Neurosyphilis presenting with Guillain-Barre syndrome: a case report.

BACKGROUND: Syphilis is associated with a wide variety of systemic presentations, earning it the moniker "The great mimicker". Neurosyphilis is classically associated with meningovasculitis in the acute-subacute stage and tabes dorsalis and dementia paralytica in later stages. However, one of the less well described presentations include Guillain-Barre Syndrome. This case presents a patient with an ascending polyneuropathy suspicious for Guillain-Barre Syndrome who also had other atypical findings including a truncal sensory loss, optic disc swelling, and rash ultimately found to have neurosyphilis. Electrodiagnostic testing was consistent with demyelination, supporting a diagnosis of neurosyphilis associated Guillain-Barre Syndrome. CASE PRESENTATION: A 37-year-old female presented to the emergency department with a weakness and difficulty swallowing. She described a three-month history of symptoms, initially starting with a persistent headache followed by one month of a pruritic rash on her chest, palms, and soles. Two weeks prior to presentation, she developed progressive weakness in her arms, numbness in her arms and chest, and difficulty swallowing. Neurological exam was notable for multiple cranial neuropathies, distal predominant weakness in all extremities, length-dependent sensory loss, and hyporeflexia. Investigation revealed a positive Venereal Disease Research Laboratory in her cerebrospinal fluid without significant pleocytosis, contrast enhancement in cranial nerves V, VII, and VIII on MRI, and a demyelinating polyneuropathy on electrodiagnostic testing. She was diagnosed with Guillain-Barre syndrome, secondary to neurosyphilis. The patient acutely declined and required intubation, and ultimately made a full recovery after treatment with plasmapheresis and penicillin. CONCLUSIONS: This case describes a clinical entity of syphilitic Guillain-Barre Syndrome and highlights the importance of including syphilis in the differential of any patient presenting with ascending polyradiculopathy, especially given the resurgence of syphilis.

Spatiotemporal evolution of focal epileptiform activity from surface and laminar field recordings in cat neocortex.

New devices that use targeted electrical stimulation to treat refractory localization-related epilepsy have shown great promise, although it is not well known which targets most effectively prevent the initiation and spread of seizures. To better understand how the brain transitions from healthy to seizing on a local scale, we induced focal epileptiform activity in the visual cortex of five anesthetized cats with local application of the GABAA blocker picrotoxin while simultaneously recording local field potentials on a high-resolution electrocorticography array and laminar depth probes. Epileptiform activity appeared in the form of isolated events, revealing a consistent temporal pattern of ictogenesis across animals with interictal events consistently preceding the appearance of seizures. Based on the number of spikes per event, there was a natural separation between seizures and shorter interictal events. Two distinct spatial regions were seen: an epileptic focus that grew in size as activity progressed, and an inhibitory surround that exhibited a distinct relationship with the focus both on the surface and in the depth of the cortex. Epileptiform activity in the cortical laminae was seen concomitant with activity on the surface. Focus spikes appeared earlier on electrodes deeper in the cortex, suggesting that deep cortical layers may be integral to recruiting healthy tissue into the epileptic network and could be a promising target for interventional devices. Our study may inform more effective therapies to prevent seizure generation and spread in localization-related epilepsies. NEW & NOTEWORTHY We induced local epileptiform activity and recorded continuous, high-resolution local field potentials from the surface and depth of the visual cortex in anesthetized cats. Our results reveal a consistent pattern of ictogenesis, characterize the spatial spread of the epileptic focus and its relationship with the inhibitory surround, and show that focus activity within events appears earliest in deeper cortical layers. These findings have potential implications for the monitoring and treatment of refractory epilepsy.

The circadian profile of epilepsy improves seizure forecasting.

It is now established that epilepsy is characterized by periodic dynamics that increase seizure likelihood at certain times of day, and which are highly patient-specific. However, these dynamics are not typically incorporated into seizure prediction algorithms due to the difficulty of estimating patient-specific rhythms from relatively short-term or unreliable data sources. This work outlines a novel framework to develop and assess seizure forecasts, and demonstrates that the predictive power of forecasting models is improved by circadian information. The analyses used long-term, continuous electrocorticography from nine subjects, recorded for an average of 320 days each. We used a large amount of out-of-sample data (a total of 900 days for algorithm training, and 2879 days for testing), enabling the most extensive post hoc investigation into seizure forecasting. We compared the results of an electrocorticography-based logistic regression model, a circadian probability, and a combined electrocorticography and circadian model. For all subjects, clinically relevant seizure prediction results were significant, and the addition of circadian information (combined model) maximized performance across a range of outcome measures. These results represent a proof-of-concept for implementing a circadian forecasting framework, and provide insight into new approaches for improving seizure prediction algorithms. The circadian framework adds very little computational complexity to existing prediction algorithms, and can be implemented using current-generation implant devices, or even non-invasively via surface electrodes using a wearable application. The ability to improve seizure prediction algorithms through straightforward, patient-specific modifications provides promise for increased quality of life and improved safety for patients with epilepsy.

