Brinkmann, Benjamin H

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.

The ability to predict seizures may enable patients with epilepsy to better manage their medications and activities, potentially reducing side effects and improving quality of life. Forecasting epileptic seizures remains a challenging problem, but machine learning methods using intracranial electroencephalographic (iEEG) measures have shown promise. A machine-learning-based pipeline was developed to process iEEG recordings and generate seizure warnings. Results support the ability to forecast seizures at rates greater than a Poisson random predictor for all feature sets and machine learning algorithms tested. In addition, subject-specific neurophysiological changes in multiple features are reported preceding lead seizures, providing evidence supporting the existence of a distinct and identifiable preictal state.

The function and connectivity of human brain is disrupted in epilepsy. We previously reported that the region of epileptic brain generating focal seizures, i.e., the seizure onset zone (SOZ), is functionally isolated from surrounding brain regions in focal neocortical epilepsy. The modulatory effect of behavioral state on the spatial and spectral scales over which the reduced functional connectivity occurs, however, is unclear. Here we use simultaneous sleep staging from scalp EEG with intracranial EEG recordings from medial temporal lobe to investigate how behavioral state modulates the spatial and spectral scales of local field potential synchrony in focal epileptic hippocampus. The local field spectral power and linear correlation between adjacent electrodes provide measures of neuronal population synchrony at different spatial scales, ∼1 and 10 mm, respectively. Our results show increased connectivity inside the SOZ and low connectivity between electrodes in SOZ and outside the SOZ. During slow-wave sleep, we observed decreased connectivity for ripple and fast ripple frequency bands within the SOZ at the 10 mm spatial scale, while the local synchrony remained high at the 1 mm spatial scale. Further study of these phenomena may prove useful for SOZ localization and help understand seizure generation, and the functional deficits seen in epileptic eloquent cortex.

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.

Tc-99m ethyl cysteinate diethylester (ECD) and Tc-99m hexamethyl propylene amine oxime (HMPAO) are commonly used for single-photon emission computed tomography (SPECT) studies of a variety of neurologic disorders. Although these tracers have been very helpful in diagnosing and guiding treatment of neurologic disease, data describing the distribution and laterality of these tracers in normal resting brain are limited. Advances in quantitative functional imaging have demonstrated the value of using resting studies from control populations as a baseline to account for physiologic fluctuations in cerebral perfusion. Here, we report results from 30 resting Tc-99m ECD SPECT scans and 14 resting Tc-99m HMPAO scans of normal volunteers with no history of neurologic disease. Scans were analyzed with regions of interest and with statistical parametric mapping, with comparisons performed laterally (left vs. right), as well as for age, gender, and handedness. The results show regions of significant asymmetry in the normal controls affecting widespread areas in the cerebral hemispheres, but most marked in superior parietotemporal region and frontal lobes. The results have important implications for the use of normal control SPECT images in the evaluation of patients with neurologic disease.

Synchronization of local and distributed neuronal assemblies is thought to underlie fundamental brain processes such as perception, learning, and cognition. In neurological disease, neuronal synchrony can be altered and in epilepsy may play an important role in the generation of seizures. Linear cross-correlation and mean phase coherence of local field potentials (LFPs) are commonly used measures of neuronal synchrony and have been studied extensively in epileptic brain. Multiple studies have reported that epileptic brain is characterized by increased neuronal synchrony except possibly prior to seizure onset when synchrony may decrease. Previous studies using intracranial electroencephalography (EEG), however, have been limited to patients with epilepsy. Here we investigate neuronal synchrony in epileptic and control brain using intracranial EEG recordings from patients with medically resistant partial epilepsy and control subjects with intractable facial pain. For both epilepsy and control patients, average LFP synchrony decreases with increasing interelectrode distance. Results in epilepsy patients show lower LFP synchrony between seizure-generating brain and other brain regions. This relative isolation of seizure-generating brain underlies the paradoxical finding that control patients without epilepsy have greater average LFP synchrony than patients with epilepsy. In conclusion, we show that in patients with focal epilepsy, the region of epileptic brain generating seizures is functionally isolated from surrounding brain regions. We further speculate that this functional isolation may contribute to spontaneous seizure generation and may represent a clinically useful electrophysiological signature for mapping epileptic brain.