Distributed brain co-processor for tracking spikes, seizures and behaviour during electrical brain stimulation.

Early implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioural inputs from patients. Recent epilepsy stimulation devices provide brain sensing but have not yet developed analytics for accurately tracking and quantifying behaviour and seizures. Here we describe a distributed brain co-processor providing an intuitive bi-directional interface between patient, implanted neural stimulation and sensing device, and local and distributed computing resources. Automated analysis of continuous streaming electrophysiology is synchronized with patient reports using a handheld device and integrated with distributed cloud computing resources for quantifying seizures, interictal epileptiform spikes and patient symptoms during therapeutic electrical brain stimulation. The classification algorithms for interictal epileptiform spikes and seizures were developed and parameterized using long-term ambulatory data from nine humans and eight canines with epilepsy, and then implemented prospectively in out-of-sample testing in two pet canines and four humans with drug-resistant epilepsy living in their natural environments. Accurate seizure diaries are needed as the primary clinical outcome measure of epilepsy therapy and to guide brain-stimulation optimization. The brain co-processor system described here enables tracking interictal epileptiform spikes, seizures and correlation with patient behavioural reports. In the future, correlation of spikes and seizures with behaviour will allow more detailed investigation of the clinical impact of spikes and seizures on patients.

Gentamicin-induced sensorineural auditory loss in healthy adult horses.

BACKGROUND: Irreversible sensorineural auditory loss has been reported in humans treated with aminoglycosides but not in horses. OBJECTIVE: Investigate if auditory loss occurs in horses treated using the recommended IV daily dosage of gentamicin for 7 consecutive days. ANIMALS: Ten healthy adult horses (7-15 years; females and males, 5 each). METHODS: Prospective study. Physical and neurological examinations and renal function tests were performed. Gentamicin sulfate was administered at a dosage of 6.6 mg/kg via the jugular vein on alternating sides for 7 days. Gentamicin peak and trough concentrations were measured. Horses were sedated using detomidine hydrochloride IV to perform brainstem auditory evoked responses (BAER) before the first dose, immediately after the last dose, and 30 days after the last dose. Peaks latencies, amplitudes, and amplitude ratios were recorded. Data from the second and last BAER were compared to results at baseline. Bone conduction was performed to rule out conduction disorders. RESULTS: Seven horses had auditory loss: complete bilateral (N = 1), complete unilateral (N = 2), and partial unilateral (N = 4). Based on physical examination and BAER results, sensorineural auditory loss was suspected. Absent bone conduction ruled out a conduction disorder and further supported sensorineural auditory loss in horses with completely absent BAER. Auditory dysfunction was reversible in 4 of 7 horses. CONCLUSIONS AND CLINICAL IMPORTANCE: Gentamicin at recommended doses may cause sensorineural auditory loss in horses that might be irreversible. Follow-up studies are needed to investigate if other dosing protocols present a similar risk.

Semi-supervised Training Data Selection Improves Seizure Forecasting in Canines with Epilepsy.

OBJECTIVE: Conventional selection of pre-ictal EEG epochs for seizure prediction algorithm training data typically assumes a continuous pre-ictal brain state preceding a seizure. This is carried out by defining a fixed duration, pre-ictal time period before seizures from which pre-ictal training data epochs are uniformly sampled. However, stochastic physiological and pathological fluctuations in EEG data characteristics and underlying brain states suggest that pre-ictal state dynamics may be more complex, and selection of pre-ictal training data segments to reflect this could improve algorithm performance. METHODS: We propose a semi-supervised technique to select pre-ictal training data most distinguishable from interictal EEG according to pre-specified data characteristics. The proposed method uses hierarchical clustering to identify optimal pre-ictal data epochs. RESULTS: In this paper we compare the performance of a seizure forecasting algorithm with and without hierarchical clustering of pre-ictal periods in chronic iEEG recordings from six canines with naturally occurring epilepsy. Hierarchical clustering of training data improved results for Time In Warning (TIW) (0.18 vs. 0.23) and False Positive Rate (FPR) (0.5 vs. 0.59) when evaluated across all subjects (p<0.001, n=6). Results were mixed when evaluating TIW, FPR, and Sensitivity for individual dogs. CONCLUSION: Hierarchical clustering is a helpful method for training data selection overall, but should be evaluated on a subject-wise basis. SIGNIFICANCE: The clustering method can be used to optimize results of forecasting towards sensitivity or TIW or FPR, and therefore can be useful for epilepsy management.

Pharmacokinetics of Intravenous and Oral Phenobarbital Sodium in Healthy Goats.

