Feasibility of Real-Time Conditional Sacral Neuromodulation Using Wireless Bladder Pressure Sensor.

Continuous sacral neuromodulation (SNM) is used to treat overactive bladder, reducing urine leakage and increasing capacity. Conditional SNM applies stimulation in response to changing bladder conditions, and is an opportunity to study neuromodulation effects in various disease states. A key advantage of this approach is saving power consumed by stimulation pulses. This study demonstrated feasibility of automatically applying neuromodulation using a wireless bladder pressure sensor, a real-time control algorithm, and the Medtronic Summit™ RC+S neurostimulation research system. This study tested feasibility of four conditional SNM paradigms over five days in 4 female sheep. Primary outcomes assessed proof of concept of closed-loop system function. While the bladder pressure sensor correlated only weakly to simultaneous catheter-based pressure measurement (correlation 0.26-0.89, median r = 0.52), the sensor and algorithm were accurate enough to automatically trigger SNM appropriately. The neurostimulator executed 98.5% of transmitted stimulation commands with a median latency of 72 ms (n = 1,206), suggesting that rapid decision-making and control is feasible with this platform. On average, bladder capacity increased for continuous SNM and algorithm-controlled paradigms. Some animals responded more strongly to conditional SNM, suggesting that treatment could be individualized. Future research in conditional SNM may elucidate the physiologic underpinnings of differential response and enable clinical translation.

Correction: Forecasting Seizures Using Bivariate Intracranial EEG Measures and SVM in Naturally Occurring Canine Epilepsy.

[This corrects the article DOI: 10.1371/journal.pone.0133900.].

Forecasting Seizures Using Intracranial EEG Measures and SVM in Naturally Occurring Canine Epilepsy.

Management of drug resistant focal epilepsy would be greatly assisted by a reliable warning system capable of alerting patients prior to seizures to allow the patient to adjust activities or medication. Such a system requires successful identification of a preictal, or seizure-prone state. Identification of preictal states in continuous long- duration intracranial electroencephalographic (iEEG) recordings of dogs with naturally occurring epilepsy was investigated using a support vector machine (SVM) algorithm. The dogs studied were implanted with a 16-channel ambulatory iEEG recording device with average channel reference for a mean (st. dev.) of 380.4 (+87.5) days producing 220.2 (+104.1) days of intracranial EEG recorded at 400 Hz for analysis. The iEEG records had 51.6 (+52.8) seizures identified, of which 35.8 (+30.4) seizures were preceded by more than 4 hours of seizure-free data. Recorded iEEG data were stratified into 11 contiguous, non-overlapping frequency bands and binned into one-minute synchrony features for analysis. Performance of the SVM classifier was assessed using a 5-fold cross validation approach, where preictal training data were taken from 90 minute windows with a 5 minute pre-seizure offset. Analysis of the optimal preictal training time was performed by repeating the cross validation over a range of preictal windows and comparing results. We show that the optimization of feature selection varies for each subject, i.e. algorithms are subject specific, but achieve prediction performance significantly better than a time-matched Poisson random predictor (p<0.05) in 5/5 dogs analyzed.

Evidence for consolidation of neuronal assemblies after seizures in humans.

The establishment of memories involves reactivation of waking neuronal activity patterns and strengthening of associated neural circuits during slow-wave sleep (SWS), a process known as "cellular consolidation" (Dudai and Morris, 2013). Reactivation of neural activity patterns during waking behaviors that occurs on a timescale of seconds to minutes is thought to constitute memory recall (O'Keefe and Nadel, 1978), whereas consolidation of memory traces may be revealed and served by correlated firing (reactivation) that appears during sleep under conditions suitable for synaptic modification (Buhry et al., 2011). Although reactivation has been observed in human neuronal recordings (Gelbard-Sagiv et al., 2008; Miller et al., 2013), reactivation during sleep has not, likely because data are difficult to obtain and the effect is subtle. Seizures, however, provide intense and synchronous, yet sparse activation (Bower et al., 2012) that could produce a stronger consolidation effect if seizures activate learning-related mechanisms similar to those activated by learned tasks. Continuous wide-bandwidth recordings from patients undergoing intracranial monitoring for drug-resistant epilepsy revealed reactivation of seizure-related neuronal activity during subsequent SWS, but not wakefulness. Those neuronal assemblies that were most strongly activated during seizures showed the largest correlation changes, suggesting that consolidation selectively strengthened neuronal circuits activated by seizures. These results suggest that seizures "hijack" physiological learning mechanisms and also suggest a novel epilepsy therapy targeting neuronal dynamics during post-seizure sleep.

