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Sleep in epilepsy is best studied in longitudinal preclinical animal models, where state changes can have significant effects on epileptic activities. Voluminous data makes it very difficult to mark sleep stages manually. This demands an automated way to detect sleep and wake states. We developed an approach to characterize sleep-wake states in continuous video-electroencephalography (EEG) recordings in animals. We compared brute force approach based on frequency band-power based thresholding with machine learning algorithms to detect sleep in 600 hours of EEG data from 4 epileptic and 2 control animals. We found that conventional delta and theta band-powers were prominent in sleep; however, this was not sufficient to detect sleep algorithmically. We therefore extracted a set of novel frequency bands to robustly differentiate individual sleep states by using brute-force algorithm and machine learning models, among which k-nearest neighbors (KNN) was the best predictor of sleep with 94% accuracy. We subsequently characterized sleep patterns in animals with chronically induced epileptic spiking in the neocortex from tetanus toxin injections using brute-force algorithm. We found that epileptic spiking animals (without seizures) sleep more frequently, with significantly longer sleep segments and overall daily sleep time, as compared to control animals. This automated algorithm could help expedite sleep studies and help us understand the relationship between sleep and patients with epilepsy.

Objective A major challenge that limits understanding and treatment of epileptic events from mesial temporal structures comes from our inability to detect and map interictal networks reproducibly using scalp electrodes. Here, we developed a novel approach to map interictal spike networks and demonstrate their relationships to seizure onset and lesions in patients with foramen ovale electrode implantations. Methods We applied the direct Directed Transfer Function to reveal interictal spike propagation from bilateral foramen ovale electrodes on 10 consecutive patients and co-registered spatially with both seizure onset zones and temporal lobe lesions. Results Highly reproducible, yet unique interictal spike networks were seen for each patient (correlation: 0.93 ± 0.13). Interictal spikes spread in both anterior and posterior directions within each temporal lobe, often reverberating between sites. Spikes propagated to the opposite temporal lobe predominantly through posterior pathways. Patients with structural lesions (N = 4), including tumors and sclerosis, developed reproducible spike networks adjacent to their lesions that were highly lateralized compared to patients without lesions. Only 5% of mesial temporal lobe spikes were time-locked with scalp electrode spikes. Our preliminary observation on two lesional patients suggested that along with lesion location, Interictal spike networks also partially co-registered with seizure onset zones suggesting interrelationship between seizure onset and a subset of spike networks. Conclusions This is the first demonstration of patient-specific, reproducible interictal spike networks in mesial temporal structures that are closely linked to both temporal lobe lesions and seizure onset zones. Significance Interictal spike connectivity is a novel approach to map epileptic networks that could help advance invasive and non-invasive epilepsy treatments.

Objective Interictal spikes are a biomarker of epilepsy, yet their precise roles are poorly understood. Using long-term neocortical recordings from epileptic patients, we investigated the spatial-temporal propagation patterns of interictal spiking. Methods Interictal spikes were detected in 10 epileptic patients. Short time direct directed transfer function was used to map the spatial-temporal patterns of interictal spike onset and propagation across different cortical topographies. Results Each patient had unique interictal spike propagation pattern that was highly consistent across times, regardless of the frequency band. High spiking brain regions were often not spike onset regions. We observed frequent spike propagations to shorter distances and that the central sulcus forms a strong barrier to spike propagation. Spike onset and seizure onset seemed to be distinct networks in most cases. Conclusions Patients in epilepsy have distinct and unique network of causal propagation pattern which are very consistent revealing the underlying epileptic network. Although spike are epileptic biomarkers, spike origin and seizure onset seems to be distinct in most cases. Significance Understanding patterns of interictal spike propagation could lead to the identification patient-specific epileptic networks amenable to surgical or other treatments.

Introduction Epilepsy is a debilitating neurological condition characterized by spontaneous seizures as well as significant comorbid behavioral abnormalities. In addition to seizures, epileptic patients exhibit interictal spikes far more frequently than seizures, often, but not always observed in the same brain areas. The exact relationship between spiking and seizures as well as their respective effects on behavior are not well understood. In fact, spiking without overt seizures is seen in various psychiatric conditions including attention-deficit hyperactivity disorder. Methods In order to study the effects of spiking and seizures on behavior in an epileptic animal model, we used long-term video-electroencephalography recordings at six cortical recording sites together with behavioral activity monitoring. Animals received unilateral injections of tetanus toxin into either the somatosensory or motor cortex. Results Somatosensory cortex-injected animals developed progressive spiking ipsilateral to the injection site, while those receiving the injection into the motor cortex developed mostly contralateral spiking and spontaneous seizures. Animals with spiking but no seizures displayed a hyperactive phenotype, while animals with both spiking and seizures displayed a hypoactive phenotype. Not all spikes were equivalent as spike location strongly correlated with distinct locomotor behaviors including ambulatory distance, vertical movements, and rotatory movement. Conclusions Together, our results demonstrate relationships between brain region-specific spiking, seizures, and behaviors in rodents that could translate into a better understanding for patients with epileptic behavioral comorbidities and other neuropsychiatric disorders.

the infrequent nature of seizure events in epilepsy has engaged researchers to find alternative ways to accurately predict seizure onset regions. We implemented a hybrid approach combining the granger causality based network estimation with subsequent measurement of graph centrality measures in the interictal electrocorticography (ECoG) data to predict seizure onset zones. Critical to the success of this method was dividing analysis into two stages: first, detecting epileptic spikes and second, identifying their functional network. We analyzed interictal data from 8 epileptic patients and found that the epileptic spike functional network is a robust predictor of physician-identified seizure onset compared to the commonly used total ECoG network. Moreover, we discovered that PageRank, indegree and hub centrality measures best predict seizure onset regions. This establishes several features for automated detection of seizure onset zones, and can assist physicians in surgical planning and decisions.