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In NeuroImage. Clinical

BACKGROUND AND OBJECTIVE : Both Alzheimer's disease (AD) and Parkinson's disease (PD) are progressive neurodegenerative diseases. Early identification is very important for the prevention and intervention of their progress. Hippocampus plays a crucial role in cognition, in which there are correlations between atrophy of Hippocampal subfields and cognitive impairment in neurodegenerative diseases. Exploring biomarkers in the prediction of early cognitive impairment in AD and PD is significant for understanding the progress of neurodegenerative diseases.

METHODS : A multi-scale attention-based deep learning method is proposed to perform computer-aided diagnosis for neurodegenerative disease based on Hippocampal subfields. First, the two dimensional (2D) Hippocampal Mapping Image (HMI) is constructed and used as input of three branches of the following network. Second, the multi-scale module and attention module are integrated into the 2D residual network to improve the diversity of the extracted features and capture significance of various voxels for classification. Finally, the role of Hippocampal subfields in the progression of different neurodegenerative diseases is analyzed using the proposed method.

RESULTS : Classification experiments between normal control (NC), mild cognitive impairment (MCI), AD, PD with normal cognition (PD-NC) and PD with mild cognitive impairment (PD-MCI) are carried out using the proposed method. Experimental results show that subfields subiculum, presubiculum, CA1, and molecular layer are strongly correlated with cognitive impairment in AD and MCI, subfields GC-DG and fimbria are sensitive in detecting early stage of cognitive impairment in MCI, subfields CA3, CA4, GC-DG, and CA1 show significant atrophy in PD. For exploring the role of Hippocampal subfields in PD cognitive impairment, we find that left parasubiculum, left HATA and left presubiculum could be important biomarkers for predicting conversion from PD-NC to PD-MCI.

CONCLUSION : The proposed multi-scale attention-based network can effectively discover the correlation between subfields and neurodegenerative diseases. Experimental results are consistent with previous clinical studies, which will be useful for further exploring the role of Hippocampal subfields in neurodegenerative disease progression.

Xu Hongbo, Liu Yan, Wang Ling, Zeng Xiangzhu, Xu Yingying, Wang Zeng


Alzheimer’s disease, Deep learning, Hippocampal subfields, Mild cognitive impairment, Parkinson disease