Hybrid High-order Functional Connectivity Networks Using Resting-state Functional MRI for Mild Cognitive Impairment Diagnosis

Abstract Conventional functional connectivity (FC), referred to as low-order FC, estimates temporal correlation of the resting-state functional magnetic resonance imaging (rs-fMRI) time series between any pair of brain regions, simply ignoring the potentially high-level relationship among these brai...

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Autores principales:	Yu Zhang, Han Zhang, Xiaobo Chen, Seong-Whan Lee, Dinggang Shen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8aa6da1328494353a6685e96cb7232c5
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spelling	oai:doaj.org-article:8aa6da1328494353a6685e96cb7232c52021-12-02T12:31:47ZHybrid High-order Functional Connectivity Networks Using Resting-state Functional MRI for Mild Cognitive Impairment Diagnosis10.1038/s41598-017-06509-02045-2322https://doaj.org/article/8aa6da1328494353a6685e96cb7232c52017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-06509-0https://doaj.org/toc/2045-2322Abstract Conventional functional connectivity (FC), referred to as low-order FC, estimates temporal correlation of the resting-state functional magnetic resonance imaging (rs-fMRI) time series between any pair of brain regions, simply ignoring the potentially high-level relationship among these brain regions. A high-order FC based on “correlation’s correlation” has emerged as a new approach for abnormality detection of brain disease. However, separate construction of the low- and high-order FC networks overlooks information exchange between the two FC levels. Such a higher-level relationship could be more important for brain diseases study. In this paper, we propose a novel framework, namely “hybrid high-order FC networks” by exploiting the higher-level dynamic interaction among brain regions for early mild cognitive impairment (eMCI) diagnosis. For each sliding window-based rs-fMRI sub-series, we construct a whole-brain associated high-order network, by estimating the correlations between the topographical information of the high-order FC sub-network from one brain region and that of the low-order FC sub-network from another brain region. With multi-kernel learning, complementary features from multiple time-varying FC networks constructed at different levels are fused for eMCI classification. Compared with other state-of-the-art methods, the proposed framework achieves superior diagnosis accuracy, and hence could be promising for understanding pathological changes of brain connectome.Yu ZhangHan ZhangXiaobo ChenSeong-Whan LeeDinggang ShenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-15 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yu Zhang Han Zhang Xiaobo Chen Seong-Whan Lee Dinggang Shen Hybrid High-order Functional Connectivity Networks Using Resting-state Functional MRI for Mild Cognitive Impairment Diagnosis
description	Abstract Conventional functional connectivity (FC), referred to as low-order FC, estimates temporal correlation of the resting-state functional magnetic resonance imaging (rs-fMRI) time series between any pair of brain regions, simply ignoring the potentially high-level relationship among these brain regions. A high-order FC based on “correlation’s correlation” has emerged as a new approach for abnormality detection of brain disease. However, separate construction of the low- and high-order FC networks overlooks information exchange between the two FC levels. Such a higher-level relationship could be more important for brain diseases study. In this paper, we propose a novel framework, namely “hybrid high-order FC networks” by exploiting the higher-level dynamic interaction among brain regions for early mild cognitive impairment (eMCI) diagnosis. For each sliding window-based rs-fMRI sub-series, we construct a whole-brain associated high-order network, by estimating the correlations between the topographical information of the high-order FC sub-network from one brain region and that of the low-order FC sub-network from another brain region. With multi-kernel learning, complementary features from multiple time-varying FC networks constructed at different levels are fused for eMCI classification. Compared with other state-of-the-art methods, the proposed framework achieves superior diagnosis accuracy, and hence could be promising for understanding pathological changes of brain connectome.
format	article
author	Yu Zhang Han Zhang Xiaobo Chen Seong-Whan Lee Dinggang Shen
author_facet	Yu Zhang Han Zhang Xiaobo Chen Seong-Whan Lee Dinggang Shen
author_sort	Yu Zhang
title	Hybrid High-order Functional Connectivity Networks Using Resting-state Functional MRI for Mild Cognitive Impairment Diagnosis
title_short	Hybrid High-order Functional Connectivity Networks Using Resting-state Functional MRI for Mild Cognitive Impairment Diagnosis
title_full	Hybrid High-order Functional Connectivity Networks Using Resting-state Functional MRI for Mild Cognitive Impairment Diagnosis
title_fullStr	Hybrid High-order Functional Connectivity Networks Using Resting-state Functional MRI for Mild Cognitive Impairment Diagnosis
title_full_unstemmed	Hybrid High-order Functional Connectivity Networks Using Resting-state Functional MRI for Mild Cognitive Impairment Diagnosis
title_sort	hybrid high-order functional connectivity networks using resting-state functional mri for mild cognitive impairment diagnosis
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/8aa6da1328494353a6685e96cb7232c5
work_keys_str_mv	AT yuzhang hybridhighorderfunctionalconnectivitynetworksusingrestingstatefunctionalmriformildcognitiveimpairmentdiagnosis AT hanzhang hybridhighorderfunctionalconnectivitynetworksusingrestingstatefunctionalmriformildcognitiveimpairmentdiagnosis AT xiaobochen hybridhighorderfunctionalconnectivitynetworksusingrestingstatefunctionalmriformildcognitiveimpairmentdiagnosis AT seongwhanlee hybridhighorderfunctionalconnectivitynetworksusingrestingstatefunctionalmriformildcognitiveimpairmentdiagnosis AT dinggangshen hybridhighorderfunctionalconnectivitynetworksusingrestingstatefunctionalmriformildcognitiveimpairmentdiagnosis
_version_	1718394290878545920

Hybrid High-order Functional Connectivity Networks Using Resting-state Functional MRI for Mild Cognitive Impairment Diagnosis

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