Emotional-state brain network analysis revealed by minimum spanning tree using EEG signals

Author(s):  
Jianhai Zhang ◽  
Shaokai Zhao ◽  
Guodong Yang ◽  
Jiajia Tang ◽  
Tao Zhang ◽  
...  
NeuroImage ◽  
2015 ◽  
Vol 104 ◽  
pp. 177-188 ◽  
Author(s):  
P. Tewarie ◽  
E. van Dellen ◽  
A. Hillebrand ◽  
C.J. Stam

2019 ◽  
Vol 15 (1) ◽  
pp. 527-536 ◽  
Author(s):  
Nadia Mammone ◽  
Simona De Salvo ◽  
Lilla Bonanno ◽  
Cosimo Ieracitano ◽  
Silvia Marino ◽  
...  

2019 ◽  
Author(s):  
Fatin Nur Amirah Mahamood ◽  
Hafizah Bahaludin ◽  
Mimi Hafizah Abdullah

2014 ◽  
Vol 576 ◽  
pp. 28-33 ◽  
Author(s):  
Michael Vourkas ◽  
Eleni Karakonstantaki ◽  
Panagiotis G. Simos ◽  
Vasso Tsirka ◽  
Marios Antonakakis ◽  
...  

2020 ◽  
Vol 14 ◽  
Author(s):  
Seyyed Bahram Borgheai ◽  
John McLinden ◽  
Kunal Mankodiya ◽  
Yalda Shahriari

Recent evidence increasingly associates network disruption in brain organization with multiple neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), a rare terminal disease. However, the comparability of brain network characteristics across different studies remains a challenge for conventional graph theoretical methods. One suggested method to address this issue is minimum spanning tree (MST) analysis, which provides a less biased comparison. Here, we assessed the novel application of MST network analysis to hemodynamic responses recorded by functional near-infrared spectroscopy (fNIRS) neuroimaging modality, during an activity-based paradigm to investigate hypothetical disruptions in frontal functional brain network topology as a marker of the executive dysfunction, one of the most prevalent cognitive deficit reported across ALS studies. We analyzed data recorded from nine participants with ALS and ten age-matched healthy controls by first estimating functional connectivity, using phase-locking value (PLV) analysis, and then constructing the corresponding individual and group MSTs. Our results showed significant between-group differences in several MST topological properties, including leaf fraction, maximum degree, diameter, eccentricity, and degree divergence. We further observed a global shift toward more centralized frontal network organizations in the ALS group, interpreted as a more random or dysregulated network in this cohort. Moreover, the similarity analysis demonstrated marginally significantly increased overlap in the individual MSTs from the control group, implying a reference network with lower topological variation in the healthy cohort. Our nodal analysis characterized the main local hubs in healthy controls as distributed more evenly over the frontal cortex, with slightly higher occurrence in the left prefrontal cortex (PFC), while in the ALS group, the most frequent hubs were asymmetrical, observed primarily in the right prefrontal cortex. Furthermore, it was demonstrated that the global PLV (gPLV) synchronization metric is associated with disease progression, and a few topological properties, including leaf fraction and tree hierarchy, are linked to disease duration. These results suggest that dysregulation, centralization, and asymmetry of the hemodynamic-based frontal functional network during activity are potential neuro-topological markers of ALS pathogenesis. Our findings can possibly support new bedside assessments of the functional status of ALS’ brain network and could hypothetically extend to applications in other neurodegenerative diseases.


Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1553
Author(s):  
Majd Abazid ◽  
Nesma Houmani ◽  
Jerome Boudy ◽  
Bernadette Dorizzi ◽  
Jean Mariani ◽  
...  

This work addresses brain network analysis considering different clinical severity stages of cognitive dysfunction, based on resting-state electroencephalography (EEG). We use a cohort acquired in real-life clinical conditions, which contains EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients, and Alzheimer’s disease (AD) patients. We propose to exploit an epoch-based entropy measure to quantify the connectivity links in the networks. This entropy measure relies on a refined statistical modeling of EEG signals with Hidden Markov Models, which allow a better estimation of the spatiotemporal characteristics of EEG signals. We also propose to conduct a comparative study by considering three other measures largely used in the literature: phase lag index, coherence, and mutual information. We calculated such measures at different frequency bands and computed different local graph parameters considering different proportional threshold values for a binary network analysis. After applying a feature selection procedure to determine the most relevant features for classification performance with a linear Support Vector Machine algorithm, our study demonstrates the effectiveness of the statistical entropy measure for analyzing the brain network in patients with different stages of cognitive dysfunction.


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