Brain gray matter changes in children at risk for sudden unexpected death in epilepsy.

BACKGROUND: Potential failing adult brain sites, stratified by risk, mediating Sudden Unexpected Death in Epilepsy (SUDEP) have been described, but are unknown in children. METHODS: We examined regional brain volumes using T1-weighted MRI images in 21 children with epilepsy at high SUDEP risk and 62 healthy children, together with SUDEP risk scores, calculated from focal seizure frequency. Gray matter tissue type was partitioned, maps normalized, smoothed, and compared between groups (SPM12; ANCOVA; covariates, age, sex, and BMI). Partial correlations between regional volumes and seizure frequency were examined (SPM12, covariates, age, sex, and BMI); 67% were at high risk for SUDEP. RESULTS: The cerebellar cortex, hippocampus, amygdala, putamen, cingulate, thalamus, and para-hippocampal gyrus showed increased gray matter volumes in epilepsy, and decreased volumes in the posterior thalamus, lingual gyrus, and temporal cortices. The cingulate, insula, and putamen showed significant positive relationships with focal seizure frequency indices using whole-brain voxel-by-voxel partial correlations. Tissue volume changes in selected sites differed in direction from adults; particularly, cerebellar sites, key for hypotensive recovery, increased rather than adult declines. CONCLUSION: The volume increases may represent expansion by inflammatory or other processes that, with sustained repetitive seizure discharge, lead to tissue volume declines described earlier in adults. IMPACT: Children with epilepsy, who are at risk for Sudden Unexplained Death, show changes in brain volume that often differ in direction of change from adults at risk for SUDEP. Sites of volume change play significant roles in mediating breathing and blood pressure, and include areas that serve recovery from prolonged apnea and marked loss of blood pressure. The extent of volume changes correlated with focal seizure frequency. Although the underlying processes contributing to regional volume changes remain speculative, regions of tissue swelling in pediatric brain areas may represent transitory conditions that later lead to tissue loss in the adult condition.

Incidence and Types of Cardiac Arrhythmias in the Peri-Ictal Period in Patients Having a Generalized Convulsive Seizure.

BACKGROUND AND OBJECTIVES: Generalized convulsive seizures (GCSs) are the main risk factor of sudden unexpected death in epilepsy (SUDEP), which is likely due to peri-ictal cardiorespiratory dysfunction. The incidence of GCS-induced cardiac arrhythmias, their relationship to seizure severity markers, and their role in SUDEP physiopathology are unknown. The aim of this study was to analyze the incidence of seizure-induced cardiac arrhythmias, their association with electroclinical features and seizure severity biomarkers, as well as their specific occurrences in SUDEP cases. METHODS: This is an observational, prospective, multicenter study of patients with epilepsy aged 18 years and older with recorded GCS during inpatient video-EEG monitoring for epilepsy evaluation. Exclusion criteria were status epilepticus and an obscured video recording. We analyzed semiologic and cardiorespiratory features through video-EEG (VEEG), electrocardiogram, thoracoabdominal bands, and pulse oximetry. We investigated the presence of bradycardia, asystole, supraventricular tachyarrhythmias (SVTs), premature atrial beats, premature ventricular beats, nonsustained ventricular tachycardia (NSVT), atrial fibrillation (Afib), ventricular fibrillation (VF), atrioventricular block (AVB), exaggerated sinus arrhythmia (ESA), and exaggerated sinus arrhythmia with bradycardia (ESAWB). A board-certified cardiac electrophysiologist diagnosed and classified the arrhythmia types. Bradycardia, asystole, SVT, NSVT, Afib, VF, AVB, and ESAWB were classified as arrhythmias of interest because these were of SUDEP pathophysiology value. The main outcome was the occurrence of seizure-induced arrhythmias of interest during inpatient VEEG monitoring. Moreover, yearly follow-up was conducted to identify SUDEP cases. Binary logistic generalized estimating equations were used to determine clinical-demographic and peri-ictal variables that were predictive of the presence of seizure-induced arrhythmias of interest. The z-score test for 2 population proportions was used to test whether the proportion of seizures and patients with postconvulsive ESAWB or bradycardia differed between SUDEP cases and survivors. RESULTS: This study includes data from 249 patients (mean age 37.2 ± 23.5 years, 55% female) who had 455 seizures. The most common arrhythmia was ESA, with an incidence of 137 of 382 seizures (35.9%) (106/224 patients [47.3%]). There were 50 of 352 seizure-induced arrhythmias of interest (14.2%) in 41 of 204 patients (20.1%). ESAWB was the commonest in 22 of 394 seizures (5.6%) (18/225 patients [8%]), followed by SVT in 18 of 397 seizures (4.5%) (17/228 patients [7.5%]). During follow-up (48.36 ± 31.34 months), 8 SUDEPs occurred. Seizure-induced bradycardia (3.8% vs 12.5%, z = -16.66, p < 0.01) and ESAWB (6.6% vs 25%; z = -3.03, p < 0.01) were over-represented in patients who later died of SUDEP. There was no association between arrhythmias of interest and seizure severity biomarkers (p > 0.05). DISCUSSION: Markers of seizure severity are not related to seizure-induced arrhythmias of interest, suggesting that other factors such as occult cardiac abnormalities may be relevant for their occurrence. Seizure-induced ESAWB and bradycardia were more frequent in SUDEP cases, although this observation was based on a very limited number of SUDEP patients. Further case-control studies are needed to evaluate the yield of arrhythmias of interest along with respiratory changes as potential SUDEP biomarkers.

