Abstract
Functional connectivity (FC) networks with the resting-state functional magnetic resonance imaging (rs-fMRI) help advance our understanding of brain disorders, such as Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). Recent studies have shown that FC networks demonstrate significant dynamic changes even in the resting state. However, previous studies typically focus on model the low-order (e.g., second-order) dynamics, without exploring the high-order dynamic properties of FC networks. In this paper, we propose to build a high-order dynamic functional connectivity network (hoDFCN) from the second-order FC networks, and define two novel measures to characterize the temporal and spatial variability of hoDFCN. Furthermore, we employ both spatial and temporal variability features for brain disease classification. Experimental results on 149 subjects with baseline resting-state functional MRI (rs-fMRI) data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) suggest the effectiveness of our proposed method in brain dementia identification.
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Change history
29 September 2020
In a former version of this paper, the CERNET Innovation Project (NGII20190621) was missing from the Acknowledgement section. This has been corrected.
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Acknowledgment
This study was supported by NSFC (61976006, 61573023, 61703301, 61902003), Anhui-NSFC (1708085MF145, 1808085MF171), AHNU-FOYHE (gxyqZD2017010), CERNET Innovation Project (NGII20190621).
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Feng, C., Jie, B., Ding, X., Zhang, D., Liu, M. (2020). Constructing High-Order Dynamic Functional Connectivity Networks from Resting-State fMRI for Brain Dementia Identification. In: Liu, M., Yan, P., Lian, C., Cao, X. (eds) Machine Learning in Medical Imaging. MLMI 2020. Lecture Notes in Computer Science(), vol 12436. Springer, Cham. https://doi.org/10.1007/978-3-030-59861-7_31
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DOI: https://doi.org/10.1007/978-3-030-59861-7_31
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