Deep Learning for Daily Monitoring of Parkinson's Disease Outside the Clinic Using Wearable Sensors.

Now that wearable sensors have become more commonplace, it is possible to monitor individual healthcare-related activity outside the clinic, unleashing potential for early detection of events in diseases such as Parkinson's disease (PD). However, the unsupervised and "open world" nature of this type of data collection make such applications difficult to develop. In this proof-of-concept study, we used inertial sensor data from Verily Study Watches worn by individuals for up to 23 h per day over several months to distinguish between seven subjects with PD and four without. Since motor-related PD symptoms such as bradykinesia and gait abnormalities typically present when a PD subject is walking, we initially used human activity recognition (HAR) techniques to identify walk-like activity in the unconstrained, unlabeled data. We then used these "walk-like" events to train one-dimensional convolutional neural networks (1D-CNNs) to determine the presence of PD. We report classification accuracies near 90% on single 5-s walk-like events and 100% accuracy when taking the majority vote over single-event classifications that span a duration of one day. Though based on a small cohort, this study shows the feasibility of leveraging unconstrained wearable sensor data to accurately detect the presence or absence of PD.

Big data to smart data in Alzheimer's disease: Real-world examples of advanced modeling and simulation.

Many disease-modifying clinical development programs in Alzheimer's disease (AD) have failed to date, and development of new and advanced preclinical models that generate actionable knowledge is desperately needed. This review reports on computer-based modeling and simulation approach as a powerful tool in AD research. Statistical data-analysis techniques can identify associations between certain data and phenotypes, such as diagnosis or disease progression. Other approaches integrate domain expertise in a formalized mathematical way to understand how specific components of pathology integrate into complex brain networks. Private-public partnerships focused on data sharing, causal inference and pathway-based analysis, crowdsourcing, and mechanism-based quantitative systems modeling represent successful real-world modeling examples with substantial impact on CNS diseases. Similar to other disease indications, successful real-world examples of advanced simulation can generate actionable support of drug discovery and development in AD, illustrating the value that can be generated for different stakeholders.

Big data to smart data in Alzheimer's disease: The brain health modeling initiative to foster actionable knowledge.

Massive investment and technological advances in the collection of extensive and longitudinal information on thousands of Alzheimer patients results in large amounts of data. These "big-data" databases can potentially advance CNS research and drug development. However, although necessary, they are not sufficient, and we posit that they must be matched with analytical methods that go beyond retrospective data-driven associations with various clinical phenotypes. Although these empirically derived associations can generate novel and useful hypotheses, they need to be organically integrated in a quantitative understanding of the pathology that can be actionable for drug discovery and development. We argue that mechanism-based modeling and simulation approaches, where existing domain knowledge is formally integrated using complexity science and quantitative systems pharmacology can be combined with data-driven analytics to generate predictive actionable knowledge for drug discovery programs, target validation, and optimization of clinical development.

Large-scale candidate gene study to identify genetic risk factors predictive of paliperidone treatment response in patients with schizophrenia.

OBJECTIVE: Clinical response to antipsychotic medications can vary markedly in patients with schizophrenia. Identifying genetic variants associated with treatment response could help optimize patient care and outcome. To this end, we carried out a large-scale candidate gene study to identify genetic risk factors predictive of paliperidone efficacy. PATIENTS AND METHODS: A central nervous system custom chip containing single nucleotide polymorphisms from 1204 candidate genes was utilized to genotype a discovery cohort of 684 schizophrenia patients from four clinical studies of paliperidone extended-release and paliperidone palmitate. Variants predictive of paliperidone efficacy were identified and further tested in four independent replication cohorts of schizophrenic patients (N=2856). RESULTS: We identified an SNP in ERBB4 that may contribute toward differential treatment response to paliperidone. The association trended in the same direction as the discovery cohort in two of the four replication cohorts, but ultimately did not survive multiple testing corrections. The association was not replicated in the other two independent cohorts. We also report several SNPs in well-known schizophrenia candidate genes that show suggestive associations with paliperidone efficacy. CONCLUSION: These preliminary findings suggest that genetic variation in the ERBB4 gene may differentially affect treatment response to paliperidone in individuals with schizophrenia. They implicate the neuregulin 1 (NRG1)-ErbB4 pathway for modulating antipsychotic response. However, these findings were not robustly reproduced in replication cohorts.

