TY - JOUR
T1 - Brain Network Classification for Accurate Detection of Alzhemier’s Disease via Manifold Harmonic Discriminant Analysis
AU - Cai, Hongmin
AU - Sheng, Xiaoqi
AU - Wu, Guorong
AU - Hu, Bin
AU - Cheung, Yiu-ming
AU - Chen, Jiazhou
N1 - 10.13039/501100012166-National Key Research and Development Program of China (Grant Number: 2022YFE0112200)
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62325204, U21A20520, 62102153 and 62172112)
Science and Technology Project of Guangdong Province (Grant Number: 2022A0505050014)
Key-Area Research and Development Program of Guangzhou City (Grant Number: 202206030009)
Natural Science Foundation of Guangdong Province of China (Grant Number: 2022A1515011162)
10.13039/501100002858-China Postdoctoral Science Foundation (Grant Number: 2021M691062 and 2023T160226)
Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Publisher Copyright:
IEEE
PY - 2024/12
Y1 - 2024/12
N2 - Mounting evidence shows that Alzheimer’s disease (AD) manifests the dysfunction of the brain network much earlier before the onset of clinical symptoms, making its early diagnosis possible. Current brain network analyses treat high-dimensional network data as a regular matrix or vector, which destroys the essential network topology, thereby seriously affecting diagnosis accuracy. In this context, harmonic waves provide a solid theoretical background for exploring brain network topology. However, the harmonic waves are originally intended to discover neurological disease propagation patterns in the brain, which makes it difficult to accommodate brain disease diagnosis with high heterogeneity. To address this challenge, this article proposes a network manifold harmonic discriminant analysis (MHDA) method for accurately detecting AD. Each brain network is regarded as an instance drawn on a Stiefel manifold. Every instance is represented by a set of orthonormal eigenvectors (i.e., harmonic waves) derived from its Laplacian matrix, which fully respects the topological structure of the brain network. An MHDA method within the Stiefel space is proposed to identify the group-dependent common harmonic waves, which can be used as group-specific references for downstream analyses. Extensive experiments are conducted to demonstrate the effectiveness of the proposed method in stratifying cognitively normal (CN) controls, mild cognitive impairment (MCI), and AD.
AB - Mounting evidence shows that Alzheimer’s disease (AD) manifests the dysfunction of the brain network much earlier before the onset of clinical symptoms, making its early diagnosis possible. Current brain network analyses treat high-dimensional network data as a regular matrix or vector, which destroys the essential network topology, thereby seriously affecting diagnosis accuracy. In this context, harmonic waves provide a solid theoretical background for exploring brain network topology. However, the harmonic waves are originally intended to discover neurological disease propagation patterns in the brain, which makes it difficult to accommodate brain disease diagnosis with high heterogeneity. To address this challenge, this article proposes a network manifold harmonic discriminant analysis (MHDA) method for accurately detecting AD. Each brain network is regarded as an instance drawn on a Stiefel manifold. Every instance is represented by a set of orthonormal eigenvectors (i.e., harmonic waves) derived from its Laplacian matrix, which fully respects the topological structure of the brain network. An MHDA method within the Stiefel space is proposed to identify the group-dependent common harmonic waves, which can be used as group-specific references for downstream analyses. Extensive experiments are conducted to demonstrate the effectiveness of the proposed method in stratifying cognitively normal (CN) controls, mild cognitive impairment (MCI), and AD.
KW - Alzheimer’s disease (AD)
KW - brain network
KW - classification
KW - manifold learning
UR - http://www.scopus.com/inward/record.url?scp=85167790662&partnerID=8YFLogxK
U2 - 10.1109/TNNLS.2023.3301456
DO - 10.1109/TNNLS.2023.3301456
M3 - Journal article
SN - 2162-237X
VL - 35
SP - 17266
EP - 17280
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
IS - 12
ER -