Temporal-spatial dynamic functional connectivity analysis in schizophrenia classification

• Published in: Frontiers in neuroscience Vol. 16; p. 965937
• Main Authors: Pan, Cong, Yu, Haifei, Fei, Xuan, Zheng, Xingjuan, Yu, Renping
• Format: Journal Article
• Language: English
• Published: Lausanne Frontiers Research Foundation 17-08-2022; Frontiers Media S.A
• Subjects: Autism; Biomarkers; Brain mapping; Brain research; Classification; Cognitive ability; Construction; Disease; dynamic functional connectivity networks; Functional magnetic resonance imaging; high-order functional connectivity network; Mental disorders; Methods; Neural networks; Neuroimaging; Neuroscience; Schizophrenia; schizophrenia classification; temporal variability; Time series
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2022.965937

With the development of resting-state functional magnetic resonance imaging (rs-fMRI) technology, the functional connectivity network (FCN) which reflects the statistical similarity of temporal activity between brain regions has shown promising results for the identification of neuropsychiatric disorders. Alteration in FCN is believed to have the potential to locate biomarkers for classifying or predicting schizophrenia (SZ) from healthy control. However, the traditional FCN analysis with stationary assumption, i.e., static functional connectivity network (SFCN) at the time only measures the simple functional connectivity among brain regions, ignoring the dynamic changes of functional connectivity and the high-order dynamic interactions. In this article, the dynamic functional connectivity network (DFCN) is constructed to delineate the characteristic of connectivity variation across time. A high-order functional connectivity network (HFCN) designed based on DFCN, could characterize more complex spatial interactions across multiple brain regions with the potential to reflect complex functional segregation and integration. Specifically, the temporal variability and the high-order network topology features, which characterize the brain FCNs from region and connectivity aspects, are extracted from DFCN and HFCN, respectively. Experiment results on SZ identification prove that our method is more effective (i.e., obtaining a significantly higher classification accuracy, 81.82%) than other competing methods. Post hoc inspection of the informative features in the individualized classification task further could serve as the potential biomarkers for identifying associated aberrant connectivity in SZ.