Adversarial Learning Based Structural Brain-Network Generative Model for Analyzing Mild Cognitive Impairment

Kong，Heng1,2; Pan，Junren2; Shen，Yanyan2; Wang，Shuqiang2

10.1007/978-3-031-18910-4_30

2022

0302-9743

1611-3349

LECT NOTES ARTIF INT

13535 LNCS

361-375

Mild cognitive impairment (MCI) is a precursor of Alzheimer’s disease (AD), and the detection of MCI is of great clinical significance. Analyzing the structural brain networks of patients is vital for the recognition of MCI. However, the current studies on structural brain networks are totally dependent on specific toolboxes, which is time-consuming and subjective. Few tools can obtain the structural brain networks from brain diffusion tensor images. In this work, an adversarial learning-based structural brain-network generative model (SBGM) is proposed to directly learn the structural connections from brain diffusion tensor images. By analyzing the differences in structural brain networks across subjects, we found that the structural brain networks of subjects showed a consistent trend from elderly normal controls (NC) to early mild cognitive impairment (EMCI) to late mild cognitive impairment (LMCI): structural connectivity progressed in a progressively weaker direction as the condition worsened. In addition, our proposed model tri-classifies EMCI, LMCI, and NC subjects, achieving a classification accuracy of 83.33% on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database.

Graph convolution Separable convolutional block Structural brain networks Structural feature extraction

First

English

2-s2.0-85142709633

Scopus

1.Southern University of Science and Technology,Shenzhen,518000,China
2.Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences,Shenzhen,518000,China

Kong，Heng,Pan，Junren,Shen，Yanyan,et al. Adversarial Learning Based Structural Brain-Network Generative Model for Analyzing Mild Cognitive Impairment[C],2022:361-375.