William C. Chen

Impaired Serotonergic Brainstem Function during and after Seizures

Impaired breathing, cardiac function, and arousal during and after seizures are important causes of morbidity and mortality. Previous work suggests that these changes are associated with depressed brainstem function in the ictal and post-ictal periods. Lower brainstem serotonergic systems are postulated to play an important role in cardiorespiratory changes during and after seizures, whereas upper brainstem serotonergic and other systems regulate arousal. However, direct demonstration of seizure-associated neuronal activity changes in brainstem serotonergic regions has been lacking. Here, we performed multiunit and single-unit recordings from medullary raphe and midbrain dorsal raphe nuclei in an established rat seizure model while measuring changes in breathing rate and depth as well as heart rate. Serotonergic neurons were identified by immunohistochemistry. Respiratory rate, tidal volume, and minute ventilation were all significantly decreased during and after seizures in this model. We found that population firing of neurons in the medullary and midbrain raphe on multiunit recordings was significantly decreased during the ictal and post-ictal periods. Single-unit recordings from identified serotonergic neurons in the medullary raphe revealed highly consistently decreased firing during and after seizures. In contrast, firing of midbrain raphe serotonergic neurons was more variable, with a mixture of increases and decreases. The markedly suppressed firing of medullary serotonergic neurons supports their possible role in simultaneously impaired cardiorespiratory function in seizures. Decreased arousal likely arises from depressed population activity of several neuronal pools in the upper brainstem and forebrain. These findings have important implications for preventing morbidity and mortality in people living with epilepsy. SIGNIFICANCE STATEMENT Seizures often cause impaired breathing, cardiac dysfunction, and loss of consciousness. The brainstem and, specifically, brainstem serotonin neurons are thought to play an important role in controlling breathing, cardiac function, and arousal. We used an established rat seizure model to study the overall neuronal activity in the brainstem as well as firing of specific serotonin neurons while measuring cardiorespiratory function. Our results demonstrated overall decreases in brainstem neuronal activity and marked downregulation of lower brainstem serotonin neuronal firing in association with decreased breathing and heart rate during and after seizures. These findings point the way toward new treatments to augment brainstem function and serotonin, aiming to prevent seizure complications and reduce morbidity and mortality in people living with epilepsy.

Ictal spread of medial temporal lobe seizures with and without secondary generalization: An intracranial electroencephalography analysis

Secondary generalization of seizures has devastating consequences for patient safety and quality of life. The aim of this intracranial electroencephalography (icEEG) study was to investigate the differences in onset and propagation patterns of temporal lobe seizures that remained focal versus those with secondary generalization, in order to better understand the mechanism of secondary generalization.

Impaired consciousness in partial seizures is bimodally distributed

Objective: To investigate whether impaired consciousness in partial seizures can usually be attributed to specific deficits in the content of consciousness or to a more general decrease in the overall level of consciousness. Methods: Prospective testing during partial seizures was performed in patients with epilepsy using the Responsiveness in Epilepsy Scale (n = 83 partial seizures, 30 patients). Results were compared with responsiveness scores in a cohort of patients with severe traumatic brain injury evaluated with the JFK Coma Recovery Scale–Revised (n = 552 test administrations, 184 patients). Results: Standardized testing during partial seizures reveals a bimodal scoring distribution, such that most patients were either fully impaired or relatively spared in their ability to respond on multiple cognitive tests. Seizures with impaired performance on initial test items remained consistently impaired on subsequent items, while other seizures showed spared performance throughout. In the comparison group, we found that scores of patients with brain injury were more evenly distributed across the full range in severity of impairment. Conclusions: Partial seizures can often be cleanly separated into those with vs without overall impaired responsiveness. Results from similar testing in a comparison group of patients with brain injury suggest that the bimodal nature of Responsiveness in Epilepsy Scale scores is not a result of scale bias but may be a finding unique to partial seizures. These findings support a model in which seizures either propagate or do not propagate to key structures that regulate overall arousal and thalamocortical function. Future investigations are needed to relate these behavioral findings to the physiology underlying impaired consciousness in partial seizures.

Seizure localization using three-dimensional surface projections of intracranial EEG power.

Intracranial EEG (icEEG) provides a critical road map for epilepsy surgery but it has become increasingly difficult to interpret as technology has allowed the number of icEEG channels to grow. Borrowing methods from neuroimaging, we aimed to simplify data analysis and increase consistency between reviewers by using 3D surface projections of intracranial EEG poweR (3D-SPIER). We analyzed 139 seizures from 48 intractable epilepsy patients (28 temporal and 20 extratemporal) who had icEEG recordings, epilepsy surgery, and at least one year of post-surgical follow-up. We coregistered and plotted icEEG β frequency band signal power over time onto MRI-based surface renderings for each patient, to create color 3D-SPIER movies. Two independent reviewers interpreted the icEEG data using visual analysis vs. 3D-SPIER, blinded to any clinical information. Overall agreement rates between 3D-SPIER and icEEG visual analysis or surgery were about 90% for side of seizure onset, 80% for lobe, and just under 80% for sublobar localization. These agreement rates were improved when flexible thresholds or frequency ranges were allowed for 3D-SPIER, especially for sublobar localization. Interestingly, agreement was better for patients with good surgical outcome than for patients with poor outcome. Localization using 3D-SPIER was measurably faster and considered qualitatively easier to interpret than visual analysis. These findings suggest that 3D-SPIER could be an improved diagnostic method for presurgical seizure localization in patients with intractable epilepsy and may also be useful for mapping normal brain function.

Rhythmic 3–4 Hz discharge is insufficient to produce cortical BOLD fMRI decreases in generalized seizures

Absence seizures are transient episodes of impaired consciousness accompanied by 3-4 Hz spike-wave discharge on electroencephalography (EEG). Human functional magnetic resonance imaging (fMRI) studies have demonstrated widespread cortical decreases in the blood oxygen-level dependent (BOLD) signal that may play an important role in the pathophysiology of these seizures. Animal models could provide an opportunity to investigate the fundamental mechanisms of these changes, however they have so far failed to consistently replicate the cortical fMRI decreases observed in human patients. This may be due to important differences between human seizures and animal models, including a lack of cortical development in rodents or differences in the frequencies of rodent (7-8 Hz) and human (3-4 Hz) spike-wave discharges. To examine the possible contributions of these differences, we developed a ferret model that exhibits 3-4 Hz spike-wave seizures in the presence of a sulcated cortex. Measurements of BOLD fMRI and simultaneous EEG demonstrated cortical fMRI increases during and following spike-wave seizures in ferrets. However unlike human patients, significant fMRI decreases were not observed. The lack of fMRI decreases was consistent across seizures of different durations, discharge frequencies, and anesthetic regimes, and using fMRI analysis models similar to human patients. In contrast, generalized tonic-clonic seizures under the same conditions elicited sustained postictal fMRI decreases, verifying that the lack of fMRI decreases with spike-wave was not due to technical factors. These findings demonstrate that 3-4 Hz spike-wave discharge in a sulcated animal model does not necessarily produce fMRI decreases, leaving the mechanism for this phenomenon open for further investigation.

Cortical Network Switching: Possible Role of the Lateral Septum and Cholinergic Arousal

BACKGROUND Cortical networks undergo large-scale switching between states of increased or decreased activity in normal sleep and cognition as well as in pathological conditions such as epilepsy. We previously found that focal hippocampal seizures in rats induce increased neuronal firing and cerebral blood flow in subcortical structures including the lateral septal area, along with frontal cortical slow oscillations resembling slow wave sleep. In addition, stimulation of the lateral septum in the absence of a seizure resulted in cortical deactivation with slow oscillations. HYPOTHESIS We hypothesized that lateral septal activation might cause neocortical deactivation indirectly, possibly through impaired subcortical arousal. But how does subcortical stimulation cause slow wave activity in frontal cortex? How do arousal neurotransmitter levels (e.g. acetylcholine) change in cortex during the excitation of inhibitory projection nuclei? METHODS AND RESULTS In the current study, we used simultaneous electrophysiology and enzyme-based amperometry in a rat model, and found a decrease in choline, along with slow wave activity in orbital frontal cortex during lateral septal stimulation in the absence of seizures. In contrast, the choline signal and local field potential in frontal cortex had no significant changes when stimulating the hippocampus, but showed increased choline and decreased slow wave activity with an arousal stimulus produced by toe pinch. CONCLUSIONS These findings indicate that the activation of subcortical inhibitory structures (such as lateral septum) can depress subcortical cholinergic arousal. This mechanism may play an important role in large-scale transitions of cortical activity in focal seizures, as well as in normal cortical function.

Human bedside evaluation versus automatic responsiveness testing in epilepsy (ARTiE)

Evaluation of behavioral impairment during epileptic seizures is critical for medical decision making, including accurate diagnosis, recommendations for driving, and presurgical evaluation. We investigated the quality of behavioral testing during inpatient video–electroencephalography (EEG) monitoring at an established epilepsy center, and introduce a technical innovation that may improve clinical care. We retrospectively reviewed video‐EEG data from 152 seizures in 33 adult or pediatric patients admitted for video‐EEG monitoring. Behavioral testing with questions or commands was performed in only 50% of seizures ictally, 73% of seizures postictally, and 80% with either ictal or postictal testing combined. Furthermore, the questions or commands were highly inconsistent and were performed by nonmedical personnel in about one fourth of cases. In an effort to improve this situation we developed and here introduce Automatic Responsiveness Testing in Epilepsy (ARTiE), a series of video‐recorded behavioral tasks automatically triggered to play in the patients room by computerized seizure detection. In initial technical testing using prerecorded or live video‐EEG data we found that ARTiE is initiated reliably by automatic seizure detection. With additional clinical testing we hope that ARTiE will succeed in providing comprehensive and reliable behavioral evaluation during seizures for people with epilepsy to greatly improve their clinical care.

PHATE: A Dimensionality Reduction Method for Visualizing Trajectory Structures in High-Dimensional Biological Data

With the advent of high-throughput technologies measuring high-dimensional biological data, there is a pressing need for visualization tools that reveal the structure and emergent patterns of data in an intuitive form. We present PHATE, a visualization method that captures both local and global nonlinear structure in data by an information-geometry distance between datapoints. We perform extensive comparison between PHATE and other tools on a variety of artificial and biological datasets, and find that it consistently preserves a range of patterns in data including continual progressions, branches, and clusters. We show that PHATE is applicable to a wide variety of datatypes including mass cytometry, single-cell RNA-sequencing, Hi-C, and gut microbiome data, where it can generate interpretable insights into the underlying systems. Finally, we use PHATE to explore a newly generated scRNA-seq dataset of human germ layer differentiation. Here, PHATE reveals a dynamic picture of the main developmental branches in unparalleled detail.In recent years, dimensionality reduction methods have become critical for visualization, exploration, and interpretation of high-throughput, high-dimensional biological data, as they enable the extraction of major trends in the data while discarding noise. However, biological data contains a type of predominant structure that is not preserved in commonly used methods such as PCA and tSNE, namely, branching progression structure. This structure, which is often non-linear, arises from underlying biological processes such as differentiation, graded responses to stimuli, and population drift, which generate cellular (or population) diversity. We propose a novel, affinity-preserving embedding called PHATE (Potential of Heat-diffusion for Affinity-based Trajectory Embedding), designed explicitly to preserve progression structure in data. PHATE provides a denoised, two or three-dimensional visualization of the complete branching trajectory structure in high-dimensional data. It uses heat-diffusion processes, which naturally denoise the data, to compute cell-cell affinities. Then, PHATE creates a diffusion-potential geometry by free-energy potentials of these processes. This geometry captures high-dimensional trajectory structures, while enabling a natural embedding of the intrinsic data geometry. This embedding accurately visualizes trajectories and data distances, without requiring strict assumptions typically used by path-finding and tree-fitting algorithms, which have recently been used for pseudotime orderings or tree-renderings of cellular data. Furthermore, PHATE supports a wide range of data exploration tasks by providing interpretable overlays on top of the visualization. We show that such overlays can emphasize and reveal trajectory end-points, branch points and associated split-decisions, progression-forming variables (e.g., specific genes), and paths between developmental events in cellular state-space. We demonstrate PHATE on single-cell RNA sequencing and mass cytometry data pertaining to embryoid body differentiation, IPSC reprogramming, and hematopoiesis in the bone marrow. We also demonstrate PHATE on non-single cell data including single-nucleotide polymorphism (SNP) measurements of European populations, and 16s sequencing of gut microbiota.Abstract In the era of ‘Big Data’ there is a pressing need for tools that provide human interpretable visualizations of emergent patterns in high-throughput high-dimensional data. Further, to enable insightful data exploration, such visualizations should faithfully capture and emphasize emergent structures and patterns without enforcing prior assumptions on the shape or form of the data. In this paper, we present PHATE (Potential of Heat-diffusion for Affinity-based Transition Embedding) - an unsupervised low-dimensional embedding for visualization of data that is aimed at solving these issues. Unlike previous methods that are commonly used for visualization, such as PCA and tSNE, PHATE is able to capture and highlight both local and global structure in the data. In particular, in addition to clustering patterns, PHATE also uncovers and emphasizes progression and transitions (when they exist) in the data, which are often missed in other visualization-capable methods. Such patterns are especially important in biological data that contain, for example, single-cell phenotypes at different phases of differentiation, patients at different stages of disease progression, and gut microbial compositions that vary gradually between individuals, even of the same enterotype. The embedding provided by PHATE is based on a novel informational distance that captures long-range nonlinear relations in the data by computing energy potentials of data-adaptive diffusion processes. We demonstrate the effectiveness of the produced visualization in revealing insights on a wide variety of biomedical data, including single-cell RNA-sequencing, mass cytometry, gut microbiome sequencing, human SNP data, Hi-C data, as well as non-biomedical data, such as facebook network and facial image data. In order to validate the capability of PHATE to enable exploratory analysis, we generate a new dataset of 31,000 single-cells from a human embryoid body differentiation system. Here, PHATE provides a comprehensive picture of the differentiation process, while visualizing major and minor branching trajectories in the data. We validate that all known cell types are recapitulated in the PHATE embedding in proper organization. Furthermore, the global picture of the system offered by PHATE allows us to connect parts of the developmental progression and characterize novel regulators associated with developmental lineages.

Epileptic auras and their role in driving safety in people with epilepsy

The aim of our study was to evaluate the role of auras in preventing motor vehicle accidents (MVAs) among patients with medically refractory epilepsy. The Multicenter Study of Epilepsy Surgery database was used to perform a case–control study by identifying patients who had seizures while driving that led to MVAs (cases) and those who had seizures while driving without MVAs (controls). We compared presence of reliable auras and other aura‐related features between the two groups. Two hundred fifteen of 553 patients reported having seizure(s) while driving; 74 were identified as “controls” and 141 as “cases.” The two groups had similar demographic and clinical features. The presence of reliable auras was not different between the two groups (67% in cases vs. 65% in controls; odds ratio [OR] 0.89, 95% confidence interval [CI] 0.49–1.61, p = 0.76). In addition, the groups did not differ in the proportion of patients who reported longer (>1 min) auras (OR 0.7, 95% CI 0.28–1.76, p = 0.47), or who thought that their auras were of sufficient duration to be protective (OR 1.19, 95% CI 0.62–2.00, p = 0.77). Our study questions the long‐held belief of a protective role of reliable auras against MVAs in people with epilepsy.

