Diagnosis of Early Mild Cognitive Impairment Based on Associated High-Order 
Functional Connection Network Generated by Multimodal MRI

Wang, Weiping; Zhang, Shunqi; Wang, Zhen; Luo, Xiong; Luan, Ping; Hramov, Alexander; Kurths, Jürgen; He, Chang; Li, Jianwu

doi:10.1109/TCDS.2023.3283406

アイテム詳細

登録内容を編集ファイル形式で保存

一時保存へ追加

タグ情報を表示リリース履歴を表示詳細要約

公開

学術論文

Diagnosis of Early Mild Cognitive Impairment Based on Associated High-Order Functional Connection Network Generated by Multimodal MRI

Authors

Wang, Weiping

Zhang, Shunqi

Wang, Zhen

Luo, Xiong

Luan, Ping

Hramov, Alexander

/persons/resource/Juergen.Kurths

Kurths, Jürgen
Potsdam Institute for Climate Impact Research;

He, Chang

Li, Jianwu

URL

There are no locators available

フルテキスト (公開)

There are no public fulltexts stored in PIKpublic

付随資料 (公開)

There is no public supplementary material available

引用

Wang, W., Zhang, S., Wang, Z., Luo, X., Luan, P., Hramov, A., Kurths, J., He, C., & Li, J. (2024). Diagnosis of Early Mild Cognitive Impairment Based on Associated High-Order Functional Connection Network Generated by Multimodal MRI. IEEE Transactions on Cognitive and Developmental Systems, 16(2), 618-627. doi:10.1109/TCDS.2023.3283406.

引用: https://publications.pik-potsdam.de/pubman/item/item_30101

要旨

Mild cognitive impairment (MCI) is highly likely to convert to Alzheimer’s disease (AD). The main approach to identifying MCI is using a functional connection network (FCN). Traditional FCN is used to study the correlation between two brain regions, but it lacks deeper brain interaction information. Neuroscientists found the internal functional activity pattern in the human brain is characterized by sparse, modular, and overlapping structures, and the FCN is restricted by the brain structural connection network (SCN). They can improve the estimation accuracy of FCN. Therefore, this article first constructs low order FCN (LFCN) based on brain sparse, modular, and overlapping activity patterns. Then, new high-order FCN (HFCN) is proposed based on the restrictive relationship between SCN and FCN. To combine high robustness of LFCN with high sensitivity of HFCN, a new combination strategy of LFCN and HFCN is proposed. It integrates the idea of brain modular and overlapping with the restricted relationship between SCN and FCN. Finally, the experimental results show that in early MCI (EMCI) recognition the best classification performance is acquired with an accuracy of 91.42%, which is better than similar methods. This method will be instrumental in the early recognition of clinical MCI.