Leveraging logical definitions and lexical features to detect missing IS-A relations in biomedical terminologies.

Biomedical terminologies play a vital role in managing biomedical data. Missing IS-A relations in a biomedical terminology could be detrimental to its downstream usages. In this paper, we investigate an approach combining logical definitions and lexical features to discover missing IS-A relations in two biomedical terminologies: SNOMED CT and the National Cancer Institute (NCI) thesaurus. The method is applied to unrelated concept-pairs within non-lattice subgraphs: graph fragments within a terminology likely to contain various inconsistencies. Our approach first compares whether the logical definition of a concept is more general than  that of the other concept. Then, we check whether the lexical features of the concept are contained in those of the other concept. If both constraints are satisfied, we suggest a potentially missing IS-A relation between the two concepts. The method identified 982 potential missing IS-A relations for SNOMED CT and 100 for NCI thesaurus. In order to assess the efficacy of our approach, a random sample of results belonging to the "Clinical Findings" and "Procedure" subhierarchies of SNOMED CT and results belonging to the "Drug, Food, Chemical or Biomedical Material" subhierarchy of the NCI thesaurus were evaluated by domain experts. The evaluation results revealed that 118 out of 150 suggestions are valid for SNOMED CT and 17 out of 20 are valid for NCI thesaurus.

Reliable detection of generalized convulsive seizures using an off-the-shelf digital watch: A multisite phase 2 study.

OBJECTIVE: The aim of this study was to develop a machine learning algorithm using an off-the-shelf digital watch, the Samsung watch (SM-R800), and evaluate its effectiveness for the detection of generalized convulsive seizures (GCS) in persons with epilepsy. METHODS: This multisite epilepsy monitoring unit (EMU) phase 2 study included 36 adult patients. Each patient wore a Samsung watch that contained accelerometer, gyroscope, and photoplethysmographic sensors. Sixty-eight time and frequency domain features were extracted from the sensor data and were used to train a random forest algorithm. A testing framework was developed that would better reflect the EMU setting, consisting of (1) leave-one-patient-out cross-validation (LOPO CV) on GCS patients, (2) false alarm rate (FAR) testing on nonseizure patients, and (3) "fixed-and-frozen" prospective testing on a prospective patient cohort. Balanced accuracy, precision, sensitivity, and FAR were used to quantify the performance of the algorithm. Seizure onsets and offsets were determined by using video-electroencephalographic (EEG) monitoring. Feature importance was calculated as the mean decrease in Gini impurity during the LOPO CV testing. RESULTS: LOPO CV results showed balanced accuracy of .93 (95% confidence interval [CI] = .8-.98), precision of .68 (95% CI = .46-.85), sensitivity of .87 (95% CI = .62-.96), and FAR of .21/24 h (interquartile range [IQR] = 0-.90). Testing the algorithm on patients without seizure resulted in an FAR of .28/24 h (IQR = 0-.61). During the "fixed-and-frozen" prospective testing, two patients had three GCS, which were detected by the algorithm, while generating an FAR of .25/24 h (IQR = 0-.89). Feature importance showed that heart rate-based features outperformed accelerometer/gyroscope-based features. SIGNIFICANCE: Commercially available wearable digital watches that reliably detect GCS, with minimum false alarm rates, may overcome usage adoption and other limitations of custom-built devices. Contingent on the outcomes of a prospective phase 3 study, such devices have the potential to provide non-EEG-based seizure surveillance and forecasting in the clinical setting.

