Discriminant subgraph learning from functional brain sensory data
提出判别性子图学习器DSL，从脑感觉数据中识别出最能区分特定疾病患者与健康对照的功能性子网络，在偏头痛数据集上优于五种现有算法。
The human brain is a complex system with many functional units interacting with each other. This interacting relationship, known as the functional connectivity network (FCN), is critical for brain functions. To learn the FCN, machine learning algorithms can be built based on brain signals captured by sensing technologies such as EEG and fMRI. In neurological diseases, past research has revealed that the FCN is altered. Also, focusing on a specific disease, some part of the FCN, i.e., a sub-network, can be more susceptible than other parts. However, the current knowledge about disease-specific sub-networks is limited. We propose a novel Discriminant Subgraph Learner (DSL) to identify a functional sub-network that best differentiates patients with a specific disease from healthy controls based on brain sensory data. We develop an integrated optimization framework for DSL to simultaneously learn the FCN of each class and identify the discriminant sub-network. Further, we develop tractable and converging algorithms to solve the optimization. We apply DSL to identify a functional sub-network that best differentiates patients with episodic migraine (EM) from healthy controls based on a fMRI dataset. DSL achieved the best accuracy compared to five state-of-the-art competing algorithms.