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Fusion of ULS Group Constrained High- and Low-Order Sparse Functional Connectivity Networks for MCI Classification

  • Yang Li
  • Jingyu LiuEmail author
  • Ziwen Peng
  • Can Sheng
  • Minjeong Kim
  • Pew-Thian Yap
  • Chong-Yaw Wee
  • Dinggang ShenEmail author
Original Article

Abstract

Functional connectivity networks, derived from resting-state fMRI data, have been found as effective biomarkers for identifying mild cognitive impairment (MCI) from healthy elderly. However, the traditional functional connectivity network is essentially a low-order network with the assumption that the brain activity is static over the entire scanning period, ignoring temporal variations among the correlations derived from brain region pairs. To overcome this limitation, we proposed a new type of sparse functional connectivity network to precisely describe the relationship of temporal correlations among brain regions. Specifically, instead of using the simple pairwise Pearson’s correlation coefficient as connectivity, we first estimate the temporal low-order functional connectivity for each region pair based on an ULS Group constrained-UOLS regression algorithm, where a combination of ultra-least squares (ULS) criterion with a Group constrained topology structure detection algorithm is applied to detect the topology of functional connectivity networks, aided by an Ultra-Orthogonal Least Squares (UOLS) algorithm to estimate connectivity strength. Compared to the classical least squares criterion which only measures the discrepancy between the observed signals and the model prediction function, the ULS criterion takes into consideration the discrepancy between the weak derivatives of the observed signals and the model prediction function and thus avoids the overfitting problem. By using a similar approach, we then estimate the high-order functional connectivity from the low-order connectivity to characterize signal flows among the brain regions. We finally fuse the low-order and the high-order networks using two decision trees for MCI classification. Experimental results demonstrate the effectiveness of the proposed method on MCI classification.

Keywords

High-order network Low-order network Mild cognitive impairment Ultra-least squares Computer-aided detection and diagnosis 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China [U1809209, 61671042, 61403016, 31871113], Beijing Natural Science Foundation [L182015, 4172037], and Open Fund Project of Fujian Provincial Key Laboratory in Minjiang University [MJUKF201702]. An earlier version of this paper was presented at the International Workshop on Machine Learning in Medical Imaging (MLMI 2017).

Compliance with Ethical Standards

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study was approved by the local ethical committee.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Conflict of Interest

The authors declare that they have no conflicts of interest.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Automation Sciences and Electrical Engineering, Beijing Advanced Innovation Center for Big Data and Brain Computing, Beijing Advanced Innovation Center for Big Date-based Precision MedicineBeihang UniversityBeijingChina
  2. 2.Shenzhen Kangning HospitalShenzhen University School of MedicineShenzhenChina
  3. 3.College of Psychology and SociologyShenzhen UniversityShenzhenChina
  4. 4.Department of NeurologyXuanWu Hospital of Capital Medical UniversityBeijingChina
  5. 5.Center of Alzheimer’s DiseaseBeijing Institute for Brain DisordersBeijingChina
  6. 6.Department of Computer ScienceUniversity of North Carolina at GreensboroGreensboroUSA
  7. 7.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA
  8. 8.Department of Biomedical EngineeringNational University of SingaporeSingaporeSingapore

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