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Node abnormality predicts seizure outcome and relates to long-term relapse after epilepsy surgery

View ORCID ProfileNishant Sinha, View ORCID ProfileYujiang Wang, Nádia Moreira da Silva, Anna Miserocchi, Andrew W. McEvoy, Jane de Tisi, Sjoerd B. Vos, View ORCID ProfileGavin P. Winston, View ORCID ProfileJohn S. Duncan, Peter Neal Taylor
doi: https://doi.org/10.1101/747725
Nishant Sinha
1, Newcastle upon Tyne, United Kingdom
2Institute of Neuroscience, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
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  • For correspondence: nishant.sinha89@gmail.com peter.taylor@newcastle.ac.uk
Yujiang Wang
1, Newcastle upon Tyne, United Kingdom
2Institute of Neuroscience, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
3NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London, United Kingdom
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Nádia Moreira da Silva
1, Newcastle upon Tyne, United Kingdom
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Anna Miserocchi
3NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London, United Kingdom
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Andrew W. McEvoy
3NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London, United Kingdom
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Jane de Tisi
3NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London, United Kingdom
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Sjoerd B. Vos
3NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London, United Kingdom
4Centre for Medical Image Computing, University College London, London, United Kingdom
5Epilepsy Society MRI Unit, Chalfont St Peter, United Kingdom
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Gavin P. Winston
3NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London, United Kingdom
5Epilepsy Society MRI Unit, Chalfont St Peter, United Kingdom
6Department of Medicine, Division of Neurology, Queen’s University, Kingston, Canada
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John S. Duncan
3NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London, United Kingdom
5Epilepsy Society MRI Unit, Chalfont St Peter, United Kingdom
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Peter Neal Taylor
1, Newcastle upon Tyne, United Kingdom
2Institute of Neuroscience, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
3NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London, United Kingdom
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  • For correspondence: nishant.sinha89@gmail.com peter.taylor@newcastle.ac.uk
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Abstract

Objective We assessed pre-operative structural brain networks and clinical characteristics of patients with drug resistant temporal lobe epilepsy (TLE) to identify correlates of post-surgical seizure outcome at 1 year and seizure relapses up to 5 years.

Methods We retrospectively examined data from 51 TLE patients who underwent anterior temporal lobe resection (ATLR) and 29 healthy controls. For each patient, using the pre-operative structural, diffusion, and post-operative structural MRI, we generated two networks: ‘pre-surgery’ network and ‘surgically-spared’ network. The pre-surgery network is the whole-brain network before surgery and the surgically-spared network is a subnetwork of the pre-surgery network which is expected to remain unaffected by surgery and hence present post-operatively. Standardising these networks with respect to controls, we determined the number of abnormal nodes before surgery and expected to remain after surgery. We incorporated these 2 abnormality measures and 13 commonly acquired clinical data from each patient in a robust machine learning framework to estimate patient-specific chances of seizures persisting after surgery.

Results Patients with more abnormal nodes had lower chance of seizure freedom at 1 year and even if seizure free at 1 year, were more likely to relapse within five years. In the surgically-spared networks of poor outcome patients, the number of abnormal nodes was greater and their locations more widespread than in good outcome patients. We achieved 0.84 ± 0.06 AUC and 0.89 ± 0.09 specificity in detecting unsuccessful seizure outcomes at 1-year. Moreover, the model-predicted likelihood of seizure relapse was significantly correlated with the grade of surgical outcome at year-one and associated with relapses up-to five years post-surgery.

Conclusion Node abnormality offers a personalised non-invasive marker, that can be combined with clinical data, to better estimate the chances of seizure freedom at 1 year, and subsequent relapse up to 5 years after ATLR.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted September 01, 2019.
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Node abnormality predicts seizure outcome and relates to long-term relapse after epilepsy surgery
Nishant Sinha, Yujiang Wang, Nádia Moreira da Silva, Anna Miserocchi, Andrew W. McEvoy, Jane de Tisi, Sjoerd B. Vos, Gavin P. Winston, John S. Duncan, Peter Neal Taylor
bioRxiv 747725; doi: https://doi.org/10.1101/747725
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Node abnormality predicts seizure outcome and relates to long-term relapse after epilepsy surgery
Nishant Sinha, Yujiang Wang, Nádia Moreira da Silva, Anna Miserocchi, Andrew W. McEvoy, Jane de Tisi, Sjoerd B. Vos, Gavin P. Winston, John S. Duncan, Peter Neal Taylor
bioRxiv 747725; doi: https://doi.org/10.1101/747725

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