Proper function of the female pelvic floor requires intact pelvic floor muscles (PFMs). The prevalence of pelvic floor disorders (PFDs) increases substantially with age, in part due to clinically identified deterioration of PFM function with age. However, the etiology of this decline remains largely unknown. We previously demonstrated that PFMs undergo age-related fibrotic changes. This study sought to determine whether aging also impacts PFMs' passive mechanical properties that are largely determined by the intramuscular extracellular matrix. Biopsies from younger (≤52y) and older (>52y) female cadaveric donors were procured from PFMs, specifically coccygeus (C) and two portions of the levator ani - iliococcygeus (IC) and pubovisceralis (PV), and the appendicular muscles - obturator internus (OI) and vastus lateralis (VL). Muscle bundles were subjected to a passive loading protocol, and stress-sarcomere length (L_s) relationships calculated. Muscle stiffness was compared between groups using 2-way ANOVA and Sidak pairwise comparisons, α < 0.05. The mean age was 43.4 ± 11.6y and 74.9 ± 11.9y in younger (N = 5) and older (N = 10) donors, respectively. In all PFMs, the quadratic coefficient of parabolic regression of the stress-L_s curve, a measure of stiffness, was lower in the younger versus older group: C: 33.7 ± 13.9 vs 87.2 ± 10.7, P = 0.02; IC: 38.3 ± 12.7 vs 84.5 ± 13.9, P = 0.04; PV: 24.7 ± 8.8 vs 74.6 ± 9.6, P = 0.04. In contrast, non-PFM stiffness was not affected by aging: OI: 14.5 ± 4.7 vs 32.9 ± 6.2, P = 0.8 and VL: 13.6 ± 5.7 vs 30.1 ± 5.3, P = 0.9. Age-associated increase in PFM stiffness is predicted to negatively impact PFM function by diminishing muscle load-bearing, excursional, contractile, and regenerative capacity, thus predisposing older women to PFDs.

Age-related pelvic floor muscle (PFM) dysfunction is a critical defect in the progression to pelvic floor disorders (PFDs). Despite dramatic prevalence of PFDs in older women, the underlying pathophysiology of age-related PFM dysfunction remains poorly understood. Using cadaveric specimens, we quantified aging effects on functionally relevant PFM properties and compared PFMs with the appendicular muscles from the same donors. PFMs, obturator internus, and vastus lateralis were procured from younger (N = 4) and older (N = 11) donors with known obstetrical and medical history. Our findings demonstrate that PFMs undergo degenerative, rather than atrophic, alterations. Importantly, age-related fibrotic degeneration disproportionally impacts PFMs compared to the appendicular muscles. We identified intramuscular lipid accumulation as another contributing factor to the pathological alterations of PFMs with aging. We observed a fourfold decrease in muscle stem cell (MuSC) pool of aged relative to younger PFMs, but the MuSC pool of appendicular muscles from the same older donors was only twofold lower than in younger group, although these differences were not statistically significant. Age-related degeneration appears to disproportionally impact PFMs relative to the appendicular muscles from the same donors. Knowledge of tissue- and cell-level changes in aged PFMs is essential to promote our understanding of the mechanisms governing PFM dysfunction in older women.

There exist significant clinical and basic research needs for accurate, automated seizure detection algorithms. These algorithms have translational potential in responsive neurostimulation devices and in automatic parsing of continuous intracranial electroencephalography data. An important barrier to developing accurate, validated algorithms for seizure detection is limited access to high-quality, expertly annotated seizure data from prolonged recordings. To overcome this, we hosted a kaggle.com competition to crowdsource the development of seizure detection algorithms using intracranial electroencephalography from canines and humans with epilepsy. The top three performing algorithms from the contest were then validated on out-of-sample patient data including standard clinical data and continuous ambulatory human data obtained over several years using the implantable NeuroVista seizure advisory system. Two hundred teams of data scientists from all over the world participated in the kaggle.com competition. The top performing teams submitted highly accurate algorithms with consistent performance in the out-of-sample validation study. The performance of these seizure detection algorithms, achieved using freely available code and data, sets a new reproducible benchmark for personalized seizure detection. We have also shared a plug and play pipeline to allow other researchers to easily use these algorithms on their own datasets. The success of this competition demonstrates how sharing code and high quality data results in the creation of powerful translational tools with significant potential to impact patient care.

Background

There is currently no formal method for predicting the range expected in an individual's seizure counts. Having access to such a prediction would be of benefit for developing more efficient clinical trials, but also for improving clinical care in the outpatient setting.