2014 Report on the Milestones for the US National Plan to Address Alzheimer's Disease.

With increasing numbers of people with Alzheimer's and other dementias across the globe, many countries have developed national plans to deal with the resulting challenges. In the United States, the National Alzheimer's Project Act, signed into law in 2011, required the creation of such a plan with annual updates thereafter. Pursuant to this, the US Department of Health and Human Services (HHS) released the National Plan to Address Alzheimer's Disease in 2012, including an ambitious research goal of preventing and effectively treating Alzheimer's disease by 2025. To guide investments, activities, and the measurement of progress toward achieving this 2025 goal, in its first annual plan update (2013) HHS also incorporated into the plan a set of short, medium and long-term milestones. HHS further committed to updating these milestones on an ongoing basis to account for progress and setbacks, and emerging opportunities and obstacles. To assist HHS as it updates these milestones, the Alzheimer's Association convened a National Plan Milestone Workgroup consisting of scientific experts representing all areas of Alzheimer's and dementia research. The workgroup evaluated each milestone and made recommendations to ensure that they collectively constitute an adequate work plan for reaching the goal of preventing and effectively treating Alzheimer's by 2025. This report presents these Workgroup recommendations.

Normative Neuroimaging Library: Designing a Comprehensive and Demographically Diverse Dataset of Healthy Controls to Support Traumatic Brain Injury Diagnostic and Therapeutic Development.

The past decade has seen impressive advances in neuroimaging, moving from qualitative to quantitative outputs. Available techniques now allow for the inference of microscopic changes occurring in white and gray matter, along with alterations in physiology and function. These existing and emerging techniques hold the potential of providing unprecedented capabilities in achieving a diagnosis and predicting outcomes for traumatic brain injury (TBI) and a variety of other neurological diseases. To see this promise move from the research lab into clinical care, an understanding is needed of what normal data look like for all age ranges, sex, and other demographic and socioeconomic categories. Clinicians can only use the results of imaging scans to support their decision-making if they know how the results for their patient compare with a normative standard. This potential for utilizing magnetic resonance imaging (MRI) in TBI diagnosis motivated the American College of Radiology and Cohen Veterans Bioscience to create a reference database of healthy individuals with neuroimaging, demographic data, and characterization of psychological functioning and neurocognitive data that will serve as a normative resource for clinicians and researchers for development of diagnostics and therapeutics for TBI and other brain disorders. The goal of this article is to introduce the large, well-curated Normative Neuroimaging Library (NNL) to the research community. NNL consists of data collected from ∼1900 healthy participants. The highlights of NNL are (1) data are collected across a diverse population, including civilians, veterans, and active-duty service members with an age range (18-64 years) not well represented in existing datasets; (2) comprehensive structural and functional neuroimaging acquisition with state-of-the-art sequences (including structural, diffusion, and functional MRI; raw scanner data are preserved, allowing higher quality data to be derived in the future; standardized imaging acquisition protocols across sites reflect sequences and parameters often recommended for use with various neurological and psychiatric conditions, including TBI, post-traumatic stress disorder, stroke, neurodegenerative disorders, and neoplastic disease); and (3) the collection of comprehensive demographic details, medical history, and a broad structured clinical assessment, including cognition and psychological scales, relevant to multiple neurological conditions with functional sequelae. Thus, NNL provides a demographically diverse population of healthy individuals who can serve as a comparison group for brain injury study and clinical samples, providing a strong foundation for precision medicine. Use cases include the creation of imaging-derived phenotypes (IDPs), derivation of reference ranges of imaging measures, and use of IDPs as training samples for artificial intelligence-based biomarker development and for normative modeling to help identify injury-induced changes as outliers for precision diagnosis and targeted therapeutic development. On its release, NNL is poised to support the use of advanced imaging in clinician decision support tools, the validation of imaging biomarkers, and the investigation of brain-behavior anomalies, moving the field toward precision medicine.

