Predicting Surgery Targets in Temporal Lobe Epilepsy through Structural Connectome Based Simulations
- Submitting institution
-
University of Newcastle upon Tyne
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 216769-82336-1292
- Type
- D - Journal article
- DOI
-
10.1371/journal.pcbi.1004642
- Title of journal
- PLoS Computational Biology
- Article number
- e1004642
- First page
- -
- Volume
- 11
- Issue
- 12
- ISSN
- 1553-7358
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2015
- URL
-
http://dx.doi.org/10.1371/journal.pcbi.1004642
- 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
-
5
- Research group(s)
-
B - Interdisciplinary Computing and Complex Biosystems (ICOS)
- Citation count
- 34
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Using brain network data of epilepsy patients, locations commonly implicated in temporal lobe epilepsy were found by our computer model to be most vulnerable to seizures. Simulations of surgery in patients predicted a surgery success rate close to 70%, matching clinical observations. This is the first article to show that information about the network organisation of individual patients can inform diagnosis and interventions. This article was highlighted in the print edition of New Scientist and in leading clinical research journals. Furthermore, it inspired our later study (Brain, 2017) where we were able to accurately predict the outcome of epilepsy surgery.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -