In recent years, functional magnetic resonance imaging has emerged as a
powerful tool for investigating the human brain's functional connectivity
networks. Related studies demonstrate that functional connectivity networks in
the human brain can help to improve the efficiency of diagnosing neurological
disorders. However, there still exist two challenges that limit the progress of
functional neuroimaging. Firstly, there exists an abundance of noise and
redundant information in functional connectivity data, resulting in poor
performance. Secondly, existing brain network models have tended to prioritize
either classification performance or the interpretation of neuroscience
findings behind the learned models. To deal with these challenges, this paper
proposes a novel brain graph learning framework called Template-induced Brain
Graph Learning (TiBGL), which has both discriminative and interpretable
abilities. Motivated by the related medical findings on functional
connectivites, TiBGL proposes template-induced brain graph learning to extract
template brain graphs for all groups. The template graph can be regarded as an
augmentation process on brain networks that removes noise information and
highlights important connectivity patterns. To simultaneously support the tasks
of discrimination and interpretation, TiBGL further develops template-induced
convolutional neural network and template-induced brain interpretation
analysis. Especially, the former fuses rich information from brain graphs and
template brain graphs for brain disorder tasks, and the latter can provide
insightful connectivity patterns related to brain disorders based on template
brain graphs. Experimental results on three real-world datasets show that the
proposed TiBGL can achieve superior performance compared with nine
state-of-the-art methods and keep coherent with neuroscience findings in recent
literatures.

提出了一种称为 TiBGL 的新型脑图学习框架，通过模板诱导的脑图学习解决了功能连接数据中的噪声和冗余信息问题，提高了脑部障碍诊断的性能并揭示了与脑部疾病相关的连接模式。