SUMMARYPurpose: Fast ripples are reported to be highly localizing to the epileptogenic or seizure-onset zone (SOZ) but may not be readily found in neocortical epilepsy, whereas ripples are insufficiently localizing. Herein we classified interictal neocortical ripples by associated characteristics to identify a subtype that may help to localize the SOZ in neocortical epilepsy. We hypothesize that ripples associated with an interictal epileptiform discharge (IED) are more pathologic, since the IED is not a normal physiologic event. Methods: We studied 35 patients with epilepsy with neocortical epilepsy who underwent invasive electroencephalography (EEG) evaluation by stereotactic EEG (SEEG) or subdural grid electrodes. Interictal fast ripples and ripples were visually marked during slow-wave sleep lasting 10-30 min. Neocortical ripples were classified as type I when superimposed on epileptiform discharges such as paroxysmal fast, spike, or sharp wave, and as type II when independent of epileptiform discharges. Key Findings: In 21 patients with a defined SOZ, neocortical fast ripples were detected in the SOZ of only four patients. Type I ripples were detected in 14 cases almost exclusively in the SOZ or primary propagation area (PP) and marked the SOZ with higher specificity than interictal spikes. In contrast, type II ripples were not correlated with the SOZ. In 14 patients with two or more presumed SOZs or nonlocalizable onset pattern, type I but not type II ripples also occurred in the SOZs. We found the areas with only type II ripples outside of the SOZ (type II-O ripples) in SEEG that localized to the primary motor cortex and primary visual cortex. Significance: Neocortical fast ripples and type I ripples are specific markers of the SOZ, whereas type II ripples are not. Type I ripples are found more readily than fast ripples in human neocortical epilepsy. Type II-O ripples may represent spontaneous physiologic ripples in the human neocortex.

Identifying an electrical biomarker of epileptogenicity would facilitate epilepsy surgery. Using time-frequency analysis during the pre-ictal-to-ictal transition, Grinenko et al. identify a fingerprint of the epileptogenic zone, which they validate through machine learning. The time-frequency pattern is consistent with a pathophysiological role of fast inhibitory interneurons in seizure onset.

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The current standard model of language production involves a sensorimotor dorsal stream connecting areas in the temporo-parietal junction with those in the inferior frontal gyrus and lateral premotor cortex. These regions have been linked to various aspects of word production such as phonological processing or articulatory programming, primarily through neuropsychological and functional imaging group studies. Most if not all the theoretical descriptions of this model imply that the same network should be identifiable across individual speakers. We tested this hypothesis by quantifying the variability of activation observed across individuals within each dorsal stream anatomical region. This estimate was based on electrical activity recorded directly from the cerebral cortex with millisecond accuracy in awake epileptic patients clinically implanted with intracerebral depth electrodes for pre-surgical diagnosis. Each region's activity was quantified using two different metrics, intra-cerebral evoked related potentials and high gamma activity, at the level of the group, the individual, and the recording contact. Using picture naming task, the two metrics show simultaneous activation of parietal and frontal regions in line with models that posit interactive processing during word retrieval. They also reveal different levels of variability across brain regions and patients except in auditory and motor regions. The independence and non-uniformity of cortical activity according to the two metrics push the current model towards sub-second and sub-region explorations focused on individualized language speech production. Several hypotheses are considered for this within-region heterogeneity.