Persistent Feature Analysis of Multimodal Brain Networks Using Generalized Fused Lasso for EMCI Identification.
Jin LiChenyuan BianDandan ChenXianglian MengHaoran LuoHong LiangLi ShenPublished in: Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (2020)
Early Mild Cognitive Impairment (EMCI) involves very subtle changes in brain pathological process, and thus identification of EMCI can be challenging. By jointly analyzing cross-information among different neuroimaging data, an increased interest recently emerges in multimodal fusion to better understand clinical measurements with respect to both structural and functional connectivity. In this paper, we propose a novel multimodal brain network modeling method for EMCI identification. Specifically, we employ the structural connectivity based on diffusion tensor imaging (DTI), as a constraint, to guide the regression of BOLD time series from resting state functional magnetic resonance imaging (rs-fMRI). In addition, we introduce multiscale persistent homology features to avoid the uncertainty of regularization parameter selection. An empirical study on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrates that the proposed method effectively improves classification performance compared with several competing approaches, and reasonably yields connectivity patterns specific to different diagnostic groups.
Keyphrases
- resting state
- functional connectivity
- mild cognitive impairment
- cognitive decline
- magnetic resonance imaging
- pain management
- machine learning
- bioinformatics analysis
- deep learning
- healthcare
- white matter
- emergency department
- magnetic resonance
- chronic pain
- contrast enhanced
- social media
- multiple sclerosis
- blood brain barrier