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Discovering hierarchical common brain networks via multimodal deep belief network
- Source :
- Med Image Anal
- Publication Year :
- 2018
-
Abstract
- Studying a common architecture reflecting both brain's structural and functional organizations across individuals and populations in a hierarchical way has been of significant interest in the brain mapping field. Recently, deep learning models exhibited ability in extracting meaningful hierarchical structures from brain imaging data, e.g., fMRI and DTI. However, deep learning models have been rarely used to explore the relation between brain structure and function yet. In this paper, we proposed a novel multimodal deep believe network (DBN) model to discover and quantitatively represent the hierarchical organizations of common and consistent brain networks from both fMRI and DTI data. A prominent characteristic of DBN is that it is capable of extracting meaningful features from complex neuroimaging data with a hierarchical manner. With our proposed DBN model, three hierarchical layers with hundreds of common and consistent brain networks across individual brains are successfully constructed through learning a large dimension of representative features from fMRI/DTI data.
- Subjects :
- Relation (database)
Computer science
Brain Structure and Function
Health Informatics
Neuroimaging
Machine learning
computer.software_genre
Brain mapping
Multimodal Imaging
Field (computer science)
Article
030218 nuclear medicine & medical imaging
03 medical and health sciences
Deep belief network
0302 clinical medicine
Connectome
Humans
Radiology, Nuclear Medicine and imaging
Dimension (data warehouse)
Radiological and Ultrasound Technology
business.industry
Deep learning
Computer Graphics and Computer-Aided Design
Magnetic Resonance Imaging
Diffusion Magnetic Resonance Imaging
Computer Vision and Pattern Recognition
Artificial intelligence
business
computer
030217 neurology & neurosurgery
Algorithms
Subjects
Details
- ISSN :
- 13618423
- Volume :
- 54
- Database :
- OpenAIRE
- Journal :
- Medical image analysis
- Accession number :
- edsair.doi.dedup.....8b2d04ab545f84b514738c2cd392522f