State-dependent effects of responsive neurostimulation depend on seizure localization.

Brain-responsive neurostimulation is firmly ensconced among treatment options for drug-resistant focal epilepsy, but over a quarter of patients treated with the RNS System do not experience meaningful seizure reduction. Initial titration of RNS therapy is typically similar for all patients, raising the possibility that treatment response might be enhanced by consideration of patient-specific variables. Indeed, small, single-center studies have yielded preliminary evidence that RNS System effectiveness depends on the brain state during which stimulation is applied. The generalizability of these findings remains unclear, however, and it is unknown whether state-dependent effects of responsive neurostimulation are also stratified by location of the seizure onset zone where stimulation is delivered. We aimed to determine whether state-dependent effects of the RNS System are evident in the large, diverse, multi-center cohort of RNS System clinical trial participants and to test whether these effects differ between mesiotemporal and neocortical epilepsies. Eighty-one of 256 patients who were treated with the RNS System across 31 centers during clinical trials met criteria for inclusion in this retrospective study. Risk states were defined in relation to phases of daily and multi-day cycles of interictal epileptiform activity that are thought to determine seizure likelihood. We found that the probabilities of risk state transitions depended on the stimulation parameter being changed, the starting seizure risk state, and the stimulated brain region. Changes in two commonly adjusted stimulation parameters, charge density and stimulation frequency, produced opposite effects on risk state transitions depending on seizure localization. Greater variance in acute risk state transitions was explained by state-dependent responsive neurostimulation for bipolar stimulation for neocortical epilepsies and for monopolar stimulation for mesiotemporal epilepsies. Variability in effectiveness of RNS System therapy across individuals may relate, at least partly, to the fact that current treatment paradigms do not account fully for fluctuations in brain states or locations of simulation sites. State-dependence of electrical brain stimulation may inform development of next-generation closed-loop devices that can detect changes in brain state and deliver adaptive, localization-specific patterns of stimulation to maximize therapeutic effects.

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.

Association of reductions in rescue medication requirements with vagus nerve stimulation: Results of long-term community collected data from a seizure diary app.

OBJECTIVE: To assess the impact of vagus nerve stimulation (VNS) on quality of life contributors such as rescue medications. METHODS: Using the seizure diary application SeizureTracker™ database, we examined trends in rescue administration frequency before and after the first recorded VNS magnet swipe in patients with drug-resistant epilepsy who had 1) At least one VNS magnet swipe recorded in the diary, and 2) Recorded usage of a benzodiazepine rescue medication (RM) within 90 days prior to the first swipe. A paired Wilcoxon rank-sum test was used to assess changes in RM usage frequency between 30-, 60-, 90-, 180- and 360-day intervals beginning 30 days after first magnet swipe. Longitudinal changes in RM usage frequency were assessed with a generalized estimating equation model. RESULTS: We analyzed data of 95 patients who met the inclusion criteria. Median baseline seizure frequency was 8.3 seizures per month, with median baseline rescue medication usage frequency of 2.1 administrations per month (SD 3.3). Significant reductions in rescue medication usage were observed in the 91 to 180 day interval after first VNS magnet swipe, and at 181 to 360 days and at 361 to 720 days, with the magnitude of reduction increasing over time. Decreases in rescue medication usage were sustained when controlling for patients who did not record rescue medication use after the first VNS magnet swipe (N=91). Significant predictors of reductions in rescue medication included baseline frequency of rescue medication usage and time after first VNS magnet swipe. SIGNIFICANCE: This retrospective analysis suggests that usage of rescue medications is reduced following the start of VNS treatment in patients with epilepsy, and that the magnitude of reduction may progressively increase over time.

Development of a core outcome set for quality of life for adults with drug-resistant epilepsy: A multistakeholder Delphi consensus study.