Interictal epileptiform activity outside the seizure onset zone impacts cognition.

See Kleen and Kirsch (doi:10.1093/awx178) for a scientific commentary on this article.Cognitive deficits are common among epilepsy patients. In these patients, interictal epileptiform discharges, also termed spikes, are seen routinely on electroencephalography and believed to be associated with transient cognitive impairments. In this study, we investigated the effect of spikes on memory encoding and retrieval, taking into account the spatial distribution of spikes in relation to the seizure onset zone as well as anatomical regions of the brain. Sixty-seven patients with medication refractory epilepsy undergoing continuous intracranial electroencephalography monitoring engaged in a delayed free recall task to test short-term memory. In this task, subjects were asked to memorize and recall lists of common nouns. We quantified the effect of each spike on the probability of successful recall using a generalized logistic mixed model. We found that in patients with left lateralized seizure onset zones, spikes outside the seizure onset zone impacted memory encoding, whereas those within the seizure onset zone did not. In addition, spikes in the left inferior temporal gyrus, middle temporal gyrus, superior temporal gyrus, and fusiform gyrus during memory encoding reduced odds of recall by as much as 15% per spike. Spikes also reduced the odds of word retrieval, an effect that was stronger with spikes outside of the seizure onset zone. These results suggest that seizure onset regions are dysfunctional at baseline, and support the idea that interictal spikes disrupt cognitive processes related to the underlying tissue.

Crowdsourcing seizure detection: algorithm development and validation on human implanted device recordings.

There exist significant clinical and basic research needs for accurate, automated seizure detection algorithms. These algorithms have translational potential in responsive neurostimulation devices and in automatic parsing of continuous intracranial electroencephalography data. An important barrier to developing accurate, validated algorithms for seizure detection is limited access to high-quality, expertly annotated seizure data from prolonged recordings. To overcome this, we hosted a kaggle.com competition to crowdsource the development of seizure detection algorithms using intracranial electroencephalography from canines and humans with epilepsy. The top three performing algorithms from the contest were then validated on out-of-sample patient data including standard clinical data and continuous ambulatory human data obtained over several years using the implantable NeuroVista seizure advisory system. Two hundred teams of data scientists from all over the world participated in the kaggle.com competition. The top performing teams submitted highly accurate algorithms with consistent performance in the out-of-sample validation study. The performance of these seizure detection algorithms, achieved using freely available code and data, sets a new reproducible benchmark for personalized seizure detection. We have also shared a 'plug and play' pipeline to allow other researchers to easily use these algorithms on their own datasets. The success of this competition demonstrates how sharing code and high quality data results in the creation of powerful translational tools with significant potential to impact patient care.

Bursts of seizures in long-term recordings of human focal epilepsy.

OBJECTIVE: We report on temporally clustered seizures detected from continuous long-term ambulatory human electroencephalographic data. The objective was to investigate short-term seizure clustering, which we have termed bursting, and consider implications for patient care, seizure prediction, and evaluating therapies. METHODS: Chronic ambulatory intracranial electroencephalography (EEG) data collected for the purpose of seizure prediction were annotated to identify seizure events. A detection algorithm was used to identify bursts of events. Burst events were compared to nonburst events to evaluate event dispersion, duration and dynamics. RESULTS: Bursts of seizures were present in 6 of 15 subjects, and detections were consistent over long-term monitoring (>2 years). Subjects with bursts of seizures had highly overdispersed seizure rates, compared to other subjects. There was a complicated relationship between bursts and clinical seizures, although bursts were associated with multimodal distributions of seizure duration, and poorer predictive outcomes. For three subjects, bursts demonstrated distinctive preictal dynamics compared to clinical seizures. SIGNIFICANCE: We have previously hypothesized that there are distinct physiologic pathways underlying short- and long-duration seizures. Herein we show that burst seizures fall almost exclusively within the short population of seizure durations; however, a short duration event was not sufficient to induce or imply bursting. We can therefore conclude that in addition to distinct mechanisms underlying seizure duration, there are separate factors regulating bursts of seizures. We show that bursts were a robust phenomenon in our patient cohort, which were consistent with overdispersed seizure rates, suggesting long-memory dynamics.

