Constructing multi-frequency high-order functional connectivity network for diagnosis of mild cognitive impairment

Yu Zhang, Han Zhang, Xiaobo Chen, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationConnectomics in NeuroImaging - 1st International Workshop, CNI 2017 Held in Conjunction with MICCAI 2017, Proceedings
PublisherSpringer Verlag
Pages9-16
Number of pages8
Volume10511 LNCS
ISBN (Print)9783319671581
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event1st International Workshop on Connectomics in NeuroImaging, CNI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: 2017 Sep 142017 Sep 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10511 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st International Workshop on Connectomics in NeuroImaging, CNI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
CountryCanada
CityQuebec City
Period17/9/1417/9/14

Fingerprint

Network Connectivity
Brain
Higher Order
Frequency bands
Connectivity
Feature extraction
Functional Magnetic Resonance Imaging
Complex networks
Electric network analysis
Support vector machines
Alzheimer's Disease
Temporal Correlation
Network Modeling
Complex Analysis
Network Analysis
Aging of materials
Interaction
Complex Networks
Feature Selection
Imaging techniques

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Zhang, Y., Zhang, H., Chen, X., & Shen, D. (2017). Constructing multi-frequency high-order functional connectivity network for diagnosis of mild cognitive impairment. In Connectomics in NeuroImaging - 1st International Workshop, CNI 2017 Held in Conjunction with MICCAI 2017, Proceedings (Vol. 10511 LNCS, pp. 9-16). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10511 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-67159-8_2

Constructing multi-frequency high-order functional connectivity network for diagnosis of mild cognitive impairment. / Zhang, Yu; Zhang, Han; Chen, Xiaobo; Shen, Dinggang.

Connectomics in NeuroImaging - 1st International Workshop, CNI 2017 Held in Conjunction with MICCAI 2017, Proceedings. Vol. 10511 LNCS Springer Verlag, 2017. p. 9-16 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10511 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zhang, Y, Zhang, H, Chen, X & Shen, D 2017, Constructing multi-frequency high-order functional connectivity network for diagnosis of mild cognitive impairment. in Connectomics in NeuroImaging - 1st International Workshop, CNI 2017 Held in Conjunction with MICCAI 2017, Proceedings. vol. 10511 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10511 LNCS, Springer Verlag, pp. 9-16, 1st International Workshop on Connectomics in NeuroImaging, CNI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017, Quebec City, Canada, 17/9/14. https://doi.org/10.1007/978-3-319-67159-8_2
Zhang Y, Zhang H, Chen X, Shen D. Constructing multi-frequency high-order functional connectivity network for diagnosis of mild cognitive impairment. In Connectomics in NeuroImaging - 1st International Workshop, CNI 2017 Held in Conjunction with MICCAI 2017, Proceedings. Vol. 10511 LNCS. Springer Verlag. 2017. p. 9-16. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-67159-8_2
Zhang, Yu ; Zhang, Han ; Chen, Xiaobo ; Shen, Dinggang. / Constructing multi-frequency high-order functional connectivity network for diagnosis of mild cognitive impairment. Connectomics in NeuroImaging - 1st International Workshop, CNI 2017 Held in Conjunction with MICCAI 2017, Proceedings. Vol. 10511 LNCS Springer Verlag, 2017. pp. 9-16 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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