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Temporal and Spatial Analysis of Alzheimer’s Disease Based on an Improved Convolutional Neural Network and a Resting-State FMRI Brain Functional Network

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  • Haijing Sun

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
    College of Intelligent Science and Engineering, Shenyang University, Shenyang 110044, China)

  • Anna Wang

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

  • Shanshan He

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

Abstract

Most current research on Alzheimer’s disease (AD) is based on transverse measurements. Given the nature of neurodegeneration in AD progression, observing longitudinal changes in the structural features of brain networks over time may improve the accuracy of the predicted transformation and provide a good measure of the progression of AD. Currently, there is no cure for patients with existing AD dementia, but patients with mild cognitive impairment (MCI) in the prodromal stage of AD dementia may be diagnosed. The study of the early diagnosis of MCI and the prediction of MCI to AD transformation is of great significance for the monitoring of the MCI to AD transformation process. Despite the high rate of MCI conversion to AD, the neuropathological cause of MCI is heterogeneous. However, many people with MCI remain stable. Treatment options are different for patients with stable MCI and those with underlying dementia. Therefore, it is of great significance for clinical practice to predict whether patients with MCI will develop AD dementia. This paper proposes an improved algorithm that is based on a convolution neural network (CNN) with residuals combined with multi-layer long short-term memory (LSTM) to diagnose AD and predict MCI. Firstly, multi-time resting-state fMRI images were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database for preprocessing, and then an AAL brain partition template was used to construct a 90 × 90 functional connectivity (FC) network matrix of a whole-brain region of interest (ROI). Secondly, the diversity of training samples was increased by generating an adversarial network (GAN). Finally, a CNN with residuals and a multi-layer LSTM model were constructed to automatically classify and predict the functional adjacency matrix. This method can not only distinguish Alzheimer’s disease from normal health conditions at multiple time points, but can also predict progressive MCI (pMCI) and stable MCI (sMCI) at multiple time points. The classification accuracies in AD vs. NC and sMCI vs.pMCI reached 93.5% and 75.5%, respectively.

Suggested Citation

  • Haijing Sun & Anna Wang & Shanshan He, 2022. "Temporal and Spatial Analysis of Alzheimer’s Disease Based on an Improved Convolutional Neural Network and a Resting-State FMRI Brain Functional Network," IJERPH, MDPI, vol. 19(8), pages 1-16, April.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:8:p:4508-:d:789789
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    References listed on IDEAS

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    1. Xin Ouyang & Kewei Chen & Li Yao & Xia Wu & Jiacai Zhang & Ke Li & Zhen Jin & Xiaojuan Guo & for the Alzheimer’s Disease Neuroimaging Initiative, 2015. "Independent Component Analysis-Based Identification of Covariance Patterns of Microstructural White Matter Damage in Alzheimer’s Disease," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-12, March.
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    Cited by:

    1. Youying Mu & Chengzhuo Duan & Xin Li & Yongbo Wu, 2023. "A Monitoring Method for Corporate Environmental Performance Based on Data Fusion in China under the Double Carbon Target," Sustainability, MDPI, vol. 15(12), pages 1-16, June.
    2. Muhammad Younas & Shuhab D. Khan & Muhammad Qasim & Younes Hamed, 2022. "Assessing Impacts of Land Subsidence in Victoria County, Texas, Using Geospatial Analysis," Land, MDPI, vol. 11(12), pages 1-20, December.

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