Diagnosis of early Alzheimer's disease based on dynamic high order networks.

Abstract:

:Machine learning methods have been widely used for early diagnosis of Alzheimer's disease (AD) via functional connectivity networks (FCNs) analysis from neuroimaging data. The conventional low-order FCNs are obtained by time-series correlation of the whole brain based on resting-state functional magnetic resonance imaging (R-fMRI). However, FCNs overlook inter-region interactions, which limits application to brain disease diagnosis. To overcome this drawback, we develop a novel framework to exploit the high-level dynamic interactions among brain regions for early AD diagnosis. Specifically, a sliding window approach is employed to generate some R-fMRI sub-series. The correlations among these sub-series are then used to construct a series of dynamic FCNs. High-order FCNs based on the topographical similarity between each pair of the dynamic FCNs are then constructed. Afterward, a local weight clustering method is used to extract effective features of the network, and the least absolute shrinkage and selection operation method is chosen for feature selection. A support vector machine is employed for classification, and the dynamic high-order network approach is evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Our experimental results demonstrate that the proposed approach not only achieves promising results for AD classification, but also successfully recognizes disease-related biomarkers.

journal_name

Brain Imaging Behav

authors

Lei B,Yu S,Zhao X,Frangi AF,Tan EL,Elazab A,Wang T,Wang S

doi

10.1007/s11682-019-00255-9

subject

Has Abstract

pub_date

2021-02-01 00:00:00

pages

276-287

issue

1

eissn

1931-7557

issn

1931-7565

pii

10.1007/s11682-019-00255-9

journal_volume

15

pub_type

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