Mining continuous intracranial EEG in focal canine epilepsy: Relating interictal bursts to seizure onsets.

OBJECTIVE: Brain regions are localized for resection during epilepsy surgery based on rare seizures observed during a short period of intracranial electroencephalography (iEEG) monitoring. Interictal epileptiform bursts, which are more prevalent than seizures, may provide complementary information to aid in epilepsy evaluation. In this study, we leverage a long-term iEEG dataset from canines with naturally occurring epilepsy to investigate interictal bursts and their electrographic relationship to seizures. METHODS: Four dogs were included in this study, each monitored previously with continuous iEEG for periods of 475.7, 329.9, 45.8, and 451.8 days, respectively, for a total of >11,000 h. Seizures and bursts were detected and validated by two board-certified epileptologists. A published Bayesian model was applied to analyze the dynamics of interictal epileptic bursts on EEG and compare them to seizures. RESULTS: In three dogs, bursts were stereotyped and found to be statistically similar to periods before or near seizure onsets. Seizures from one dog during status epilepticus were markedly different from other seizures in terms of burst similarity. SIGNIFICANCE: Shorter epileptic bursts explored in this work have the potential to yield significant information about the distribution of epileptic events. In our data, bursts are at least an order of magnitude more prevalent than seizures and occur much more regularly. Our finding that bursts often display pronounced similarity to seizure onsets suggests that they contain relevant information about the epileptic networks from which they arise and may aide in the clinical evaluation of epilepsy in patients.

Temporal behavior of seizures and interictal bursts in prolonged intracranial recordings from epileptic canines.

OBJECTIVE: Epilepsy is a chronic disorder, but seizure recordings are usually obtained in the acute setting. The chronic behavior of seizures and the interictal bursts that sometimes initiate them is unknown. We investigate the variability of these electrographic patterns over an extended period of time using chronic intracranial recordings in canine epilepsy. METHODS: Continuous, yearlong intracranial electroencephalography (iEEG) recordings from four dogs with naturally occurring epilepsy were analyzed for seizures and interictal bursts. Following automated detection and clinician verification of interictal bursts and seizures, temporal trends of seizures, burst count, and burst-burst similarities were determined. One dog developed status epilepticus, the recordings of which were also investigated. RESULTS: Multiple seizure types, determined by onset channels, were observed in each dog, with significant temporal variation between types. The first 14 days of invasive recording, analogous to the average duration of clinical invasive recordings in humans, did not capture the entirety of seizure types. Seizures typically occurred in clusters, and isolated seizures were rare. The count and dynamics of interictal bursts form distinct groups and do not stabilize until several weeks after implantation. SIGNIFICANCE: There is significant temporal variability in seizures and interictal bursts after electrode implantation that requires several weeks to reach steady state. These findings, comparable to those reported in humans implanted with the NeuroPace Responsive Neurostimulator System (RNS) device, suggest that transient network changes following electrode implantation may need to be taken into account when interpreting or analyzing iEEG during evaluation for epilepsy surgery. Chronic, ambulatory iEEG may be better suited to accurately map epileptic networks in appropriate individuals.

Multivariate classification of structural MRI data detects chronic low back pain.

Chronic low back pain (cLBP) has a tremendous personal and socioeconomic impact, yet the underlying pathology remains a mystery in the majority of cases. An objective measure of this condition, that augments self-report of pain, could have profound implications for diagnostic characterization and therapeutic development. Contemporary research indicates that cLBP is associated with abnormal brain structure and function. Multivariate analyses have shown potential to detect a number of neurological diseases based on structural neuroimaging. Therefore, we aimed to empirically evaluate such an approach in the detection of cLBP, with a goal to also explore the relevant neuroanatomy. We extracted brain gray matter (GM) density from magnetic resonance imaging scans of 47 patients with cLBP and 47 healthy controls. cLBP was classified with an accuracy of 76% by support vector machine analysis. Primary drivers of the classification included areas of the somatosensory, motor, and prefrontal cortices--all areas implicated in the pain experience. Differences in areas of the temporal lobe, including bordering the amygdala, medial orbital gyrus, cerebellum, and visual cortex, were also useful for the classification. Our findings suggest that cLBP is characterized by a pattern of GM changes that can have discriminative power and reflect relevant pathological brain morphology.