OBJECTIVE: In 2017, the American Academy of Neurology (AAN) convened the AAN Quality Measurement Set working group to define the improvement and maintenance of quality of life (QOL) as a key outcome measure in epilepsy clinical practice. A core outcome set (COS), defined as an accepted, standardized set of outcomes that should be minimally measured and reported in an area of health care research and practice, has not previously been defined for QOL in adult epilepsy. METHODS: A cross-sectional Delphi consensus study was employed to attain consensus from patients and caregivers on the QOL outcomes that should be minimally measured and reported in epilepsy clinical practice. Candidate items were compiled from QOL scales recommended by the AAN 2017 Quality Measurement Set. Inclusion criteria to participate in the Delphi study were adults with drug-resistant epilepsy diagnosed by a physician, no prior diagnosis of psychogenic nonepileptic seizures or a cognitive and/or developmental disability, or caregivers of patients meeting these criteria. RESULTS: A total of 109 people satisfied inclusion/exclusion criteria and took part in Delphi Round 1 (patients, n = 95, 87.2%; caregivers, n = 14, 12.8%), and 55 people from Round 1 completed Round 2 (patients, n = 43, 78.2%; caregivers, n = 12, 21.8%). One hundred three people took part in the final consensus round. Consensus was attained by patients/caregivers on a set of 36 outcomes that should minimally be included in the QOL COS. Of these, 32 of the 36 outcomes (88.8%) pertained to areas outside of seizure frequency and severity. SIGNIFICANCE: Using patient-centered Delphi methodology, this study defines the first COS for QOL measurement in clinical practice for adults with drug-resistant epilepsy. This set highlights the diversity of factors beyond seizure frequency and severity that impact QOL in epilepsy.

Spinal epidural arteriovenous fistula with nerve root enhancement mimicking myeloradiculitis: a case report.

BACKGROUND: Gadolinium enhancement of spinal nerve roots on magnetic resonance imaging (MRI) has rarely been reported in spinal dural arteriovenous fistula (SDAVF). Nerve root enhancement and cerebrospinal fluid (CSF) pleocytosis can be deceptive and lead to a misdiagnosis of myeloradiculitis. We report a patient who was initially diagnosed with neurosarcoid myeloradiculitis due to spinal nerve root enhancement, mildly inflammatory cerebrospinal fluid, and pulmonary granulomas, who ultimately was found to have an extensive symptomatic SDAVF. CASE PRESENTATION: A 52-year-old woman presented with a longitudinally extensive spinal cord lesion with associated gadolinium enhancement of the cord and cauda equina nerve roots, and mild lymphocytic pleocytosis. Pulmonary lymph node biopsy revealed non-caseating granulomas and neurosarcoid myeloradiculitis was suspected. She had rapid and profound clinical deterioration after a single dose of steroids. Further work-up with spinal angiography revealed a thoracic SDAVF, which was surgically ligated leading to clinical improvement. CONCLUSIONS: This case highlights an unexpected presentation of SDAVF with nerve root enhancement and concurrent pulmonary non-caseating granulomas, leading to an initial misdiagnosis with neurosarcoidosis. Nerve root enhancement has only rarely been described in cases of SDAVF; however, as this case highlights, it is an important consideration in the differential diagnosis of non-inflammatory causes of longitudinally extensive myeloradiculopathy with nerve root enhancement. This point is highly salient due to the importance of avoiding misdiagnosis of SDAVF, as interventions such as steroids or epidural injections used to treat inflammatory or infiltrative mimics may worsen symptoms in SDAVF. We review the presentation, diagnosis, and management of SDAVF as well as a proposed diagnostic approach to differentiating SDAVF from inflammatory myeloradiculitis.

Bilateral temporal lobe epilepsy: How many seizures are required in chronic ambulatory electrocorticography to estimate the laterality ratio?