Ictal Central Apnea Is Predictive of Mesial Temporal Seizure Onset: An Intracranial Investigation.

OBJECTIVE: Ictal central apnea (ICA) is a semiological sign of focal epilepsy, associated with temporal and frontal lobe seizures. In this study, using qualitative and quantitative approaches, we aimed to assess the localizational value of ICA. We also aimed to compare ICA clinical utility in relation to other seizure semiological features of focal epilepsy. METHODS: We analyzed seizures in patients with medically refractory focal epilepsy undergoing intracranial stereotactic electroencephalographic (SEEG) evaluations with simultaneous multimodal cardiorespiratory monitoring. A total of 179 seizures in 72 patients with reliable artifact-free respiratory signal were analyzed. RESULTS: ICA was seen in 55 of 179 (30.7%) seizures. Presence of ICA predicted a mesial temporal seizure onset compared to those without ICA (odds ratio = 3.8, 95% confidence interval = 1.3-11.6, p = 0.01). ICA specificity was 0.82. ICA onset was correlated with increased high-frequency broadband gamma (60-150Hz) activity in specific mesial or basal temporal regions, including amygdala, hippocampus, and fusiform and lingual gyri. Based on our results, ICA has an almost 4-fold greater association with mesial temporal seizure onset zones compared to those without ICA and is highly specific for mesial temporal seizure onset zones. As evidence of symptomatogenic areas, onset-synchronous increase in high gamma activity in mesial or basal temporal structures was seen in early onset ICA, likely representing anatomical substrates for ICA generation. INTERPRETATION: ICA recognition may help anatomoelectroclinical localization of clinical seizure onset to specific mesial and basal temporal brain regions, and the inclusion of these regions in SEEG evaluations may help accurately pinpoint seizure onset zones for resection. ANN NEUROL 2024;95:998-1008.

Personalization of SUDEP risk: A survey of transient subclinical comorbid changes.

OBJECTIVE: Preclinical data report within subject modifiable ailments emerge weeks prior to SUDEP, including sleep disorders and cardiorespiratory changes; findings which support anecdotal clinical data. Here, we bridge preclinical findings with future clinical/preclinical studies, and survey whether caretakers or family members of victims noticed transient changes prior to SUDEP. The aim of this pilot study is to identify potential modifiable changes that may synergistically increase SUDEP risk for future research. METHODS: A mobile electronic survey was posted on SUDEP community websites. The survey queried whether changes in seizures, sleep, physical well-being, emotional well-being, cognition, breathing, or heart rate were noticed before SUDEP. RESULTS: The most profound finding was that 85% of victims had multiple transient ailments prior to SUDEP. Changes in seizures (28/54), and sleep (30/58) occurred in more than 50% of the victims and represent the most influential changes identified. The second and third most influential changes were a reduction in physical well-being (25/57) and emotional well-being (26/56). Changes were observed within the last two months of life in approximately one third of the cases, and more than four months prior to SUDEP in approximately one third of cases, indicating a potential time frame for proactive preventative strategies. Respondents also noted changes in cognition (16/55), breathing (9/54) or heart rate (8/55). Data indicate these changes may be associated with increased SUDEP risk within subject. Study limitations include the responses were based on memory, there was a potential for data to be over reported, and caretakers were not prompted to observe changes a priori, thus some existing changes may have gone unnoticed. SIGNIFICANCE: Data support the preclinical findings that transient, subclinical (i.e., not severe enough to require medical intervention), modifiable ailments may increase risk of SUDEP. This suggests that just as an epilepsy type can change over a lifetime and epilepsy type-specific treatments can reduce SUDEP risk, further personalization of SUDEP risk will improve our understanding as to whether variables contribute to risk differently across lifespan. Thus, with a dynamic capacity to change, differing factors may contribute to the distribution of risk probability within an individual at any given time. Understanding whether different combinations of transient changes are specific to epilepsy type, age, or sex needs to be determined to move the field forward in hopes of developing a personalized approach to preventative strategies.

Stimulation-induced respiratory enhancement in corticothalamic regions.

