A Bayesian switching linear dynamical system for estimating seizure chronotypes.

Epilepsy is a disorder characterized by paroxysmal transitions between multistable states. Dynamical systems have been useful for modeling the paroxysmal nature of seizures. At the same time, intracranial electroencephalography (EEG) recordings have recently discovered that an electrographic measure of epileptogenicity, interictal epileptiform activity, exhibits cycling patterns ranging from ultradian to multidien rhythmicity, with seizures phase-locked to specific phases of these latent cycles. However, many mechanistic questions about seizure cycles remain unanswered. Here, we provide a principled approach to recast the modeling of seizure chronotypes within a statistical dynamical systems framework by developing a Bayesian switching linear dynamical system (SLDS) with variable selection to estimate latent seizure cycles. We propose a Markov chain Monte Carlo algorithm that employs particle Gibbs with ancestral sampling to estimate latent cycles in epilepsy and apply unsupervised learning on spectral features of latent cycles to uncover clusters in cycling tendency. We analyze the largest database of patient-reported seizures in the world to comprehensively characterize multidien cycling patterns among 1,012 people with epilepsy, spanning from infancy to older adulthood. Our work advances knowledge of cycling in epilepsy by investigating how multidien seizure cycles vary in people with epilepsy, while demonstrating an application of an SLDS to frame seizure cycling within a nonlinear dynamical systems framework. It also lays the groundwork for future studies to pursue data-driven hypothesis generation regarding the mechanistic drivers of seizure cycles.

Health care utilization of Hispanic/Latino veterans with epilepsy: A national population-based study.

OBJECTIVE: Hispanic/Latino people with epilepsy are a growing population that has been understudied in clinical epilepsy research. U.S. veterans are at a higher risk of epilepsy due to greater exposures including traumatic brain injury. Hispanic/Latino Veterans with Epilepsy (HL-VWEs) represent a growing population; however the treatment utilization patterns of this population have been vastly understudied. METHODS: HL-VWE were identified from administrative databases during fiscal year 2019. Variables compared between Hispanic and non-Hispanic VWEs included demographics, rurality, service era, utilization of clinical services/investigations, and service-connected injury. Chi-square and Student's t tests were used for comparisons. RESULTS: Among 56 556 VWEs, 3247 (5.7%) were HL. HL-VWEs were younger (59.2 vs 63.2 years; p < .01) and more commonly urban-dwelling (81.6% vs 63.2%, p < .01) compared to non-HL-VWEs. They were also more likely to have served in recent missions such as the Persian Gulf War and post- 9/11 wars (p < .01). HL-VWEs had a higher utilization of all neurology services examined including neurology clinic visits, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, electroencephalography (EEG), epilepsy monitoring, and comprehensive epilepsy care (p < .01 for all). HL-VWEs were more likely to visit an emergency room or have seizure-related hospitalizations (p < .01). HL-VWEs were more likely to have a service-connected disability greater or equal to 50% (p < .01). SIGNIFICANCE: This study is one of the largest cohorts examining HL-VWEs. We found higher utilization of services in neurology, epilepsy, and neuroimaging by HL-VWEs. HL-VWE are younger, more commonly urban-dwelling, and more likely to have served during recent combat periods and have higher amounts of service-connected disability. Given that the proportion of Hispanic veterans is projected to rise over time, more research is needed to provide the best interventions and mitigate the long-term impact of epilepsy on this diverse patient group.

Computer vision for automated seizure detection and classification: A systematic review.

Computer vision (CV) shows increasing promise as an efficient, low-cost tool for video seizure detection and classification. Here, we provide an overview of the fundamental concepts needed to understand CV and summarize the structure and performance of various model architectures used in video seizure analysis. We conduct a systematic literature review of the PubMed, Embase, and Web of Science databases from January 1, 2000 to September 15, 2023, to identify the strengths and limitations of CV seizure analysis methods and discuss the utility of these models when applied to different clinical seizure phenotypes. Reviews, nonhuman studies, and those with insufficient or poor quality data are excluded from the review. Of the 1942 records identified, 45 meet inclusion criteria and are analyzed. We conclude that the field has shown tremendous growth over the past 2 decades, leading to several model architectures with impressive accuracy and efficiency. The rapid and scalable detection offered by CV models holds the potential to reduce sudden unexpected death in epilepsy and help alleviate resource limitations in epilepsy monitoring units. However, a lack of standardized, thorough validation measures and concerns about patient privacy remain important obstacles for widespread acceptance and adoption. Investigation into the performance of models across varied datasets from clinical and nonclinical environments is an essential area for further research.

Comorbidities in Hispanic/Latino Veterans with epilepsy.

BACKGROUND: Hispanic/Latino people with epilepsy may be at a differential risk of medical and psychiatric comorbidities given genetic, environmental, sociocultural, and quality of care factors. In people with epilepsy, comorbidities are especially crucial to investigate given the well-known impact on quality of life and risk of adverse outcomes. Yet, Hispanic/Latino Veterans with Epilepsy (HL-VWE) remain an understudied population. The present nationwide population study sought to investigate medical and psychiatric comorbidities in this group. METHODS: Data from the Veterans Health Administration (VHA) Corporate Data Warehouse administrative data were used to identify 56,556 VWE (5.7 % HL-VWE) using a one-year cross-sectional analysis of ICD codes. Elixhauser Comorbidity Index scores and psychiatric diagnoses were calculated based on ICD-9/ICD-10-CM diagnoses using a lookback period. Comparisons were made between HL-VWE and non-HL-VWE using chi-squared and student t-tests. Regression analyses were then performed to examine group differences while accounting for age. RESULTS: HL-VWE had higher probability of being diagnosed with several psychiatric conditions when accounting for age, including depression (OR 1.21, 95 % CI 1.13-1.31) and schizophrenia (OR 1.56, 95 % CI 1.31-1.84). There were no significant differences in medical comorbidities between the HL-VWE and non-HL-VWE groups. CONCLUSIONS: We present results from the largest known study of HL people with epilepsy examining their psychiatric and medical comorbidities and one of the first to specifically study HL-VWE. Compared to non-HL-VWE, the Hispanic/Latino group had comparable medical comorbidity, but higher rates of multiple psychiatric conditions. Results indicate a need for increased screening and interventions in this population to reduce psychiatric disease burden.

Differentiating Epileptic and Psychogenic Non-Epileptic Seizures Using Machine Learning Analysis of EEG Plot Images.

The treatment of epilepsy, the second most common chronic neurological disorder, is often complicated by the failure of patients to respond to medication. Treatment failure with anti-seizure medications is often due to the presence of non-epileptic seizures. Distinguishing non-epileptic from epileptic seizures requires an expensive and time-consuming analysis of electroencephalograms (EEGs) recorded in an epilepsy monitoring unit. Machine learning algorithms have been used to detect seizures from EEG, typically using EEG waveform analysis. We employed an alternative approach, using a convolutional neural network (CNN) with transfer learning using MobileNetV2 to emulate the real-world visual analysis of EEG images by epileptologists. A total of 5359 EEG waveform plot images from 107 adult subjects across two epilepsy monitoring units in separate medical facilities were divided into epileptic and non-epileptic groups for training and cross-validation of the CNN. The model achieved an accuracy of 86.9% (Area Under the Curve, AUC 0.92) at the site where training data were extracted and an accuracy of 87.3% (AUC 0.94) at the other site whose data were only used for validation. This investigation demonstrates the high accuracy achievable with CNN analysis of EEG plot images and the robustness of this approach across EEG visualization software, laying the groundwork for further subclassification of seizures using similar approaches in a clinical setting.

Neurostimulation in generalized epilepsy: A systematic review and meta-analysis.

