scholarly journals Transition of brain networks from an interictal to a preictal state preceding a seizure revealed by scalp EEG network analysis

2019 ◽  
Vol 13 (2) ◽  
pp. 175-181 ◽  
Author(s):  
Fali Li ◽  
Yi Liang ◽  
Luyan Zhang ◽  
Chanlin Yi ◽  
Yuanyuan Liao ◽  
...  
Keyword(s):  
Network Analysis ◽  
Brain Networks ◽  
Scalp Eeg ◽  
Preictal State

scholarly journals Altered Functional Connectivity in Children with ADHD Revealed by Scalp EEG: An ERP Study

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Chunli Chen ◽  
Huan Yang ◽  
Yasong Du ◽  
Guangzhi Zhai ◽  
Hesheng Xiong ◽  
...  
Keyword(s):  
Cognitive Processing ◽  
Early Stage ◽  
Brain Activity ◽  
Brain Networks ◽  
Brain Network ◽  
Event Related Potential ◽  
Children With Adhd ◽  
Scalp Eeg ◽  
Adhd Children ◽  
The One

Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental brain disorders in childhood. Despite extensive researches, the neurobiological mechanism underlying ADHD is still left unveiled. Since the deficit functions, such as attention, have been demonstrated in ADHD, in our present study, based on the oddball P3 task, the corresponding electroencephalogram (EEG) of both healthy controls (HCs) and ADHD children was first collected. And we then not only focused on the event-related potential (ERP) evoked during tasks but also investigated related brain networks. Although an insignificant difference in behavior was found between the HCs and ADHD children, significant electrophysiological differences were found in both ERPs and brain networks. In detail, the dysfunctional attention occurred during the early stage of the designed task; as compared to HCs, the reduced P2 and N2 amplitudes in ADHD children were found, and the atypical information interaction might further underpin such a deficit. On the one hand, when investigating the cortical activity, HCs recruited much stronger brain activity mainly in the temporal and frontal regions, compared to ADHD children; on the other hand, the brain network showed atypical enhanced long-range connectivity between the frontal and occipital lobes but attenuated connectivity among frontal, parietal, and temporal lobes in ADHD children. We hope that the findings in this study may be instructive for the understanding of cognitive processing in children with ADHD.

Constructing large-scale cortical brain networks from scalp EEG with Bayesian nonnegative matrix factorization

2020 ◽  
Vol 125 ◽  
pp. 338-348 ◽  
Author(s):  
Chanlin Yi ◽  
Chunli Chen ◽  
Yajing Si ◽  
Fali Li ◽  
Tao Zhang ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Large Scale ◽  
Nonnegative Matrix Factorization ◽  
Brain Networks ◽  
Nonnegative Matrix ◽  
Scalp Eeg

scholarly journals Functional Connectivity Network Analysis with Discriminative Hub Detection for Brain Disease Identification

2019 ◽  
Vol 33 ◽  
pp. 1198-1205 ◽  
Author(s):  
Mingliang Wang ◽  
Jiashuang Huang ◽  
Mingxia Liu ◽  
Daoqiang Zhang
Keyword(s):  
Network Analysis ◽  
Human Brain ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Disease Diagnosis ◽  
Brain Network ◽  
Brain Disease ◽  
Brain Diseases ◽  
Central Position ◽  
Connectivity Patterns

Brain network analysis can help reveal the pathological basis of neurological disorders and facilitate automated diagnosis of brain diseases, by exploring connectivity patterns in the human brain. Effectively representing the brain network has always been the fundamental task of computeraided brain network analysis. Previous studies typically utilize human-engineered features to represent brain connectivity networks, but these features may not be well coordinated with subsequent classifiers. Besides, brain networks are often equipped with multiple hubs (i.e., nodes occupying a central position in the overall organization of a network), providing essential clues to describe connectivity patterns. However, existing studies often fail to explore such hubs from brain connectivity networks. To address these two issues, we propose a Connectivity Network analysis method with discriminative Hub Detection (CNHD) for brain disease diagnosis using functional magnetic resonance imaging (fMRI) data. Specifically, we incorporate both feature extraction of brain networks and network-based classification into a unified model, while discriminative hubs can be automatically identified from data via ℓ1-norm and ℓ2,1-norm regularizers. The proposed CNHD method is evaluated on three real-world schizophrenia datasets with fMRI scans. Experimental results demonstrate that our method not only outperforms several state-of-the-art approaches in disease diagnosis, but also is effective in automatically identifying disease-related network hubs in the human brain.

Brain Network Reconfiguration During Motor Imagery Revealed by a Large-Scale Network Analysis of Scalp EEG

2018 ◽  
Vol 32 (2) ◽  
pp. 304-314 ◽  
Author(s):  
Fali Li ◽  
Chanlin Yi ◽  
Limeng Song ◽  
Yuanling Jiang ◽  
Wenjing Peng ◽  
...  
Keyword(s):  
Network Analysis ◽  
Motor Imagery ◽  
Large Scale ◽  
Brain Network ◽  
Network Reconfiguration ◽  
Scalp Eeg ◽  
Large Scale Network ◽  
Scale Network

scholarly journals Complex Brain Network Analysis and Its Applications to Brain Disorders: A Survey

