Brain functional connectivity analysis based on multi-graph fusion

Gan, Jiangzhang; Peng, Ziwen; Zhu, Xiaofeng; Hu, Rongyao; Ma, Junbo; Wu, Guorong

doi:10.1016/j.media.2021.102057

Cited by 36 publications

(13 citation statements)

References 42 publications

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“…The preprocessed BOLD signal is used in some high‐dimensional models 46,47 ; however, functional connectivity measures are the most prominent fMRI measure. There are different methods of quantifying functional connectivity for analysis 16,34,35,48,49 . The main two measures used in the reviewed literature are Pearson's correlation and ICA.…”

Section: Resultsmentioning

confidence: 99%

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Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review

Warren

Moustafa

2022

Journal of Neuroimaging

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Alzheimer's disease (AD) is currently diagnosed using a mixture of psychological tests and clinical observations. However, these diagnoses are not perfect, and additional diagnostic tools (e.g., MRI) can help improve our understanding of AD as well as our ability to detect the disease. Accordingly, a large amount of research has been invested into innovative diagnostic methods for AD. Functional MRI (fMRI) is a form of neuroimaging technology that has been used to diagnose AD; however, fMRI is incredibly noisy, complex, and thus lacks clinical use. Nonetheless, recent innovations in deep learning technology could enable the simplified and streamlined analysis of fMRI. Deep learning is a form of artificial intelligence that uses computer algorithms based on human neural networks to solve complex problems. For example, in fMRI research, deep learning models can automatically denoise images and classify AD by detecting patterns in participants' brain scans. In this systematic review, we investigate how fMRI (specifically resting-state fMRI) and deep learning methods are used to diagnose AD. In turn, we outline the common deep neural network, preprocessing, and classification methods used in the literature. We also discuss the accuracy, strengths, limitations, and future direction of fMRI deep learning methods. In turn, we aim to summarize the current field for new researchers, suggest specific areas for future research, and highlight the potential of fMRI to aid AD diagnoses.

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Section: Resultsmentioning

confidence: 99%

“…Deep learning models use computer algorithms based on human learning to solve complex problems 34 . In the case of fMRI, some studies have shown that typical problems—such as the high computational cost associated with cleaning and analyzing fMRI data—can be overcome using deep learning models 35,36 . For example, Parmar et al 37 .…”

Section: Introductionmentioning

confidence: 99%

Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review

Warren

Moustafa

2022

Journal of Neuroimaging

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“…Wee et al ( 2012 ) designed a multimodality classification framework based on a multi-kernel support vector machine (SVM). Gan et al ( 2021 ) developed a multi-graph fusion method to fuse FC networks, and employed sparse-SVM to jointly conduct brain region selection and disease diagnosis. Several studies have integrated multiple properties of FC networks for the diagnosis of MCI (Zanin et al, 2012 ; Jie et al, 2018 ), autism (Anderson et al, 2011 ), and early tourette syndrome in children (Wen et al, 2017 ).…”

Section: Related Studymentioning

confidence: 99%

Convolutional Recurrent Neural Network for Dynamic Functional MRI Analysis and Brain Disease Identification

et al. 2022

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Dynamic functional connectivity (dFC) networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) help us understand fundamental dynamic characteristics of human brains, thereby providing an efficient solution for automated identification of brain diseases, such as Alzheimer's disease (AD) and its prodromal stage. Existing studies have applied deep learning methods to dFC network analysis and achieved good performance compared with traditional machine learning methods. However, they seldom take advantage of sequential information conveyed in dFC networks that could be informative to improve the diagnosis performance. In this paper, we propose a convolutional recurrent neural network (CRNN) for automated brain disease classification with rs-fMRI data. Specifically, we first construct dFC networks from rs-fMRI data using a sliding window strategy. Then, we employ three convolutional layers and long short-term memory (LSTM) layer to extract high-level features of dFC networks and also preserve the sequential information of extracted features, followed by three fully connected layers for brain disease classification. Experimental results on 174 subjects with 563 rs-fMRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate the effectiveness of our proposed method in binary and multi-category classification tasks.

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“…Resting-state functional magnetic resonance imaging (rs-fMRI) based on blood oxygen level dependent (BOLD) signal imaging is an important tool to explore brain mechanism and pathology (Chen et al, 2016;Gan et al, 2021). Rs-fMRI can realize non-invasive study of brain function high spatial resolution, which cannot only reflect the local spatial function information of the brain, but also maintain detailed functional connectivity maps of the brain (Zhi et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Multi-View Feature Enhancement Based on Self-Attention Mechanism Graph Convolutional Network for Autism Spectrum Disorder Diagnosis

Zhao

Pan

et al. 2022

Front. Hum. Neurosci.

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Functional connectivity (FC) network based on resting-state functional magnetic resonance imaging (rs-fMRI) has become an important tool to explore and understand the brain, which can provide objective basis for the diagnosis of neurodegenerative diseases, such as autism spectrum disorder (ASD). However, most functional connectivity (FC) networks only consider the unilateral features of nodes or edges, and the interaction between them is ignored. In fact, their integration can provide more comprehensive and crucial information in the diagnosis. To address this issue, a new multi-view brain network feature enhancement method based on self-attention mechanism graph convolutional network (SA-GCN) is proposed in this article, which can enhance node features through the connection relationship among different nodes, and then extract deep-seated and more discriminative features. Specifically, we first plug the pooling operation of self-attention mechanism into graph convolutional network (GCN), which can consider the node features and topology of graph network at the same time and then capture more discriminative features. In addition, the sample size is augmented by a “sliding window” strategy, which is beneficial to avoid overfitting and enhance the generalization ability. Furthermore, to fully explore the complex connection relationship among brain regions, we constructed the low-order functional graph network (Lo-FGN) and the high-order functional graph network (Ho-FGN) and enhance the features of the two functional graph networks (FGNs) based on SA-GCN. The experimental results on benchmark datasets show that: (1) SA-GCN can play a role in feature enhancement and can effectively extract more discriminative features, and (2) the integration of Lo-FGN and Ho-FGN can achieve the best ASD classification accuracy (79.9%), which reveals the information complementarity between them.

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Brain functional connectivity analysis based on multi-graph fusion

Cited by 36 publications

References 42 publications

Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review

Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review

Convolutional Recurrent Neural Network for Dynamic Functional MRI Analysis and Brain Disease Identification

Multi-View Feature Enhancement Based on Self-Attention Mechanism Graph Convolutional Network for Autism Spectrum Disorder Diagnosis

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