OBJECTIVE: There are three neurostimulation devices available to treat generalized epilepsy: vagus nerve stimulation (VNS), deep brain stimulation (DBS), and responsive neurostimulation (RNS). However, the choice between them is unclear due to lack of head-to-head comparisons. A systematic comparison of neurostimulation outcomes in generalized epilepsy has not been performed previously. The goal of this meta-analysis was to determine whether one of these devices is better than the others to treat generalized epilepsy. METHODS: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a systematic review of PubMed, Embase, and Web of Science was performed for studies reporting seizure outcomes following VNS, RNS, and DBS implantation in generalized drug-resistant epilepsy between the first pivotal trial study for each modality through August 2022. Specific search criteria were used for VNS ("vagus", "vagal", or "VNS" in the title and "epilepsy" or "seizure"), DBS ("deep brain stimulation", "DBS", "anterior thalamic nucleus", "centromedian nucleus", or "thalamic stimulation" in the title and "epilepsy" or "seizure"), and RNS ("responsive neurostimulation" or "RNS" in the title and "epilepsy" or "seizure"). From 4409 articles identified, 319 underwent full-text reviews, and 20 studies were included. Data were pooled using a random-effects model using the meta package in R. RESULTS: Sufficient data for meta-analysis were available from seven studies for VNS (n = 510) and nine studies for DBS (n = 87). Data from RNS (five studies, n = 18) were insufficient for meta-analysis. The mean (SD) follow-up durations were as follows: VNS, 39.1 (23.4) months; DBS, 23.1 (19.6) months; and RNS, 22.3 (10.6) months. Meta-analysis showed seizure reductions of 48.3% (95% confidence interval [CI] = 38.7%-57.9%) for VNS and 64.8% (95% CI = 54.4%-75.2%) for DBS (p = .02). SIGNIFICANCE: Our meta-analysis indicates that the use of DBS may lead to greater seizure reduction than VNS in generalized epilepsy. Results from RNS use are promising, but further research is required.

Neuro-stimulation in focal epilepsy: A systematic review and meta-analysis.

OBJECTIVES: Different neurostimulation modalities are available to treat drug-resistant focal epilepsy when surgery is not an option including vagus nerve stimulation (VNS), responsive neurostimulation (RNS), and deep brain stimulation (DBS). Head-to-head comparisons of efficacy do not exist between them nor are likely to be available in the future. We performed a meta-analysis on VNS, RNS, and DBS outcomes to compare seizure reduction efficacy for focal epilepsy. METHODS: We systematically reviewed the literature for reported seizure outcomes following implantation with VNS, RNS, and DBS in focal-onset seizures and performed a meta-analysis. Prospective or retrospective clinical studies were included. RESULTS: Sufficient data were available at years one (n = 642, two (n = 480), and three (n = 385) for comparing the three modalities with each other. Seizure reduction for the devices at years one, two, and three respectively were: RNS: 66.3%, 56.0%, 68.4%; DBS- 58.4%, 57.5%, 63.8%; VNS 32.9%, 44.4%, 53.5%. Seizure reduction at year one was greater for RNS (p < 0.01) and DBS (p < 0.01) compared to VNS. CONCLUSIONS: Our findings indicate the seizure reduction efficacy of RNS is similar to DBS, and both had greater seizure reductions compared to VNS in the first-year post-implantation, with the differences diminishing with longer-term follow-up. SIGNIFICANCE: The results help guide neuromodulation treatment in eligible patients with drug-resistant focal epilepsy.

Psychiatric co-morbidity of drug-resistant epilepsy in Veterans.

OBJECTIVE: Psychiatric conditions are frequently co-morbid in epilepsy and studies examining Veterans with epilepsy suggest this population may present with unique psychiatric and clinical features Drug-resistant epilepsy (DRE) may confer a greater risk of psychiatric dysfunction; however, there is a paucity of literature documenting this. To expand our clinical understanding of Veterans with DRE, we assessed a comprehensive Veterans Health Administration (VHA)-wide sample, describing psychiatric conditions, medications, and healthcare utilization. METHODS: Psychiatric and hospitalization data were collected on 52,579 Veterans enrolled in VHA healthcare between FY2014-2ndQtr.FY2020 from the VHA Corporate Data Warehouse administrative data. Data examined include psychiatric diagnosis, psychotropic medication use, and utilization of hospital services. RESULTS: At least one psychiatric diagnosis was present in 70.2% of patients, while 49.8% had two or more diagnoses. Depression (51.7%), posttraumatic stress disorder (PTSD) (38.8%), and anxiety (38.0%) represented the most common psychiatric co-morbidities. Psychiatric medication use was present in 73.3%. Emergency room (ER) visits were highest in those with suicidality (mean 14.9 visits), followed by bipolar disorder (10.3), and schizophrenia (12.1). Psychiatric-related hospitalizations were highest for schizophrenia (mean 2.5 admissions) and bipolar disorder (2.3). Females had more psychiatric diagnoses (2.4 vs. 1.6, p < 0.001), psychiatric medications (3.4 vs. 2.3, p < 0.001), and ER utilization than males (6.9 vs. 5.5, p < 0.001). SIGNIFICANCE: A substantial psychiatric burden exists among Veterans with DRE. Compared to prior epilepsy literature, results suggest that Veterans with DRE evidence more prevalent psychiatric comorbidity, emergency care usage, and inpatient psychiatric admissions. Females were especially impacted, with greater rates of psychiatric conditions and treatment. Considering the relationship of psychiatric comorbidities in epilepsy with psychosocial functioning and quality of life, our findings highlight the need for screening and provision of services for those with DRE.

