Adaptive Critical Subgraph Mining for Cognitive Impairment Conversion
  Prediction with T1-MRI-based Brain Network
Yilin Leng Wenju Cui Bai Chen Xi Jiang Shuangqing Chen Jian Zheng
Prediction the conversion to early-stage dementia is critical for mitigating
its progression but remains challenging due to subtle cognitive impairments and
structural brain changes. Traditional T1-weighted magnetic resonance imaging
(T1-MRI) research focus on identifying brain atrophy regions but often fails to
address the intricate connectivity between them. This limitation underscores
the necessity of focuing on inter-regional connectivity for a comprehensive
understand of the brain's complex network. Moreover, there is a pressing demand
for methods that adaptively preserve and extract critical information,
particularly specialized subgraph mining techniques for brain networks. These
are essential for developing high-quality feature representations that reveal
critical spatial impacts of structural brain changes and its topology. In this
paper, we propose Brain-SubGNN, a novel graph representation network to mine
and enhance critical subgraphs based on T1-MRI. This network provides a
subgraph-level interpretation, enhancing interpretability and insights for
graph analysis. The process begins by extracting node features and a
correlation matrix between nodes to construct a task-oriented brain network.
Brain-SubGNN then adaptively identifies and enhances critical subgraphs,
capturing both loop and neighbor subgraphs. This method reflects the loop
topology and local changes, indicative of long-range connections, and maintains
local and global brain attributes. Extensive experiments validate the
effectiveness and advantages of Brain-SubGNN, demonstrating its potential as a
powerful tool for understanding and diagnosing early-stage dementia. Source
code is available at https://github.com/Leng-10/Brain-SubGNN.