Adversarial Learning Based Structural Brain-network Generative Model for Analyzing Mild Cognitive Impairment
This work addresses the time-consuming and subjective nature of current methods for analyzing structural brain networks in MCI, which is a precursor to Alzheimer's disease, by providing a more automated tool for clinical applications.
The authors tackled the problem of analyzing structural brain networks for mild cognitive impairment (MCI) detection by proposing an adversarial learning-based generative model that directly learns structural connections from brain diffusion tensor images, achieving a classification accuracy of 83.33% for tri-classifying EMCI, LMCI, and NC subjects.
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.