In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 17, No. 2 ( 2021-2-17), p. e1008689-
Abstract:
Surgical interventions in epileptic patients aimed at the removal of the epileptogenic zone have success rates at only 60-70%. This failure can be partly attributed to the insufficient spatial sampling by the implanted intracranial electrodes during the clinical evaluation, leading to an incomplete picture of spatio-temporal seizure organization in the regions that are not directly observed. Utilizing the partial observations of the seizure spreading through the brain network, complemented by the assumption that the epileptic seizures spread along the structural connections, we infer if and when are the unobserved regions recruited in the seizure. To this end we introduce a data-driven model of seizure recruitment and propagation across a weighted network, which we invert using the Bayesian inference framework. Using a leave-one-out cross-validation scheme on a cohort of 45 patients we demonstrate that the method can improve the predictions of the states of the unobserved regions compared to an empirical estimate that does not use the structural information, yet it is on the same level as the estimate that takes the structure into account. Furthermore, a comparison with the performed surgical resection and the surgery outcome indicates a link between the inferred excitable regions and the actual epileptogenic zone. The results emphasize the importance of the structural connectome in the large-scale spatio-temporal organization of epileptic seizures and introduce a novel way to integrate the patient-specific connectome and intracranial seizure recordings in a whole-brain computational model of seizure spread.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1008689
DOI:
10.1371/journal.pcbi.1008689.g001
DOI:
10.1371/journal.pcbi.1008689.g002
DOI:
10.1371/journal.pcbi.1008689.g003
DOI:
10.1371/journal.pcbi.1008689.g004
DOI:
10.1371/journal.pcbi.1008689.g005
DOI:
10.1371/journal.pcbi.1008689.g006
DOI:
10.1371/journal.pcbi.1008689.g007
DOI:
10.1371/journal.pcbi.1008689.g008
DOI:
10.1371/journal.pcbi.1008689.s001
DOI:
10.1371/journal.pcbi.1008689.s002
DOI:
10.1371/journal.pcbi.1008689.s003
DOI:
10.1371/journal.pcbi.1008689.r001
DOI:
10.1371/journal.pcbi.1008689.r002
DOI:
10.1371/journal.pcbi.1008689.r003
DOI:
10.1371/journal.pcbi.1008689.r004
DOI:
10.1371/journal.pcbi.1008689.r005
DOI:
10.1371/journal.pcbi.1008689.r006
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2193340-6
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