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Sparse representation of whole-brain fMRI signals for identification of functional networks
- Source :
- Medical image analysis. 20(1)
- Publication Year :
- 2014
-
Abstract
- There have been several recent studies that used sparse representation for fMRI signal analysis and activation detection based on the assumption that each voxel's fMRI signal is linearly composed of sparse components. Previous studies have employed sparse coding to model functional networks in various modalities and scales. These prior contributions inspired the exploration of whether/how sparse representation can be used to identify functional networks in a voxel-wise way and on the whole brain scale. This paper presents a novel, alternative methodology of identifying multiple functional networks via sparse representation of whole-brain task-based fMRI signals. Our basic idea is that all fMRI signals within the whole brain of one subject are aggregated into a big data matrix, which is then factorized into an over-complete dictionary basis matrix and a reference weight matrix via an effective online dictionary learning algorithm. Our extensive experimental results have shown that this novel methodology can uncover multiple functional networks that can be well characterized and interpreted in spatial, temporal and frequency domains based on current brain science knowledge. Importantly, these well-characterized functional network components are quite reproducible in different brains. In general, our methods offer a novel, effective and unified solution to multiple fMRI data analysis tasks including activation detection, de-activation detection, and functional network identification.
- Subjects :
- Adult
Current (mathematics)
Computer science
Health Informatics
Machine learning
computer.software_genre
Matrix (mathematics)
Voxel
Humans
Radiology, Nuclear Medicine and imaging
Signal processing
Radiological and Ultrasound Technology
Basis (linear algebra)
business.industry
Brain
Pattern recognition
Sparse approximation
Computer Graphics and Computer-Aided Design
Magnetic Resonance Imaging
Identification (information)
Computer Vision and Pattern Recognition
Artificial intelligence
Nerve Net
Neural coding
business
computer
Algorithms
Subjects
Details
- ISSN :
- 13618423
- Volume :
- 20
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Medical image analysis
- Accession number :
- edsair.doi.dedup.....59c91d795b00add2229eb0a5aaac8729