Multi-level fusion network for mild cognitive impairment identification using multi-modal neuroimages

Abstract

Objective: Mild cognitive impairment (MCI) is a precursor to Alzheimer's Disease (AD) which is an irreversible progressive neurodegenerative disease and its early diagnosis and intervention are of great significance. Recently, many deep learning methods have demonstrated the advantages of multi-modal neuroimages in MCI identification task. However, previous studies frequently simply concatenate patch-level features for prediction without modeling the dependencies among local features. Also, many methods only focus on modality-sharable information or modality-specific features and ignore their incorporation. This work aims to address above-mentioned issues and construct a model for accurate MCI identification. Approach: In this paper, we propose a multi-level fusion network for MCI identification using multi-modal neuroimages, which consists of local representation learning and dependency-aware global representation learning stages. Specifically, for each patient, we first extract multi-pair of patches from multiple same position in multi-modal neuroimages. After that, in the local representation learning stage, multiple dual-channel sub-networks, each of which consists of two modality-specific feature extraction branches and three sine-cosine fusion modules, are constructed to learn local features that preserve modality-sharable and modality specific representations simultaneously. In the dependency-aware global representation learning stage, we further capture long-range dependencies among local representations and integrate them into global ones for MCI identification. Main results: Experiments on ADNI-1/ADNI-2 datasets demonstrate the superior performance of the proposed method in MCI identification tasks (Accuracy: 0.802, Sensitivity: 0.821, Specificity: 0.767 in MCI diagnosis task; Accuracy: 0.849, Sensitivity: 0.841, Specificity: 0.856 in MCI conversion task) when compared with state-of-the-art methods. The proposed classification model has demonstrated a promising potential to predict MCI conversion and identify the disease-related regions in the brain. Significance: We propose a multi-level fusion network for MCI identification using multi-modal neuroimage. The results on ADNI datasets have demonstrated its feasibility and superiority.

Multimodal magnetic resonance scans of patients with mild cognitive impairment

“…In recent years, neuroimaging techniques such as structural magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), diffusion weighted imaging (DWI), magnetic resonance spectroscopy imaging (MRS), and arterial spin label (ASL) have provided insights into brain activity, water molecule diffusion, and metabolite levels, among others. Furthermore, these methods assist in identifying changes related to neuronal damage, cerebral blood flow, and metabolic processes 3 , 5 .…”