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

Liu, Jin; Li, Min; Pan, Yi; Lan, Wei; Zheng, Ruiqing; Wu, Fang‐Xiang; Wang, Jianxin

doi:10.1155/2017/8362741

Cited by 115 publications

(81 citation statements)

References 172 publications

(187 reference statements)

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“…Accidental damage or degeneration of this area due to a brain disease, such as AD or frontotemporal dementia, causes various problems in daily life in our rapidly aging society. Several studies have used functional connectivity as a marker for detecting reduced cognitive performance, as it is directly related to cognitive performance [14,[78][79][80][81][82]. Therefore, fNIRS imaging is an important tool for the early detection of brain diseases, and combining it with tDCS is ideal due to the optical nature of fNIRS, which is not affected by the electric field during stimulation.…”

Section: Discussionmentioning

confidence: 99%

Effects of HD-tDCS on Resting-State Functional Connectivity in the Prefrontal Cortex: An fNIRS Study

2018

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Functional connectivity is linked to several degenerative brain diseases prevalent in our aging society. Electrical stimulation is used for the clinical treatment and rehabilitation of patients with many cognitive disorders. In this study, the effects of high-definition transcranial direct current stimulation (HD-tDCS) on resting-state brain networks in the human prefrontal cortex were investigated by using functional near-infrared spectroscopy (fNIRS). The intrahemispheric as well as interhemispheric connectivity changes induced by 1 mA HD-tDCS were examined in 15 healthy subjects. Pearson correlation coefficient-based correlation matrices were generated from filtered time series oxyhemoglobin (ΔHbO) signals and converted into binary matrices. Common graph theory metrics were computed to evaluate the network changes. Systematic interhemispheric, intrahemispheric, and intraregional connectivity analyses demonstrated that the stimulation positively affected the resting-state connectivity in the prefrontal cortex. The poststimulation connectivity was increased throughout the prefrontal region, while focal HD-tDCS effects induced an increased rate of connectivity in the stimulated hemisphere. The graph theory metrics clearly distinguished the prestimulation and poststimulation networks for a range of thresholds. The results of this study suggest that HD-tDCS can be used to increase functional connectivity in the prefrontal cortex. The increase in functional connectivity can be explored clinically for neurorehabilitation of patients with degenerative brain diseases.

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Section: Discussionmentioning

confidence: 99%

Effects of HD-tDCS on Resting-State Functional Connectivity in the Prefrontal Cortex: An fNIRS Study

2018

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“…Brain network analysis has been an emerging research area, as it yields new insights concerning the understanding of brain function and many neurological disorders [35]. Existing works in brain networks mainly focus on discovering brain network from spatio-temporal voxel-level data or mining from brain networks for neurological analysis [36], [23], [37], [10], [7], [38].…”

Section: Related Workmentioning

confidence: 99%

Community-preserving Graph Convolutions for Structural and Functional Joint Embedding of Brain Networks

Jiang

et al. 2019

2019 IEEE International Conference on Big Data (Big Data)

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Brain networks have received considerable attention given the critical significance for understanding human brain organization, for investigating neurological disorders and for clinical diagnostic applications. Structural brain network (e.g. DTI) and functional brain network (e.g. fMRI) are the primary networks of interest. Most existing works in brain network analysis focus on either structural or functional connectivity, which cannot leverage the complementary information from each other. Although multi-view learning methods have been proposed to learn from both networks (or views), these methods aim to reach a consensus among multiple views, and thus distinct intrinsic properties of each view may be ignored. How to jointly learn representations from structural and functional brain networks while preserving their inherent properties is a critical problem.In this paper, we propose a framework of Siamese communitypreserving graph convolutional network (SCP-GCN) to learn the structural and functional joint embedding of brain networks. Specifically, we use graph convolutions to learn the structural and functional joint embedding, where the graph structure is defined with structural connectivity and node features are from the functional connectivity. Moreover, we propose to preserve the community structure of brain networks in the graph convolutions by considering the intra-community and inter-community properties in the learning process. Furthermore, we use Siamese architecture which models the pair-wise similarity learning to guide the learning process. To evaluate the proposed approach, we conduct extensive experiments on two real brain network datasets. The experimental results demonstrate the superior performance of the proposed approach in structural and functional joint embedding for neurological disorder analysis, indicating its promising value for clinical applications.

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“…Nodes of a functional brain network could be the voxels of fMRI data, ROIs defined by brain atlas or the discrete regions with similar size by randomly parcellating the brain (Fornito et al, 2013). Links of a functional brain network could be determined by the correlations estimated from time courses between pairs of nodes (Liu et al, 2017a). For example, Yu et al (2015) created functional brain network using group ICA and Pearson correlation coefficient, and they found the new evidence about altered dynamic brain graphs in SZ.…”

Section: Introductionmentioning

confidence: 99%

“…To quantitatively analyze functional brain networks, graph theoretical analysis is employed for investigating the topological organization of functional connectivity (Anderson and Cohen, 2013;Brier et al, 2014). The most commonly used graph measures include betweenness centrality, degree, local efficiency, participation coefficient, average clustering coefficient, average path length, global efficiency, and small-worldness (Liu et al, 2017a). These topological measures have been applied in the brain disease classifications (Cheng et al, 2015;Khazaee et al, 2015Khazaee et al, , 2017Moghimi et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Schizophrenia Identification Using Multi-View Graph Measures of Functional Brain Networks

Xiang

Wang

Tan

et al. 2020

Front. Bioeng. Biotechnol.

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Schizophrenia (SZ) is a functional mental disorder that seriously affects the social life of patients. Therefore, accurate diagnosis of SZ has raised extensive attention of researchers. At present, study of brain network based on resting-state functional magnetic resonance imaging (rs-fMRI) has provided promising results for SZ identification by studying functional network alteration. However, previous studies based on brain network analysis are not very effective for SZ identification. Therefore, we propose an improved SZ identification method using multi-view graph measures of functional brain networks. Firstly, we construct an individual functional connectivity network based on Brainnetome atlas for each subject. Then, multi-view graph measures are calculated by the brain network analysis method as feature representations. Next, in order to consider the relationships between measures within the same brain region in feature selection, multi-view measures are grouped according to the corresponding regions and Sparse Group Lasso is applied to identify discriminative features based on this feature grouping structure. Finally, a support vector machine (SVM) classifier is employed to perform SZ identification task. To evaluate our proposed method, computational experiments are conducted on 145 subjects (71 schizophrenic patients and 74 healthy controls) using a leave-one-out cross-validation (LOOCV) scheme. The results show that our proposed method can obtain an accuracy of 93.10% for SZ identification. By comparison, our method is more effective for SZ identification than some existing methods.

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

Cited by 115 publications

References 172 publications

Effects of HD-tDCS on Resting-State Functional Connectivity in the Prefrontal Cortex: An fNIRS Study

Effects of HD-tDCS on Resting-State Functional Connectivity in the Prefrontal Cortex: An fNIRS Study

Community-preserving Graph Convolutions for Structural and Functional Joint Embedding of Brain Networks

Schizophrenia Identification Using Multi-View Graph Measures of Functional Brain Networks

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