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.

Modeling the Complex Dynamics and Changing Correlations of Epileptic Events.

Patients with epilepsy can manifest short, sub-clinical epileptic "bursts" in addition to full-blown clinical seizures. We believe the relationship between these two classes of events-something not previously studied quantitatively-could yield important insights into the nature and intrinsic dynamics of seizures. A goal of our work is to parse these complex epileptic events into distinct dynamic regimes. A challenge posed by the intracranial EEG (iEEG) data we study is the fact that the number and placement of electrodes can vary between patients. We develop a Bayesian nonparametric Markov switching process that allows for (i) shared dynamic regimes between a variable number of channels, (ii) asynchronous regime-switching, and (iii) an unknown dictionary of dynamic regimes. We encode a sparse and changing set of dependencies between the channels using a Markov-switching Gaussian graphical model for the innovations process driving the channel dynamics and demonstrate the importance of this model in parsing and out-of-sample predictions of iEEG data. We show that our model produces intuitive state assignments that can help automate clinical analysis of seizures and enable the comparison of sub-clinical bursts and full clinical seizures.

Defining and benchmarking open problems in single-cell analysis.

With the growing number of single-cell analysis tools, benchmarks are increasingly important to guide analysis and method development. However, a lack of standardisation and extensibility in current benchmarks limits their usability, longevity, and relevance to the community. We present Open Problems, a living, extensible, community-guided benchmarking platform including 10 current single-cell tasks that we envision will raise standards for the selection, evaluation, and development of methods in single-cell analysis.

Temporal changes of neocortical high-frequency oscillations in epilepsy.

High-frequency (100-500 Hz) oscillations (HFOs) recorded from intracranial electrodes are a potential biomarker for epileptogenic brain. HFOs are commonly categorized as ripples (100-250 Hz) or fast ripples (250-500 Hz), and a third class of mixed frequency events has also been identified. We hypothesize that temporal changes in HFOs may identify periods of increased the likelihood of seizure onset. HFOs (86,151) from five patients with neocortical epilepsy implanted with hybrid (micro + macro) intracranial electrodes were detected using a previously validated automated algorithm run over all channels of each patient's entire recording. HFOs were characterized by extracting quantitative morphologic features and divided into four time epochs (interictal, preictal, ictal, and postictal) and three HFO clusters (ripples, fast ripples, and mixed events). We used supervised classification and nonparametric statistical tests to explore quantitative changes in HFO features before, during, and after seizures. We also analyzed temporal changes in the rates and proportions of events from each HFO cluster during these periods. We observed patient-specific changes in HFO morphology linked to fluctuation in the relative rates of ripples, fast ripples, and mixed frequency events. These changes in relative rate occurred in pre- and postictal periods up to thirty min before and after seizures. We also found evidence that the distribution of HFOs during these different time periods varied greatly between individual patients. These results suggest that temporal analysis of HFO features has potential for designing custom seizure prediction algorithms and for exploring the relationship between HFOs and seizure generation.

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.

An unsupervised method for identifying regions that initiate seizures on intracranial EEG.

Epilepsy patients who do not respond to pharmacological treatments currently have only brain surgery as a major alternative therapy. Identifying which brain areas to remove is thus of critical importance for physicians and the patient. Currently, this process is almost entirely manual, can vary greatly between clinical experts and centers, and depends only on qualitative EEG features, all of which may help explain the only modest success of extratemperal lobe epilepsy surgery. In this study, we explore an unsupervised, quantitative method for identifying seizure onset regions. A Gaussian mixture model (GMM) was used to cluster 500 ms epochs of intracranial electroencephalogram (EEG) prior to (preictal) and during (ictal) seizures in week-long continuous recordings from three patients during evalulation for epilepsy surgery. The GMM learning paradigm determines the optimal number of clusters for each patient. For the two patients whose epochs sorted into two clusters, we found that one cluster was predominantly composed of seizure epochs, and a subset of the channels made brief "forays" into that cluser in the time leading up to seizure onset. This observation is in keeping with the clinical hypothesis that certain brain areas may be the initiators of seizure activity, and we find that the channels independently labeled by physicians as seizure onset zones (SOZs) are statistically overrepesented in the seizure-defined cluster. Nevertheless, we also find that a subset of channels not labeled as SOZs has similar properties as those labeled SOZs. In this study we have tried to avoid many of the assumptions commonly made about what features and events are indicative of epileptogenic activity and believe that such analysis can help avoid many of the pitfalls of manual, non-objective human SOZ marking.

Flexible, foldable, actively multiplexed, high-density electrode array for mapping brain activity in vivo.

Arrays of electrodes for recording and stimulating the brain are used throughout clinical medicine and basic neuroscience research, yet are unable to sample large areas of the brain while maintaining high spatial resolution because of the need to individually wire each passive sensor at the electrode-tissue interface. To overcome this constraint, we developed new devices that integrate ultrathin and flexible silicon nanomembrane transistors into the electrode array, enabling new dense arrays of thousands of amplified and multiplexed sensors that are connected using fewer wires. We used this system to record spatial properties of cat brain activity in vivo, including sleep spindles, single-trial visual evoked responses and electrographic seizures. We found that seizures may manifest as recurrent spiral waves that propagate in the neocortex. The developments reported here herald a new generation of diagnostic and therapeutic brain-machine interface devices.

Mice that lack matrix metalloproteinase-9 display delayed wound healing associated with delayed reepithelization and disordered collagen fibrillogenesis.

Matrix metalloproteinase- (MMP-9) is involved in processes that occur during cutaneous wound healing such as inflammation, matrix remodeling, and epithelialization, To investigate its role in healing, full thickness skin wounds were made in the dorsal region of MMP-9-null and control mice and harvested up to 14 days post wounding. Gross examination and histological and immunohistochemical analysis indicated delayed healing in MMP-9-null mice. Specifically, MMP-9-null wounds displayed compromised reepithelialization and reduced clearance of fibrin clots. In addition, they exhibited abnormal matrix deposition, as evidenced by the irregular alignment of immature collagen fibers. Despite the presence of matrix abnormalities, MMP-9-null wounds displayed normal tensile strength. Ultrastructural analysis of wounds revealed the presence of large collagen fibrils, some with irregular shape. Keratinocyte proliferation, inflammation, and angiogenesis were found to be normal in MMP-9-null wounds. In addition, VEGF levels were similar in control and MMP-9-null wound extracts. To investigate the importance of MMP-9 in wound reepithelialization we tested human and murine keratinocytes in a wound migration assay and found that antibody-based blockade of MMP-9 function or MMP-9 deficiency retarded migration. Collectively, our observations reveal defective healing in MMP-9-null mice and suggest that MMP-9 is required for normal progression of wound closure.