Depression Classification Using Frequent Subgraph Mining Based on Pattern Growth of Frequent Edge in Functional Magnetic Resonance Imaging Uncertain Network

The brain network structure is highly uncertain due to the noise in imaging signals and evaluation methods. Recent works have shown that uncertain brain networks could capture uncertain information with regards to functional connections. Most of the existing research studies covering uncertain brain...

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Main Authors: Yao Li, Zihao Zhou, Qifan Li, Tao Li, Ibegbu Nnamdi Julian, Hao Guo, Junjie Chen
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-04-01
Series:Frontiers in Neuroscience
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fnins.2022.889105/full
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author Yao Li
Zihao Zhou
Qifan Li
Tao Li
Ibegbu Nnamdi Julian
Hao Guo
Junjie Chen
author_facet Yao Li
Zihao Zhou
Qifan Li
Tao Li
Ibegbu Nnamdi Julian
Hao Guo
Junjie Chen
author_sort Yao Li
collection DOAJ
description The brain network structure is highly uncertain due to the noise in imaging signals and evaluation methods. Recent works have shown that uncertain brain networks could capture uncertain information with regards to functional connections. Most of the existing research studies covering uncertain brain networks used graph mining methods for analysis; for example, the mining uncertain subgraph patterns (MUSE) method was used to mine frequent subgraphs and the discriminative feature selection for uncertain graph classification (DUG) method was used to select discriminant subgraphs. However, these methods led to a lack of effective discriminative information; this reduced the classification accuracy for brain diseases. Therefore, considering these problems, we propose an approximate frequent subgraph mining algorithm based on pattern growth of frequent edge (unFEPG) for uncertain brain networks and a novel discriminative feature selection method based on statistical index (dfsSI) to perform graph mining and selection. Results showed that compared with the conventional methods, the unFEPG and dfsSI methods achieved a higher classification accuracy. Furthermore, to demonstrate the efficacy of the proposed method, we used consistent discriminative subgraph patterns based on thresholding and weighting approaches to compare the classification performance of uncertain networks and certain networks in a bidirectional manner. Results showed that classification performance of the uncertain network was superior to that of the certain network within a defined sparsity range. This indicated that if a better classification performance is to be achieved, it is necessary to select a certain brain network with a higher threshold or an uncertain brain network model. Moreover, if the uncertain brain network model was selected, it is necessary to make full use of the uncertain information of its functional connection.
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spelling doaj.art-bbea5b2303fa441fad8eb055aabac6472022-12-22T02:07:01ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2022-04-011610.3389/fnins.2022.889105889105Depression Classification Using Frequent Subgraph Mining Based on Pattern Growth of Frequent Edge in Functional Magnetic Resonance Imaging Uncertain NetworkYao Li0Zihao Zhou1Qifan Li2Tao Li3Ibegbu Nnamdi Julian4Hao Guo5Junjie Chen6College of Information and Computer, Taiyuan University of Technology, Taiyuan, ChinaCollege of Mathematics, Taiyuan University of Technology, Taiyuan, ChinaCollege of Information and Computer, Taiyuan University of Technology, Taiyuan, ChinaCollege of Information and Computer, Taiyuan University of Technology, Taiyuan, ChinaCollege of Information and Computer, Taiyuan University of Technology, Taiyuan, ChinaCollege of Information and Computer, Taiyuan University of Technology, Taiyuan, ChinaCollege of Information and Computer, Taiyuan University of Technology, Taiyuan, ChinaThe brain network structure is highly uncertain due to the noise in imaging signals and evaluation methods. Recent works have shown that uncertain brain networks could capture uncertain information with regards to functional connections. Most of the existing research studies covering uncertain brain networks used graph mining methods for analysis; for example, the mining uncertain subgraph patterns (MUSE) method was used to mine frequent subgraphs and the discriminative feature selection for uncertain graph classification (DUG) method was used to select discriminant subgraphs. However, these methods led to a lack of effective discriminative information; this reduced the classification accuracy for brain diseases. Therefore, considering these problems, we propose an approximate frequent subgraph mining algorithm based on pattern growth of frequent edge (unFEPG) for uncertain brain networks and a novel discriminative feature selection method based on statistical index (dfsSI) to perform graph mining and selection. Results showed that compared with the conventional methods, the unFEPG and dfsSI methods achieved a higher classification accuracy. Furthermore, to demonstrate the efficacy of the proposed method, we used consistent discriminative subgraph patterns based on thresholding and weighting approaches to compare the classification performance of uncertain networks and certain networks in a bidirectional manner. Results showed that classification performance of the uncertain network was superior to that of the certain network within a defined sparsity range. This indicated that if a better classification performance is to be achieved, it is necessary to select a certain brain network with a higher threshold or an uncertain brain network model. Moreover, if the uncertain brain network model was selected, it is necessary to make full use of the uncertain information of its functional connection.https://www.frontiersin.org/articles/10.3389/fnins.2022.889105/fullfrequent subgraph miningdiscriminative feature selectionmachine learningclassificationfMRIdepression
spellingShingle Yao Li
Zihao Zhou
Qifan Li
Tao Li
Ibegbu Nnamdi Julian
Hao Guo
Junjie Chen
Depression Classification Using Frequent Subgraph Mining Based on Pattern Growth of Frequent Edge in Functional Magnetic Resonance Imaging Uncertain Network
Frontiers in Neuroscience
frequent subgraph mining
discriminative feature selection
machine learning
classification
fMRI
depression
title Depression Classification Using Frequent Subgraph Mining Based on Pattern Growth of Frequent Edge in Functional Magnetic Resonance Imaging Uncertain Network
title_full Depression Classification Using Frequent Subgraph Mining Based on Pattern Growth of Frequent Edge in Functional Magnetic Resonance Imaging Uncertain Network
title_fullStr Depression Classification Using Frequent Subgraph Mining Based on Pattern Growth of Frequent Edge in Functional Magnetic Resonance Imaging Uncertain Network
title_full_unstemmed Depression Classification Using Frequent Subgraph Mining Based on Pattern Growth of Frequent Edge in Functional Magnetic Resonance Imaging Uncertain Network
title_short Depression Classification Using Frequent Subgraph Mining Based on Pattern Growth of Frequent Edge in Functional Magnetic Resonance Imaging Uncertain Network
title_sort depression classification using frequent subgraph mining based on pattern growth of frequent edge in functional magnetic resonance imaging uncertain network
topic frequent subgraph mining
discriminative feature selection
machine learning
classification
fMRI
depression
url https://www.frontiersin.org/articles/10.3389/fnins.2022.889105/full
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