OBJECTIVE: This study was undertaken to measure the duration of chronic electrocorticography (ECoG) needed to attain stable estimates of the seizure laterality ratio in patients with drug-resistant bilateral temporal lobe epilepsy (BTLE). METHODS: We studied 13 patients with drug-resistant BTLE who were implanted for at least 1 year with a responsive neurostimulation device (RNS System) that provides chronic ambulatory ECoG. Bootstrap analysis and nonlinear regression were applied to model the relationship between chronic ECoG duration and the probability of capturing at least one seizure. Laterality of electrographic seizures in chronic ECoG was compared with the seizure laterality ratio from Phase 1 scalp video-electroencephalographic (vEEG) monitoring. The Kaplan-Meier estimator was used to evaluate time to seizure laterality ratio convergence. RESULTS: Seizure laterality ratios from Phase 1 scalp vEEG monitoring correlated poorly with those from RNS chronic ECoG (r = .31, p = .30). Across the 13 patients, average electrographic seizure frequencies ranged from 1.4 seizures/month to 5.1 seizures/day. A 50% probability of recording at least one electrographic seizure required 9.1 days of chronic ECoG, and 90% probability required 44.3 days of chronic ECoG. A median recording duration of 150.9 days (5 months), corresponding to a median of 16 seizures, was needed before confidence intervals for the seizure laterality ratio reliably contained the long-term value. The median recording duration before the point estimate of the seizure laterality ratio converged to a stationary value was 236.8 days (7.9 months). SIGNIFICANCE: RNS chronic ECoG overcomes temporal sampling limitations intrinsic to inpatient Phase 1 vEEG evaluations. In patients with drug-resistant BTLE, approximately 8 months of chronic RNS ECoG are needed to precisely estimate the seizure laterality ratio, with 75% of people with BTLE achieving convergence after 1 year of RNS recording. For individuals who are candidates for unilateral resection based on seizure laterality, optimized recording duration may help avert morbidity associated with delay to definitive treatment.

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.

An optimized QF-binary expression system for use in zebrafish.

The zebrafish model organism has been of exceptional utility for the study of vertebrate development and disease through the application of tissue-specific labelling and overexpression of genes carrying patient-derived mutations. However, there remains a need for a binary expression system that is both non-toxic and not silenced over animal generations by DNA methylation. The Q binary expression system derived from the fungus Neurospora crassa is ideal, because the consensus binding site for the QF transcription factor lacks CpG dinucleotides, precluding silencing by CpG-meditated methylation. To optimize this system for zebrafish, we systematically tested several variants of the QF transcription factor: QF full length; QF2, which lacks the middle domain; QF2w, which is an attenuated version of QF2; and chimeric QFGal4. We found that full length QF and QF2 were strongly toxic to zebrafish embryos, QF2w was mildly toxic, and QFGal4 was well tolerated, when injected as RNA or expressed ubiquitously from stable transgenes. In addition, QFGal4 robustly activated a Tg(QUAS:GFPNLS) reporter transgene. To increase the utility of this system, we also modified the QF effector sequence termed QUAS, which consists of five copies of the QF binding site. Specifically, we decreased both the CpG dinucleotide content, as well as the repetitiveness of QUAS, to reduce the risk of transgene silencing via CpG methylation. Moreover, these modifications to QUAS removed leaky QF-independent neural expression that we detected in the original QUAS sequence. To demonstrate the utility of our QF optimizations, we show how the Q-system can be used for lineage tracing using a Cre-dependent Tg(ubi:QFGal4-switch) transgene. We also demonstrate that QFGal4 can be used in transient injections to tag and label endogenous genes by knocking in QFGal4 into sox2 and ubiquitin C genes.

Impact of intellectual and developmental disability on quality-of-life priorities in adults with epilepsy.