Seizure count forecasting to aid diagnostic testing in epilepsy.

OBJECTIVE: Epilepsy monitoring unit (EMU) admissions are critical for presurgical evaluation of drug-resistant epilepsy but may be nondiagnostic if an insufficient number of seizures are recorded. Seizure forecasting algorithms have shown promise for estimating the likelihood of seizures as a binary event in individual patients, but methods to predict how many seizures will occur remain elusive. Such methods could increase the diagnostic yield of EMU admissions and help patients mitigate seizure-related morbidity. Here, we evaluated the performance of a state-space method that uses prior seizure count data to predict future counts. METHODS: A Bayesian negative-binomial dynamic linear model (DLM) was developed to forecast daily electrographic seizure counts in 19 patients implanted with a responsive neurostimulation (RNS) device. Holdout validation was used to evaluate performance in predicting the number of electrographic seizures for forecast horizons ranging 1-7 days ahead. RESULTS: One-day-ahead prediction of the number of electrographic seizures using a negative-binomial DLM resulted in improvement over chance in 73.1% of time segments compared to a random chance forecaster and remained >50% for forecast horizons of up to 7 days. Superior performance (mean error = .99) was obtained in predicting the number of electrographic seizures in the next day compared to three traditional methods for count forecasting (integer-valued generalized autoregressive conditional heteroskedasticity model or INGARCH, 1.10; Croston, 1.06; generalized linear autoregressive moving average model or GLARMA, 2.00). Number of electrographic seizures in the preceding day and laterality of electrographic pattern detections had highest predictive value, with greater number of electrographic seizures and RNS magnet swipes in the preceding day associated with a higher number of electrographic seizures the next day. SIGNIFICANCE: This study demonstrates that DLMs can predict the number of electrographic seizures a patient will experience days in advance with above chance accuracy. This study represents an important step toward the translation of seizure forecasting methods into the optimization of EMU admissions.

Predictive value of video alone in diagnosis of epileptic vs paroxysmal nonepileptic events in children.

OBJECTIVE: Previous studies examined the use of video-based diagnosis and the predictive value of videos for differentiation of epileptic seizures (ES) from paroxysmal nonepileptic events (PNEE) in the adult population. However, there are no such published studies strictly on the pediatric population. Using video-EEG diagnosis as a gold standard, we aimed to determine the diagnostic predictive value of videos of habitual events with or without additional clinical data in differentiating the PNEE from ES in children. METHODS: Consecutive admissions to our epilepsy monitoring unit between June 2020 and December 2020 were analyzed for events of interest. Four child neurologists blinded to the patient's diagnosis formulated a diagnostic impression based upon the review of the video alone and again after having access to basic clinical information, in addition to the video. Features of the video which helped to make a diagnosis were identified by the reviewers as a part of a survey. RESULTS: A total of 54 patients were included (ES n = 24, PNEE n = 30). Diagnostic accuracy was calculated for each reviewer and combined across all the ratings. Diagnostic accuracy by video alone was 74.5% (sensitivity 80.8%, specificity 66.7%). Providing reviewers with basic clinical information in addition to the videos significantly improved diagnostic accuracy compared to viewing the videos alone. Inter-rater reliability between four reviewers based on the video alone showed moderate agreement (κ = 0.51) and unchanged when additional clinical data were presented (κ = 0.51). The ES group was significantly more likely to demonstrate changes in facial expression, generalized stiffening, repetitive eye blinks, and eye deviation when compared with the PNEE group, which was more likely to display bilateral myoclonic jerking. CONCLUSIONS: Video review of habitual events by Child Neurologists may be helpful in reliably distinguishing ES from PNEE in children, even without included clinical information.