Visualizing Transitions and Structure for High Dimensional Data Exploration

With the advent of high-throughput technologies measuring high-dimensional biological data, there is a pressing need for visualization tools that reveal the structure and emergent patterns of data in an intuitive form. We present PHATE, a visualization method that captures both local and global nonlinear structure in data by an information-geometry distance between datapoints. We perform extensive comparison between PHATE and other tools on a variety of artificial and biological datasets, and find that it consistently preserves a range of patterns in data including continual progressions, branches, and clusters. We show that PHATE is applicable to a wide variety of datatypes including mass cytometry, single-cell RNA-sequencing, Hi-C, and gut microbiome data, where it can generate interpretable insights into the underlying systems. Finally, we use PHATE to explore a newly generated scRNA-seq dataset of human germ layer differentiation. Here, PHATE reveals a dynamic picture of the main developmental branches in unparalleled detail.In recent years, dimensionality reduction methods have become critical for visualization, exploration, and interpretation of high-throughput, high-dimensional biological data, as they enable the extraction of major trends in the data while discarding noise. However, biological data contains a type of predominant structure that is not preserved in commonly used methods such as PCA and tSNE, namely, branching progression structure. This structure, which is often non-linear, arises from underlying biological processes such as differentiation, graded responses to stimuli, and population drift, which generate cellular (or population) diversity. We propose a novel, affinity-preserving embedding called PHATE (Potential of Heat-diffusion for Affinity-based Trajectory Embedding), designed explicitly to preserve progression structure in data. PHATE provides a denoised, two or three-dimensional visualization of the complete branching trajectory structure in high-dimensional data. It uses heat-diffusion processes, which naturally denoise the data, to compute cell-cell affinities. Then, PHATE creates a diffusion-potential geometry by free-energy potentials of these processes. This geometry captures high-dimensional trajectory structures, while enabling a natural embedding of the intrinsic data geometry. This embedding accurately visualizes trajectories and data distances, without requiring strict assumptions typically used by path-finding and tree-fitting algorithms, which have recently been used for pseudotime orderings or tree-renderings of cellular data. Furthermore, PHATE supports a wide range of data exploration tasks by providing interpretable overlays on top of the visualization. We show that such overlays can emphasize and reveal trajectory end-points, branch points and associated split-decisions, progression-forming variables (e.g., specific genes), and paths between developmental events in cellular state-space. We demonstrate PHATE on single-cell RNA sequencing and mass cytometry data pertaining to embryoid body differentiation, IPSC reprogramming, and hematopoiesis in the bone marrow. We also demonstrate PHATE on non-single cell data including single-nucleotide polymorphism (SNP) measurements of European populations, and 16s sequencing of gut microbiota.Abstract In the era of ‘Big Data’ there is a pressing need for tools that provide human interpretable visualizations of emergent patterns in high-throughput high-dimensional data. Further, to enable insightful data exploration, such visualizations should faithfully capture and emphasize emergent structures and patterns without enforcing prior assumptions on the shape or form of the data. In this paper, we present PHATE (Potential of Heat-diffusion for Affinity-based Transition Embedding) - an unsupervised low-dimensional embedding for visualization of data that is aimed at solving these issues. Unlike previous methods that are commonly used for visualization, such as PCA and tSNE, PHATE is able to capture and highlight both local and global structure in the data. In particular, in addition to clustering patterns, PHATE also uncovers and emphasizes progression and transitions (when they exist) in the data, which are often missed in other visualization-capable methods. Such patterns are especially important in biological data that contain, for example, single-cell phenotypes at different phases of differentiation, patients at different stages of disease progression, and gut microbial compositions that vary gradually between individuals, even of the same enterotype. The embedding provided by PHATE is based on a novel informational distance that captures long-range nonlinear relations in the data by computing energy potentials of data-adaptive diffusion processes. We demonstrate the effectiveness of the produced visualization in revealing insights on a wide variety of biomedical data, including single-cell RNA-sequencing, mass cytometry, gut microbiome sequencing, human SNP data, Hi-C data, as well as non-biomedical data, such as facebook network and facial image data. In order to validate the capability of PHATE to enable exploratory analysis, we generate a new dataset of 31,000 single-cells from a human embryoid body differentiation system. Here, PHATE provides a comprehensive picture of the differentiation process, while visualizing major and minor branching trajectories in the data. We validate that all known cell types are recapitulated in the PHATE embedding in proper organization. Furthermore, the global picture of the system offered by PHATE allows us to connect parts of the developmental progression and characterize novel regulators associated with developmental lineages.