Functional brain network analysis of schizophrenic patients with positive and negative syndrome based on mutual information of EEG time series

2017 ◽  
Vol 31 ◽  
pp. 331-338 ◽  
Author(s):  
Zhongliang Yin ◽  
Jun Li ◽  
Yun Zhang ◽  
Aifeng Ren ◽  
Karen M. Von Meneen ◽  
...  
Keyword(s):  
Time Series ◽  
Network Analysis ◽  
Mutual Information ◽  
Brain Network ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Schizophrenic Patients ◽  
Negative Syndrome

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

Functional brain network analysis using minimum spanning trees in Multiple Sclerosis: An MEG source-space study

NeuroImage ◽  
2014 ◽  
Vol 88 ◽  
pp. 308-318 ◽  
Author(s):  
P. Tewarie ◽  
A. Hillebrand ◽  
M.M. Schoonheim ◽  
B.W. van Dijk ◽  
J.J.G. Geurts ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Network Analysis ◽  
Spanning Trees ◽  
Brain Network ◽  
Minimum Spanning Trees ◽  
Functional Brain ◽  
Functional Brain Network

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.

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