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.

scholarly journals Generative models of rich clubs in Hebbian neuronal networks and large-scale human brain networks

2014 ◽  
Vol 369 (1653) ◽  
pp. 20130531 ◽  
Author(s):  
Petra E. Vértes ◽  
Aaron Alexander-Bloch ◽  
Edward T. Bullmore
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Large Scale ◽  
Hebbian Learning ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Brain Network ◽  
Generative Models ◽  
Adaptive Behaviour ◽  
Topological Term

Rich clubs arise when nodes that are ‘rich’ in connections also form an elite, densely connected ‘club’. In brain networks, rich clubs incur high physical connection costs but also appear to be especially valuable to brain function. However, little is known about the selection pressures that drive their formation. Here, we take two complementary approaches to this question: firstly we show, using generative modelling, that the emergence of rich clubs in large-scale human brain networks can be driven by an economic trade-off between connection costs and a second, competing topological term. Secondly we show, using simulated neural networks, that Hebbian learning rules also drive the emergence of rich clubs at the microscopic level, and that the prominence of these features increases with learning time. These results suggest that Hebbian learning may provide a neuronal mechanism for the selection of complex features such as rich clubs. The neural networks that we investigate are explicitly Hebbian, and we argue that the topological term in our model of large-scale brain connectivity may represent an analogous connection rule. This putative link between learning and rich clubs is also consistent with predictions that integrative aspects of brain network organization are especially important for adaptive behaviour.

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 Multimodal Brain Network Jointly Construction and Fusion for Diagnosis of Epilepsy

2021 ◽  
Vol 15 ◽  
Author(s):  
Qi Zhu ◽  
Jing Yang ◽  
Bingliang Xu ◽  
Zhenghua Hou ◽  
Liang Sun ◽  
...  
Keyword(s):  
Network Analysis ◽  
Structural Information ◽  
Brain Networks ◽  
Structural Data ◽  
Disease Diagnosis ◽  
Brain Regions ◽  
Brain Network ◽  
Low Rank ◽  
Unified Framework ◽  
Network Construction

Brain network analysis has been proved to be one of the most effective methods in brain disease diagnosis. In order to construct discriminative brain networks and improve the performance of disease diagnosis, many machine learning–based methods have been proposed. Recent studies show that combining functional and structural brain networks is more effective than using only single modality data. However, in the most of existing multi-modal brain network analysis methods, it is a common strategy that constructs functional and structural network separately, which is difficult to embed complementary information of different modalities of brain network. To address this issue, we propose a unified brain network construction algorithm, which jointly learns both functional and structural data and effectively face the connectivity and node features for improving classification. First, we conduct space alignment and brain network construction under a unified framework, and then build the correlation model among all brain regions with functional data by low-rank representation so that the global brain region correlation can be captured. Simultaneously, the local manifold with structural data is embedded into this model to preserve the local structural information. Second, the PageRank algorithm is adaptively used to evaluate the significance of different brain regions, in which the interaction of multiple brain regions is considered. Finally, a multi-kernel strategy is utilized to solve the data heterogeneity problem and merge the connectivity as well as node information for classification. We apply the proposed method to the diagnosis of epilepsy, and the experimental results show that our method can achieve a promising performance.

scholarly journals Percolation Analysis of Brain Structural Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Shu Guo ◽  
Xiaoqi Chen ◽  
Yimeng Liu ◽  
Rui Kang ◽  
Tao Liu ◽  
...  
Keyword(s):  
Failure Modes ◽  
Critical Infrastructure ◽  
Network Reliability ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Structural Features ◽  
Brain Network ◽  
Structure Design ◽  
Reliability Design ◽  
The Brain

The brain network is one specific type of critical infrastructure networks, which supports the cognitive function of biological systems. With the importance of network reliability in system design, evaluation, operation, and maintenance, we use the percolation methods of network reliability on brain networks and study the network resistance to disturbances and relevant failure modes. In this paper, we compare the brain networks of different species, including cat, fly, human, mouse, and macaque. The differences in structural features reflect the requirements for varying levels of functional specialization and integration, which determine the reliability of brain networks. In the percolation process, we apply different forms of disturbances to the brain networks based on metrics that characterize the network structure. Our findings suggest that the brain networks are mostly reliable against random or k-core-based percolation with their structure design, yet becomes vulnerable under betweenness or degree-based percolation. Our results might be useful to identify and distinguish brain connectivity failures that have been shown to be related to brain disorders, as well as the reliability design of other technological networks.

