TY - JOUR
T1 - SozRank
T2 - A new approach for localizing the epileptic seizure onset zone
AU - Murin, Yonathan
AU - Kim, Jeremy
AU - Parvizi, Josef
AU - Goldsmith, Andrea
N1 - Funding Information:
The work of YM, JK, and AG was supported by the National Science Foundation, Center for Science of Information (CSoI), under grant NSF-CCF-0939370. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank Nima Soltani, Rakesh Malladi and Behnaam Aazhang for discussions regarding earlier versions of this research.
Publisher Copyright:
© 2018 Murin et al.
PY - 2018/1
Y1 - 2018/1
N2 - Epilepsy is one of the most common neurological disorders affecting about 1% of the world population. For patients with focal seizures that cannot be treated with antiepileptic drugs, the common treatment is a surgical procedure for removal of the seizure onset zone (SOZ). In this work we introduce an algorithm for automatic localization of the seizure onset zone (SOZ) in epileptic patients based on electrocorticography (ECoG) recordings. The proposed algorithm builds upon the hypothesis that the abnormal excessive (or synchronous) neuronal activity in the brain leading to seizures starts in the SOZ and then spreads to other areas in the brain. Thus, when this abnormal activity starts, signals recorded at electrodes close to the SOZ should have a relatively large causal influence on the rest of the recorded signals. The SOZ localization is executed in two steps. First, the algorithm represents the set of electrodes using a directed graph in which nodes correspond to recording electrodes and the edges’ weights quantify the pair-wise causal influence between the recorded signals. Then, the algorithm infers the SOZ from the estimated graph using a variant of the PageRank algorithm followed by a novel post-processing phase. Inference results for 19 patients show a close match between the SOZ inferred by the proposed approach and the SOZ estimated by expert neurologists (success rate of 17 out of 19).
AB - Epilepsy is one of the most common neurological disorders affecting about 1% of the world population. For patients with focal seizures that cannot be treated with antiepileptic drugs, the common treatment is a surgical procedure for removal of the seizure onset zone (SOZ). In this work we introduce an algorithm for automatic localization of the seizure onset zone (SOZ) in epileptic patients based on electrocorticography (ECoG) recordings. The proposed algorithm builds upon the hypothesis that the abnormal excessive (or synchronous) neuronal activity in the brain leading to seizures starts in the SOZ and then spreads to other areas in the brain. Thus, when this abnormal activity starts, signals recorded at electrodes close to the SOZ should have a relatively large causal influence on the rest of the recorded signals. The SOZ localization is executed in two steps. First, the algorithm represents the set of electrodes using a directed graph in which nodes correspond to recording electrodes and the edges’ weights quantify the pair-wise causal influence between the recorded signals. Then, the algorithm infers the SOZ from the estimated graph using a variant of the PageRank algorithm followed by a novel post-processing phase. Inference results for 19 patients show a close match between the SOZ inferred by the proposed approach and the SOZ estimated by expert neurologists (success rate of 17 out of 19).
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U2 - 10.1371/journal.pcbi.1005953
DO - 10.1371/journal.pcbi.1005953
M3 - Article
C2 - 29381703
AN - SCOPUS:85041406945
SN - 1553-734X
VL - 14
JO - PLoS computational biology
JF - PLoS computational biology
IS - 1
M1 - e1005953
ER -