Label-guided low-rank Approximation for functional brain network learning in identifying subcortical vascular cognitive impairment

Xiao Jiang, Guangyu Wang, Limei Zhang, Xiaoming Xi, Renato De Leone, Lishan Qiao

Abstract: Highlights•A novel label-guided low-rank FBN learning framework is proposed, which explicitly integrates the label information of data samples by the regularization terms into the learning model. This integration ensures that brain network learning is not entirely isolated from subsequent classification tasks. Moreover, the presence of label information typically enhances the discriminative power of the learned network. Guided by the label information, the proposed method implicitly focuses on features relevant to brain disorders. As a result, the resulting network structure aligns with the specific cognitive or disease-related features of interest, leading to more accurate and biologically meaningful representations.•The proposed method concurrently considers FBNs across all the subjects, distinguishing itself from most existing methods that independently learn FBNs for each subject. Concretely, the method considers relationships among different individuals and utilizes their similar topological structures by the low-rank constraint. Moreover, such a constraint transfers the information learned during the optimization of brain networks guided by labels to the testing subject.•The presented method constitutes a versatile framework that can be effectively employed in the analysis of various brain pathologies. Our method addresses the interplay between label information and data characteristics by flexibly adjusting the weighting of regularization terms, thereby enhancing adaptability to diverse tasks and datasets. This flexibility allows for its potential application in studying other brain disorders and exploring the relationships between brain connectivity and various cognitive conditions. The obtained outcomes in the detection of distinct cognitive states of SVCI corroborate this assertion, as elaborated upon in the section on Experiments.