From Structure to Activity: Using Centrality Measures to Predict Neuronal Activity
- Submitting institution
-
University of Plymouth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 882
- Type
- D - Journal article
- DOI
-
10.1142/S0129065717500137
- Title of journal
- International Journal of Neural Systems
- Article number
- 1750013
- First page
- -
- Volume
- 28
- Issue
- 2
- ISSN
- 0129-0657
- Open access status
- Compliant
- Month of publication
- -
- 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
- No
- Number of additional authors
-
1
- Research group(s)
-
-
- Citation count
- 19
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Network and graph theoretical measures can help to elucidate structure-function relationships in neural networks from data sets. The use of centrality measures is particularly common in determining hubs in the brains' information processing. By comparing a range of centrality measures and network topologies, this work demonstrates that Katz centrality significantly outperforms other measures in most contexts and should therefore be given preferential use. The results have significance beyond computational neuroscience, and are important for other fields including neurosurgery, social sciences, and complexity physics.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -