Discriminant subgraph learning from functional brain sensory data. (2nd November 2022)

• Record Type: Journal Article
• Authors: Wang, Lujia; Schwedt, Todd J.; Chong, Catherine D.; Wu, Teresa; Li, Jing
• Is Part Of: IISE transactions. Volume 54:Number 11(2022)
• Page Start: 1084
• Page End: 1097
• Publication Date: 2022-11-02
• Subjects: Statistical models; machine learning; brain sensory data; health care; Industrial engineering -- Periodicals; Systems engineering -- Periodicals; Industrial engineering; Systems engineering; Electronic journals; Periodicals; 670.285
• DOI: 10.1080/24725854.2021.1987592
• Languages: English
• ISSNs: 2472-5854

Abstract

Abstract: 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 from healthy controls based on a fMRI dataset. DSL achieved the best accuracy compared to five state-of-the-art competing algorithms.