scholarly journals Life-span development of functional brain networks as assessed with minimum spanning tree analysis of resting state EEG.

2015 ◽  
Author(s):  
Dirk Smit ◽  
Eco J.C. de Geus ◽  
Maria Boersma ◽  
Dorret I Boomsma ◽  
Cornelis J Stam
Keyword(s):  
Life Span ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Age Groups ◽  
Brain Network ◽  
Connectivity Pattern ◽  
Phase Lag ◽  
Quantitative Changes ◽  
Graph Parameters ◽  
The Brain

The brain matures with large quantitative changes in anatomy and function. Graph analysis of EEG has previously revealed increased connectivity between distant brain areas and a decrease in randomness and increased integration in the brain network with concurrent increased modularity. Comparisons of graph parameters across age groups, however, may be confounded with network degree distributions. Here, we analyzed graph parameters from minimum spanning tree (MST) graphs. MST graphs are constructed by selecting only the strongest available connections avoiding loops resulting in a backbone graph that is thought reflect the major qualitative properties of connectivity while allowing a better comparison across age groups by avoiding the degree distribution confound. EEG was recorded in a large (N=1500) population-based sample aged 5 to 71 years. Connectivity was assessed using Phase Lag Index to reduce effects of volume conduction. As previously reported, connectivity increased from childhood to adolescence, continuing to grow nonsignificantly into adulthood decreasing only after ~30 years of age. Leaf number, degree, degree correlation, maximum centrality from the MST graph indicated a pattern of increased integration and decreased randomness from childhood into early adulthood. The observed development in network topology suggested that maturation at the neuronal level is aimed to increase connectivity as well as increase integration of the brain network. We confirm that brain network connectivity shows quantitative changes across the life span, and additionally demonstrate parallel qualitative changes in the connectivity pattern.

scholarly journals Life-Span Development of Brain Network Integration Assessed with Phase Lag Index Connectivity and Minimum Spanning Tree Graphs

2016 ◽  
Vol 6 (4) ◽  
pp. 312-325 ◽  
Author(s):  
Dirk J.A. Smit ◽  
Eco J.C. de Geus ◽  
Maria Boersma ◽  
Dorret I. Boomsma ◽  
Cornelis J. Stam
Keyword(s):  
Life Span ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Brain Network ◽  
Phase Lag ◽  
Network Integration ◽  
Life Span Development ◽  
Tree Graphs

The minimum spanning tree: An unbiased method for brain network analysis

NeuroImage ◽  
2015 ◽  
Vol 104 ◽  
pp. 177-188 ◽  
Author(s):  
P. Tewarie ◽  
E. van Dellen ◽  
A. Hillebrand ◽  
C.J. Stam
Keyword(s):  
Network Analysis ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Brain Network

scholarly journals Frontal Functional Network Disruption Associated with Amyotrophic Lateral Sclerosis: An fNIRS-Based Minimum Spanning Tree Analysis

2020 ◽  
Vol 14 ◽  
Author(s):  
Seyyed Bahram Borgheai ◽  
John McLinden ◽  
Kunal Mankodiya ◽  
Yalda Shahriari
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Prefrontal Cortex ◽  
Neurodegenerative Diseases ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Brain Network ◽  
Healthy Controls ◽  
Topological Properties ◽  
Network Disruption ◽  
Lateral Sclerosis

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.

scholarly journals Convolutional Sparse Coded Dynamic Brain Functional Connectivity

2018 ◽  
Author(s):  
Jin Yan ◽  
Yingying Zhu
Keyword(s):  
Functional Connectivity ◽  
Brain Connectivity ◽  
Sliding Window ◽  
Brain Network ◽  
Clinical Applications ◽  
Connectivity Pattern ◽  
Brain Disorder ◽  
Connectivity Patterns ◽  
Brain Functional Connectivity ◽  
The Brain

AbstractFunctional brain network has been widely studied in many previous work for brain disorder diagnosis and brain network analysis. However, most previous work focus on static dynamic brain network research. Lots of recent work reveals that the brain shows dynamic activity even in resting state. Such dynamic brain functional connectivity reveals discriminative patterns for identifying many brain disorders. Current sliding window based dynamic brain connectivity framework are not easy to be applied to real clinical applications due to many issues: First, how to set up the optimal sliding window size and how to determine the threshold for the brain connectivity patterns. Secondly, how to represent the high dimensional dynamic brain connectivity pattern in a low dimensional representations for diagnosis purpose. Last, how to deal with the different length dynamic brain network patterns especially when the raw data are of different length. In order to address all those above issues, we proposed a new framework, which employs multiple scale sliding windows and automatically learns a sparse and low ran dynamic brain functional connectivity patterns from raw fMRI data. Furthermore, we are able to measure different length dynamic brain functional connectivity patterns in an equal space by learning a sparse coded convolutional filters. We have evaluated our method with state of the art dynamic brain network methods and the results demonstrated the strong potential of our methods for brain disorder diagnosis in real clinical applications.

scholarly journals EEG Feature Extraction Based on a Bilevel Network: Minimum Spanning Tree and Regional Network

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 203 ◽  
Author(s):  
Zhizeng Luo ◽  
Xianju Lu ◽  
Xugang Xi
Keyword(s):  
Feature Extraction ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Movement Patterns ◽  
Brain Network ◽  
Network Size ◽  
Global Network ◽  
Regional Network ◽  
Hybrid Features ◽  
Network Layer

Feature extraction is essential for classifying different motor imagery (MI) tasks in a brain–computer interface (BCI). Although the methods of brain network analysis have been widely studied in the BCI field, these methods are limited by differences in network size, density, and standardization. To address this issue and improve classification accuracy, we propose a novel method, in which the hybrid features of the brain function based on the bilevel network are extracted. Minimum spanning tree (MST) based on electroencephalogram (EEG) signal nodes in different MIs is constructed as the first network layer to solve the global network connectivity problem. In addition, the regional network in different movement patterns is constructed as the second network layer to determine the network characteristics, which is consistent with the correspondence between limb movement patterns and cerebral cortex in neurophysiology. We attempt to apply MST to the classification of the MI EEG signals, and the bilevel network has better interpretability. Thereafter, a vector is formed by combining the MST fundamental features with the directional features of the regional network. Our method is validated using the BCI Competition IV Dataset I. Experimental results verify the feasibility of the bilevel network framework. Furthermore, the average classification performance of the proposed method reaches 89.50%, which is higher than that of other competing methods, thereby indicating that the bilevel network is effective for MI classification.

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