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Article

A Cascaded Multi-modality Analysis in Mild Cognitive Impairment

Authors:

Dajiang ZhuAuthors Info & Claims

Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings

Pages 557 - 565

https://doi.org/10.1007/978-3-030-32692-0_64

Published: 13 October 2019 Publication History

Abstract

Though reversing the pathology of Alzheimer’s disease (AD) has so far not been possible, a more tractable goal may be the prevention or slowing of the disease when diagnosed in its earliest stage, such as mild cognitive impairment (MCI). Recent advances in deep modeling approaches trigger a new era for AD/MCI classification. However, it is still difficult to integrate multi-modal imaging data into a single deep model, to gain benefit from complementary datasets as much as possible. To address this challenge, we propose a cascaded deep model to capture both brain structural and functional characteristic for MCI classification. With diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) data, a graph convolution network (GCN) is constructed based on brain structural connectome and it works with a one-layer recurrent neural network (RNN) which is responsible for inferring the temporal features from brain functional activities. We named this cascaded deep model as Graph Convolutional Recurrent Neural Network (GCRNN). Using Alzheimer’s Disease Neuroimaging Initiative (ADNI-3) dataset as a test-bed, our method can achieve 97.3% accuracy between normal controls (NC) and MCI patients.

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Digital Library

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Cited By

Zhang LNa SLiu TZhu DHuang J(2023)Multimodal Deep Fusion in Hyperbolic Space for Mild Cognitive Impairment StudyMedical Image Computing and Computer Assisted Intervention – MICCAI 202310.1007/978-3-031-43904-9_65(674-684)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-43904-9_65
Yu XHu DZhang LHuang YWu ZLiu TWang LLin WZhu DLi G(2022)Longitudinal Infant Functional Connectivity Prediction via Conditional Intensive Triplet NetworkMedical Image Computing and Computer Assisted Intervention – MICCAI 202210.1007/978-3-031-16452-1_25(255-264)Online publication date: 18-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-16452-1_25
Lyu YYu XZhang LZhu DMakedon F(2021)Classification of Mild Cognitive Impairment by Fusing Neuroimaging and Gene Expression DataProceedings of the 14th PErvasive Technologies Related to Assistive Environments Conference10.1145/3453892.3453906(26-32)Online publication date: 29-Jun-2021
https://dl.acm.org/doi/10.1145/3453892.3453906

Index Terms

A Cascaded Multi-modality Analysis in Mild Cognitive Impairment
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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Abstract
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Information & Contributors

Information

Published In

cover image Guide Proceedings

Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings

Oct 2019

710 pages

ISBN:978-3-030-32691-3

DOI:10.1007/978-3-030-32692-0

Editors:
Heung-Il Suk
Korea University, Seoul, Korea (Republic of)
,
Mingxia Liu
University of North Carolina, Chapel Hill, NC, USA
,
Pingkun Yan
Rensselaer Polytechnic Institute, Troy, NY, USA
,
Chunfeng Lian
University of North Carolina, Chapel Hill, NC, USA

© Springer Nature Switzerland AG 2019.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 13 October 2019

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Cited By

Zhang LNa SLiu TZhu DHuang J(2023)Multimodal Deep Fusion in Hyperbolic Space for Mild Cognitive Impairment StudyMedical Image Computing and Computer Assisted Intervention – MICCAI 202310.1007/978-3-031-43904-9_65(674-684)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-43904-9_65
Yu XHu DZhang LHuang YWu ZLiu TWang LLin WZhu DLi G(2022)Longitudinal Infant Functional Connectivity Prediction via Conditional Intensive Triplet NetworkMedical Image Computing and Computer Assisted Intervention – MICCAI 202210.1007/978-3-031-16452-1_25(255-264)Online publication date: 18-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-16452-1_25
Lyu YYu XZhang LZhu DMakedon F(2021)Classification of Mild Cognitive Impairment by Fusing Neuroimaging and Gene Expression DataProceedings of the 14th PErvasive Technologies Related to Assistive Environments Conference10.1145/3453892.3453906(26-32)Online publication date: 29-Jun-2021
https://dl.acm.org/doi/10.1145/3453892.3453906

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