{"title":"构建多频高阶功能连接网络诊断轻度认知障碍。","authors":"Yu Zhang, Han Zhang, Xiaobo Chen, Dinggang Shen","doi":"10.1007/978-3-319-67159-8_2","DOIUrl":null,"url":null,"abstract":"<p><p>Human brain functional connectivity (FC) networks, estimated based on resting-state functional magnetic resonance imaging (rs-fMRI), has become a promising tool for imaging-based brain disease diagnosis. Conventional low-order FC network (LON) usually characterizes pairwise temporal correlation of rs-fMRI signals between any pair of brain regions. Meanwhile, high-order FC network (HON) has provided an alternative brain network modeling strategy, characterizing more complex interactions among low-order FC sub-networks that involve multiple brain regions. However, both LON and HON are usually constructed within a fixed and relatively wide frequency band, which may fail in capturing (sensitive) frequency-specific FC changes caused by pathological attacks. To address this issue, we propose a novel \"multi-frequency HON construction\" method. Specifically, we construct <i>not only</i> multiple frequency-specific HONs (<i>intra-spectrum</i> HONs), <i>but also</i> a series of cross-frequency interaction-based HONs (<i>inter-spectrum</i> HONs) based on the low-order FC sub-networks constructed at different frequency bands. Both types of these HONs, together with the frequency-specific LONs, are used for the complex network analysis-based feature extraction, followed by sparse regression-based feature selection and the classification between mild cognitive impairment (MCI) patients and normal aging subjects using a support vector machine. Compared with the previous methods, our proposed method achieves the best diagnosis accuracy in early diagnosis of Alzheimer's disease.</p>","PeriodicalId":92190,"journal":{"name":"Connectomics in neuroimaging : First International Workshop, CNI 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, proceedings. CNI (Workshop) (1st : 2017 : Quebec, Quebec)","volume":"10511 ","pages":"9-16"},"PeriodicalIF":0.0000,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-67159-8_2","citationCount":"11","resultStr":"{\"title\":\"Constructing Multi-frequency High-Order Functional Connectivity Network for Diagnosis of Mild Cognitive Impairment.\",\"authors\":\"Yu Zhang, Han Zhang, Xiaobo Chen, Dinggang Shen\",\"doi\":\"10.1007/978-3-319-67159-8_2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Human brain functional connectivity (FC) networks, estimated based on resting-state functional magnetic resonance imaging (rs-fMRI), has become a promising tool for imaging-based brain disease diagnosis. Conventional low-order FC network (LON) usually characterizes pairwise temporal correlation of rs-fMRI signals between any pair of brain regions. Meanwhile, high-order FC network (HON) has provided an alternative brain network modeling strategy, characterizing more complex interactions among low-order FC sub-networks that involve multiple brain regions. However, both LON and HON are usually constructed within a fixed and relatively wide frequency band, which may fail in capturing (sensitive) frequency-specific FC changes caused by pathological attacks. To address this issue, we propose a novel \\\"multi-frequency HON construction\\\" method. Specifically, we construct <i>not only</i> multiple frequency-specific HONs (<i>intra-spectrum</i> HONs), <i>but also</i> a series of cross-frequency interaction-based HONs (<i>inter-spectrum</i> HONs) based on the low-order FC sub-networks constructed at different frequency bands. Both types of these HONs, together with the frequency-specific LONs, are used for the complex network analysis-based feature extraction, followed by sparse regression-based feature selection and the classification between mild cognitive impairment (MCI) patients and normal aging subjects using a support vector machine. Compared with the previous methods, our proposed method achieves the best diagnosis accuracy in early diagnosis of Alzheimer's disease.</p>\",\"PeriodicalId\":92190,\"journal\":{\"name\":\"Connectomics in neuroimaging : First International Workshop, CNI 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, proceedings. 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Constructing Multi-frequency High-Order Functional Connectivity Network for Diagnosis of Mild Cognitive Impairment.
Human brain functional connectivity (FC) networks, estimated based on resting-state functional magnetic resonance imaging (rs-fMRI), has become a promising tool for imaging-based brain disease diagnosis. Conventional low-order FC network (LON) usually characterizes pairwise temporal correlation of rs-fMRI signals between any pair of brain regions. Meanwhile, high-order FC network (HON) has provided an alternative brain network modeling strategy, characterizing more complex interactions among low-order FC sub-networks that involve multiple brain regions. However, both LON and HON are usually constructed within a fixed and relatively wide frequency band, which may fail in capturing (sensitive) frequency-specific FC changes caused by pathological attacks. To address this issue, we propose a novel "multi-frequency HON construction" method. Specifically, we construct not only multiple frequency-specific HONs (intra-spectrum HONs), but also a series of cross-frequency interaction-based HONs (inter-spectrum HONs) based on the low-order FC sub-networks constructed at different frequency bands. Both types of these HONs, together with the frequency-specific LONs, are used for the complex network analysis-based feature extraction, followed by sparse regression-based feature selection and the classification between mild cognitive impairment (MCI) patients and normal aging subjects using a support vector machine. Compared with the previous methods, our proposed method achieves the best diagnosis accuracy in early diagnosis of Alzheimer's disease.