OBJECTIVE: We aimed to identify corticothalamic areas and electrical stimulation paradigms that optimally enhance breathing. METHODS: Twenty-nine patients with medically intractable epilepsy were prospectively recruited in an epilepsy monitoring unit while undergoing stereoelectroencephalographic evaluation. Direct electrical stimulation in cortical and thalamic regions was carried out using low (<1 Hz) and high (≥10 Hz) frequencies, and low (<5 mA) and high (≥5 mA) current intensities, with pulse width of .1 ms. Electrocardiography, arterial oxygen saturation (SpO2 ), end-tidal carbon dioxide (ETCO2 ), oronasal airflow, and abdominal and thoracic plethysmography were monitored continuously during stimulations. Airflow signal was used to estimate breathing rate, tidal volume, and minute ventilation (MV) changes during stimulation, compared to baseline. RESULTS: Electrical stimulation increased MV in the amygdala, anterior cingulate, anterior insula, temporal pole, and thalamus, with an average increase in MV of 20.8% ± 28.9% (range = 0.2%-165.6%) in 19 patients. MV changes were associated with SpO2 and ETCO2 changes (p < .001). Effects on respiration were parameter and site dependent. Within amygdala, low-frequency stimulation of the medial region produced 78.49% greater MV change (p < .001) compared to high-frequency stimulation. Longer stimulation produced greater MV changes (an increase of 4.47% in MV for every additional 10 s, p = .04). SIGNIFICANCE: Stimulation of amygdala, anterior cingulate gyrus, anterior insula, temporal pole, and thalamus, using certain stimulation paradigms, enhances respiration. Among tested paradigms, low-frequency, low-intensity, long-duration stimulation of the medial amygdala is the most effective breathing enhancement stimulation strategy. Such approaches may pave the way for the future development of neuromodulatory techniques that aid rescue from seizure-related apnea, potentially as a targeted sudden unexpected death in epilepsy prevention method.

Volumetric and microstructural abnormalities of the amygdala in focal epilepsy with varied levels of SUDEP risk.

Although the mechanisms of sudden unexpected death in epilepsy (SUDEP) are not yet well understood, generalised- or focal-to-bilateral tonic-clonic seizures (TCS) are a major risk factor. Previous studies highlighted alterations in structures linked to cardio-respiratory regulation; one structure, the amygdala, was enlarged in people at high risk of SUDEP and those who subsequently died. We investigated volume changes and the microstructure of the amygdala in people with epilepsy at varied risk for SUDEP since that structure can play a key role in triggering apnea and mediating blood pressure. The study included 53 healthy subjects and 143 patients with epilepsy, the latter separated into two groups according to whether TCS occur in years before scan. We used amygdala volumetry, derived from structural MRI, and tissue microstructure, derived from diffusion MRI, to identify differences between the groups. The diffusion metrics were obtained by fitting diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) models. The analyses were performed at the whole amygdala level and at the scale of amygdaloid nuclei. Patients with epilepsy showed larger amygdala volumes and lower neurite density indices (NDI) than healthy subjects; the left amygdala volumes were especially enhanced. Microstructural changes, reflected by NDI differences, were more prominent on the left side and localized in the lateral, basal, central, accessory basal and paralaminar amygdala nuclei; basolateral NDI lowering appeared bilaterally. No significant microstructural differences appeared between epilepsy patients with and without current TCS. The central amygdala nuclei, with prominent interactions from surrounding nuclei of that structure, project to cardiovascular regions and respiratory phase switching areas of the parabrachial pons, as well as to the periaqueductal gray. Consequently, they have the potential to modify blood pressure and heart rate, and induce sustained apnea or apneusis. The findings here suggest that lowered NDI, indicative of reduced dendritic density, could reflect an impaired structural organization influencing descending inputs that modulate vital respiratory timing and drive sites and areas critical for blood pressure control.

Volumetric and microstructural abnormalities of the amygdala in focal epilepsy with varied levels of SUDEP risk.

