A causal brain network estimation method leveraging Bayesian analysis and the PC algorithm

2020 ◽  
Author(s):  
Gemeng Zhang ◽  
Aiying Zhang ◽  
Vince D. Calhoun ◽  
Yu-Ping Wang
Keyword(s):  
Bayesian Analysis ◽  
Estimation Method ◽  
Brain Network ◽  
Pc Algorithm ◽  
Network Estimation

scholarly journals Functional Brain Network Estimation with Time Series Self-scrubbing

2017 ◽  
Author(s):  
Weikai Li ◽  
Lishan Qiao ◽  
Zhengxia Wang ◽  
Dinggang Shen
Keyword(s):  
Time Series ◽  
Data Quality ◽  
Latent Variable ◽  
Estimation Method ◽  
Brain Network ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Data Points ◽  
Network Estimation

AbstractFunctional brain network (FBN) has been becoming an increasingly important measurement for exploring the cerebral working mechanism and mining informative biomarkers for assisting diagnosis of some neurodegenerative disorders. Despite its potential performance in discovering the valuable patterns hidden in the brains, the estimated FBNs are often heavily influenced by the quality of the observed data (e.g., BOLD signal series). In practice, a preprocessing pipeline is usually employed for improving the data quality prior to the FBN estimation; but, even so, some data points in the time series are still not clean enough, possibly including original artifacts (e.g., micro head motion), non-resting functional disturbing (e.g., mind-wandering), and new “noises” caused by the preprocessing pipeline per se. Therefore, not all data points in the time series can contribute to the subsequent FBN estimation. To address this issue, in this paper, we propose a novel FBN estimation method by introducing a latent variable as an indicator of the data quality, and develop an alternating optimization algorithm for scrubbing the data and estimating FBN simultaneously in a single framework. As a result, we can obtain more accurate FBNs with the self-scrubbing data. To illustrate the effectiveness of the proposed method, we conduct experiments on two publicly available datasets to identify mild cognitive impairment (MCI) patients from normal control (NC) subjects based on the estimated FBNs. Experimental results show that the proposed FBN modelling method can achieve higher classification accuracy, significantly outperforming the baseline methods.

scholarly journals Functional Brain Network Estimation With Time Series Self-Scrubbing

2019 ◽  
Vol 23 (6) ◽  
pp. 2494-2504 ◽  
Author(s):  
Weikai Li ◽  
Lishan Qiao ◽  
Limei Zhang ◽  
Zhengxia Wang ◽  
Dinggang Shen
Keyword(s):  
Time Series ◽  
Brain Network ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Network Estimation

scholarly journals Estimating Factors with Psychometric Networks: A Monte Carlo Simulation Comparing Community Detection Algorithms

2020 ◽  
Author(s):  
Alexander P. Christensen ◽  
Hudson Golino
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Community Detection ◽  
Partial Correlation ◽  
Estimation Method ◽  
Detection Algorithm ◽  
Systematic Investigation ◽  
Correlation Network ◽  
Detection Algorithms ◽  
Network Estimation

Estimating the number of factors in multivariate data is at the crux of psychological measurement. Factor analysis has a long tradition in the field but it’s been challenged recently by exploratory graph analysis (EGA), an approach based on network psychometrics. EGA first estimates a regularized partial correlation network using the graphical least absolute shrinkage and selection operator (GLASSO), and then applies the Walktrap community detection algorithm, which identifies communities (or factors) in the network. Simulation studies have demonstrated that EGA has comparable or better accuracy than contemporary state-of-the-art factor analytic methods (e.g., parallel analysis), while providing some additional advantages such as not requiring rotations and deterministic allocation of items into factors. Despite EGA’s effectiveness, there has yet to be an investigation into whether other community detection algorithms could achieve equivalent or better perfomance. In the present study, we performed a Monte Carlo simulation using the GLASSO and two variants of a non-regularized partial correlation network estimation method and several community detection algorithms in the open-source igraph package in R. The purpose of the present study was to critically examine whether the network estimation and community detection components of EGA are optimal for estimating factors in psychological data as well as to provide a systematic investigation into how different community detection algorithms perform “out-of-the-box.” The results indicate that the Fast-greedy, Louvain, and Walktrap algorithms paired with the GLASSO method were consistently among the most accurate and least biased across conditions.

scholarly journals Group Similarity Constraint Functional Brain Network Estimation for Mild Congititive Impairment Classification

2019 ◽  
Author(s):  
Xin Gao ◽  
Xiaowen Xu ◽  
Weikai Li ◽  
Rui Li
Keyword(s):  
Poor Quality ◽  
Brain Network ◽  
Estimation Model ◽  
Activation Patterns ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Network Estimation ◽  
Post Hoc ◽  
Group Similarity

AbstractFunctional brain network (FBN) provides an effective biomarker for understanding brain activation patterns, which also improve the diagnostic criteria for neurodegenerative diseases or the information transmission of brain. Unfortunately, despite its efficiency, FBN still suffers several challenges for accurately estimate the biological meaningful or discriminative FBNs, under the poor quality of functional magnetic resonance imaging (fMRI) data as well as the limited understanding of human brain. Hence, there still a motivation to alleviate those issues above, it is currently still an open field to discover. In this paper, a novel FBN estimation model based on group similarity constraints is proposed. In particular, we extend the FBN estimation model to the tensor form and incorporate the trace-norm regularizer for formulating the group similarity constraint. In order to verify the proposed method, we conduct experiments on identifying Mild Cognitive Impairments (MCIs) from normal controls (NCs) based on the estimated FBNs. The experimental results illustrated that the proposed method can construct a more discriminative brain network. Consequently, we achieved an 91.97% classification accuracy which outperforms the baseline methods. The post hoc analysis further shown more biologically meaningful functional brain connections obtained by our proposed method.

scholarly journals Comparison of Causality Network Estimation in the Sensor and Source Space: Simulation and Application on EEG

2021 ◽  
Vol 1 ◽  
Author(s):  
Christos Koutlis ◽  
Vasilios K. Kimiskidis ◽  
Dimitris Kugiumtzis
Keyword(s):  
Small World ◽  
Brain Network ◽  
Discriminative Ability ◽  
Scale Free ◽  
Deterministic Systems ◽  
Epileptiform Discharges ◽  
Eeg Data ◽  
Network Estimation ◽  
Source Level ◽  
Electroencephalogram Eeg

The usage of methods for the estimation of the true underlying connectivity among the observed variables of a system is increasing, especially in the domain of neuroscience. Granger causality and similar concepts are employed for the estimation of the brain network from electroencephalogram (EEG) data. Also source localization techniques, such as the standardized low resolution electromagnetic tomography (sLORETA), are widely used for obtaining more reliable data in the source space. In this work, connectivity structures are estimated in the sensor and in the source space making use of the sLORETA transformation for simulated and for EEG data with episodes of spontaneous epileptiform discharges (ED). From the comparative simulation study on high-dimensional coupled stochastic and deterministic systems originating in the sensor space, we conclude that the structure of the estimated causality networks differs in the sensor space and in the source space. Moreover, different network types, such as random, small-world and scale-free, can be better discriminated on the basis of the data in the original sensor space than on the transformed data in the source space. Similarly, in EEG epochs containing epileptiform discharges, the discriminative ability of network topological indices was significantly better in the sensor compared to the source level. In conclusion, causality networks constructed at the sensor and source level, for both simulated and empirical data, exhibit significant structural differences. These observations indicate that further studies are warranted in order to clarify the exact relationship between data registered in the sensor and source space.

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