Social determinants do not affect access to specialized epilepsy care in veterans.

INTRODUCTION: It is well established that sociodemographic and neighborhood determinants impact access to healthcare. Veterans with epilepsy (VWE) face unique challenges that may limit access to specialized epilepsy care, though institutional initiatives have aimed to minimize disparities. We assessed the extent to which surrogate markers of access to quality care in VWE were impacted by sociodemographic and neighborhood determinants. METHODS: The sample included 180 VWE. Surrogate markers included time between initial diagnosis and admission to epilepsy monitoring unit (EMU) (time to referral, TTR), and the number of CT, MRI, and EEGs conducted prior to initial EMU evaluation. Sociodemographic and neighborhood determinants included age, sex, race, education, neighborhood advantage, rural status, distance from home to the nearest VAMC, and number of service connection (SC) conditions. Significant correlations across variables of interest were entered into a linear regression. Group differences between social factors were assessed for early and late TTR groups (based on 1st and 4th quartiles). RESULTS: The mean TTR was 12 years (SD ±â€¯13.18). Longer TTR was correlated to older age (p < 0.001) and fewer SC conditions (p = 0.03). None of the other factors were significantly correlated to TTR. Older age significantly predicted longer TTR on regression. The earlier TTR group was younger, had more SC conditions, lived closer to a VAMC, and was more likely to be female. Greater geographic distance was correlated with fewer CT scans (p = 0.01). A greater number of MRIs was correlated with older age (p = 0.04). Younger age (p < 0.01) and greater education (p = 0.01) were correlated with more SC. CONCLUSION: Access to epilepsy care among VWE was largely unimpacted by social determinants, with the exception of older age leading to longer TTR. The TTR in VWE was considerably shorter than has been reported in the literature for civilian patients. The Veterans Health Administration model of care may harbor certain advantages in epilepsy treatment.

Predictive utility of the Minnesota Multiphasic Personality Inventory-2-RF (MMPI-2-RF) in differentiating psychogenic nonepileptic seizures and epileptic seizures in male veterans.

OBJECTIVE: While psychogenic nonepileptic seizures (PNES) and epileptic seizures (ES) often present similarly, they are etiologically distinct, and correct diagnosis is essential for ensuring appropriate treatment and improving outcomes. The Minnesota Multiphasic Personality Inventory-2-RF (MMPI-2-RF) may assist in differential diagnosis, but prior investigations have been limited by disproportionately female samples, inconsistent accounting for profile invalidity, and limited intra-scale variability from dichotomizing variables. The current investigation addressed these gaps by assessing diagnostic utility of the MMPI-2-RF in differentiating PNES and ES in a male sample of veterans while conservatively accounting for profile invalidity and using a statistical approach that allows for consideration of continuous independent variables to better appreciate intra-scale variance. METHOD: One hundred and forty-four veterans completed the MMPI-2-RF and were diagnosed with PNES (57.6%) or ES (42.4%) by a board-certified neurologist following continuous video-EEG monitoring. Participants with validity scores falling in the definitely or likely invalid ranges were excluded to ensure construct validity among clinical/substantive scales. Independent samples t-tests assessed differences in MMPI-2-RF variables by diagnostic groups. Hierarchical stepwise logistical regressions assessed predictive utility of MMPI-2-RF indices. A clinical calculator was derived from regression findings to help with diagnostic prediction. RESULTS: Males with PNES endorsed significantly higher scores on F-r, FBS-r, RBS, RC1, RC7, HPC, and NUC (medium to large effect sizes). The regression block that contained validity, restructured clinical (RC1), and substantive scales (GIC, SUI) had a hit rate of 75.69%, which was an improvement from the baseline model hit rate of 57.64%. Higher endorsement on RC1 and lower reporting on GIC significantly predicted PNES diagnosis for males. CONCLUSIONS: Minnesota Multiphasic Personality Inventory-2-RF improved diagnostic accuracy of PNES versus ES among male veterans, and RC1 (somatic complaints) emerged as a significant predictor for males with PNES, in line with hypotheses. Several clinical/substantive scales assisted with differential diagnosis after careful accounting for profile validity. Future studies can validate findings among males outside of veteran samples.