OBJECTIVE: Adults living with intellectual and developmental disability (IDD) and epilepsy (IDD-E) face challenges in addition to those faced by the general population of adults with epilepsy, which may be associated with distinct priorities for improving health-related quality of life (HR-QOL). This study sought to (1) conduct a survey of HR-QOL priorities identified by adults with IDD-E and caregivers, and (2) perform an exploratory cross-sectional comparison to adults with epilepsy who do not have IDD. METHODS: This cross-sectional study recruited 65 adults with IDD-E and 134 adults with epilepsy without IDD and caregivers. Using a three-step development process, 256 items from existing quality-of-life scales recommended by the American Academy of Neurology (AAN) were rated by patients/caregivers for their importance as HR-QOL priorities. HR-QOL items identified as critical to the majority of the sample of adults with IDD-E were reported. Health-related quality of life priorities were compared between adults with IDD-E and adults with epilepsy without IDD. RESULTS: Health-related quality of life was significantly lower in adults with IDD-E. Health-related quality of life domains identified as critical priorities by adults with IDD-E included seizure burden, anti-seizure medication side effects, seizure unpredictability, and family impact. Priorities for improving HR-QOL differed between adults with and without IDD-E, with concerns about family impact, difficulty finding appropriate living conditions, inadequate assistance, and difficulty transitioning from pediatric-to-adult care valued significantly more among those with IDD-E. SIGNIFICANCE: Intellectual and developmental disability is an important determinant of HR-QOL among adults with epilepsy. We report HR-QOL priorities identified by adults with IDD-E and their caregivers. These results may help epilepsy clinicians and researchers develop tailored strategies to address priorities of the patient with IDD-E/caregiver community.

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.

Seizure detection devices and health-related quality of life: A patient- and caregiver-centered evaluation.

INTRODUCTION: The unpredictability of epilepsy has a severe impact on health-related quality of life (HR-QOL) for people with epilepsy. Seizure detection devices have the potential to improve HR-QOL by improving seizure safety, reducing caregiver hypervigilance, and reducing seizure anxiety. Emerging data have led to an improved understanding of characteristics that promote acceptability of detection devices for people with epilepsy and caregivers. However, whether usage of seizure detection devices is associated with clinically meaningful improvement in anxiety and HR-QOL remains poorly understood. METHODS: We analyzed cross-sectional survey data collected first-hand from 371 people with epilepsy and caregivers on seizure detection device and HR-QOL using an enriched population of electronic seizure diary users. Metrics related to quality of life and anxiety reduction were compared between users and nonusers of seizure detection devices. RESULTS: Compared with nonusers of seizure detection devices, device users were significantly more likely to have been impacted by epilepsy in multiple HR-QOL domains, including anxiety, mood, emotional regulation/aggression, speech/language, sleep quality, social life, activities of daily living, independence, and education/academic potential. The majority (80.2%) of people using seizure detection devices experienced moderate or greater anxiety reduction from seizure detection device usage, while 11.1% reported that detection devices did not help at all with anxiety. Despite potential benefit, seizure detection devices were used only by a minority (21.8%) of people with epilepsy surveyed, and usage tended to be skewed toward younger patient age, higher income, and caregivers. There was no significant difference in overall HR-QOL between users and nonusers. CONCLUSIONS: Seizure detection devices provide moderate or greater anxiety reduction among the majority of people with epilepsy and their caregivers, but current translatability into improvements in overall HR-QOL may be limited. Affordability and technological support are potential barriers to maximizing benefit equally among the epilepsy community. These considerations may be useful to help guide future device development and inform patient-clinician discussions on device usage and HR-QOL.

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.

A big data approach to the development of mixed-effects models for seizure count data.

