NeuroBooster Array: A Genome-Wide Genotyping Platform to Study Neurological Disorders Across Diverse Populations.

Genome-wide genotyping platforms have the capacity to capture genetic variation across different populations, but there have been disparities in the representation of population-dependent genetic diversity. The motivation for pursuing this endeavor was to create a comprehensive genome-wide array capable of encompassing a wide range of neuro-specific content for the Global Parkinson's Genetics Program (GP2) and the Center for Alzheimer's and Related Dementias (CARD). CARD aims to increase diversity in genetic studies, using this array as a tool to foster inclusivity. GP2 is the first supported resource project of the Aligning Science Across Parkinson's (ASAP) initiative that aims to support a collaborative global effort aimed at significantly accelerating the discovery of genetic factors contributing to Parkinson's disease and atypical parkinsonism by generating genome-wide data for over 200,000 individuals in a multi-ancestry context. Here, we present the Illumina NeuroBooster array (NBA), a novel, high-throughput and cost-effective custom-designed content platform to screen for genetic variation in neurological disorders across diverse populations. The NBA contains a backbone of 1,914,934 variants (Infinium Global Diversity Array) complemented with custom content of 95,273 variants implicated in over 70 neurological conditions or traits with potential neurological complications. Furthermore, the platform includes over 10,000 tagging variants to facilitate imputation and analyses of neurodegenerative disease-related GWAS loci across diverse populations. The NBA can identify low frequency variants and accurately impute over 15 million common variants from the latest release of the TOPMed Imputation Server as of August 2023 (reference of over 300 million variants and 90,000 participants). We envisage this valuable tool will standardize genetic studies in neurological disorders across different ancestral groups, allowing researchers to perform genetic research inclusively and at a global scale.

Proceedings of the 2020 Epilepsy Foundation Pipeline Conference: Emerging Drugs and Devices.

From August 27-28, 2020 the Epilepsy Foundation hosted the Pipeline Conference, exploring emerging issues related to antiepileptic drug and device development. The conference featured epilepsy therapeutic companies and academic laboratories developing drugs for focal epilepsies, innovations for rare and ultra-rare diseases, and devices both in clinical trials and approved for use. In this paper, we outline the virtual presentations by the authors, including novel data from their development pipeline.

Seizure Forecasting: Patient and Caregiver Perspectives.

Accurate seizure forecasting is emerging as a near-term possibility due to recent advancements in machine learning and EEG technology improvements. Large-scale data curation and new data element collection through consumer wearables and digital health tools such as longitudinal seizure diary data has uncovered new possibilities for personalized algorithm development that may be used to predict the likelihood of future seizures. The Epilepsy Foundation recognized the unmet need for development in seizure forecasting following a 2016 survey where an overwhelming majority of respondents across all seizure types and frequencies reported that unpredictability of seizures had the strongest impact on their life while living with or caring for someone living with epilepsy. In early 2021, the Epilepsy Foundation conducted an updated survey among those living with epilepsies and/or their caregivers to better understand the use-cases that best suit the needs of our community as seizure forecast research advances. These results will provide researchers with insight into user-acceptance of using a forecasting tool and incorporation into their daily life. Ultimately, this input from people living with epilepsy and caregivers will provide timely feedback on what the community needs are and ensure researchers and companies first and foremost consider these needs in seizure forecasting tools/product development.

Seizure Diaries and Forecasting With Wearables: Epilepsy Monitoring Outside the Clinic.

