Adversarial Learning Based Structural Brain-network Generative Model for
Analyzing Mild Cognitive Impairment
- URL: http://arxiv.org/abs/2208.08896v1
- Date: Tue, 9 Aug 2022 02:45:53 GMT
- Title: Adversarial Learning Based Structural Brain-network Generative Model for
Analyzing Mild Cognitive Impairment
- Authors: Heng Kong and Shuqiang Wang
- Abstract summary: Mild cognitive impairment(MCI) is a precursor of Alzheimer's disease(AD)
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.
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.
- Score: 7.403660531145136
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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