OBJECTIVE: Our objective was to develop a generalized linear mixed model for predicting seizure count that is useful in the design and analysis of clinical trials. This model also may benefit the design and interpretation of seizure-recording paradigms. Most existing seizure count models do not include children, and there is currently no consensus regarding the most suitable model that can be applied to children and adults. Therefore, an additional objective was to develop a model that accounts for both adult and pediatric epilepsy. METHODS: Using data from SeizureTracker.com, a patient-reported seizure diary tool with >1.2 million recorded seizures across 8 years, we evaluated the appropriateness of Poisson, negative binomial, zero-inflated negative binomial, and modified negative binomial models for seizure count data based on minimization of the Bayesian information criterion. Generalized linear mixed-effects models were used to account for demographic and etiologic covariates and for autocorrelation structure. Holdout cross-validation was used to evaluate predictive accuracy in simulating seizure frequencies. RESULTS: For both adults and children, we found that a negative binomial model with autocorrelation over 1 day was optimal. Using holdout cross-validation, the proposed model was found to provide accurate simulation of seizure counts for patients with up to four seizures per day. SIGNIFICANCE: The optimal model can be used to generate more realistic simulated patient data with very few input parameters. The availability of a parsimonious, realistic virtual patient model can be of great utility in simulations of phase II/III clinical trials, epilepsy monitoring units, outpatient biosensors, and mobile Health (mHealth) applications.

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.

Risk Factors for Dehiscence of Stapled Functional End-to-End Intestinal Anastomoses in Dogs: 53 Cases (2001-2012).

OBJECTIVE: To identify risk factors for dehiscence in stapled functional end-to-end anastomoses (SFEEA) in dogs. STUDY DESIGN: Retrospective case series. ANIMALS: Dogs (n = 53) requiring an enterectomy. METHODS: Medical records from a single institution for all dogs undergoing an enterectomy (2001-2012) were reviewed. Surgeries were included when gastrointestinal (GIA) and thoracoabdominal (TA) stapling equipment was used to create a functional end-to-end anastomosis between segments of small intestine or small and large intestine in dogs. Information regarding preoperative, surgical, and postoperative factors was recorded. RESULTS: Anastomotic dehiscence was noted in 6 of 53 cases (11%), with a mortality rate of 83%. The only preoperative factor significantly associated with dehiscence was the presence of inflammatory bowel disease (IBD). Surgical factors significantly associated with dehiscence included the presence, duration, and number of intraoperative hypotensive periods, and location of anastomosis, with greater odds of dehiscence in anastomoses involving the large intestine. CONCLUSION: IBD, location of anastomosis, and intraoperative hypotension are risk factors for intestinal anastomotic dehiscence after SFEEA in dogs. Previously suggested risk factors (low serum albumin concentration, preoperative septic peritonitis, and intestinal foreign body) were not confirmed in this study.

Taq DNA Polymerase Mutants and 2'-Modified Sugar Recognition.

Chemical modifications to DNA, such as 2' modifications, are expected to increase the biotechnological utility of DNA; however, these modified forms of DNA are limited by their inability to be effectively synthesized by DNA polymerase enzymes. Previous efforts have identified mutant Thermus aquaticus DNA polymerase I (Taq) enzymes capable of recognizing 2'-modified DNA nucleotides. While these mutant enzymes recognize these modified nucleotides, they are not capable of synthesizing full length modified DNA; thus, further engineering is required for these enzymes. Here, we describe comparative biochemical studies that identify useful, but previously uncharacterized, properties of these enzymes; one enzyme, SFM19, is able to recognize a range of 2'-modified nucleotides much wider than that previously examined, including fluoro, azido, and amino modifications. To understand the molecular origins of these differences, we also identify specific amino acids and combinations of amino acids that contribute most to the previously evolved unnatural activity. Our data suggest that a negatively charged amino acid at 614 and mutation of the steric gate residue, E615, to glycine make up the optimal combination for modified oligonucleotide synthesis. These studies yield an improved understanding of the mutational origins of 2'-modified substrate recognition as well as identify SFM19 as the best candidate for further engineering, whether via rational design or directed evolution.

Computer-automated focus lateralization of temporal lobe epilepsy using fMRI.

