Abstract

Objective We present a novel quantitative method to statistically analyze the distribution of multichannel intracerebral interictal spikes (multi-IIS) in stereoelectroencephalographic (SEEG) recordings. The method automatically extracts groups of brain structures conjointly and frequently involved in the generation of interictal activity. These groups are referred to as ‘subsets of co-activated structures’ (SCAS). We applied the method to long duration interictal recordings in patients with mesial temporal lobe epilepsy (MTLE) and analyzed the reproducibility of subsets of structures involved in the generation of multi-IIS for each patient and among patients. Methods Fifteen patients underwent long-term intracerebral EEG recording (SEEG technique) using depth electrodes. A 1 h period of continuous interictal EEG recording was selected for each patient with precautions regarding the time after anesthesia pre-SEEG, the temporal distance with respect to seizures, the vigilance state of the patient, and the anti-epileptic drug withdrawal. A research of SCAS was conducted on each recording using the developed method that includes 3 steps: (i) automatic detection of monochannel intracerebral interictal spikes (mono-IIS), (ii) formation of multi-IIS using a temporal sliding window, and (iii) extraction of SCAS. In the third step, statistical tests are used to evaluate the frequency of multi-IIS as well as their significance (with respect to the ‘random distribution of mono-IIS’ case). Results In each patient, several thousands of multi-IIS (mean±SD, 3322±2190) were formed and several SCAS (mean±SD, 3.80±1.47) were automatically extracted. Results show that reproducible subsets of brain structures are involved in the generation of interictal activity. Although SCAS were found to be variable from one patient to another, some invariant information was pointed up. In all patients, multi-IIS distribute over two distinct groups of structures: mesial structures (15/15) and lateral structures (7/15). Moreover, two particular structures, the internal temporal pole and the temporo-basal cortex, may be conjointly involved with either the first or the second group. Finally, some extracted SCAS seem to match well-defined anatomo-functional circuits of the temporal lobe. Conclusions and Significance During interictal activity in MTLE, similar subsets of temporal lobe structures are involved in the generation of spikes. This paper brings statistical evidence for the existence of these subsets and presents a method to automatically extract them from SEEG recordings. Interictal activity is spatially organized in the temporal lobe and preferentially involves two functional systems of the temporal lobe (either mesial or lateral).

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