Although the mechanisms of sudden unexpected death in epilepsy (SUDEP) are not yet well understood, generalised- or focal-to-bilateral tonic-clonic seizures (TCS) are a major risk factor. Previous studies highlighted alterations in structures linked to cardio-respiratory regulation; one structure, the amygdala, was enlarged in people at high risk of SUDEP and those who subsequently died. We investigated volume changes and the microstructure of the amygdala in people with epilepsy at varied risk for SUDEP since that structure can play a key role in triggering apnea and mediating blood pressure. The study included 53 healthy subjects and 143 patients with epilepsy, the latter separated into two groups according to whether TCS occur in years before scan. We used amygdala volumetry, derived from structural MRI, and tissue microstructure, derived from diffusion MRI, to identify differences between the groups. The diffusion metrics were obtained by fitting diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) models. The analyses were performed at the whole amygdala level and at the scale of amygdaloid nuclei. Patients with epilepsy showed larger amygdala volumes and lower neurite density indices (NDI) than healthy subjects; the left amygdala volumes were especially enhanced. Microstructural changes, reflected by NDI differences, were more prominent on the left side and localized in the lateral, basal, central, accessory basal and paralaminar amygdala nuclei; basolateral NDI lowering appeared bilaterally. No significant microstructural differences appeared between epilepsy patients with and without current TCS. The central amygdala nuclei, with prominent interactions from surrounding nuclei of that structure, project to cardiovascular regions and respiratory phase switching areas of the parabrachial pons, as well as to the periaqueductal gray. Consequently, they have the potential to modify blood pressure and heart rate, and induce sustained apnea or apneusis. The findings here suggest that lowered NDI, indicative of reduced dendritic density, could reflect an impaired structural organization influencing descending inputs that modulate vital respiratory timing and drive sites and areas critical for blood pressure control.

Subject-Specific Activation of Central Respiratory Centers during Breath-Holding Functional Magnetic Resonance Imaging.

BACKGROUND: Voluntary breath-holding (BH) triggers responses from central neural control and respiratory centers in order to restore breathing. Such responses can be observed using functional MRI (fMRI). OBJECTIVES: We used this paradigm in healthy volunteers with the view to develop a biomarker that could be used to investigate disorders of the central control of breathing at the individual patient level. METHOD: In 21 healthy human subjects (mean age±SD, 32.8 ± 9.9 years old), fMRI was used to determine, at both the individual and group levels, the physiological neural response to expiratory and inspiratory voluntary apneas, within respiratory control centers in the brain and brainstem. RESULTS: Group analysis showed that expiratory BH, but not inspiratory BH, triggered activation of the pontine respiratory group and raphe nuclei at the group level, with a significant relationship between the levels of activation and drop in SpO2. Using predefined ROIs, expiratory BH, and to a lesser extent, inspiratory BH were associated with activation of most respiratory centers. The right ventrolateral nucleus of the thalamus, right pre-Bötzinger complex, right VRG, right nucleus ambiguus, and left Kölliker-Fuse-parabrachial complex were only activated during inspiratory BH. Individual analysis identified activations of cortical/subcortical and brainstem structures related to respiratory control in 19 out of 21 subjects. CONCLUSION: Our study shows that BH paradigm allows to reliably trigger fMRI response from brainstem and cortical areas involved in respiratory control at the individual level, suggesting that it might serve as a clinically relevant biomarker to investigate conditions associated with an altered central control of respiration.

Safety and efficacy of bihemispheric sampling via transmidline stereoelectroencephalography.

OBJECTIVE: Stereoelectroencephalography (SEEG) is designed to target distributed cortical networks responsible for electroclinical seizure syndrome and to enable localization of the site of seizure onset in patients with intractable epilepsy. When the preimplantation hypothesis invokes the bilateral mesial frontal lobes, sampling of several deep-seated cortical sites in both hemispheres is required. In this study, the authors have demonstrated the feasibility of sampling bihemispheric areas with intentional implantation of an SEEG electrode crossing the midline (SECM) for sampling the cortex on both sides of the interhemispheric fissure. METHODS: An analysis of 231 consecutive SEEG procedures over 8 years was used to identify instances of bihemispheric sampling by using the transmidline SEEG technique. RESULTS: The authors identified 53 SEEG cases, with a total of 126 electrodes that crossed the interhemispheric fissure; all were in the frontal lobes. Eighty-three electrodes targeted the cingulate gyrus (18 rostral, 43 anterior, and 22 middle), 31 targeted the posterior orbitofrontal region, 8 sampled the medial prefrontal cortex, and 4 targeted nodular heterotopia around the frontal horns. The ictal onset zone was localized to the frontal lobe in 16 cases. SECM isolated interictal and ictal activity in the contralateral hemisphere in 6 cases and independent bihemispheric seizure activity in 2 cases. No hemorrhagic or infectious complications were noted in any of these cases. CONCLUSIONS: Based on this extensive experience of bihemispheric sampling, the authors concluded that this technique is safe and effective. In this series, SECM showed contralateral interictal and/or ictal epileptiform activity in 8 (15%) cases, and 9 (16%) cases (with unilateral implantation) had sufficient data to discard contralateral involvement, contributing to support of the epileptogenic network. SECM may reduce the number of electrodes used to sample bilateral mesial frontal or orbitofrontal cortices, and such an approach may lower the risk of hemorrhage and costs.

