Chapter title |
Multimodal Hyper-connectivity Networks for MCI Classification
|
---|---|
Chapter number | 50 |
Book title |
Medical Image Computing and Computer Assisted Intervention − MICCAI 2017
|
Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, January 2017
|
DOI | 10.1007/978-3-319-66182-7_50 |
Pubmed ID | |
Book ISBNs |
978-3-31-966181-0, 978-3-31-966182-7
|
Authors |
Li, Yang, Gao, Xinqiang, Jie, Biao, Yap, Pew-Thian, Kim, Min-jeong, Wee, Chong-Yaw, Shen, Dinggang |
Abstract |
Hyper-connectivity network is a network where every edge is connected to more than two nodes, and can be naturally denoted using a hyper-graph. Hyper-connectivity brain network, either based on structural or functional interactions among the brain regions, has been used for brain disease diagnosis. However, the conventional hyper-connectivity network is constructed solely based on single modality data, ignoring potential complementary information conveyed by other modalities. The integration of complementary information from multiple modalities has been shown to provide a more comprehensive representation about the brain disruptions. In this paper, a novel multimodal hyper-network modelling method was proposed for improving the diagnostic accuracy of mild cognitive impairment (MCI). Specifically, we first constructed a multimodal hyper-connectivity network by simultaneously considering information from diffusion tensor imaging and resting-state functional magnetic resonance imaging data. We then extracted different types of network features from the hyper-connectivity network, and further exploited a manifold regularized multi-task feature selection method to jointly select the most discriminative features. Our proposed multimodal hyper-connectivity network demonstrated a better MCI classification performance than the conventional single modality based hyper-connectivity networks. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 18 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 4 | 22% |
Researcher | 3 | 17% |
Student > Master | 2 | 11% |
Student > Doctoral Student | 2 | 11% |
Lecturer > Senior Lecturer | 1 | 6% |
Other | 2 | 11% |
Unknown | 4 | 22% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 3 | 17% |
Medicine and Dentistry | 3 | 17% |
Neuroscience | 3 | 17% |
Physics and Astronomy | 1 | 6% |
Pharmacology, Toxicology and Pharmaceutical Science | 1 | 6% |
Other | 2 | 11% |
Unknown | 5 | 28% |