It is a major challenge in clinical epilepsy to diagnose and treat a disease characterized by infrequent seizures based on patient or caregiver reports and limited duration clinical testing. The poor reliability of self-reported seizure diaries for many people with epilepsy is well-established, but these records remain necessary in clinical care and therapeutic studies. A number of wearable devices have emerged, which may be capable of detecting seizures, recording seizure data, and alerting caregivers. Developments in non-invasive wearable sensors to measure accelerometry, photoplethysmography (PPG), electrodermal activity (EDA), electromyography (EMG), and other signals outside of the traditional clinical environment may be able to identify seizure-related changes. Non-invasive scalp electroencephalography (EEG) and minimally invasive subscalp EEG may allow direct measurement of seizure activity. However, significant network and computational infrastructure is needed for continuous, secure transmission of data. The large volume of data acquired by these devices necessitates computer-assisted review and detection to reduce the burden on human reviewers. Furthermore, user acceptability of such devices must be a paramount consideration to ensure adherence with long-term device use. Such devices can identify tonic-clonic seizures, but identification of other seizure semiologies with non-EEG wearables is an ongoing challenge. Identification of electrographic seizures with subscalp EEG systems has recently been demonstrated over long (>6 month) durations, and this shows promise for accurate, objective seizure records. While the ability to detect and forecast seizures from ambulatory intracranial EEG is established, invasive devices may not be acceptable for many individuals with epilepsy. Recent studies show promising results for probabilistic forecasts of seizure risk from long-term wearable devices and electronic diaries of self-reported seizures. There may also be predictive value in individuals' symptoms, mood, and cognitive performance. However, seizure forecasting requires perpetual use of a device for monitoring, increasing the importance of the system's acceptability to users. Furthermore, long-term studies with concurrent EEG confirmation are lacking currently. This review describes the current evidence and challenges in the use of minimally and non-invasive devices for long-term epilepsy monitoring, the essential components in remote monitoring systems, and explores the feasibility to detect and forecast impending seizures via long-term use of these systems.

Cycles of self-reported seizure likelihood correspond to yield of diagnostic epilepsy monitoring.

OBJECTIVE: Video-electroencephalography (vEEG) is an important component of epilepsy diagnosis and management. Nevertheless, inpatient vEEG monitoring fails to capture seizures in up to one third of patients. We hypothesized that personalized seizure forecasts could be used to optimize the timing of vEEG. METHODS: We used a database of ambulatory vEEG studies to select a cohort with linked electronic seizure diaries of more than 20 reported seizures over at least 8 weeks. The total cohort included 48 participants. Diary seizure times were used to detect individuals' multiday seizure cycles and estimate times of high seizure risk. We compared whether estimated seizure risk was significantly different between conclusive and inconclusive vEEGs, and between vEEG with and without recorded epileptic activity. vEEGs were conducted prior to self-reported seizures; hence, the study aimed to provide a retrospective proof of concept that cycles of seizure risk were correlated with vEEG outcomes. RESULTS: Estimated seizure risk was significantly higher for conclusive vEEGs and vEEGs with epileptic activity. Across all cycle strengths, the average time in high risk during vEEG was 29.1% compared with 14% for the conclusive/inconclusive groups and 32% compared to 18% for the epileptic activity/no epileptic activity groups. On average, 62.5% of the cohort showed increased time in high risk during their previous vEEG when epileptic activity was recorded (compared to 28% of the cohort where epileptic activity was not recorded). For conclusive vEEGs, 50% of the cohort had increased time in high risk, compared to 21.5% for inconclusive vEEGs. SIGNIFICANCE: Although retrospective, this study provides a proof of principle that scheduling monitoring times based on personalized seizure risk forecasts can improve the yield of vEEG. Forecasts can be developed at low cost from mobile seizure diaries. A simple scheduling tool to improve diagnostic outcomes may reduce cost and risks associated with delayed or missed diagnosis in epilepsy.

230 days of ultra long-term subcutaneous EEG: seizure cycle analysis and comparison to patient diary.

We describe the longest period of subcutaneous EEG (sqEEG) monitoring to date, in a 35-year-old female with refractory epilepsy. Over 230 days, 4791/5520 h of sqEEG were recorded (86%, mean 20.8 [IQR 3.9] hours/day). Using an electronic diary, the patient reported 22 seizures, while automatically-assisted visual sqEEG review detected 32 seizures. There was substantial agreement between days of reported and recorded seizures (Cohen's kappa 0.664), although multiple clustered seizures remained undocumented. Circular statistics identified significant sqEEG seizure cycles at circadian (24-hour) and multidien (5-day) timescales. Electrographic seizure monitoring and analysis of long-term seizure cycles are possible with this neurophysiological tool.

Proceedings of the 15th Antiepileptic Drug and Device Trials Meeting: State of the Science.

