TY - GEN
T1 - Classifying Stages of Mild Cognitive Impairment via Augmented Graph Embedding
AU - Tang, Haoteng
AU - Guo, Lei
AU - Dennis, Emily
AU - Thompson, Paul M.
AU - Huang, Heng
AU - Ajilore, Olusola
AU - Leow, Alex D.
AU - Zhan, Liang
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Mild Cognitive Impairment (MCI) is a clinically intermediate stage in the course of Alzheimer’s disease (AD). MCI does not always lead to dementia. Some MCI patients may stay in the MCI status for the rest of their life, while others will develop AD eventually. Therefore, classification methods that help to distinguish MCI from earlier or later stages of the disease are important to understand the progression of AD. In this paper, we propose a novel computational framework - named Augmented Graph Embedding, or AGE - to tackle this challenge. In this new AGE framework, the random walk approach is first applied to brain structural networks derived from diffusion-weighted MRI to extract nodal feature vectors. A technique adapted from natural language processing is used to analyze these nodal feature vectors, and a multimodal augmentation procedure is adopted to improve classification accuracy. We validated this new AGE framework on data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Results show advantages of the proposed framework, compared to a range of existing methods.
AB - Mild Cognitive Impairment (MCI) is a clinically intermediate stage in the course of Alzheimer’s disease (AD). MCI does not always lead to dementia. Some MCI patients may stay in the MCI status for the rest of their life, while others will develop AD eventually. Therefore, classification methods that help to distinguish MCI from earlier or later stages of the disease are important to understand the progression of AD. In this paper, we propose a novel computational framework - named Augmented Graph Embedding, or AGE - to tackle this challenge. In this new AGE framework, the random walk approach is first applied to brain structural networks derived from diffusion-weighted MRI to extract nodal feature vectors. A technique adapted from natural language processing is used to analyze these nodal feature vectors, and a multimodal augmentation procedure is adopted to improve classification accuracy. We validated this new AGE framework on data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Results show advantages of the proposed framework, compared to a range of existing methods.
KW - Brain structural network
KW - Data augmentation
KW - Graph embedding
KW - Mild Cognitive Impairment
KW - Natural Language Processing
KW - Random walk
UR - https://www.scopus.com/pages/publications/85075586588
U2 - 10.1007/978-3-030-33226-6_4
DO - 10.1007/978-3-030-33226-6_4
M3 - Conference contribution
AN - SCOPUS:85075586588
SN - 9783030332259
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 30
EP - 38
BT - Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy - 4th International Workshop, MBIA 2019, and 7th International Workshop, MFCA 2019, Held in Conjunction with MICCAI 2019, Proceedings
A2 - Zhu, Dajiang
A2 - Yan, Jingwen
A2 - Huang, Heng
A2 - Shen, Li
A2 - Thompson, Paul M.
A2 - Westin, Carl-Fredrik
A2 - Pennec, Xavier
A2 - Joshi, Sarang
A2 - Nielsen, Mads
A2 - Sommer, Stefan
A2 - Fletcher, Tom
A2 - Durrleman, Stanley
PB - Springer
T2 - 4th International Workshop on Multimodal Brain Image Analysis, MBAI 2019, and the 7th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer Assisted Intervention, MICCAI 2019
Y2 - 17 October 2019 through 17 October 2019
ER -