Selecting Multiple Node Statistics Jointly from Functional Connectivity Networks for Brain Disorders Identification

Zhang, Yangyang; Xue, Yanfang; Wu, Xiao Ping; Qiao, Liyan; Wang, Zhengxia; Shen, Dinggang

doi:10.1007/s10548-022-00914-z

Cited by 5 publications

(4 citation statements)

References 47 publications

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“…In this section, we study the influence of feature selection and network modeling parameters on the identification outcome ( Jiang et al, 2019 ; Zhang et al, 2022 ; Xue et al, 2020 ; Jiao et al, 2022 ). Then, we discuss the most representative features obtained through the proposed approach to study the relationship with brain diseases.…”

Section: Discussionmentioning

confidence: 99%

Neighborhood structure-guided brain functional networks estimation for mild cognitive impairment identification

Liang,

Zhu,

et al. 2024

PeerJ

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The adoption and growth of functional magnetic resonance imaging (fMRI) technology, especially through the use of Pearson’s correlation (PC) for constructing brain functional networks (BFN), has significantly advanced brain disease diagnostics by uncovering the brain’s operational mechanisms and offering biomarkers for early detection. However, the PC always tends to make for a dense BFN, which violates the biological prior. Therefore, in practice, researchers use hard-threshold to remove weak connection edges or introduce l1-norm as a regularization term to obtain sparse BFNs. However, these approaches neglect the spatial neighborhood information between regions of interest (ROIs), and ROI with closer distances has higher connectivity prospects than ROI with farther distances due to the principle of simple wiring costs in resent studies. Thus, we propose a neighborhood structure-guided BFN estimation method in this article. In detail, we figure the ROIs’ Euclidean distances and sort them. Then, we apply the K-nearest neighbor (KNN) to find out the top K neighbors closest to the current ROIs, where each ROI’s K neighbors are independent of each other. We establish the connection relationship between the ROIs and these K neighbors and construct the global topology adjacency matrix according to the binary network. Connect ROI nodes with k nearest neighbors using edges to generate an adjacency graph, forming an adjacency matrix. Based on adjacency matrix, PC calculates the correlation coefficient between ROIs connected by edges, and generates the BFN. With the purpose of evaluating the performance of the introduced method, we utilize the estimated BFN for distinguishing individuals with mild cognitive impairment (MCI) from the healthy ones. Experimental outcomes imply this method attains better classification performance than the baselines. Additionally, we compared it with the most commonly used time series methods in deep learning. Results of the performance of K-nearest neighbor-Pearson’s correlation (K-PC) has some advantage over deep learning.

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Section: Discussionmentioning

confidence: 99%

Neighborhood structure-guided brain functional networks estimation for mild cognitive impairment identification

Liang,

Zhu,

et al. 2024

PeerJ

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“…(3) MNSFS: The method extracts multiple measures for each node from the estimated FCN, including local efficiency, three different definitions of local clustering coefficients, and four centralities. The measures belonging to each node are assigned to a group, followed by sparse group LASSO for feature selection and SVM for classification [46].…”

Section: B Competition Methodsmentioning

confidence: 99%

Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification

Fang

Wang

Potter

et al. 2023

Medical Image Analysis

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“…Node Statistics (NS): We design eight node statistics and concatenate them into a feature vector ( Zhang et al, 2022 ). In particular, these statistics include three definitions of local clustering coefficients ( Li, Shang & Yang, 2017 ), four centralities ( Hamilton, 2020 ) ( i.e., degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality), as well as local efficiency.…”

Section: Methodsmentioning

confidence: 99%

The effect of node features on GCN-based brain network classification: an empirical study

Wang

Zhang

Qiao

2023

PeerJ

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Brain functional network (BFN) analysis has become a popular technique for identifying neurological/mental diseases. Due to the fact that BFN is a graph, a graph convolutional network (GCN) can be naturally used in the classification of BFN. Different from traditional methods that directly use the adjacency matrices of BFNs to train a classifier, GCN requires an additional input-node features. To our best knowledge, however, there is no systematic study to analyze their influence on the performance of GCN-based brain disorder classification. Therefore, in this study, we conduct an empirical study on various node feature measures, including (1) original fMRI signals, (2) one-hot encoding, (3) node statistics, (4) node correlation, and (5) their combination. Experimental results on two benchmark databases show that different node feature inputs to GCN significantly affect the brain disease classification performance, and node correlation usually contributes higher accuracy compared to original signals and manually extracted statistical features.

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Selecting Multiple Node Statistics Jointly from Functional Connectivity Networks for Brain Disorders Identification

Cited by 5 publications

References 47 publications

Neighborhood structure-guided brain functional networks estimation for mild cognitive impairment identification

Neighborhood structure-guided brain functional networks estimation for mild cognitive impairment identification

Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification

The effect of node features on GCN-based brain network classification: an empirical study

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