Li WX, Lin QH, Zhao BH, Kuang LD, Zhang CY, Han Y, Calhoun VD. Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia.
J Neurosci Methods 2024;
403:110049. [PMID:
38151187 DOI:
10.1016/j.jneumeth.2023.110049]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 12/12/2023] [Accepted: 12/21/2023] [Indexed: 12/29/2023]
Abstract
BACKGROUND
Dynamic spatial functional network connectivity (dsFNC) has shown advantages in detecting functional alterations impacted by mental disorders using magnitude-only fMRI data. However, complete fMRI data are complex-valued with unique and useful phase information.
METHODS
We propose dsFNC of spatial source phase (SSP) maps, derived from complex-valued fMRI data (named SSP-dsFNC), to capture the dynamics elicited by the phase. We compute mutual information for connectivity quantification, employ statistical analysis and Markov chains to assess dynamics, ultimately classifying schizophrenia patients (SZs) and healthy controls (HCs) based on connectivity variance and Markov chain state transitions across windows.
RESULTS
SSP-dsFNC yielded greater dynamics and more significant HC-SZ differences, due to the use of complete brain information from complex-valued fMRI data.
COMPARISON WITH EXISTING METHODS
Compared with magnitude-dsFNC, SSP-dsFNC detected additional and meaningful connections across windows (e.g., for right frontal parietal) and achieved 14.6% higher accuracy for classifying HCs and SZs.
CONCLUSIONS
This work provides new evidence about how SSP-dsFNC could be impacted by schizophrenia, and this information could be used to identify potential imaging biomarkers for psychotic diagnosis.
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