Ontology-based feature engineering in machine learning workflows for heterogeneous epilepsy patient records.

Biomedical ontologies are widely used to harmonize heterogeneous data and integrate large volumes of clinical data from multiple sources. This study analyzed the utility of ontologies beyond their traditional roles, that is, in addressing a challenging and currently underserved field of feature engineering in machine learning workflows. Machine learning workflows are being increasingly used to analyze medical records with heterogeneous phenotypic, genotypic, and related medical terms to improve patient care. We performed a retrospective study using neuropathology reports from the German Neuropathology Reference Center for Epilepsy Surgery at Erlangen, Germany. This cohort included 312 patients who underwent epilepsy surgery and were labeled with one or more diagnoses, including dual pathology, hippocampal sclerosis, malformation of cortical dysplasia, tumor, encephalitis, and gliosis. We modeled the diagnosis terms together with their microscopy, immunohistochemistry, anatomy, etiologies, and imaging findings using the description logic-based Web Ontology Language (OWL) in the Epilepsy and Seizure Ontology (EpSO). Three tree-based machine learning models were used to classify the neuropathology reports into one or more diagnosis classes with and without ontology-based feature engineering. We used five-fold cross validation to avoid overfitting with a fixed number of repetitions while leaving out one subset of data for testing, and we used recall, balanced accuracy, and hamming loss as performance metrics for the multi-label classification task. The epilepsy ontology-based feature engineering approach improved the performance of all the three learning models with an improvement of 35.7%, 54.5%, and 33.3% in logistics regression, random forest, and gradient tree boosting models respectively. The run time performance of all three models improved significantly with ontology-based feature engineering with gradient tree boosting model showing a 93.8% reduction in the time required for training and testing of the model. Although, all three models showed an overall improved performance across the three-performance metrics using ontology-based feature engineering, the rate of improvement was not consistent across all input features. To analyze this variation in performance, we computed feature importance scores and found that microscopy had the highest importance score across the three models, followed by imaging, immunohistochemistry, and anatomy in a decreasing order of importance scores. This study showed that ontologies have an important role in feature engineering to make heterogeneous clinical data accessible to machine learning models and also improve the performance of machine learning models in multilabel multiclass classification tasks.

A multimodal clinical data resource for personalized risk assessment of sudden unexpected death in epilepsy.

Epilepsy affects ~2-3 million individuals in the United States, a third of whom have uncontrolled seizures. Sudden unexpected death in epilepsy (SUDEP) is a catastrophic and fatal complication of poorly controlled epilepsy and is the primary cause of mortality in such patients. Despite its huge public health impact, with a ~1/1,000 incidence rate in persons with epilepsy, it is an uncommon enough phenomenon to require multi-center efforts for well-powered studies. We developed the Multimodal SUDEP Data Resource (MSDR), a comprehensive system for sharing multimodal epilepsy data in the NIH funded Center for SUDEP Research. The MSDR aims at accelerating research to address critical questions about personalized risk assessment of SUDEP. We used a metadata-guided approach, with a set of common epilepsy-specific terms enforcing uniform semantic interpretation of data elements across three main components: (1) multi-site annotated datasets; (2) user interfaces for capturing, managing, and accessing data; and (3) computational approaches for the analysis of multimodal clinical data. We incorporated the process for managing dataset-specific data use agreements, evidence of Institutional Review Board review, and the corresponding access control in the MSDR web portal. The metadata-guided approach facilitates structural and semantic interoperability, ultimately leading to enhanced data reusability and scientific rigor. MSDR prospectively integrated and curated epilepsy patient data from seven institutions, and it currently contains data on 2,739 subjects and 10,685 multimodal clinical data files with different data formats. In total, 55 users registered in the current MSDR data repository, and 6 projects have been funded to apply MSDR in epilepsy research, including three R01 projects and three R21 projects.

Regional variation in brain tissue texture in patients with tonic-clonic seizures.

