Collective sparse symmetric non-negative matrix factorization for identifying overlapping communities in resting-state brain functional networks
- Submitting institution
-
The University of Essex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1213
- Type
- D - Journal article
- DOI
-
10.1016/j.neuroimage.2017.11.003
- Title of journal
- NeuroImage
- Article number
- -
- First page
- 259
- Volume
- 166
- Issue
- -
- ISSN
- 1053-8119
- Open access status
- Compliant
- Month of publication
- November
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
2
- Research group(s)
-
B - Brain Computer Interfaces and Neural Engineering (BCI-NE)
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposes a novel method to identify overlapping community structures in the brain functional network based on rs-fMRI data. This work is significant because the new method can accurately characterise the brain network organisation at both group and individual levels with high reproducibility and reliability. Furthermore, with the novel sparsity and non-negativity constraints in the method its identification results are interpretable, providing new insights for understanding the functional network of the human brain. The proposed method and algorithms are described and analysed using mathematical equations, leading to a solid foundation for practical applications.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -