  Functional alterations in the relevant neural circuits occur from drug
addiction over a certain period. And these significant alterations are also
revealed by analyzing fMRI. However, because of fMRI's high dimensionality and
poor signal-to-noise ratio, it is challenging to encode efficient and robust
brain regional embeddings for both graph-level identification and region-level
biomarkers detection tasks between nicotine addiction (NA) and healthy control
(HC) groups. In this work, we represent the fMRI of the rat brain as a graph
with biological attributes and propose a novel feature-selected graph spatial
attention network(FGSAN) to extract the biomarkers of addiction and identify
from these brain networks. Specially, a graph spatial attention encoder is
employed to capture the features of spatiotemporal brain networks with spatial
information. The method simultaneously adopts a Bayesian feature selection
strategy to optimize the model and improve classification task by constraining
features. Experiments on an addiction-related neural imaging dataset show that
the proposed model can obtain superior performance and detect interpretable
biomarkers associated with addiction-relevant neural circuits.