On May 22-24, 2019, the 15th Antiepileptic Drug and Device (AEDD) Trials Conference was held, which focused on current issues related to AEDD development from preclinical models to clinical prognostication. The conference featured regulatory agencies, academic laboratories, and healthcare companies involved in emerging epilepsy therapies and research. The program included discussions around funding and support for investigations in epilepsy and neurologic research, clinical trial design and integrated outcome measures for people with epilepsy, and drug development and upcoming disease-modifying therapies. Finally, the conference included updates from the preclinical, clinical, and device pipeline. Summaries of the talks are provided in this paper, with the various pipeline therapeutics in the listed tables to be outlined in a subsequent publication.

Forecasting cycles of seizure likelihood.

OBJECTIVE: Seizure unpredictability is rated as one of the most challenging aspects of living with epilepsy. Seizure likelihood can be influenced by a range of environmental and physiological factors that are difficult to measure and quantify. However, some generalizable patterns have been demonstrated in seizure onset. A majority of people with epilepsy exhibit circadian rhythms in their seizure times, and many also show slower, multiday patterns. Seizure cycles can be measured using a range of recording modalities, including self-reported electronic seizure diaries. This study aimed to develop personalized forecasts from a mobile seizure diary app. METHODS: Forecasts based on circadian and multiday seizure cycles were tested pseudoprospectively using data from 50 app users (mean of 109 seizures per subject). Individuals' strongest cycles were estimated from their reported seizure times and used to derive the likelihood of future seizures. The forecasting approach was validated using self-reported events and electrographic seizures from the Neurovista dataset, an existing database of long-term electroencephalography that has been widely used to develop forecasting algorithms. RESULTS: The validation dataset showed that forecasts of seizure likelihood based on self-reported cycles were predictive of electrographic seizures for approximately half the cohort. Forecasts using only mobile app diaries allowed users to spend an average of 67.1% of their time in a low-risk state, with 14.8% of their time in a high-risk warning state. On average, 69.1% of seizures occurred during high-risk states and 10.5% of seizures occurred in low-risk states. SIGNIFICANCE: Seizure diary apps can provide personalized forecasts of seizure likelihood that are accurate and clinically relevant for electrographic seizures. These results have immediate potential for translation to a prospective seizure forecasting trial using a mobile diary app. It is our hope that seizure forecasting apps will one day give people with epilepsy greater confidence in managing their daily activities.

Patient and caregiver preferences for the potential benefits and risks of a seizure forecasting device: A best-worst scaling.

BACKGROUND: Epilepsy is the 4th most common neurological disorder and is characterized by recurrent, unpredictable seizures. The ability to forecast seizures is a significant unmet need and would have a transformative effect on the lives of people living with epilepsy. In an effort to address this need, the Epilepsy Foundation has committed effort and resources to promote the development of seizure forecasting devices (SFD). OBJECTIVE: To promote user-centered design of future SFD, we sought to quantify patient and caregiver preferences for the potential benefits and risks of SFD. METHODS: A community-centered approach was used to develop a survey incorporating a novel best-worst scaling (BWS) to assess preferences for SFD. A main-effect orthogonal array was used to design and generate 18 "prototypes" that systematically varied across six attributes: seizure forecasting probability, seizure forecasting range, inaccuracy of forecasting, amount of time required to use the device, how the device is worn, and cost. The dependent variable was the attributes that respondents selected the best and worst in each profile, and a choice model was estimated using conditional logistic regression, which was also stratified and compared across patients and caregivers. Respondents also indicated that they would accept each of the prototype SFDs if it were real. These acceptance data and net monetary benefits (relative to the least preferred SFD) were explored. RESULTS: There were 633 eligible respondents; 493 (78%) completed at least one task. Responses indicated that 346 (68%) had epilepsy, and 147 (29%) were primary caregivers or family members of someone with epilepsy. The data show that short forecasting range is the most favored among experimental attributes, followed by mid forecasting range and notification of high chance of seizure. Having the device implanted is the least favorable attribute. Stated preferences differed between patients and caregivers (p < 0.001) for range of forecasting and inaccuracy of device. Caregivers preferred any range of forecasting, regardless of length, more than patients. Patients cared less about inaccuracy of the device compared to caregivers. The groups also differ in impact of fear of having seizures (versus actually having seizures) (p = 0.034) and on device acceptance. The acceptance of devices ranged from 42.3% to 95%, with caregivers being more likely to use a device (p < 0.05) for the majority of device profiles. Acceptance of devices varied with net monetary benefit of the best device being $717.44 more per month relative to the least preferred device. CONCLUSION: Our finding extends previous calls for seizure forecasting devices by demonstrating the value that they might provide to patients and caregivers affected by epilepsy and the feature that might be most and least desirable. In addition to guiding device development, the data can help inform regulatory decisions makers.