Machine learning within the Parkinson's progression markers initiative: Review of the current state of affairs.

The Parkinson's Progression Markers Initiative (PPMI) has collected more than a decade's worth of longitudinal and multi-modal data from patients, healthy controls, and at-risk individuals, including imaging, clinical, cognitive, and 'omics' biospecimens. Such a rich dataset presents unprecedented opportunities for biomarker discovery, patient subtyping, and prognostic prediction, but it also poses challenges that may require the development of novel methodological approaches to solve. In this review, we provide an overview of the application of machine learning methods to analyzing data from the PPMI cohort. We find that there is significant variability in the types of data, models, and validation procedures used across studies, and that much of what makes the PPMI data set unique (multi-modal and longitudinal observations) remains underutilized in most machine learning studies. We review each of these dimensions in detail and provide recommendations for future machine learning work using data from the PPMI cohort.

Models and methods: a perspective of the impact of six IMI translational data-centric initiatives for Alzheimer's disease and other neuropsychiatric disorders.

The Innovative Medicines Initiative (IMI), was a European public-private partnership (PPP) undertaking intended to improve the drug development process, facilitate biomarker development, accelerate clinical trial timelines, improve success rates, and generally increase the competitiveness of European pharmaceutical sector research. Through the IMI, pharmaceutical research interests and the research agenda of the EU are supported by academic partnership and financed by both the pharmaceutical companies and public funds. Since its inception, the IMI has funded dozens of research partnerships focused on solving the core problems that have consistently obstructed the translation of research into clinical success. In this post-mortem review paper, we focus on six research initiatives that tackled foundational challenges of this nature: Aetionomy, EMIF, EPAD, EQIPD, eTRIKS, and PRISM. Several of these initiatives focused on neurodegenerative diseases; we therefore discuss the state of neurodegenerative research both at the start of the IMI and now, and the contributions that IMI partnerships made to progress in the field. Many of the initiatives we review had goals including, but not limited to, the establishment of translational, data-centric initiatives and the implementation of trans-diagnostic approaches that move beyond the candidate disease approach to assess symptom etiology without bias, challenging the construct of disease diagnosis. We discuss the successes of these initiatives, the challenges faced, and the merits and shortcomings of the IMI approach with participating senior scientists for each. Here, we distill their perspectives on the lessons learned, with an aim to positively impact funding policy and approaches in the future.

Correction: Chaby et al. Cross-Platform Evaluation of Commercially Targeted and Untargeted Metabolomics Approaches to Optimize the Investigation of Psychiatric Disease. Metabolites 2021, 11, 609.

In the original publication [...].

Global synergistic actions to improve brain health for human development.

The global burden of neurological disorders is substantial and increasing, especially in low-resource settings. The current increased global interest in brain health and its impact on population wellbeing and economic growth, highlighted in the World Health Organization's new Intersectoral Global Action Plan on Epilepsy and other Neurological Disorders 2022-2031, presents an opportunity to rethink the delivery of neurological services. In this Perspective, we highlight the global burden of neurological disorders and propose pragmatic solutions to enhance neurological health, with an emphasis on building global synergies and fostering a 'neurological revolution' across four key pillars - surveillance, prevention, acute care and rehabilitation - termed the neurological quadrangle. Innovative strategies for achieving this transformation include the recognition and promotion of holistic, spiritual and planetary health. These strategies can be deployed through co-design and co-implementation to create equitable and inclusive access to services for the promotion, protection and recovery of neurological health in all human populations across the life course.

A Framework to Advance Biomarker Development in the Diagnosis, Outcome Prediction, and Treatment of Traumatic Brain Injury.