High frequency oscillations are associated with cognitive processing in human recognition memory.

High frequency oscillations are associated with normal brain function, but also increasingly recognized as potential biomarkers of the epileptogenic brain. Their role in human cognition has been predominantly studied in classical gamma frequencies (30-100 Hz), which reflect neuronal network coordination involved in attention, learning and memory. Invasive brain recordings in animals and humans demonstrate that physiological oscillations extend beyond the gamma frequency range, but their function in human cognitive processing has not been fully elucidated. Here we investigate high frequency oscillations spanning the high gamma (50-125 Hz), ripple (125-250 Hz) and fast ripple (250-500 Hz) frequency bands using intracranial recordings from 12 patients (five males and seven females, age 21-63 years) during memory encoding and recall of a series of affectively charged images. Presentation of the images induced high frequency oscillations in all three studied bands within the primary visual, limbic and higher order cortical regions in a sequence consistent with the visual processing stream. These induced oscillations were detected on individual electrodes localized in the amygdala, hippocampus and specific neocortical areas, revealing discrete oscillations of characteristic frequency, duration and latency from image presentation. Memory encoding and recall significantly modulated the number of induced high gamma, ripple and fast ripple detections in the studied structures, which was greater in the primary sensory areas during the encoding (Wilcoxon rank sum test, P = 0.002) and in the higher-order cortical association areas during the recall (Wilcoxon rank sum test, P = 0.001) of memorized images. Furthermore, the induced high gamma, ripple and fast ripple responses discriminated the encoded and the affectively charged images. In summary, our results show that high frequency oscillations, spanning a wide range of frequencies, are associated with memory processing and generated along distributed cortical and limbic brain regions. These findings support an important role for fast network synchronization in human cognition and extend our understanding of normal physiological brain activity during memory processing.

Familial risk of epilepsy: a population-based study.

Almost all previous studies of familial risk of epilepsy have had potentially serious methodological limitations. Our goal was to address these limitations and provide more rigorous estimates of familial risk in a population-based study. We used the unique resources of the Rochester Epidemiology Project to identify all 660 Rochester, Minnesota residents born in 1920 or later with incidence of epilepsy from 1935-94 (probands) and their 2439 first-degree relatives who resided in Olmsted County. We assessed incidence of epilepsy in relatives by comprehensive review of the relatives' medical records, and estimated age-specific cumulative incidence and standardized incidence ratios for epilepsy in relatives compared with the general population, according to proband and relative characteristics. Among relatives of all probands, cumulative incidence of epilepsy to age 40 was 4.7%, and risk was increased 3.3-fold (95% confidence interval 2.75-5.99) compared with population incidence. Risk was increased to the greatest extent in relatives of probands with idiopathic generalized epilepsies (standardized incidence ratio 6.0) and epilepsies associated with intellectual or motor disability presumed present from birth, which we denoted 'prenatal/developmental cause' (standardized incidence ratio 4.3). Among relatives of probands with epilepsy without identified cause (including epilepsies classified as 'idiopathic' or 'unknown cause'), risk was significantly increased for epilepsy of prenatal/developmental cause (standardized incidence ratio 4.1). Similarly, among relatives of probands with prenatal/developmental cause, risk was significantly increased for epilepsies without identified cause (standardized incidence ratio 3.8). In relatives of probands with generalized epilepsy, standardized incidence ratios were 8.3 (95% confidence interval 2.93-15.31) for generalized epilepsy and 2.5 (95% confidence interval 0.92-4.00) for focal epilepsy. In relatives of probands with focal epilepsy, standardized incidence ratios were 1.0 (95% confidence interval 0.00-2.19) for generalized epilepsy and 2.6 (95% confidence interval 1.19-4.26) for focal epilepsy. Epilepsy incidence was greater in offspring of female probands than in offspring of male probands, and this maternal effect was restricted to offspring of probands with focal epilepsy. The results suggest that risks for epilepsies of unknown and prenatal/developmental cause may be influenced by shared genetic mechanisms. They also suggest that some of the genetic influences on generalized and focal epilepsies are distinct. However, the similar increase in risk for focal epilepsy among relatives of probands with either generalized (2.5-fold) or focal epilepsy (2.6-fold) may reflect some coexisting shared genetic influences.

