Longitudinal study of early mild cognitive impairment via similarity-constrained group learning and self-attention based SBi-LSTM

Lei，Baiying1,2; Zhang，Yuwen1; Liu，Dongdong1; Xu，Yanwu4; Yue，Guanghui1; Cao，Jiuwen3; Hu，Huoyou5; Yu，Shuangzhi1; Yang，Peng7; Wang，Tianfu1; Qiu，Yali1; Xiao，Xiaohua5; Wang，Shuqiang6

2022-10-27

10.1016/j.knosys.2022.109466

KNOWLEDGE-BASED SYSTEMS   Impact Factor & Quartile

0950-7051

Alzheimer's disease (AD) is an incurable neurodegenerative disease. Mild cognitive impairment (MCI) is often considered a critical time window for predicting early conversion to Alzheimer's disease (AD), with approximately 80% of amnestic MCI patients developing AD within 6 years. MCI can be further categorized into two stages (i.e., early MCI (EMCI) and late MCI (LMCI)). To identify EMCI effectively and understand how it changes brain function, the brain functional connectivity network (BFCN) has been widely used. However, the conventional methods mainly focused on detection from a single time-point data, which could not discover the changes during the disease progression without using multi-time points data. Therefore, in this work, we carry out a longitudinal study based on multi-time points data to detect EMCI and validate them on two public datasets. Specifically, we first construct a similarity-constrained group network (SGN) from the resting state functional magnetic resonance imaging (rs-fMRI) data at different time-points, and then use a stacked bidirectional long short term memory (SBi-LSTM) network to extract features for longitudinal analysis. Also, we use a self-attention mechanism to leverage high-level features to further improve the detection accuracy. Evaluated on the public Alzheimer's Disease Neuroimaging Initiative Phase II and III (ADNI-2 and ADNI-3) databases, the proposed method outperforms several state-of-the-art methods.

Longitudinal study Mild cognitive impairment SBi-LSTM Self-attention Similarity-constrained group learning

EI

English

Others

Natural Science Foundation of Guangdong Province[2019A1515111205];National Natural Science Foundation of China[61871274];National Natural Science Foundation of China[62101338];National Natural Science Foundation of China[U1909209];

20223512628928

Functional neuroimaging ; Magnetic resonance imaging ; Neurodegenerative diseases

Biomedical Engineering:461.1 ; Medicine and Pharmacology:461.6 ; Magnetism: Basic Concepts and Phenomena:701.2 ; Imaging Techniques:746

COMPUTER SCIENCE

2-s2.0-85136487070

Scopus

1.National-Regional Key Technology Engineering Laboratory for Medical Ultrasound,Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging,School of Biomedical Engineering,Health Science Center,Shenzhen University,Shenzhen,518060,China
2.Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation,Southern University of Science and Technology,Shenzhen,518055,China
3.Artificial Intelligence Institute,Hangzhou Dianzi University,Zhejiang,310010,China
4.Ningbo Institute of Industrial Technology,Chinese Academy of Sciences,Ningbo,China
5.Affiliated Hospital of Shenzhen University,Health Science Center,Shenzhen University,Shenzhen Second People's Hospital,Shenzhen,China
6.Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences,Shenzhen,Shenzhen,518000,China
7.Shenzhen University,China

Lei，Baiying,Zhang，Yuwen,Liu，Dongdong,et al. Longitudinal study of early mild cognitive impairment via similarity-constrained group learning and self-attention based SBi-LSTM[J]. KNOWLEDGE-BASED SYSTEMS,2022,254.

Lei，Baiying.,Zhang，Yuwen.,Liu，Dongdong.,Xu，Yanwu.,Yue，Guanghui.,...&Wang，Shuqiang.(2022).Longitudinal study of early mild cognitive impairment via similarity-constrained group learning and self-attention based SBi-LSTM.KNOWLEDGE-BASED SYSTEMS,254.

Lei，Baiying,et al."Longitudinal study of early mild cognitive impairment via similarity-constrained group learning and self-attention based SBi-LSTM".KNOWLEDGE-BASED SYSTEMS 254(2022).