Multi-modal biomarkers (e.g., imaging, blood-based, physiological) of unique traumatic brain injury (TBI) endophenotypes are necessary to guide the development of personalized and targeted therapies for TBI. Optimal biomarkers will be specific, sensitive, rapidly and easily accessed, minimally invasive, cost effective, and bidirectionally translatable for clinical and research use. For both uses, understanding how TBI biomarkers change over time is critical to reliably identify appropriate time windows for an intervention as the injury evolves. Biomarkers that enable researchers and clinicians to identify cellular injury and monitor clinical improvement, inflection, arrest, or deterioration in a patient's clinical trajectory are needed for precision healthcare. Prognostic biomarkers that reliably predict outcomes and recovery windows to assess neurodegenerative change and guide decisions for return to play or duty are also important. TBI biomarkers that fill these needs will transform clinical practice and could reduce the patient's risk for long-term symptoms and lasting deficits. This article summarizes biomarkers currently under investigation and outlines necessary steps to achieve short- and long-term goals, including how biomarkers can advance TBI treatment and improve care for patients with TBI.

Cross-Platform Evaluation of Commercially Targeted and Untargeted Metabolomics Approaches to Optimize the Investigation of Psychiatric Disease.

Metabolomics methods often encounter trade-offs between quantification accuracy and coverage, with truly comprehensive coverage only attainable through a multitude of complementary assays. Due to the lack of standardization and the variety of metabolomics assays, it is difficult to integrate datasets across studies or assays. To inform metabolomics platform selection, with a focus on posttraumatic stress disorder (PTSD), we review platform use and sample sizes in psychiatric metabolomics studies and then evaluate five prominent metabolomics platforms for coverage and performance, including intra-/inter-assay precision, accuracy, and linearity. We found performance was variable between metabolite classes, but comparable across targeted and untargeted approaches. Within all platforms, precision and accuracy were highly variable across classes, ranging from 0.9-63.2% (coefficient of variation) and 0.6-99.1% for accuracy to reference plasma. Several classes had high inter-assay variance, potentially impeding dissociation of a biological signal, including glycerophospholipids, organooxygen compounds, and fatty acids. Coverage was platform-specific and ranged from 16-70% of PTSD-associated metabolites. Non-overlapping coverage is challenging; however, benefits of applying multiple metabolomics technologies must be weighed against cost, biospecimen availability, platform-specific normative levels, and challenges in merging datasets. Our findings and open-access cross-platform dataset can inform platform selection and dataset integration based on platform-specific coverage breadth/overlap and metabolite-specific performance.

Systematic Review and Methodological Considerations for the Use of Single Prolonged Stress and Fear Extinction Retention in Rodents.

Posttraumatic stress disorder (PTSD) is a mental health condition triggered by experiencing or witnessing a terrifying event that can lead to lifelong burden that increases mortality and adverse health outcomes. Yet, no new treatments have reached the market in two decades. Thus, screening potential interventions for PTSD is of high priority. Animal models often serve as a critical translational tool to bring new therapeutics from bench to bedside. However, the lack of concordance of some human clinical trial outcomes with preclinical animal efficacy findings has led to a questioning of the methods of how animal studies are conducted and translational validity established. Thus, we conducted a systematic review to determine methodological variability in studies that applied a prominent animal model of trauma-like stress, single prolonged stress (SPS). The SPS model has been utilized to evaluate a myriad of PTSD-relevant outcomes including extinction retention. Rodents exposed to SPS express an extinction retention deficit, a phenotype identified in humans with PTSD, in which fear memory is aberrantly retained after fear memory extinction. The current systematic review examines methodological variation across all phases of the SPS paradigm, as well as strategies for behavioral coding, data processing, statistical approach, and the depiction of data. Solutions for key challenges and sources of variation within these domains are discussed. In response to methodological variation in SPS studies, an expert panel was convened to generate methodological considerations to guide researchers in the application of SPS and the evaluation of extinction retention as a test for a PTSD-like phenotype. Many of these guidelines are applicable to all rodent paradigms developed to model trauma effects or learned fear processes relevant to PTSD, and not limited to SPS. Efforts toward optimizing preclinical model application are essential for enhancing the reproducibility and translational validity of preclinical findings, and should be conducted for all preclinical psychiatric research models.