Patients with epilepsy, who later succumb to sudden unexpected death, show altered brain tissue volumes in selected regions. It is unclear whether the alterations in brain tissue volume represent changes in neurons or glial properties, since volumetric procedures have limited sensitivity to assess the source of volume changes (e.g., neuronal loss or glial cell swelling). We assessed a measure, entropy, which can determine tissue homogeneity by evaluating tissue randomness, and thus, shows tissue integrity; the measure is easily calculated from T1-weighted images. T1-weighted images were collected with a 3.0-Tesla MRI from 53 patients with tonic-clonic (TC) seizures and 53 healthy controls; images were bias-corrected, entropy maps calculated, normalized to a common space, smoothed, and compared between groups (TC patients and controls using ANCOVA; covariates, age and sex; SPM12, family-wise error correction for multiple comparisons, p<0.01). Decreased entropy, indicative of increased tissue homogeneity, appeared in major autonomic (ventromedial prefrontal cortex, hippocampus, dorsal and ventral medulla, deep cerebellar nuclei), motor (sensory and motor cortex), or both motor and autonomic regulatory sites (basal-ganglia, ventral-basal cerebellum), and external surfaces of the pons. The anterior and posterior thalamus and midbrain also showed entropy declines. Only a few isolated regions showed increased entropy. Among the spared autonomic regions was the anterior cingulate and anterior insula; the posterior insula and cingulate were, however, affected. The entropy alterations overlapped areas of tissue changes found earlier with volumetric measures, but were more extensive, and indicate widespread injury to tissue within critical autonomic and breathing regulatory areas, as well as prominent damage to more-rostral sites that exert influences on both breathing and cardiovascular regulation. The entropy measures provide easily-collected supplementary information using only T1-weighted images, showing aspects of tissue integrity other than volume change that are important for assessing function.

Protocols for multimodal polygraphy for cardiorespiratory monitoring in the epilepsy monitoring unit. Part I: Clinical acquisition.

Multimodal polygraphy including cardiorespiratory monitoring in the Epilepsy Monitoring is becoming increasingly important. In addition to simultaneous recording of video and EEG, the combination of these techniques not only improves seizure detection, it enhances patient safety and provides information on autonomic clinical symptoms, which may be contributory to localization of seizure foci. However, there are currently no consensus guidelines, nor adequate information on devices available for multimodal polygraphy for cardiorespiratory monitoring in the Epilepsy Monitoring Unit. Our purpose here is to provide protocols and information on devices for such monitoring. Suggested parameters include respiratory inductance plethysmography (thoraco-abdominal belts for respiratory rate), pulse oximetry and four-lead electrocardiography. Detailed knowledge of devices, their operability and acquisition optimization enables accurate interpretation of signal and differentiation of abnormalities from artifacts. Multimodal polygraphy brings new opportunities for identification of peri-ictal cardiorespiratory abnormalities, and may identify high SUDEP risk individuals.

Functional MRI Correlates of Carbon Dioxide Chemosensing in Persons With Epilepsy.

Objectives: Sudden unexpected death in epilepsy (SUDEP) is a catastrophic epilepsy outcome for which there are no reliable premortem imaging biomarkers of risk. Percival respiratory depression is seen in monitored SUDEP and near SUDEP cases, and abnormal chemosensing of raised blood carbon dioxide (CO2) is thought to contribute. Damage to brainstem respiratory control and chemosensing structures has been demonstrated in structural imaging and neuropathological studies of SUDEP. We hypothesized that functional MRI (fMRI) correlates of abnormal chemosensing are detectable in brainstems of persons with epilepsy (PWE) and are different from healthy controls (HC). Methods: We analyzed fMRI BOLD activation and brain connectivity in 10 PWE and 10 age- and sex-matched HCs during precisely metered iso-oxic, hypercapnic breathing challenges. Segmented brainstem responses were of particular interest, along with characterization of functional connectivity metrics between these structures. Regional BOLD activations during hypercapnic challenges were convolved with hemodynamic responses, and the resulting activation maps were passed on to group-level analyses. For the functional connectivity analysis, significant clusters from BOLD results were used as seeds. Each individual seed time-series activation map was extracted for bivariate correlation coefficient analyses to study changes in brain connectivity between PWE and HCs. Results: (1) Greater brainstem BOLD activations in PWE were observed compared to HC during hypercapnic challenges in several structures with respiratory/chemosensing properties. Group comparison between PWE vs. HC showed significantly greater activation in the dorsal raphe among PWE (p < 0.05) compared to HCs. (2) PWE had significantly greater seed-seed connectivity and recruited more structures during hypercapnia compared to HC. Significance: The results of this study show that BOLD responses to hypercapnia in human brainstem are detectable and different in PWE compared to HC. Increased dorsal raphe BOLD activation in PWE and increased seed-seed connectivity between brainstem and adjacent subcortical areas may indicate abnormal chemosensing in these individuals. Imaging investigation of brainstem respiratory centers involved in respiratory regulation in PWE is an important step toward identifying suspected dysfunction of brainstem breathing control that culminates in SUDEP and deserve further study as potential imaging SUDEP biomarkers.

