Joint selection of brain network nodes and edges for MCI identification

Xiao, Jiang; Qiao, Liyan; Leone, Renato De; Shen, Dinggang

doi:10.1016/j.cmpb.2022.107082

Cited by 8 publications

(4 citation statements)

References 23 publications

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“…In general, different BFN estimation methods have a significant influence on the classification performance ( Sun et al, 2021 ; Jiang et al, 2022 ). Therefore, we discuss the effect of node features based on BFNs estimated by different methods, including PC, sparse representation (SR) ( Qiao et al, 2018 ), and low-rank representation (LR) ( Qiao, Chen & Tan, 2010 ).…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

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

Wang

Zhang

Qiao

2023

PeerJ

Self Cite

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“…This indicates that the feature dimension is much larger than the sample size (i.e., the number of subjects), which not only brings about expensive computation, but also has the potential to negatively impact classification accuracy due to the so-called curse of dimensionality. To alleviate this situation, researchers have proposed many methods for feature selection such as t-test, least absolute shrinkage and selection operator (lasso) [21], GA [22], sparse group least absolute shrinkage and selection operator (sgLASSO) [23] and so on. In this paper, we use the popular t-test method for feature selection, and set the p-value to 0.05 and in order to provide a more comprehensive assessment of the effectiveness of the proposed method, we have incorporated the sgLASSO as a benchmark for feature selection.…”

Section: ( )mentioning

confidence: 99%

Estimating Brain Functional Networks Based on Spatiotemporal Higher-Order Correlations for Autism Identification

Pang,

Zhang,

Liu

et al. 2023

JCC

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Brain functional network (BFN) has become an important tool for the analysis and diagnosis of brain diseases, and how to build a high-quality BFN based on resting-state functional magnetic resonance imaging (rs-fMRI) has become a growing concern in the neuroscience community. Although some methods have been proposed to construct a high-quality BFN, they only encode the spatial characteristics of the ROIs, ignoring the temporal characteristics. As a result, it becomes challenging to accurately capture the true state of the brain. To address this problem, we propose a novel method to construct a higher-order BFN, considering both temporal and spatial domain characteristics. In particular, we get the characteristics of the temporal domain by differentiating the rs-fMRI signal itself, and then we integrate the information of the spatial domain and temporal domain to build a high-order BFN. To evaluate the proposed method, we conduct our experiments on ABIDE database to identify subjects with Autism Spectrum Disorder (ASD) from normal controls. Experimental results show that our method can achieve higher performance than baseline methods.

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“…In general, different BFN estimation methods have a significant influence on the classification performance [17, 18]. Therefore, we discuss the effect of node features based on BFNs estimated by different methods, including PC, sparse representation (SR) [19], and low-rank representation (LR) [20].…”

Section: Empirical Studiesmentioning

confidence: 99%

The Effect of Node Features on GCN-based Brain Network Classification: An Empirical Study

Wang

Zhang

Qiao

2022

Preprint

Self Cite

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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, 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 i.e.,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 per, 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.

show abstract

Joint selection of brain network nodes and edges for MCI identification

Cited by 8 publications

References 23 publications

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

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

Estimating Brain Functional Networks Based on Spatiotemporal Higher-Order Correlations for Autism Identification

The Effect of Node Features on GCN-based Brain Network Classification: An Empirical Study

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