Carisbamate as adjunctive treatment of partial onset seizures in adults in two randomized, placebo-controlled trials.

PURPOSE: To assess the efficacy, safety, and tolerability of the investigational drug carisbamate as adjunctive treatment for partial-onset seizures (POS). METHODS: Two identical, randomized, placebo-controlled, double-blind studies were conducted in adults with POS uncontrolled for >or=1 year. Therapy-refractory epilepsy patients (>or=16 years) remained on stable doses of prescribed antiepileptic drugs (or=50% reduction in POS frequency) during the double-blind phase compared with the prospective baseline phase. RESULTS: Of the 565 patients randomized in study 1, 93% completed the study; of the 562 randomized in study 2, 94% completed the study. Patient characteristics were similar across both studies and treatment arms: mean age, 35 years (study 1, range 16-75 years) and 36 years (study 2, range 16-74 years); approximately 50% were men. Treatment with carisbamate 400 mg/day resulted in significant improvement (p < 0.01) in both efficacy measures compared with placebo in study 1 but not in study 2. Carisbamate 200 mg/day did not differ statistically from placebo in either study. Among the most common treatment-emergent adverse events (>or=5% in any group), those with an incidence exceeding placebo (>or=3%) were dizziness (400 mg/day group) and somnolence. CONCLUSIONS: Carisbamate 400 mg/day was effective in patients with refractory partial-onset seizures in one of these global studies. More than 200 mg/day of carisbamate is required for efficacy. Carisbamate was well-tolerated in both studies.

Cross-platform comparison of highly sensitive immunoassay technologies for cytokine markers: Platform performance in post-traumatic stress disorder and Parkinson's disease.

There is mounting evidence of systemic inflammation in post-traumatic stress disorder (PTSD) and Parkinson's disease (PD), yet inconsistency and a lack of replicability in findings of putative biological markers have delayed progress in this space. Variability in performance between platforms may contribute to the lack of consensus in the biomarker literature, as has been seen for a number of psychiatric disorders, including PTSD. Thus, there is a need for high-performance, scalable, and validated platforms for the discovery and development of biomarkers of inflammation for use in drug development and as clinical diagnostics. To identify the best platform for use in future biomarker discovery efforts, we conducted a comprehensive cross-platform and cross-assay evaluation across five leading platform technologies. This initial assessment focused on four cytokines that have been implicated PTSD - interleukin (IL)-1ß, IL-6, tumor necrosis factor (TNF)-α, and interferon (IFN)-γ. To assess platform performance and understand likely measurements in individuals with brain disorders, serum and plasma samples were obtained from individuals with PTSD (n = 13) or Parkinson's Disease (n = 14) as well as healthy controls (n = 5). We compared platform performance across a number of common analytic parameters, including assay precision, sensitivity, frequency of endogenous analyte detection (FEAD), correlation between platforms, and parallelism in measurement of cytokines using a serial dilution series. The single molecule array (Simoa™) ultra-sensitive platform (Quanterix), MESO V-Plex (Mesoscale Discovery), and Luminex xMAP® (Myriad) were conducted by their respective vendors, while Luminex® and Quantikine® high-sensitivity ELISA assays were evaluated by R&D System's Biomarker Testing Services. The assay with the highest sensitivity in detecting endogenous analytes across all analytes and clinical populations (i.e. the highest FEAD), was the Simoa™ platform. In contrast, more variable performance was observed for MESO V-plex, R&D Luminex® and Quantikine®, while Myriad's Luminex xMAP® exhibited low FEAD across all analytes and samples. Simoa™ also demonstrated high precision in detecting endogenous cytokines, as reflected in < 20 percent coefficient of variance (%CV) across replicate runs for samples from the healthy controls, PTSD patients, and PD patients. In contrast, MESO V-Plex, R&D Luminex® and Quantikine® had variable performance in terms of precision across cytokines. Myriad Luminex xMAP® could not be included in precision estimates because the vendor did not run samples in duplicate. For cross-platform performance comparisons, the highest cross-platform correlations were observed for IL-6 such that all platforms - except for Myriad's Luminex xMAP® - had strong correlations with one another in measurements of IL-6 (r range = 0.59 - 0.86). For the other cytokines, there was low to no correlation across platforms, such that reported measurements of IL-1ß, TNF-α, and IFN-γ varied across assays. Taken together, these findings provide novel evidence that the choice of immunoassay could greatly impact reported cytokine findings. The current study provides crucial information on the variability in performance between platforms and across immunoassays that may help inform the selection of assay in future research studies. Further, the results emphasize the need for performing comparative evaluations of immunoassays as new technologies emerge over time, particularly given the lack of reference standards for the quantitative assessments of cytokines.