Methods

Using three independently collected patient diary datasets, we explored the predictability of seizure frequency. Three independent seizure diary databases were explored: SeizureTracker (n = 3016), Human Epilepsy Project (n = 93), and NeuroVista (n = 15). First, the relationship between mean and standard deviation in seizure frequency was assessed. Using that relationship, a prediction for the range of possible seizure frequencies was compared with a traditional prediction scheme commonly used in clinical trials. A validation dataset was obtained from a separate data export of SeizureTracker to further verify the predictions.

Results

A consistent mathematical relationship was observed across datasets. The logarithm of the average seizure count was linearly related to the logarithm of the standard deviation with a high correlation (R² > 0.83). The three datasets showed high predictive accuracy for this log-log relationship of 94%, compared with a predictive accuracy of 77% for a traditional prediction scheme. The independent validation set showed that the log-log predicted 94% of the correct ranges while the RR50 predicted 77%.

Conclusion

Reliably predicting seizure frequency variability is straightforward based on knowledge of mean seizure frequency, across several datasets. With further study, this may help to increase the power of RCTs, and guide clinical practice.

Objective

Seizure frequency variability is associated with placebo responses in randomized controlled trials (RCT). Increased variability can result in drug misclassification and, hence, decreased statistical power. We investigated a new method that directly incorporated variability into RCT analysis, Z_V.

Methods

Two models were assessed: the traditional 50%-responder rate (RR50), and the variability-corrected score, Z_V. Each predicted seizure frequency upper and lower limits using prior seizures. Accuracy was defined as percentage of time-intervals when the observed seizure frequencies were within the predicted limits. First, we tested the Z_V method on three datasets (SeizureTracker: n=3016, Human Epilepsy Project: n=107, and NeuroVista: n=15). An additional independent SeizureTracker validation dataset was used to generate a set of 200 simulated trials each for 5 different sample sizes (total N=100 to 500 by 100), assuming 20% dropout and 30% drug efficacy. "Power" was determined as the percentage of trials successfully distinguishing placebo from drug (p<0.05).

Results

Prediction accuracy across datasets was, Z_V: 91-100%, RR50: 42-80%. Simulated RCT Z_V analysis achieved >90% power at N=100 per arm while RR50 required N=200 per arm.

Significance

Z_V may increase the statistical power of an RCT relative to the traditional RR50.

Early implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioural inputs from patients. Recent epilepsy stimulation devices provide brain sensing but have not yet developed analytics for accurately tracking and quantifying behaviour and seizures. Here we describe a distributed brain co-processor providing an intuitive bi-directional interface between patient, implanted neural stimulation and sensing device, and local and distributed computing resources. Automated analysis of continuous streaming electrophysiology is synchronized with patient reports using a handheld device and integrated with distributed cloud computing resources for quantifying seizures, interictal epileptiform spikes and patient symptoms during therapeutic electrical brain stimulation. The classification algorithms for interictal epileptiform spikes and seizures were developed and parameterized using long-term ambulatory data from nine humans and eight canines with epilepsy, and then implemented prospectively in out-of-sample testing in two pet canines and four humans with drug-resistant epilepsy living in their natural environments. Accurate seizure diaries are needed as the primary clinical outcome measure of epilepsy therapy and to guide brain-stimulation optimization. The brain co-processor system described here enables tracking interictal epileptiform spikes, seizures and correlation with patient behavioural reports. In the future, correlation of spikes and seizures with behaviour will allow more detailed investigation of the clinical impact of spikes and seizures on patients.

Pelvic floor disorders, including pelvic organ prolapse and urinary and fecal incontinence, affect millions of women globally and represent a major public health concern. Pelvic floor muscle (PFM) dysfunction has been identified as one of the leading risk factors for the development of these morbid conditions. Childbirth, specifically vaginal delivery, has been recognized as the most important potentially modifiable risk factor for PFM injury; however, the precise mechanisms of PFM dysfunction after parturition remain elusive. In this study, we demonstrated that PFMs exhibit atrophy and fibrosis in parous women with symptomatic pelvic organ prolapse. These pathological alterations were recapitulated in a preclinical rat model of simulated birth injury (SBI). The transcriptional signature of PFMs after injury demonstrated an impairment in muscle anabolism, persistent expression of genes that promote extracellular matrix (ECM) deposition, and a sustained inflammatory response. We also evaluated the administration of acellular injectable skeletal muscle ECM hydrogel for the prevention of these pathological alterations. Treatment of PFMs with the ECM hydrogel either at the time of birth injury or 4 weeks after injury mitigated PFM atrophy and fibrosis. By evaluating gene expression, we demonstrated that these changes are mainly driven by the hydrogel-induced enhancement of endogenous myogenesis, ECM remodeling, and modulation of the immune response. This work furthers our understanding of PFM birth injury and demonstrates proof of concept for future investigations of proregenerative biomaterial approaches for the treatment of injured pelvic soft tissues.

