Data-driven approach for the delineation of the irritative zone in epilepsy in MEG.

The reliable identification of the irritative zone (IZ) is a prerequisite for the correct clinical evaluation of medically refractory patients affected by epilepsy. Given the complexity of MEG data, visual analysis of epileptiform neurophysiological activity is highly time consuming and might leave clinically relevant information undetected. We recorded and analyzed the interictal activity from seven patients affected by epilepsy (Vectorview Neuromag), who successfully underwent epilepsy surgery (Engel > = II). We visually marked and localized characteristic epileptiform activity (VIS). We implemented a two-stage pipeline for the detection of interictal spikes and the delineation of the IZ. First, we detected candidate events from peaky ICA components, and then clustered events around spatio-temporal patterns identified by convolutional sparse coding. We used the average of clustered events to create IZ maps computed at the amplitude peak (PEAK), and at the 50% of the peak ascending slope (SLOPE). We validated our approach by computing the distance of the estimated IZ (VIS, SLOPE and PEAK) from the border of the surgically resected area (RA). We identified 25 spatiotemporal patterns mimicking the underlying interictal activity (3.6 clusters/patient). Each cluster was populated on average by 22.1 [15.0-31.0] spikes. The predicted IZ maps had an average distance from the resection margin of 8.4 ± 9.3 mm for visual analysis, 12.0 ± 16.5 mm for SLOPE and 22.7 ±. 16.4 mm for PEAK. The consideration of the source spread at the ascending slope provided an IZ closer to RA and resembled the analysis of an expert observer. We validated here the performance of a data-driven approach for the automated detection of interictal spikes and delineation of the IZ. This computational framework provides the basis for reproducible and bias-free analysis of MEG recordings in epilepsy.

Dataset of spiking and LFP activity invasively recorded in the human amygdala during aversive dynamic stimuli.

We present an electrophysiological dataset collected from the amygdalae of nine participants attending a visual dynamic stimulation of emotional aversive content. The participants were patients affected by epilepsy who underwent preoperative invasive monitoring in the mesial temporal lobe. Participants were presented with dynamic visual sequences of fearful faces (aversive condition), interleaved with sequences of neutral landscapes (neutral condition). The dataset contains the simultaneous recording of intracranial EEG (iEEG) and neuronal spike times and waveforms, and localization information for iEEG electrodes. Participant characteristics and trial information are provided. We technically validated this dataset and provide here the spike sorting quality metrics and the spectra of iEEG signals. This dataset allows the investigation of amygdalar response to dynamic aversive stimuli at multiple spatial scales, from the macroscopic EEG to the neuronal firing in the human brain.