Multi-Modal Neuroimaging Neural Network-Based Feature Detection for Diagnosis of Alzheimer’s Disease - Meng, Xianglian; Liu, Junlong; Fan, Xiang; Bian, Chenyuan; Wei, Qingpeng; Wang, Ziwei; Liu, Wenjie; Jiao, Zhuqing (Frontiers in Aging Neuroscience, 2022)
Alzheimer's disease (AD) is a neurodegenerative brain disease and it is challenging to mine features that distinguish AD and healthy control (HC) from multiple data sets. Brain network modeling technology in AD using single-modal image often lacks supplementary information of multi-source resolution, and has poor spatiotemporal sensitivity. In this paper, we proposed a novel multi-modal LassoNet framework with neural network for AD-related feature detection and classification. Specifically, data including two modalities of resting state functional magnetic resonance imaging (rsfMRI) and diffusion tensor imaging (DTI) were adopted for predicting pathological brain areas related to AD. The results of 10 repeated experiments and validation experiments in three groups proves that our proposed framework outperforms well in classification performance, generalization and reproducibility. And we found discriminative brain regions such as Hippocampus, Frontal_Inf_Orb_L, Parietal_Sup_L, Putamen_L, Fusiform_R, etc. These discoveries provide a novel method for AD research and the experimental study demonstrates that the framework will further our understanding of the mechanisms of AD.