Opportunities for epilepsy: Outcomes of the Milken Institute State of the Science Retreat.

The Milken Institute Center for Strategic Philanthropy has launched a Giving Smarter Program in Epilepsy to inform philanthropists on the state of the science for the epilepsy field, key challenges, and solutions to address them. As part of the program, the Milken Institute Center for Strategic Philanthropy hosted a retreat to identify strategic investments that would accelerate epilepsy research to ultimately improve care. The top three prioritized opportunities from the retreat were to 1) invest in data standards and analytical tool development, 2) support young investigators, and 3) promote cross-sector collaborations especially between basic scientists, preclinical researchers, clinicians, and patients.

Seizure Forecasting from Idea to Reality. Outcomes of the My Seizure Gauge Epilepsy Innovation Institute Workshop.

The Epilepsy Innovation Institute (Ei2) is a new research program of the Epilepsy Foundation designed to be an innovation incubator for epilepsy. Ei2 research areas are selected based on community surveys that ask people impacted by epilepsy what they would like researchers to focus on. In their 2016 survey, unpredictability was selected as a top issue regardless of seizure frequency or severity. In response to this need, Ei2 launched the My Seizure Gauge challenge, with the end goal of creating a personalized seizure advisory system device. Prior to moving forward, Ei2 convened a diverse group of stakeholders from people impacted by epilepsy and clinicians, to device developers and data scientists, to basic science researchers and regulators, for a state of the science assessment on seizure forecasting. From the discussions, it was clear that we are at an exciting crossroads. With the advances in bioengineering, we can utilize digital markers, wearables, and biosensors as parameters for a seizure-forecasting algorithm. There are also over a thousand individuals who have been implanted with ambulatory intracranial EEG recording devices. Pairing up peripheral measurements to brain states could identify new relationships and insights. Another key component is the heterogeneity of the relationships indicating that pooling findings across groups is suboptimal, and that data collection will need to be done on longer time scales to allow for individualization of potential seizure-forecasting algorithms.

Identification and modification of amyloid-independent phenotypes of APOE4 mice.

BACKGROUND: Over 70 million Americans inherit the strongest genetic risk factor for Alzheimer's disease (AD), apolipoprotein E4 (APOE4), but have no course for reducing their risk. The association of non-steroidal anti-inflammatory drug (NSAID) use with reduced risk of AD for APOE4-carriers suggests that NSAIDs may be useful in AD prevention. METHODS: We identified phenotypes associated with APOE4 in APOE knock-in mice in order to define modifiable measures that correlate with risk of AD. RESULTS: APOE4 mouse brains showed altered post-translational modifications and biochemical distribution of APOE compared to APOE3 mice; these differences were also observed in brains of human APOE4 carriers. Two-month treatment with ibuprofen significantly altered the expression pattern of APOE in APOE4 mice to that of APOE3 mice; PPAR-γ agonist pioglitazone also had a significant effect. APOE4 mice also show deficits in dendritic spine density, and ibuprofen and pioglitazone significantly increased dendritic spine density. CONCLUSIONS: We report new phenotypes associated with APOE4 in human and APOE knock-in mice and their mitigation with NSAID treatment, through COX-2 inhibition and PPAR-γ activation.

Distinct Functions for Anterograde and Retrograde Sorting of SORLA in Amyloidogenic Processes in the Brain.

