Signal sampling for efficient sparse representation of resting state FMRI data

Autor: Jinglei Lv, Milad Makkie, Bao Ge, Tianming Liu, Xiang Li, Wei Zhang, Xi Jiang, Shu Zhang, Jin Wang, Junwei Han, Shijie Zhao, Lei Guo
Rok vydání: 2015
Předmět:
Speedup
Time Factors
Computer science
Rest
Cognitive Neuroscience
Computation
0206 medical engineering
02 engineering and technology
Machine learning
computer.software_genre
Signal
Article
03 medical and health sciences
Behavioral Neuroscience
Cellular and Molecular Neuroscience
0302 clinical medicine
Encoding (memory)
Neural Pathways
Humans
Radiology
Nuclear Medicine and imaging
Sparse matrix
Brain Mapping
Human Connectome Project
K-SVD
Resting state fMRI
business.industry
Brain
Sampling (statistics)
Pattern recognition
Sparse approximation
Magnetic Resonance Imaging
020601 biomedical engineering
Psychiatry and Mental health
Neurology
Data Interpretation
Statistical
Connectome
Neurology (clinical)
Artificial intelligence
Neural coding
Scale (map)
business
computer
Algorithms
030217 neurology & neurosurgery
Zdroj: ISBI
ISSN: 1931-7565
1931-7557
Popis: As the size of brain imaging data such as fMRI grows explosively, it provides us with unprecedented and abundant information about the brain. How to reduce the size of fMRI data but not lose much information becomes a more and more pressing issue. Recent literature studies tried to deal with it by dictionary learning and sparse representation methods, however, their computation complexities are still high, which hampers the wider application of sparse representation method to large scale fMRI datasets. To effectively address this problem, this work proposes to represent resting state fMRI (rs-fMRI) signals of a whole brain via a statistical sampling based sparse representation. First we sampled the whole brain's signals via different sampling methods, then the sampled signals were aggregate into an input data matrix to learn a dictionary, finally this dictionary was used to sparsely represent the whole brain's signals and identify the resting state networks. Comparative experiments demonstrate that the proposed signal sampling framework can speed-up by ten times in reconstructing concurrent brain networks without losing much information. The experiments on the 1000 Functional Connectomes Project further demonstrate its effectiveness and superiority.
Databáze: OpenAIRE
Externí odkaz: