Chapter title |
Structural Connectivity Guided Sparse Effective Connectivity for MCI Identification
|
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Chapter number | 35 |
Book title |
Machine Learning in Medical Imaging
|
Published in |
Machine learning in medical imaging. MLMI (Workshop), September 2017
|
DOI | 10.1007/978-3-319-67389-9_35 |
Pubmed ID | |
Book ISBNs |
978-3-31-967388-2, 978-3-31-967389-9
|
Authors |
Yang Li, Jingyu Liu, Meilin Luo, Ke Li, Pew-Thian Yap, Minjeong Kim, Chong-Yaw Wee, Dinggang Shen |
Abstract |
Recent advances in network modelling techniques have enabled the study of neurological disorders at a whole-brain level based on functional connectivity inferred from resting-state magnetic resonance imaging (rs-fMRI) scan possible. However, constructing a directed effective connectivity, which provides a more comprehensive characterization of functional interactions among the brain regions, is still a challenging task particularly when the ultimate goal is to identify disease associated brain functional interaction anomalies. In this paper, we propose a novel method for inferring effective connectivity from multimodal neuroimaging data for brain disease classification. Specifically, we apply a newly devised weighted sparse regression model on rs-fMRI data to determine the network structure of effective connectivity with the guidance from diffusion tensor imaging (DTI) data. We further employ a regression algorithm to estimate the effective connectivity strengths based on the previously identified network structure. We finally utilize a bagging classifier to evaluate the performance of the proposed sparse effective connectivity network through identifying mild cognitive impairment from healthy aging. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 14 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 4 | 29% |
Student > Bachelor | 2 | 14% |
Student > Ph. D. Student | 2 | 14% |
Professor | 1 | 7% |
Lecturer > Senior Lecturer | 1 | 7% |
Other | 1 | 7% |
Unknown | 3 | 21% |
Readers by discipline | Count | As % |
---|---|---|
Neuroscience | 4 | 29% |
Computer Science | 3 | 21% |
Psychology | 1 | 7% |
Medicine and Dentistry | 1 | 7% |
Unknown | 5 | 36% |