Modality-aware discriminative fusion network for integrated analysis of brain imaging genomics
Publication Type
Journal Article
Publication Date
8-2024
Abstract
Mild cognitive impairment (MCI) represents an early stage of Alzheimer's disease (AD), characterized by subtle clinical symptoms that pose challenges for accurate diagnosis. The quest for the identification of MCI individuals has highlighted the importance of comprehending the underlying mechanisms of disease causation. Integrated analysis of brain imaging and genomics offers a promising avenue for predicting MCI risk before clinical symptom onset. However, most existing methods face challenges in: 1) mining the brain network-specific topological structure and addressing the single nucleotide polymorphisms (SNPs)-related noise contamination and 2) extracting the discriminative properties of brain imaging genomics, resulting in limited accuracy for MCI diagnosis. To this end, a modality-aware discriminative fusion network (MA-DFN) is proposed to integrate the complementary information from brain imaging genomics to diagnose MCI. Specifically, we first design two modality-specific feature extraction modules: the graph convolutional network with edge-augmented self-attention module (GCN-EASA) and the deep adversarial denoising autoencoder module (DAD-AE), to capture the topological structure of brain networks and the intrinsic distribution of SNPs. Subsequently, a discriminative-enhanced fusion network with correlation regularization module (DFN-CorrReg) is employed to enhance inter-modal consistency and between-class discrimination in brain imaging and genomics. Compared to other state-of-the-art approaches, MA-DFN not only exhibits superior performance in stratifying cognitive normal (CN) and MCI individuals but also identifies disease-related brain regions and risk SNPs locus, which hold potential as putative biomarkers for MCI diagnosis.
Keywords
Brain imaging genomics, early diagnosis, mild cognitive impairment (MCI), multimodal fusion
Discipline
Artificial Intelligence and Robotics | OS and Networks
Research Areas
Intelligent Systems and Optimization
Publication
IEEE Transactions on Neural Networks and Learning Systems
ISSN
2162-237X
Identifier
10.1109/TNNLS.2024.3439530
Publisher
Institute of Electrical and Electronics Engineers
Citation
SHENG, Xiaoqi; CAI, Hongmin; NIE, Yongwei; HE, Shengfeng; CHEUNG, Yiu-Ming; and CHEN, Jiazhou.
Modality-aware discriminative fusion network for integrated analysis of brain imaging genomics. (2024). IEEE Transactions on Neural Networks and Learning Systems.
Available at: https://ink.library.smu.edu.sg/sis_research/9800
Additional URL
https://doi.org/10.1109/TNNLS.2024.3439530