Consistency of Long-Term Subdural Electrocorticography in Humans.

OBJECTIVE: Subdural electrocorticography (ECoG) can provide a robust control signal for a brain-computer interface (BCI). However, the long-term recording properties of ECoG are poorly understood as most ECoG studies in the BCI field have only used signals recorded for less than 28 days. We assessed human ECoG recordings over durations of many months to investigate changes to recording quality that occur with long-term implantation. METHODS: We examined changes in signal properties over time from 15 ambulatory humans who had continuous subdural ECoG monitoring for 184-766 days. RESULTS: Individual electrodes demonstrated varying changes in frequency power characteristics over time within individual patients and between patients. Group level analyses demonstrated that there were only small changes in effective signal bandwidth and spectral band power across months. High-gamma signals could be recorded throughout the study, though there was a decline in signal power for some electrodes. CONCLUSION: ECoG-based BCI systems can robustly record high-frequency activity over multiple years, albeit with marked intersubject variability. SIGNIFICANCE: Group level results demonstrated that ECoG is a promising modality for long-term BCI and neural prosthesis applications.

Intracranial EEG fluctuates over months after implanting electrodes in human brain.

OBJECTIVE: Implanting subdural and penetrating electrodes in the brain causes acute trauma and inflammation that affect intracranial electroencephalographic (iEEG) recordings. This behavior and its potential impact on clinical decision-making and algorithms for implanted devices have not been assessed in detail. In this study we aim to characterize the temporal and spatial variability of continuous, prolonged human iEEG recordings. APPROACH: Intracranial electroencephalography from 15 patients with drug-refractory epilepsy, each implanted with 16 subdural electrodes and continuously monitored for an average of 18 months, was included in this study. Time and spectral domain features were computed each day for each channel for the duration of each patient's recording. Metrics to capture post-implantation feature changes and inflexion points were computed on group and individual levels. A linear mixed model was used to characterize transient group-level changes in feature values post-implantation and independent linear models were used to describe individual variability. MAIN RESULTS: A significant decline in features important to seizure detection and prediction algorithms (mean line length, energy, and half-wave), as well as mean power in the Berger and high gamma bands, was observed in many patients over 100 d following implantation. In addition, spatial variability across electrodes declines post-implantation following a similar timeframe. All selected features decreased by 14-50% in the initial 75 d of recording on the group level, and at least one feature demonstrated this pattern in 13 of the 15 patients. Our findings indicate that iEEG signal features demonstrate increased variability following implantation, most notably in the weeks immediately post-implant. SIGNIFICANCE: These findings suggest that conclusions drawn from iEEG, both clinically and for research, should account for spatiotemporal signal variability and that properly assessing the iEEG in patients, depending upon the application, may require extended monitoring.

A novel seizure detection algorithm informed by hidden Markov model event states.

OBJECTIVE: Recently the FDA approved the first responsive, closed-loop intracranial device to treat epilepsy. Because these devices must respond within seconds of seizure onset and not miss events, they are tuned to have high sensitivity, leading to frequent false positive stimulations and decreased battery life. In this work, we propose a more robust seizure detection model. APPROACH: We use a Bayesian nonparametric Markov switching process to parse intracranial EEG (iEEG) data into distinct dynamic event states. Each event state is then modeled as a multidimensional Gaussian distribution to allow for predictive state assignment. By detecting event states highly specific for seizure onset zones, the method can identify precise regions of iEEG data associated with the transition to seizure activity, reducing false positive detections associated with interictal bursts. The seizure detection algorithm was translated to a real-time application and validated in a small pilot study using 391 days of continuous iEEG data from two dogs with naturally occurring, multifocal epilepsy. A feature-based seizure detector modeled after the NeuroPace RNS System was developed as a control. MAIN RESULTS: Our novel seizure detection method demonstrated an improvement in false negative rate (0/55 seizures missed versus 2/55 seizures missed) as well as a significantly reduced false positive rate (0.0012 h versus 0.058 h(-1)). All seizures were detected an average of 12.1 ± 6.9 s before the onset of unequivocal epileptic activity (unequivocal epileptic onset (UEO)). SIGNIFICANCE: This algorithm represents a computationally inexpensive, individualized, real-time detection method suitable for implantable antiepileptic devices that may considerably reduce false positive rate relative to current industry standards.