Kucewicz, Michal

Ultrafast oscillations (UFOs) are brief bursts of high-frequency (2–8 kHz) activity recorded using depth electrodes in epileptic brain tissue. While reliably detected at small scales using microelectrodes, UFOs have thus far likely not been observed at larger scales using macroelectrodes. Here we show that faint echoes of these bursting patterns can be detected in macroelectrode signals using a model of cross-scale propagation. To model this cross-scale effect, we extend the classical damped wave equation to include an explicit scale coordinate governed by the dilation operator. We then use Green's functions to obtain an expression for the spectral echo expected at the macroelectrode scale. This echo takes the form of a carrier envelope modulated by the temporal structure of the original microscale bursting event. We test the predicted signature echo pattern in human intracranial recordings from eleven epilepsy patients implanted with both micro- and macroelectrodes. Statistically significant echoes are confirmed in two out of eleven patients, appearing in 2% of all tested micro-macro pairs.

Abstract Objective Memory impairments are common in neurological disorders, especially drug‐resistant epilepsy, where cognitive deficits such as accelerated long‐term forgetting may occur before surgery due to interictal/ictal discharges, antiseizure medications, and disease progression. Postsurgical memory decline affects 40%–60% of patients. Understanding the neural mechanisms of memory encoding is essential for identifying critical network hubs and guiding surgical interventions that preserve cognitive function. Methods We analyzed intracranial electroencephalographic recordings from epilepsy patients ( n = 15) performing a verbal memory encoding task to identify key structures involved in successful memory formation. Functional connectivity was computed using multiple features across several frequency bands. Temporal dynamics of connectivity were examined to characterize pre‐encoding and encoding phases. Results The middle temporal gyrus (MTG) consistently emerged as a verbal memory hub across features and frequency bands. The inferior frontal gyrus (IFG) was identified as a beta‐band‐specific hub via phase synchrony. Temporal analyses revealed that MTG exhibited stable and widespread connectivity differences across all encoding phases, suggesting a role in pre‐encoding network priming. In contrast, IFG displayed a transient decrease in connectivity and spatial extent during early encoding, indicating a more focal and dynamic contribution. Significance These findings suggest that successful memory encoding relies on widespread, frequency‐specific connectivity patterns centered on MTG, complemented by transient, localized IFG activity. Identifying such hubs may inform surgical planning by highlighting regions critical for cognitive outcomes, ultimately supporting strategies to preserve memory in epilepsy surgery.

Abstract Objective . Automated behavioral state classification can benefit next generation implantable epilepsy devices. In this study we explored the feasibility of automated awake (AW) and slow wave sleep (SWS) classification using wide bandwidth intracranial EEG (iEEG) in patients undergoing evaluation for epilepsy surgery. Approach . Data from seven patients (age 34 ± 12 , 4 women) who underwent intracranial depth electrode implantation for iEEG monitoring were included. Spectral power features (0.1–600 Hz) spanning several frequency bands from a single electrode were used to train and test a support vector machine classifier. Main results . Classification accuracy of 97.8 ± 0.3% (normal tissue) and 89.4 ± 0.8% (epileptic tissue) across seven subjects using multiple spectral power features from a single electrode was achieved. Spectral power features from electrodes placed in normal temporal neocortex were found to be more useful (accuracy 90.8 ± 0.8%) for sleep-wake state classification than electrodes located in normal hippocampus (87.1 ± 1.6%). Spectral power in high frequency band features (Ripple (80–250 Hz), Fast Ripple (250–600 Hz)) showed comparable performance for AW and SWS classification as the best performing Berger bands (Alpha, Beta, low Gamma) with accuracy ⩾90% using a single electrode contact and single spectral feature. Significance . Automated classification of wake and SWS should prove useful for future implantable epilepsy devices with limited computational power, memory, and number of electrodes. Applications include quantifying patient sleep patterns and behavioral state dependent detection, prediction, and electrical stimulation therapies.

SummaryObjectiveLocal field potentials (LFPs) arise from synchronous activation of millions of neurons, producing seemingly consistent waveform shapes and relative synchrony across electrodes. Interictal spikes (IISs) are LFPs associated with epilepsy that are commonly used to guide surgical resection. Recently, changes in neuronal firing patterns observed in the minutes preceding seizure onset were found to be reactivated during postseizure sleep, a process called seizure‐related consolidation (SRC), due to similarities with learning‐related consolidation. Because IISs arise from summed neural activity, we hypothesized that changes in IIS shape and relative synchrony would be observed in the minutes preceding seizure onset and would be reactivated preferentially during postseizure slow‐wave sleep (SWS).MethodsScalp and intracranial recordings were obtained continuously across multiple days from clinical macroelectrodes implanted in patients undergoing treatment for intractable epilepsy. Data from scalp electrodes were used to stage sleep. Data from intracranial electrodes were used to detect IISs using a previously established algorithm. Partial correlations were computed for sleep and wake periods before and after seizures as a function of correlations observed in the minutes preceding seizures. Magnetic resonance imaging (MRI) and computed tomography (CT) scans were co‐registered with electroencephalography (EEG) to determine the location of the seizure‐onset zone (SOZ).ResultsChanges in IIS shape and relative synchrony were observed on a subset of macroelectrodes minutes before seizure onset, and these changes were reactivated preferentially during postseizure SWS. Changes in synchrony were greatest for pairs of electrodes where at least one electrode was located in the SOZ.SignificanceThese data suggest preseizure changes in neural activity and their subsequent reactivation occur across a broad spatiotemporal scale: from single neurons to LFPs, both within and outside the SOZ. The preferential reactivation of seizure‐related changes in IISs during postseizure SWS adds to a growing body of literature suggesting that pathologic neural processes may utilize physiologic mechanisms of synaptic plasticity.

High-frequency oscillations (HFO; gamma: 40–100 Hz, ripples: 100–200 Hz, and fast ripples: 250–500 Hz) have been widely studied in health and disease. These phenomena may serve as biomarkers for epileptic brain; however, a means of differentiating between pathological and normal physiological HFO is essential. We categorized task-induced physiological HFO during periods of HFO induced by a visual or motor task by measuring frequency, duration, and spectral amplitude of each event in single trial time-frequency spectra and compared them to pathological HFO similarly measured. Pathological HFO had higher mean spectral amplitude, longer mean duration, and lower mean frequency than physiological-induced HFO. In individual patients, support vector machine analysis correctly classified pathological HFO with sensitivities ranging from 70–98% and specificities >90% in all but one patient. In this patient, infrequent high-amplitude HFO were observed in the motor cortex just before movement onset in the motor task. This finding raises the possibility that in epileptic brain physiological-induced gamma can assume higher spectral amplitudes similar to those seen in pathologic HFO. This method if automated and validated could provide a step towards differentiating physiological HFO from pathological HFO and improving localization of epileptogenic brain.