Home video prediction of epileptic vs. nonepileptic seizures in US veterans.

OBJECTIVE: Using video-EEG (v-EEG) diagnosis as a gold standard, we assessed the predictive diagnostic value of home videos of spells with or without additional limited demographic data in US veterans referred for evaluation of epilepsy. Veterans, in particular, stand to benefit from improved diagnostic tools given higher rates of PNES and limited accessibility to care. METHODS: This was a prospective, blinded diagnostic accuracy study in adults conducted at the Houston VA Medical Center from 12/2015-06/2019. Patients with a definitive diagnosis of epileptic seizures (ES), psychogenic nonepileptic seizures (PNES), or physiologic nonepileptic events (PhysNEE) from v-EEG monitoring were asked to submit home videos. Four board-certified epileptologists blinded to the original diagnosis formulated a diagnostic impression based upon the home video review alone and video plus limited demographic data. RESULTS: Fifty patients (30 males; mean age 47.7 years) submitted home videos. Of these, 14 had ES, 33 had PNES, and three had PhysNEE diagnosed by v-EEG. The diagnostic accuracy by video alone was 88.0%, with a sensitivity of 83.9% and specificity of 89.6%. Providing raters with basic patient demographic information in addition to the home videos did not significantly improve diagnostic accuracy when comparing to reviewing the videos alone. Inter-rater agreement between four raters based on video was moderate with both videos alone (kappa = 0.59) and video plus limited demographic data (kappa = 0.60). SIGNIFICANCE: This study demonstrated that home videos of paroxysmal events could be an important tool in reliably diagnosing ES vs. PNES in veterans referred for evaluation of epilepsy when interpreted by experts. A moderate inter-rater reliability was observed in this study.

Prospective validation study of an epilepsy seizure risk system for outpatient evaluation.

OBJECTIVE: We conducted clinical testing of an automated Bayesian machine learning algorithm (Epilepsy Seizure Assessment Tool [EpiSAT]) for outpatient seizure risk assessment using seizure counting data, and validated performance against specialized epilepsy clinician experts. METHODS: We conducted a prospective longitudinal study of EpiSAT performance against 24 specialized clinician experts at three tertiary referral epilepsy centers in the United States. Accuracy, interrater reliability, and intra-rater reliability of EpiSAT for correctly identifying changes in seizure risk (improvements, worsening, or no change) were evaluated using 120 seizures from four synthetic seizure diaries (seizure risk known) and 120 seizures from four real seizure diaries (seizure risk unknown). The proportion of observed agreement between EpiSAT and clinicians was evaluated to assess compatibility of EpiSAT with clinical decision patterns by epilepsy experts. RESULTS: EpiSAT exhibited substantial observed agreement (75.4%) with clinicians for assessing seizure risk. The mean accuracy of epilepsy providers for correctly assessing seizure risk was 74.7%. EpiSAT accurately identified seizure risk in 87.5% of seizure diary entries, corresponding to a significant improvement of 17.4% (P = .002). Clinicians exhibited low-to-moderate interrater reliability for seizure risk assessment (Krippendorff's α = 0.46) with good intrarater reliability across a 4- to 12-week evaluation period (Scott's π = 0.89). SIGNIFICANCE: These results validate the ability of EpiSAT to yield objective clinical recommendations on seizure risk which follow decision patterns similar to those from specialized epilepsy providers, but with improved accuracy and reproducibility. This algorithm may serve as a useful clinical decision support system for quantitative analysis of clinical seizure frequency in clinical epilepsy practice.

A somatic mutation in MEN1 gene detected in periventricular nodular heterotopia tissue obtained from depth electrodes.

