Predicting Surgery Targets in Temporal Lobe Epilepsy through Structural Connectome Based Simulations

Temporal lobe epilepsy (TLE) is a prevalent neurological disorder resulting in disruptive seizures. In the case of drug resistant epilepsy resective surgery is often considered. This is a procedure hampered by unpredictable success rates, with many patients continuing to have seizures even after sur...

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Vydané v:PLoS computational biology Ročník 11; číslo 12; s. e1004642
Hlavní autori: Hutchings, Frances, Han, Cheol E., Keller, Simon S., Weber, Bernd, Taylor, Peter N., Kaiser, Marcus
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Public Library of Science 01.12.2015
Public Library of Science (PLoS)
Predmet:
Adult
Aged
Care and treatment
Computer Simulation
Connectome - methods
Councils
Diffusion Tensor Imaging - methods
Epilepsy
Epilepsy, Temporal Lobe - pathology
Epilepsy, Temporal Lobe - surgery
Excision (Surgery)
Female
Hippocampus - pathology
Hippocampus - surgery
Humans
Male
Methods
Middle Aged
Models, Anatomic
Models, Neurological
Monitoring, Intraoperative - methods
Neurophysiological Monitoring - methods
NMR
Nuclear magnetic resonance
Patient outcomes
Patient-Specific Modeling
Patients
Preoperative Care - methods
Public access
Standard scores
Success
Surgery
Surgery, Computer-Assisted - methods
Temporal lobe epilepsy
Treatment Outcome
ISSN:1553-7358, 1553-734X, 1553-7358
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Popis
Shrnutí:Temporal lobe epilepsy (TLE) is a prevalent neurological disorder resulting in disruptive seizures. In the case of drug resistant epilepsy resective surgery is often considered. This is a procedure hampered by unpredictable success rates, with many patients continuing to have seizures even after surgery. In this study we apply a computational model of epilepsy to patient specific structural connectivity derived from diffusion tensor imaging (DTI) of 22 individuals with left TLE and 39 healthy controls. We validate the model by examining patient-control differences in simulated seizure onset time and network location. We then investigate the potential of the model for surgery prediction by performing in silico surgical resections, removing nodes from patient networks and comparing seizure likelihood post-surgery to pre-surgery simulations. We find that, first, patients tend to transit from non-epileptic to epileptic states more often than controls in the model. Second, regions in the left hemisphere (particularly within temporal and subcortical regions) that are known to be involved in TLE are the most frequent starting points for seizures in patients in the model. In addition, our analysis also implicates regions in the contralateral and frontal locations which may play a role in seizure spreading or surgery resistance. Finally, the model predicts that patient-specific surgery (resection areas chosen on an individual, model-prompted, basis and not following a predefined procedure) may lead to better outcomes than the currently used routine clinical procedure. Taken together this work provides a first step towards patient specific computational modelling of epilepsy surgery in order to inform treatment strategies in individuals.
Bibliografia:ObjectType-Article-2
SourceType-Scholarly Journals-1
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PNT and MK are joint senior authors.
The authors have declared that no competing interests exist.
Conceived and designed the experiments: PNT MK FH. Performed the experiments: FH. Analyzed the data: FH PNT MK SSK. Contributed reagents/materials/analysis tools: BW CEH PNT. Wrote the paper: FH PNT MK SSK CEH BW.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1004642