scholarly journals Brain connectivity during Alzheimer’s disease progression and its cognitive impact in a transgenic rat model

2019 ◽  
Author(s):  
Emma Muñoz-Moreno ◽  
Raúl Tudela ◽  
Xavier López-Gil ◽  
Guadalupe Soria
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Transgenic Animals ◽  
Brain Network ◽  
Cognitive Outcome ◽  
Structural Network ◽  
New Treatments

ABSTRACTThe research of Alzheimer’s disease (AD) in their early stages and its progression till symptomatic onset is essential to understand the pathology and investigate new treatments. Animal models provide a helpful approach to this research, since they allow for controlled follow-up during the disease evolution. In this work, transgenic TgF344-AD rats were longitudinally evaluated starting at 6 months of age. Every 3 months, cognitive abilities were assessed by a memory-related task and magnetic resonance imaging (MRI) was acquired. Structural and functional brain networks were estimated and characterized by graph metrics to identify differences between the groups in connectivity, its evolution with age, and its influence on cognition. Structural networks of transgenic animals were altered since the earliest stage. Likewise, aging significantly affected network metrics in TgF344-AD, but not in the control group. In addition, while the structural brain network influenced cognitive outcome in transgenic animals, functional network impacted how control subjects performed. TgF344-AD brain network alterations were present from very early stages, difficult to identify in clinical research. Likewise, the characterization of aging in these animals, involving structural network reorganization and its effects on cognition, opens a window to evaluate new treatments for the disease.AUTHOR SUMMARYWe have applied magnetic resonance image based connectomics to characterize TgF344-AD rats, a transgenic model of Alzheimer’s disease (AD). This represents a highly translational approach, what is essential to investigate potential treatments. TgF344-AD animals were evaluated from early to advanced ages to describe alterations in brain connectivity and how brain networks are affected by age. Results showed that aging had a bigger impact in the structural connectivity of the TgF344-AD than in control animals, and that changes in the structural network, already observed at early ages, significantly influenced cognitive outcome of transgenic animals. Alterations in connectivity were similar to the described in AD human studies, and complement them providing insights into earlier stages and a plot of AD effects throughout the whole life span.

scholarly journals Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Ruedeerat Keerativittayayut ◽  
Ryuta Aoki ◽  
Mitra Taghizadeh Sarabi ◽  
Koji Jimura ◽  
Kiyoshi Nakahara
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Large Scale ◽  
Brain Regions ◽  
Brain Network ◽  
Time Varying ◽  
Memory Encoding ◽  
Graph Metrics ◽  
Connectivity Patterns ◽  
Network States

Although activation/deactivation of specific brain regions has been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here, we investigated time-varying functional connectivity patterns across the human brain in periods of 30–40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding.

scholarly journals Advances in Electrical Neuroimaging, Brain Networks and Neurofeedback Protocols

2020 ◽  
Author(s):  
Robert W. Thatcher ◽  
Carl J. Biver ◽  
Ernesto Palermero Soler ◽  
Joel Lubar ◽  
J. Lucas Koberda
Keyword(s):  
Human Brain ◽  
Real Time ◽  
Brain Mapping ◽  
Brain Networks ◽  
Three Dimensional ◽  
Brain Network ◽  
Eeg Biofeedback ◽  
Reference Database ◽  
Z Score ◽  
Electrical Neuroimaging

