Dementia analysis from functional connectivity network with graph neural networks

作者：

Highlights：

• A novel framework with self-attention structure learning to characterize the relations among the brain regions.

• Our model can provide an interpretable result, which has substantial clinical implications for brain disease diagnosis.

• This paper is the first effort that jointly learns deep graph learning and selects discriminative brain regions.

摘要

•A novel framework with self-attention structure learning to characterize the relations among the brain regions.•Our model can provide an interpretable result, which has substantial clinical implications for brain disease diagnosis.•This paper is the first effort that jointly learns deep graph learning and selects discriminative brain regions.

论文关键词：Brain Functional Connectivity,Graph neural networks,Structure learning,Self-attention,Feature selection

论文评审过程：Received 2 October 2021, Revised 9 January 2022, Accepted 9 February 2022, Available online 8 April 2022, Version of Record 8 April 2022.

论文官网地址：https://doi.org/10.1016/j.ipm.2022.102901