Periventricular nodular heterotopia (PNH) is a common structural malformation of cortical development. Mutations in the filamin A gene are frequent in familial cases with X-linked PNH. However, many cases with sporadic PNH remain genetically unexplained. Although medically refractory epilepsy often brings attention to the underlying PNH, patients are often not candidates for surgical resection. This limits access to neuronal tissue harboring causal mutations. We evaluated a patient with PNH and medically refractory focal epilepsy who underwent a presurgical evaluation with stereotactically placed electroencephalographic (SEEG) depth electrodes. Following SEEG explantation, we collected trace tissue adherent to the electrodes and extracted the DNA. Whole-exome sequencing performed in a Clinical Laboratory Improvement Amendments-approved genetic diagnostic laboratory uncovered a de novo heterozygous pathogenic variant in novel candidate PNH gene MEN1 (multiple endocrine neoplasia type 1; c.1546dupC, p.R516PfsX15). The variant was absent in an earlier exome profiling of the venous blood-derived DNA. The MEN1 gene encodes the ubiquitously expressed, nuclear scaffold protein menin, a known tumor suppressor gene with an established role in the regulation of transcription, proliferation, differentiation, and genomic integrity. Our study contributes a novel candidate gene in PNH generation and a novel practical approach that integrates electrophysiological and genetic explorations of epilepsy.

Bayesian vector autoregressive model for multi-subject effective connectivity inference using multi-modal neuroimaging data.

In this article a multi-subject vector autoregressive (VAR) modeling approach was proposed for inference on effective connectivity based on resting-state functional MRI data. Their framework uses a Bayesian variable selection approach to allow for simultaneous inference on effective connectivity at both the subject- and group-level. Furthermore, it accounts for multi-modal data by integrating structural imaging information into the prior model, encouraging effective connectivity between structurally connected regions. They demonstrated through simulation studies that their approach resulted in improved inference on effective connectivity at both the subject- and group-level, compared with currently used methods. It was concluded by illustrating the method on temporal lobe epilepsy data, where resting-state functional MRI and structural MRI were used. Hum Brain Mapp 38:1311-1332, 2017. © 2016 Wiley Periodicals, Inc.

Use of resting-state fMRI in planning epilepsy surgery.

Epileptic seizures result from abnormal neuronal excitability and synchronization, affecting 0.5-1% of the population worldwide. Although anti-seizure drugs are often effective, a significant number of patients with epilepsy continue to experience refractory seizures and are candidates for surgical resection. Whereas standard presurgical evaluation has relied on intracranial electroencephalography (icEEG) and direct cortical stimulation to identify epileptogenic tissue and areas of cortex for which resection would produce clinical deficits, the invasive nature and limited spatial extent of icEEG has led to the investigation of less invasive imaging modalities as adjunctive tools in the presurgical workup. In the past few decades, functional connectivity MRI has emerged as a promising approach for presurgical mapping, leading to a surge in the number of proposed methods and biomarkers for identifying epileptogenic tissue. This review focuses on recent advances in the use of functional connectivity MRI toward its application for presurgical planning, including epilepsy localization and eloquent cortex mapping.

Time-dependence of graph theory metrics in functional connectivity analysis.

Brain graphs provide a useful way to computationally model the network structure of the connectome, and this has led to increasing interest in the use of graph theory to quantitate and investigate the topological characteristics of the healthy brain and brain disorders on the network level. The majority of graph theory investigations of functional connectivity have relied on the assumption of temporal stationarity. However, recent evidence increasingly suggests that functional connectivity fluctuates over the length of the scan. In this study, we investigate the stationarity of brain network topology using a Bayesian hidden Markov model (HMM) approach that estimates the dynamic structure of graph theoretical measures of whole-brain functional connectivity. In addition to extracting the stationary distribution and transition probabilities of commonly employed graph theory measures, we propose two estimators of temporal stationarity: the S-index and N-index. These indexes can be used to quantify different aspects of the temporal stationarity of graph theory measures. We apply the method and proposed estimators to resting-state functional MRI data from healthy controls and patients with temporal lobe epilepsy. Our analysis shows that several graph theory measures, including small-world index, global integration measures, and betweenness centrality, may exhibit greater stationarity over time and therefore be more robust. Additionally, we demonstrate that accounting for subject-level differences in the level of temporal stationarity of network topology may increase discriminatory power in discriminating between disease states. Our results confirm and extend findings from other studies regarding the dynamic nature of functional connectivity, and suggest that using statistical models which explicitly account for the dynamic nature of functional connectivity in graph theory analyses may improve the sensitivity of investigations and consistency across investigations.