Phenobarbital is a common drug used to manage epilepsy in goats. However, the recommended dose and dosing frequency are based on studies in dogs and horses. Studies describing the pharmacokinetics of phenobarbital when administered orally and assessing changes in behavior with concurrent electroencephalogram (EEG) readings are warranted in goats. The objectives of this study were to determine the bioavailability of orally administered phenobarbital and determine the effect of phenobarbital on brain activity using EEG in healthy goats. A cross-over design with 8 non-pregnant goats was performed. The goats were administered phenobarbital intravenously at 10 mg/kg, followed by a 2 week wash out period, and then administered phenobarbital, orally, at 10 mg/kg. Plasma sample determination of phenobarbital concentrations were collected at 13 time points. Continuous EEG readings with simultaneous video recording for 12 h was performed to determine the state of vigilance using a behavior scoring system prior to and after phenobarbital administration. Bioavailability of phenobarbital was 24.9%. Mean ± SD for half-life was similar between the oral (3.80 ± 0.826 h) and intravenous (4.0 ± 0.619 h) routes. Time to observed maximum concentration (Tmax), and maximum plasma concentration (Cmax) for the oral administration were 1.75 ± 0.46 h and 4,478.7 ± 962.4 ng/mL, respectively. Clearance was 152.5 ± 102.7 ml/h/kg. Area under the curve from zero to infinity (AUC0→∞) was 155,813 ± 218,448 and 38,763 ± 9,832 h*ng/mL for the intravenous and oral administration routes, respectively. Behavior score at 3 h after phenobarbital administration was different (P = 0.0002) from the score prior to administration for the oral administration route. In contrast, behavior scores before administration of phenobarbital and each time point after administration were not different (P >0.05) for the intravenous administration route or other oral administration route time points. Bioavailability of phenobarbital was poor, and the half-life was very short due to a high clearance. Doses >10 mg/kg should be considered when phenobarbital is administered orally in goats.

Clinicopathological characteristics of histiocytic sarcoma affecting the central nervous system in dogs.

BACKGROUND: Histiocytic sarcoma affecting the central nervous system (CNS HS) in dogs may present as primary or disseminated disease, often characterized by inflammation. Prognosis is poor, and imaging differentiation from other CNS tumors can be problematic. OBJECTIVE: To characterize the clinicopathological inflammatory features, breed predisposition, and survival in dogs with CNS HS. ANIMALS: One hundred two dogs with HS, 62 dogs with meningioma. METHODS: Retrospective case series. Records were reviewed for results of cerebrospinal fluid (CSF) analysis, CBC, treatment, and outcome data. RESULTS: Predisposition for CNS HS was seen in Bernese Mountain Dogs, Golden Retrievers, Rottweilers, Corgis, and Shetland Sheepdogs (P ≤ .001). Corgis and Shetland Sheepdogs had predominantly primary tumors; Rottweilers had exclusively disseminated tumors. Marked CSF inflammation was characteristic of primary rather than disseminated HS, and neoplastic cells were detected in CSF of 52% of affected dogs. Increased neutrophil to lymphocyte ratios were seen in all groups relative to controls (P <.008) but not among tumor subtypes. Definitive versus palliative treatment resulted in improved survival times (P < .001), but overall prognosis was poor. CONCLUSIONS AND CLINICAL IMPORTANCE: Clinicopathological differences between primary and disseminated HS suggest that tumor biological behavior and origin may be different. Corgis and Shetland Sheepdogs are predisposed to primary CNS HS, characterized by inflammatory CSF. High total nucleated cell count and the presence of neoplastic cells support the use of CSF analysis as a valuable diagnostic test. Prognosis for CNS HS is poor, but further evaluation of inflammatory mechanisms may provide novel therapeutic opportunities.

Integrating Brain Implants With Local and Distributed Computing Devices: A Next Generation Epilepsy Management System.

Brain stimulation has emerged as an effective treatment for a wide range of neurological and psychiatric diseases. Parkinson's disease, epilepsy, and essential tremor have FDA indications for electrical brain stimulation using intracranially implanted electrodes. Interfacing implantable brain devices with local and cloud computing resources have the potential to improve electrical stimulation efficacy, disease tracking, and management. Epilepsy, in particular, is a neurological disease that might benefit from the integration of brain implants with off-the-body computing for tracking disease and therapy. Recent clinical trials have demonstrated seizure forecasting, seizure detection, and therapeutic electrical stimulation in patients with drug-resistant focal epilepsy. In this paper, we describe a next-generation epilepsy management system that integrates local handheld and cloud-computing resources wirelessly coupled to an implanted device with embedded payloads (sensors, intracranial EEG telemetry, electrical stimulation, classifiers, and control policy implementation). The handheld device and cloud computing resources can provide a seamless interface between patients and physicians, and realtime intracranial EEG can be used to classify brain state (wake/sleep, preseizure, and seizure), implement control policies for electrical stimulation, and track patient health. This system creates a flexible platform in which low demand analytics requiring fast response times are embedded in the implanted device and more complex algorithms are implemented in offthebody local and distributed cloud computing environments. The system enables tracking and management of epileptic neural networks operating over time scales ranging from milliseconds to months.