
Local And Global Dynamics ◽  
Temporal Graphs

Visibility algorithms are a family of methods that map time series into graphs, such that the tools of graph theory and network science can be used for the characterization of time series. This approach has proved a convenient tool, and visibility graphs have found applications across several disciplines. Recently, an approach has been proposed to extend this framework to multivariate time series, allowing a novel way to describe collective dynamics. Here we test their application to fMRI time series, following two main motivations, namely that (a) this approach allows vs to simultaneously capture and process relevant aspects of both local and global dynamics in an easy and intuitive way, and (b) this provides a suggestive bridge between time series and network theory that nicely fits the consolidating field of network neuroscience. Our application to a large open dataset reveals differences in the similarities of temporal networks (and thus in correlated dynamics) across resting-state networks, and gives indications that some differences in brain activity connected to psychiatric disorders could be picked up by this approach.

scholarly journals Visibility graphs for fMRI data: multiplex temporal graphs and their modulations across resting state networks

2017 ◽  
Author(s):  
Speranza Sannino ◽  
Sebastiano Stramaglia ◽  
Lucas Lacasa ◽  
Daniele Marinazzo
Keyword(s):  
Time Series ◽  
Resting State ◽  
Brain Activity ◽  
Multivariate Time Series ◽  
Global Dynamics ◽  
Resting State Networks ◽  
Visibility Graphs ◽  
Local And Global Dynamics ◽  
Temporal Graphs

AbstractVisibility algorithms are a family of methods that map time series into graphs, such that the tools of graph theory and network science can be used for the characterization of time series. This approach has proved a convenient tool and visibility graphs have found applications across several disciplines. Recently, an approach has been proposed to extend this framework to multivariate time series, allowing a novel way to describe collective dynamics. Here we test their application to fMRI time series, following two main motivations, namely that (i) this approach allows to simultaneously capture and process relevant aspects of both local and global dynamics in an easy and intuitive way, and (ii) this provides a suggestive bridge between time series and network theory which nicely fits the consolidating field of network neuroscience. Our application to a large open dataset reveals differences in the similarities of temporal networks (and thus in correlated dynamics) across resting state networks, and gives indications that some differences in brain activity connected to psychiatric disorders could be picked up by this approach.

2092-P: Impact of Insulin Resistance on Response to a Meal by Salience, Executive Control, and Default Mode Resting State Networks in Humans

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 2092-P
Author(s):  
LETICIA ESPOSITO SEWAYBRICKER ◽  
SUSAN J. MELHORN ◽  
MARY K. ASKREN ◽  
MARY WEBB ◽  
VIDHI TYAGI ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Executive Control ◽  
Resting State ◽  
Resting State Networks ◽  
Default Mode

scholarly journals Intra and inter: Alterations in functional brain resting‐state networks after peripheral nerve injury

2020 ◽  
Vol 10 (9) ◽  
Author(s):  
Xiang‐Xin Xing ◽  
Xu‐Yun Hua ◽  
Mou‐Xiong Zheng ◽  
Zhen‐Zhen Ma ◽  
Bei‐Bei Huo ◽  
...  
Keyword(s):  
Peripheral Nerve ◽  
Nerve Injury ◽  
Resting State ◽  
Peripheral Nerve Injury ◽  
Resting State Networks ◽  
Functional Brain

scholarly journals An improved conflicting-evidence combination method based on the redistribution of the basic probability assignment

2021 ◽  
Author(s):  
Zezheng Yan ◽  
Hanping Zhao ◽  
Xiaowen Mei
Keyword(s):  
Evidence Theory ◽  
Combination Method ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Combination Methods ◽  
Conflicting Evidence ◽  
Basic Probability ◽  
The Right ◽  
Conflict Intensity ◽  
Dempster’S Rule Of Combination

AbstractDempster–Shafer evidence theory is widely applied in various fields related to information fusion. However, the results are counterintuitive when highly conflicting evidence is fused with Dempster’s rule of combination. Many improved combination methods have been developed to address conflicting evidence. Nevertheless, all of these approaches have inherent flaws. To solve the existing counterintuitive problem more effectively and less conservatively, an improved combination method for conflicting evidence based on the redistribution of the basic probability assignment is proposed. First, the conflict intensity and the unreliability of the evidence are calculated based on the consistency degree, conflict degree and similarity coefficient among the evidence. Second, the redistribution equation of the basic probability assignment is constructed based on the unreliability and conflict intensity, which realizes the redistribution of the basic probability assignment. Third, to avoid excessive redistribution of the basic probability assignment, the precision degree of the evidence obtained by information entropy is used as the correction factor to modify the basic probability assignment for the second time. Finally, Dempster’s rule of combination is used to fuse the modified basic probability assignment. Several different types of examples and actual data sets are given to illustrate the effectiveness and potential of the proposed method. Furthermore, the comparative analysis reveals the proposed method to be better at obtaining the right results than other related methods.

scholarly journals Stochastic Modeling of Hydroclimatic Processes Using Vine Copulas

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2156
Author(s):  
George Pouliasis ◽  
Gina Alexandra Torres-Alves ◽  
Oswaldo Morales-Napoles
Keyword(s):  
Time Series ◽  
Stochastic Modeling ◽  
Copula Models ◽  
Multiple Processes ◽  
Spatial Dependencies ◽  
Probabilistic Dependence ◽  
Temporal And Spatial ◽  
Vine Copula ◽  
Synthetic Time Series ◽  
Hydroclimatic Variables

The generation of synthetic time series is important in contemporary water sciences for their wide applicability and ability to model environmental uncertainty. Hydroclimatic variables often exhibit highly skewed distributions, intermittency (that is, alternating dry and wet intervals), and spatial and temporal dependencies that pose a particular challenge to their study. Vine copula models offer an appealing approach to generate synthetic time series because of their ability to preserve any marginal distribution while modeling a variety of probabilistic dependence structures. In this work, we focus on the stochastic modeling of hydroclimatic processes using vine copula models. We provide an approach to model intermittency by coupling Markov chains with vine copula models. Our approach preserves first-order auto- and cross-dependencies (correlation). Moreover, we present a novel framework that is able to model multiple processes simultaneously. This method is based on the coupling of temporal and spatial dependence models through repetitive sampling. The result is a parsimonious and flexible method that can adequately account for temporal and spatial dependencies. Our method is illustrated within the context of a recent reliability assessment of a historical hydraulic structure in central Mexico. Our results show that by ignoring important characteristics of probabilistic dependence that are well captured by our approach, the reliability of the structure could be severely underestimated.

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