Forecasting seizures in dogs with naturally occurring epilepsy.

Seizure forecasting has the potential to create new therapeutic strategies for epilepsy, such as providing patient warnings and delivering preemptive therapy. Progress on seizure forecasting, however, has been hindered by lack of sufficient data to rigorously evaluate the hypothesis that seizures are preceded by physiological changes, and are not simply random events. We investigated seizure forecasting in three dogs with naturally occurring focal epilepsy implanted with a device recording continuous intracranial EEG (iEEG). The iEEG spectral power in six frequency bands: delta (0.1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), low-gamma (30-70 Hz), and high-gamma (70-180 Hz), were used as features. Logistic regression classifiers were trained to discriminate labeled pre-ictal and inter-ictal data segments using combinations of the band spectral power features. Performance was assessed on separate test data sets via 10-fold cross-validation. A total of 125 spontaneous seizures were detected in continuous iEEG recordings spanning 6.5 to 15 months from 3 dogs. When considering all seizures, the seizure forecasting algorithm performed significantly better than a Poisson-model chance predictor constrained to have the same time in warning for all 3 dogs over a range of total warning times. Seizure clusters were observed in all 3 dogs, and when the effect of seizure clusters was decreased by considering the subset of seizures separated by at least 4 hours, the forecasting performance remained better than chance for a subset of algorithm parameters. These results demonstrate that seizures in canine epilepsy are not randomly occurring events, and highlight the feasibility of long-term seizure forecasting using iEEG monitoring.

Validation of a brief screening instrument for the ascertainment of epilepsy.

PURPOSE: To validate a brief screening instrument for identifying people with epilepsy in epidemiologic or genetic studies. METHODS: We designed a nine-question screening instrument for epilepsy and administered it by telephone to individuals with medical record-documented epilepsy (lifetime history of >or=2 unprovoked seizures, n = 168) or isolated unprovoked seizure (n = 54), and individuals who were seizure-free on medical record review (n = 120), from a population-based study using Rochester Epidemiology Project resources. Interviewers were blinded to record-review findings. RESULTS: Sensitivity (the proportion of individuals who screened positive among affected individuals) was 96% for epilepsy and 87% for isolated unprovoked seizure. The false positive rate (FPR, the proportion who screened positive among seizure-free individuals) was 7%. The estimated positive predictive value (PPV) for epilepsy was 23%, assuming a lifetime prevalence of 2% in the population. Use of only a single question asking whether the subject had ever had epilepsy or a seizure disorder resulted in sensitivity 76%, FPR 0.8%, and estimated PPV 66%. Subjects with epilepsy were more likely to screen positive with this question if they were diagnosed after 1964 or continued to have seizures for at least 5 years after diagnosis. DISCUSSION: Given its high sensitivity, our instrument may be useful for the first stage of screening for epilepsy; however, the PPV of 23% suggests that only about one in four screen-positive individuals will be truly affected. Screening with a single question asking about epilepsy yields a higher PPV but lower sensitivity, and screen-positive subjects may be biased toward more severe epilepsy.