LAUSR

Background: Understanding functional connectivity (FC) patterns of epileptic brain networks as they relate to the presence or absence of interictal epileptiform discharges (IEDs) can enhance machine learning (ML) algorithms identifying them. Methods: Changes in brain dynamics induced by the presence of IEDs are demonstrated by constructing FC maps from scalp electroencephalography (EEG) data. The intent is to demonstrate how unique IED characteristics present in the FC maps could be useful in training ML algorithms to yield an effective IED detection process. Results: a) The active frontal-temporal (FT) region as predetermined by the neurologists during an IED segment is found to be characterized by a statistically significant increase in the average local FC over the other FT region and over FT regions with the highest average local FC when using non-IED (NIED) segments of the same patient. This statistical significance is found for the theta, alpha, and beta sub-bands. b) Distant connections coupling one region to another also show a statistically significant difference between IED and NIED segments. Depending on the IED morphology, the significant sub-band matching those findings differs from patient to patient. Hence, while the theta sub-band results in the highest area under receiver operating characteristic curve (ROC-AUC) among the rest, it is still important to include features of other sub-bands since together they yield even higher ROC-AUC. Conclusions: FC maps intrinsically reflect the significant changes occurring in the dynamics of the epileptic brain. The obtained results provide added confidence in utilizing FC maps as biomarkers for detecting IEDs.