Template-guided Functional Network Identification via Supervised Dictionary Learning
| Autor: | Tianming Liu, Xiang Li, Binbin Lin, Milad Makkie, Shannon Quinn, Jieping Ye, Yu Zhao |
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| Rok vydání: | 2017 |
| Předmět: |
Computer science
business.industry 05 social sciences Supervised learning Pattern recognition Machine learning computer.software_genre 050105 experimental psychology Image (mathematics) Data modeling Matrix decomposition Constraint (information theory) 03 medical and health sciences Identification (information) 0302 clinical medicine Unsupervised learning 0501 psychology and cognitive sciences Algorithm design Artificial intelligence business computer 030217 neurology & neurosurgery |
| Zdroj: | ISBI |
| Popis: | Functional network analysis based on matrix decomposition/factorization methods including ICA and dictionary learning models have become a popular approach in fMRI study. Yet it is still a challenging issue in interpreting the result networks because of the inter-subject variability and image noises, thus in many cases, manual inspection on the obtained networks is needed. Aiming to provide a fast and reliable functional network identification tool for both normal and diseased brain fMRI data analysis, in this work, we propose a novel supervised dictionary learning model based on rank-1 matrix decomposition algorithm (S-r1DL) with sparseness constraint. Application on the Autism Brain Imaging Data Exchange (ABIDE) database showed that S-r1DL can fast and accurately identify the functional networks based on the given templates, comparing to unsupervised learning method. |
| Databáze: | OpenAIRE |
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