Human EEG biofeedback (neurofeedback) started in the 1940s using 1 EEG recording channel, then to 4 channels in the 1990s. New advancements in electrical neuroimaging expanded EEG biofeedback to 19 channels using Low Resolution Electromagnetic Tomography (LORETA) three-dimensional current sources of the EEG. In 2004–2006 the concept of a “real-time” comparison of the EEG to a healthy reference database was developed and tested using surface EEG z-score neurofeedback based on a statistical bell curve called “real-time” z-scores. The “real-time” or “live” normative reference database comparison was developed to help reduce the uncertainty of what threshold to select to activate a feedback signal and to unify all EEG measures to a single value, i.e., the distance from the mean of an age matched reference sample. In 2009 LORETA z-score neurofeedback further increased the specificity by targeting brain network hubs referred to as Brodmann areas. A symptom check list program to help link symptoms to dysregulation of brain networks based on fMRI and PET and neurology was created in 2009. The symptom checklist and NIH based networks linking symptoms to brain networks grew out of the human brain mapping program starting in 1990 which is continuing today. A goal is to increase specificity of EEG biofeedback by targeting brain network hubs and connections between hubs likely linked to the patient’s symptoms. New advancements in electrical neuroimaging introduced in 2017 provide increased resolution of three-dimensional source localization with 12,700 voxels using swLORETA with the capacity to conduct cerebellar neurofeedback and neurofeedback of subcortical brain hubs such as the thalamus, amygdala and habenula. Future applications of swLORETA z-score neurofeedback represents another example of the transfer of knowledge gained by the human brain mapping initiatives to further aid in helping people with cognition problems as well as balance problems and parkinsonism. A brief review of the past, present and future predictions of z-score neurofeedback are discussed with special emphasis on new developments that point toward a bright and enlightened future in the field of EEG biofeedback.

scholarly journals Mindfulness Meditation Is Related to Long-Lasting Changes in Hippocampal Functional Topology during Resting State: A Magnetoencephalography Study

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Anna Lardone ◽  
Marianna Liparoti ◽  
Pierpaolo Sorrentino ◽  
Rosaria Rucco ◽  
Francesca Jacini ◽  
...  
Keyword(s):  
Resting State ◽  
Mindfulness Meditation ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Brain Network ◽  
Long Term Effects ◽  
Age Related ◽  
Functional Topology ◽  
The Right ◽  
The Brain

It has been suggested that the practice of meditation is associated to neuroplasticity phenomena, reducing age-related brain degeneration and improving cognitive functions. Neuroimaging studies have shown that the brain connectivity changes in meditators. In the present work, we aim to describe the possible long-term effects of meditation on the brain networks. To this aim, we used magnetoencephalography to study functional resting-state brain networks in Vipassana meditators. We observed topological modifications in the brain network in meditators compared to controls. More specifically, in the theta band, the meditators showed statistically significant (p corrected = 0.009) higher degree (a centrality index that represents the number of connections incident upon a given node) in the right hippocampus as compared to controls. Taking into account the role of the hippocampus in memory processes, and in the pathophysiology of Alzheimer’s disease, meditation might have a potential role in a panel of preventive strategies.

scholarly journals A Generative Network Model of the Human Brain Normal Aging Process

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 91
Author(s):  
Xiao Liu ◽  
Shuaizong Si ◽  
Bo Hu ◽  
Hai Zhao ◽  
Jian Zhu
Keyword(s):  
Human Brain ◽  
Network Model ◽  
Aging Process ◽  
Brain Aging ◽  
Brain Networks ◽  
Normal Aging ◽  
Brain Network ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Generative Mechanisms

The human brain is approximately a symmetric structure, although the functional brain does not exhibit symmetry. Functional brain aging process modelling is essential for the understanding of hypothesized generative mechanisms for human brain networks throughout one’s lifespan. We present a novel generative network model of the human functional brain network, which is the hybrid of the local naïve Bayes model and the anatomical similarity correction (LNBE). We use LNBE, as well as published generative network models to simulate the aging process of the functional brain network, to construct artificial brain networks and to reveal the generative mechanisms and evolutionary patterns of human functional brain across human lifespans. It is suggested that the idea of classifying common neighbours while considering anatomical distances during network formation can provide a much more similar generative mechanism of the human fMRI brain aging process as well as a more practical generative network model of it. We hold that studies on brain normal aging process modelling have the potential to improve the way in which early warnings for latent injury or disease are practised today and advance healthcare.

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