PURPOSE: To compare the performance of computer-automated diagnosis using functional magnetic resonance imaging (fMRI) interictal graph theory (CADFIG) to that achieved in standard clinical practice with MRI, for lateralizing the affected hemisphere in temporal lobe epilepsy (TLE). MATERIALS AND METHODS: Interictal resting state fMRI and high-resolution MRI were performed on 14 left and 10 right TLE patients. Functional topology measures were calculated from fMRI using graph theory, and used to lateralize the epileptogenic hemisphere using quadratic discriminant analysis. Leave-one-out cross-validation prediction accuracy of CADFIG was compared to performance based on expert manual analysis (MA) of MRI, using video EEG as the "gold standard" for focus lateralization. RESULTS: CADFIG correctly lateralized 95.8% (23/24) of cases, compared to 66.7% (16/24) with expert MA of MRI. Combining MA with CADFIG allowed all cases (24/24) to be correctly lateralized. CADFIG correctly identified the affected hemisphere for all patients (8/8) where MRI failed to lateralize. CONCLUSION: CADFIG based on fMRI lateralized the affected hemisphere in TLE with superior performance compared to expert MA of MRI. These results demonstrate that functional patterns in fMRI can be used with automated machine learning for diagnostic lateralization in TLE. Addition of fMRI-based tests to existing protocols for identifying the affected hemisphere in presurgical assessment can improve diagnostic accuracy and surgical outcome in TLE.

Review-of-systems questionnaire as a predictive tool for psychogenic nonepileptic seizures.

Patients with refractory epilepsy undergo video-electroencephalography for seizure characterization, among whom approximately 10-30% will be discharged with the diagnosis of psychogenic nonepileptic seizures (PNESs). Clinical PNES predictors have been described but in general are not sensitive or specific. We evaluated whether multiple complaints in a routine review-of-system (ROS) questionnaire could serve as a sensitive and specific marker of PNESs. We performed a retrospective analysis of a standardized ROS questionnaire completed by patients with definite PNESs and epileptic seizures (ESs) diagnosed in our adult epilepsy monitoring unit. A multivariate analysis of covariance (MANCOVA) was used to determine whether groups with PNES and ES differed with respect to the percentage of complaints in the ROS questionnaire. Tenfold cross-validation was used to evaluate the predictive error of a logistic regression classifier for PNES status based on the percentage of positive complaints in the ROS questionnaire. A total of 44 patients were included for analysis. Patients with PNESs had a significantly higher number of complaints in the ROS questionnaire compared to patients with epilepsy. A threshold of 17% positive complaints achieved a 78% specificity and 85% sensitivity for discriminating between PNESs and ESs. We conclude that the routine ROS questionnaire may be a sensitive and specific predictive tool for discriminating between PNESs and ESs.

Functional connectivity homogeneity correlates with duration of temporal lobe epilepsy.

Temporal lobe epilepsy (TLE) is often associated with progressive changes to seizures, memory, and mood during its clinical course. However, the cerebral changes related to this progression are not well understood. Because the changes may be related to changes in brain networks, we used functional connectivity MRI (fcMRI) to determine whether brain network parameters relate to the duration of TLE. Graph theory-based analysis of the sites of reported regions of TLE abnormality was performed on resting-state fMRI data in 48 subjects: 24 controls, 13 patients with left TLE, and 11 patients with right TLE. Various network parameters were analyzed including betweenness centrality (BC), clustering coefficient (CC), path length (PL), small-world index (SWI), global efficiency (GE), connectivity strength (CS), and connectivity diversity (CD). These were compared for patients with TLE as a group, compared to controls, and for patients with left and right TLE separately. The association of changes in network parameters with epilepsy duration was also evaluated. We found that CC, CS, and CD decreased in subjects with TLE compared to control subjects. Analyzed according to epilepsy duration, patients with TLE showed a progressive reduction in CD. In conclusion, we found that several network parameters decreased in patients with TLE compared to controls, which suggested reduced connectivity in TLE. Reduction in CD associated with epilepsy duration suggests a homogenization of connections over time in TLE, indicating a reduction of the normal repertoire of stronger and weaker connections to other brain regions.