s2MRI-ADNet: an interpretable deep learning framework integrating Euclidean-graph representations of Alzheimer’s disease solely from structural MRI-Reference-Cited by-同舟云学术

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s2MRI-ADNet: an interpretable deep learning framework integrating Euclidean-graph representations of Alzheimer’s disease solely from structural MRI

Published:2024-06-13 Issue: Volume: Page:
ISSN:1352-8661
Container-title:Magnetic Resonance Materials in Physics, Biology and Medicine
language:en
Short-container-title:Magn Reson Mater Phy

Author:

Song Zhiwei,Li Honglun,Zhang Yiyu,Zhu Chuanzhen,Jiang Minbo,Song Limei,Wang Yi,Ouyang Minhui,Hu Fang,Zheng Qiang^ORCID

Funder

National Natural Science Foundation of China

Open Project of Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Xiangnan University

Yantai City Science and Technology Innovation Development Plan

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10334-024-01178-3.pdf

Reference51 articles.

1. Querfurth HW, Laferla FM (2010) Alzheimer’s disease. N Engl J Med 362(4):329

2. Logothetis NK (2008) What we can do and what we cannot do with fMRI. Nature 453(7197):869–878

3. Oishi K, Mielke MM, Albert M, Lyketsos CG, Mori S (2011) DTI analyses and clinical applications in Alzheimer’s disease. J Alzheimers Dis 26(s3):287–296

4. Zhang N, Gordon ML, Goldberg TE (2017) Cerebral blood flow measured by arterial spin labeling MRI at resting state in normal aging and Alzheimer’s disease. Neurosci Biobehav Rev 72:168–175

5. Liu M, Li F, Yan H, Wang K, Ma Y, Shen L, Xu M, AsDN I (2020) A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer’s disease. Neuroimage 208:116459

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