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Edge-centric effective connection network based on muti-modal MRI for the diagnosis of Alzheimer’s disease

Urheber*innen

Zhang,  Shunqi
External Organizations;

Zhao,  Haiyan
External Organizations;

Wang,  Weiping
External Organizations;

Wang,  Zhen
External Organizations;

Luo,  Xiong
External Organizations;

Hramov,  Alexander
External Organizations;

/persons/resource/Juergen.Kurths

Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;

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Zitation

Zhang, S., Zhao, H., Wang, W., Wang, Z., Luo, X., Hramov, A., Kurths, J. (2023): Edge-centric effective connection network based on muti-modal MRI for the diagnosis of Alzheimer’s disease. - Neurocomputing, 552, 126512.
https://doi.org/10.1016/j.neucom.2023.126512


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_28934
Zusammenfassung
Alzheimer’s disease (AD) is an irreversible neurodegenerative disease. But if AD is detected early, it can greatly reduce the severity of the disease. Functional connection networks (FCNs) can be used for the early diagnosis of AD, but they are undirected graphs and lack the description of causal information. Moreover, most of FCNs take brain regions as nodes, and few studies have been carried out focusing on the connections of the brain network. Although effective connection networks (ECNs) are digraphs, they do not reflect the causal relationships between brain connections. Therefore, we innovatively propose an edge-centric ECN (EECN) to explore the causality of the co-fluctuating connection in brain networks. Firstly, the traditional conditional Granger causality (GC) method is improved for constructing ECNs based on the suppression relationship between structural connection network (SCN) and FCN. Then based on the improved GC method, edge time series and EECNs are constructed. Finally, we perform dichotomous tasks on four stages of AD to verify the accuracy of our proposed method. The results show that this method achieves good results in six classification tasks. Finally, we present some brain connections that may be essential for early AD classification tasks. This study may have a positive impact on the application of brain networks.