  One of the main reasons for Alzheimer's disease (AD) is the disorder of some
neural circuits. Existing methods for AD prediction have achieved great
success, however, detecting abnormal neural circuits from the perspective of
brain networks is still a big challenge. In this work, a novel decoupling
generative adversarial network (DecGAN) is proposed to detect abnormal neural
circuits for AD. Concretely, a decoupling module is designed to decompose a
brain network into two parts: one part is composed of a few sparse graphs which
represent the neural circuits largely determining the development of AD; the
other part is a supplement graph, whose influence on AD can be ignored.
Furthermore, the adversarial strategy is utilized to guide the decoupling
module to extract the feature more related to AD. Meanwhile, by encoding the
detected neural circuits to hypergraph data, an analytic module associated with
the hyperedge neurons algorithm is designed to identify the neural circuits.
More importantly, a novel sparse capacity loss based on the spatial-spectral
hypergraph similarity is developed to minimize the intrinsic topological
distribution of neural circuits, which can significantly improve the accuracy
and robustness of the proposed model. Experimental results demonstrate that the
proposed model can effectively detect the abnormal neural circuits at different
stages of AD, which is helpful for pathological study and early treatment.