Protocols for multimodal polygraphy for cardiorespiratory monitoring in the epilepsy monitoring unit. Part II - Research acquisition.

Multimodal polygraphy including cardiorespiratory monitoring is a valuable tool for epilepsy and sudden unexpected death in epilepsy (SUDEP) research. Broader applications in research into stress, anxiety, mood and other domains exist. Polygraphy techniques used during video electroencephalogram (EEG) recordings provide information on cardiac and respiratory changes in the peri-ictal period. In addition, such monitoring in brain mapping during chronic intracranial EEG evaluations has helped the understanding of pathomechanisms that lead to seizure induced cardiorespiratory dysfunction. Our aim here is to provide protocols and information on devices that may be used in the Epilepsy Monitoring Unit, in addition to proposed standard of care data acquisition. These devices include oronasal thermistors, oronasal pressure transducers, capnography, transcutaneous CO2 sensors, and continuous noninvasive blood pressure monitoring. Standard protocols for cardiorespiratory monitoring simultaneously with video EEG recording, may be useful in the study of cardiorespiratory phenomena in persons with epilepsy.

Forecasting seizure clusters from chronic ambulatory electrocorticography.

Seizure clusters are seizures that occur in rapid succession during periods of heightened seizure risk and are associated with substantial morbidity and sudden unexpected death in epilepsy. The objective of this feasibility study was to evaluate the performance of a novel seizure cluster forecasting algorithm. Chronic ambulatory electrocorticography recorded over an average of 38 months in 10 subjects with drug-resistant epilepsies was analyzed pseudoprospectively by dividing data into training (first 85%) and validation periods. For each subject, the probability of seizure clustering, derived from the Kolmogorov-Smirnov statistic using a novel algorithm, was forecasted in the validation period using individualized autoregressive models that were optimized from training data. The primary outcome of this study was the mean absolute scaled error (MASE) of 1-day horizon forecasts. From 10 subjects, 394 ± 142 (mean ± SD) electrocorticography-based seizure events were extracted for analysis, representing a span of 38 ± 27 months of recording. MASE across all subjects was .74 ± .09, .78 ± .09, and .83 ± .07 at .5-, 1-, and 2-day horizons. The feasibility study demonstrates that seizure clusters are quasiperiodic and can be forecasted to clinically meaningful horizons. Pending validation in larger cohorts, the forecasting approach described herein may herald chronotherapy during imminent heightened seizure vulnerability.

Thalamic neuromodulation for epilepsy: A clinical perspective.

Thalamic neuromodulation can be an effective therapeutic option for select patients with medically refractory epilepsy. However, successful outcome depends on several factors, beginning with appropriate patient and thalamic target selection. Among thalamic targets, the anterior (ANT) and centromedian (CeM) nuclei have the greatest clinical evidence for efficacy. However, the place of thalamic neuromodulation in the treatment armamentarium for intractable seizures is at the tail end of a long list of options. It's relative efficacy, if any, in relation to other treatment modalities however, can be inferred. As we will discuss, considerable work remains to be done in optimal targeting of thalamic nuclei, appropriate to the epilepsy syndrome and seizure type of the individual patient, which may change our current understanding of the place of thalamic neuromodulation on a range of treatment modality efficacies. Currently, it appears that ANT DBS is most efficacious for limbic epilepsies whereas CM, for generalized, multifocal (especially frontotemporal) epilepsies. Based on controlled studies, the efficacy of ANT and CeM DBS is roughly in line with other neuromodulatory therapies (i.e. RNS, VNS) when assessed within the cohort of patients for which the therapy is indicated. Much improvement is needed to render thalamic DBS more efficacious, and use of optimal targeting strategies, especially direct targeting, can positively affect outcomes. Thalamic neuromodulation is still in its infancy; however, clinical advances in this therapy are being realized.