Aided Diagnosis Method of Alzheimer’s Disease Based on Sequential Discriminative Subgraph

doi:10.12068/j.issn.1005-3026.2022.08.004

Abstract

Abstract: In order to solve the problem of ignoring the dynamic connection changes of the brain network in the aided diagnosis of Alzheimer’s disease by the existing discriminative subgraph method， an aided diagnosis method based on the sequential discriminative subgraph was proposed. The functional magnetic resonance imaging (fMRI) is processed to form binary matrices， and multiple dynamic brain networks of the same subject form sequential difference graphs， and then frequent subgraph mining and frequent sequence mining are performed to screen out biomarkers(sequential discriminative subgraph)that retain the time sequence characteristics of the brain network. A set of data from the ADNI public data set was obtained for experiments， and a large number of experimental comparisons were conducted with the existing early Alzheimer’s disease aided diagnosis methods， which proves that the aided diagnosis accuracy of this method is improved by 12.7% on this data set and the effectiveness of the proposed method.

Key words: Alzheimer’s disease; sequential discriminative subgraph; dynamic brain network； functional magnetic resonance imaging(fMRI)； gSpan algorithm

CLC Number:

TP391

XIN Jun-chang， GUO En-ming， ZHANG Jia-zheng. Aided Diagnosis Method of Alzheimer’s Disease Based on Sequential Discriminative Subgraph[J]. Journal of Northeastern University(Natural Science), 2022, 43(8): 1089-1096.

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