SORLA is a neuronal sorting receptor implicated both in sporadic and familial forms of AD. SORLA reduces the amyloidogenic burden by two mechanisms, either by rerouting internalized APP molecules from endosomes to the trans-Golgi network (TGN) to prevent proteolytic processing or by directing newly produced Aß to lysosomes for catabolism. Studies in cell lines suggested that the interaction of SORLA with cytosolic adaptors retromer and GGA is required for receptor sorting to and from the TGN. However, the relevance of anterograde or retrograde trafficking for SORLA activity in vivo remained largely unexplored. Here, we generated mouse models expressing SORLA variants lacking binding sites for GGA or retromer to query this concept in the brain. Disruption of retromer binding resulted in a retrograde-sorting defect with accumulation of SORLA in endosomes and depletion from the TGN, and in an overall enhanced APP processing. In contrast, disruption of the GGA interaction did not impact APP processing but caused increased brain Aß levels, a mechanism attributed to a defect in anterograde lysosomal targeting of Aß. Our findings substantiated the significance of adaptor-mediated sorting for SORLA activities in vivo, and they uncovered that anterograde and retrograde sorting paths may serve discrete receptor functions in amyloidogenic processes. SIGNIFICANCE STATEMENT: SORLA is a sorting receptor that directs target proteins to distinct intracellular compartments in neurons. SORLA has been identified as a genetic risk factor for sporadic, but recently also for familial forms of AD. To confirm the relevance of SORLA sorting for AD processes in the brain, we generated mouse lines, which express trafficking mutants instead of the wild-type form of this receptor. Studying neuronal activities in these mutant mice, we dissected distinct trafficking routes for SORLA guided by two cytosolic adaptors termed GGA and retromer. We show that these sorting pathways serve discrete functions in control of amyloidogenic processes and may represent unique therapeutic targets to interfere with specific aspects of neurodegenerative processes in the diseased brain.

Very low density lipoprotein receptor regulates dendritic spine formation in a RasGRF1/CaMKII dependent manner.

Very Low Density Lipoprotein Receptor (VLDLR) is an apolipoprotein E receptor involved in synaptic plasticity, learning, and memory. However, it is unknown how VLDLR can regulate synaptic and cognitive function. In the present study, we found that VLDLR is present at the synapse both pre- and post-synaptically. Overexpression of VLDLR significantly increases, while knockdown of VLDLR decreases, dendritic spine number in primary hippocampal cultures. Additionally, knockdown of VLDLR significantly decreases synaptophysin puncta number while differentially regulating cell surface and total levels of glutamate receptor subunits. To identify the mechanism by which VLDLR induces these synaptic effects, we investigated whether VLDLR affects dendritic spine formation through the Ras signaling pathway, which is involved in spinogenesis and neurodegeneration. Interestingly, we found that VLDLR interacts with RasGRF1, a Ras effector, and knockdown of RasGRF1 blocks the effect of VLDLR on spinogenesis. Moreover, we found that VLDLR did not rescue the deficits induced by the absence of Ras signaling proteins CaMKIIα or CaMKIIß. Taken together, our results suggest that VLDLR requires RasGRF1/CaMKII to alter dendritic spine formation.

From student to steward: the Interdisciplinary Program in Neuroscience at Georgetown University as a case study in professional development during doctoral training.

A key facet of professional development is the formation of professional identity. At its most basic level, professional identity for a scientist centers on mastery of a discipline and the development of research skills during doctoral training. To develop a broader understanding of professional identity in the context of doctoral training, the Carnegie Initiative on the Doctorate (CID) ran a multi-institutional study from 2001 to 2005. A key outcome of the CID was the development of the concept of 'stewards of the discipline'. The Interdisciplinary Program in Neuroscience (IPN) at Georgetown University participated in CID from 2003 to 2005. Here, we describe the IPN and highlight the programmatic developments resulting from participation in the CID. In particular, we emphasize programmatic activities that are designed to promote professional skills in parallel with scientific development. We describe activities in the domains of leadership, communication, teaching, public outreach, ethics, collaboration, and mentorship. Finally, we provide data that demonstrate that traditional metrics of academic success are not adversely affected by the inclusion of professional development activities in the curricula. By incorporating these seven 'professional development' activities into the required coursework and dissertation research experience, the IPN motivates students to become stewards of the discipline.