Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography
Fig 7
(A-C) Example of a virtual resection on a seizure from patient 17, cf. Fig 7 in S1 Text. (A) Inferred probability of high excitability. Anatomical abbreviations as in Fig 2. (B) Pre-operative seizure dynamics as inferred from the data. (C) Post-operative seizure dynamics. The resected regions were removed from the model, and the dynamics was simulated using the excitabilities inferred from the pre-operative observations. The seizure activity is not completely stopped, but the number of seizing regions is reduced. (D) Outcome of virtual surgeries on a group level. Each point corresponds to an operated patient (n = 18). Top panel shows the number of post-operative seizing regions npostop (i.e. the regions with recruitment probability above 50% at t = tlim), bottom panel shows the relative reduction of the seizing regions compared to the pre-operative level, (npreop − npostop)/npreop. For patients where multiple seizures were available, the values were averaged across seizures. (E) Precision-recall curves for evaluating the match between the performed resection and the inferred epileptogenicity. The precision and recall values were calculated for varying threshold pt on high epileptogenicity, p(c > ch) > pt; the threshold on high excitability ch = 2 was kept constant.