Objective

Common data elements (CDEs) are currently unavailable for mobile health (mHealth) in epilepsy devices and related applications. As a result, despite expansive growth of new digital services for people with epilepsy, information collected is often not interoperable or directly comparable. We aim to correct this problem through development of industry-wide standards for mHealth epilepsy data.

Methods

Using a group of stakeholders from industry, academia, and patient advocacy organizations, we offer a consensus statement for the elements that may facilitate communication among different systems.

Results

A consensus statement is presented for epilepsy mHealth CDEs.

Significance

Although it is not exclusive, we believe that the use of a minimal common information denominator, specifically these CDEs, will promote innovation, accelerate scientific discovery, and enhance clinical usage across applications and devices in the epilepsy mHealth space. As a consequence, people with epilepsy will have greater flexibility and ultimately more powerful tools to improve their lives.

BACKGROUND AND OBJECTIVES: Mood, anxiety disorders, and suicidality are more frequent in people with epilepsy than in the general population. Yet, their prevalence and the types of mood and anxiety disorders associated with suicidality at the time of the epilepsy diagnosis are not established. We sought to answer these questions in patients with newly diagnosed focal epilepsy and to assess their association with suicidal ideation and attempts. METHODS: The data were derived from the Human Epilepsy Project study. A total of 347 consecutive adults aged 18-60 years with newly diagnosed focal epilepsy were enrolled within 4 months of starting treatment. The types of mood and anxiety disorders were identified with the Mini International Neuropsychiatric Interview, whereas suicidal ideation (lifetime, current, active, and passive) and suicidal attempts (lifetime and current) were established with the Columbia Suicidality Severity Rating Scale (CSSRS). Statistical analyses included the t test, χ2 statistics, and logistic regression analyses. RESULTS: A total of 151 (43.5%) patients had a psychiatric diagnosis; 134 (38.6%) met the criteria for a mood and/or anxiety disorder, and 75 (21.6%) reported suicidal ideation with or without attempts. Mood (23.6%) and anxiety (27.4%) disorders had comparable prevalence rates, whereas both disorders occurred together in 43 patients (12.4%). Major depressive disorders (MDDs) had a slightly higher prevalence than bipolar disorders (BPDs) (9.5% vs 6.9%, respectively). Explanatory variables of suicidality included MDD, BPD, panic disorders, and agoraphobia, with BPD and panic disorders being the strongest variables, particularly for active suicidal ideation and suicidal attempts. DISCUSSION: In patients with newly diagnosed focal epilepsy, the prevalence of mood, anxiety disorders, and suicidality is higher than in the general population and comparable to those of patients with established epilepsy. Their recognition at the time of the initial epilepsy evaluation is of the essence.

With more than 6000 attendees between in-person and virtual offerings, the American Epilepsy Society Meeting 2022 in Nashville, felt as busy as in prepandemic times. An ever-growing number of physicians, scientists, and allied health professionals gathered to learn a variety of topics about epilepsy. The program was carefully tailored to meet the needs of professionals with different interests and career stages. This article summarizes the different symposia presented at the meeting. Basic science lectures addressed the primary elements of seizure generation and pathophysiology of epilepsy in different disease states. Scientists congregated to learn about anti-seizure medications, mechanisms of action, and new tools to treat epilepsy including surgery and neurostimulation. Some symposia were also dedicated to discuss epilepsy comorbidities and practical issues regarding epilepsy care. An increasing number of patient advocates discussing their stories were intertwined within scientific activities. Many smaller group sessions targeted more specific topics to encourage member participation, including Special Interest Groups, Investigator, and Skills Workshops. Special lectures included the renown Hoyer and Lombroso, an ILAE/IBE joint session, a spotlight on the impact of Dobbs v. Jackson on reproductive health in epilepsy, and a joint session with the NAEC on coding and reimbursement policies. The hot topics symposium was focused on traumatic brain injury and post-traumatic epilepsy. A balanced collaboration with the industry allowed presentations of the latest pharmaceutical and engineering advances in satellite symposia.