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-27 ◽  
Author(s):  
Jin Liu ◽  
Min Li ◽  
Yi Pan ◽  
Wei Lan ◽  
Ruiqing Zheng ◽  
...  
Keyword(s):  
Network Analysis ◽  
Brain Networks ◽  
Small World ◽  
Brain Regions ◽  
Brain Network ◽  
Brain Diseases ◽  
Brain Disorders ◽  
Comprehensive Overview ◽  
Rich Club ◽  
New Perspective

It is well known that most brain disorders are complex diseases, such as Alzheimer’s disease (AD) and schizophrenia (SCZ). In general, brain regions and their interactions can be modeled as complex brain network, which describe highly efficient information transmission in a brain. Therefore, complex brain network analysis plays an important role in the study of complex brain diseases. With the development of noninvasive neuroimaging and electrophysiological techniques, experimental data can be produced for constructing complex brain networks. In recent years, researchers have found that brain networks constructed by using neuroimaging data and electrophysiological data have many important topological properties, such as small-world property, modularity, and rich club. More importantly, many brain disorders have been found to be associated with the abnormal topological structures of brain networks. These findings provide not only a new perspective to explore the pathological mechanisms of brain disorders, but also guidance for early diagnosis and treatment of brain disorders. The purpose of this survey is to provide a comprehensive overview for complex brain network analysis and its applications to brain disorders.

scholarly journals Dynamic network properties of the interictal brain determine whether seizures appear focal or generalised

2019 ◽  
Author(s):  
Wessel Woldman ◽  
Helmut Schmidt ◽  
Eugenio Abela ◽  
Fahmida A. Chowdhury ◽  
Adam D. Pawley ◽  
...  
Keyword(s):  
Computer Model ◽  
Phase Locking ◽  
Brain Networks ◽  
Dynamic Network ◽  
Brain Network ◽  
Healthy Controls ◽  
Seizure Onset ◽  
Scalp Eeg ◽  
Network Properties ◽  
Interictal Discharges

AbstractObjectiveCurrent explanatory concepts suggest seizures emerge from ongoing dynamics of brain networks. It is unclear how brain network properties determine focal or generalised seizure onset, or how network properties can be described in a clinically-useful manner. Understanding network properties would cast light on seizure-generating mechanisms and allow to quantify in the clinic the extent to which a seizure is focal or generalised.Methods68 people with epilepsy and 38 healthy controls underwent 19 channel scalp EEG recording. Functional brain networks were estimated in each subject using phase-locking between EEG channels in the 6-9Hz band from segments of 20s without interictal discharges. Simplified brain dynamics were simulated using a computer model. We introduce three concepts: Critical Coupling (Cc), the ability of a network to generate seizures; Onset Index (OI), the tendency of a region to generate seizures; and Participation Index (PI), the tendency of a region to become involved in seizures.ResultsCc was lower in both patient groups compared with controls. OI and PI were more variable in focal-onset than generalised-onset cases. No regions showed higher OI and PI in generalised-onset cases than in healthy controls; in focal cases, the regions with highest OI and PI corresponded to the side of seizure onset.ConclusionsProperties of interictal functional networks from scalp EEG can be estimated using a computer model and used to predict seizure likelihood and onset patterns. Our framework, consisting of three clinically-meaningful measures, could be implemented in the clinic to quantify the diagnosis and seizure onset pattern.

P2-070: Increased randomness of functional brain networks in Alzheimer's disease: ‘small-world' network analysis of non-linear functional connectivity

2008 ◽  
Vol 4 ◽  
pp. T387-T388
Author(s):  
Ernesto J. Sanz-Arigita ◽  
Menno M. Schoonheim ◽  
Jeske S. Damoiseaux ◽  
Serge A.R.B. Rombouts ◽  
Frederik Barkhof ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Functional Connectivity ◽  
Network Analysis ◽  
Linear Functional ◽  
Brain Networks ◽  
Small World ◽  
Small World Network ◽  
Functional Brain ◽  
Non Linear ◽  
Functional Brain Networks

The Dynamic Brain Networks of Motor Imagery: Time-Varying Causality Analysis of Scalp EEG

2019 ◽  
Vol 29 (01) ◽  
pp. 1850016 ◽  
Author(s):  
Fali Li ◽  
Wenjing Peng ◽  
Yuanling Jiang ◽  
Limeng Song ◽  
Yuanyuan Liao ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Large Scale ◽  
Brain Networks ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Motor Preparation ◽  
Causality Analysis ◽  
Left Hand ◽  
Scalp Eeg ◽  
Dynamic Processing

Motor imagery (MI) requires subjects to visualize the requested motor behaviors, which involves a large-scale network that spans multiple brain areas. The corresponding cortical activity reflected on the scalp is characterized by event-related desynchronization (ERD) and then by event-related synchronization (ERS). However, the network mechanisms that account for the dynamic information processing of MI during the ERD and ERS periods remain unknown. Here, we combined ERD/ERS analysis with the dynamic networks in different MI stages (i.e. motor preparation, ERD and ERS) to probe the dynamic processing of MI information. Our results show that specific dynamic network structures correspond to the ERD/ERS evolution patterns. Specifically, ERD mainly shows the contralateral networks, while ERS has the symmetric networks. Moreover, different dynamic network patterns are also revealed between the two types of MIs, in which the left-hand MIs exhibit a relatively less sustained contralateral network, which may be the network mechanism that accounts for the bilateral ERD/ERS observed for the left-hand MIs. Similar to the network topologies, the three MI stages also appear to be characterized by different network properties. The above findings all demonstrate that different MI stages that involve specific brain networks for dynamically processing the MI information.

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