Genomic influences on self-reported childhood maltreatment.

Childhood maltreatment is highly prevalent and serves as a risk factor for mental and physical disorders. Self-reported childhood maltreatment appears heritable, but the specific genetic influences on this phenotype are largely unknown. The aims of this study were to (1) identify genetic variation associated with self-reported childhood maltreatment, (2) estimate SNP-based heritability (h2snp), (3) assess predictive value of polygenic risk scores (PRS) for childhood maltreatment, and (4) quantify genetic overlap of childhood maltreatment with mental and physical health-related phenotypes, and condition the top hits from our analyses when such overlap is present. Genome-wide association analysis for childhood maltreatment was undertaken, using a discovery sample from the UK Biobank (UKBB) (n = 124,000) and a replication sample from the Psychiatric Genomics Consortium-posttraumatic stress disorder group (PGC-PTSD) (n = 26,290). h2snp for childhood maltreatment and genetic correlations with mental/physical health traits were calculated using linkage disequilibrium score regression. PRS was calculated using PRSice and mtCOJO was used to perform conditional analysis. Two genome-wide significant loci associated with childhood maltreatment (rs142346759, p = 4.35 × 10-8, FOXP1; rs10262462, p = 3.24 × 10-8, FOXP2) were identified in the discovery dataset but were not replicated in PGC-PTSD. h2snp for childhood maltreatment was ~6% and the PRS derived from the UKBB was significantly predictive of childhood maltreatment in PGC-PTSD (r2 = 0.0025; p = 1.8 × 10-15). The most significant genetic correlation of childhood maltreatment was with depressive symptoms (rg = 0.70, p = 4.65 × 10-40), although we show evidence that our top hits may be specific to childhood maltreatment. This is the first large-scale genetic study to identify specific variants associated with self-reported childhood maltreatment. Speculatively, FOXP genes might influence externalizing traits and so be relevant to childhood maltreatment. Alternatively, these variants may be associated with a greater likelihood of reporting maltreatment. A clearer understanding of the genetic relationships of childhood maltreatment, including particular abuse subtypes, with a range of phenotypes, may ultimately be useful in in developing targeted treatment and prevention strategies.

Efficacy, safety and tolerability of two dosing regimens in adolescent schizophrenia: double-blind study.

BACKGROUND: Effective treatments for adolescent schizophrenia are needed. AIMS: To compare efficacy and safety of two dosing regimens of risperidone. METHOD: Double-blind, 8-week study. Patients, 13-17 years, with an acute episode of schizophrenia, randomised 1:1 to risperidone 1.5-6.0 mg/day (regimen A; n=125) or 0.15-0.6 mg/day (regimen B; n=132). TRIAL REGISTRATION NUMBER: NCT00034749. RESULTS: Mean total Positive and Negative Syndrome Scale (PANSS) score improved significantly (P<0.001; effect size=0.49) from baseline to end-point for regimen A (mean=96.4 (s.d.=15.39) to mean=72.8 (s.d.=22.52)) compared with regimen B (mean=93.3 (s.d.=14.14) to mean=80.8 (s.d.=24.33)). Treatment-emergent adverse events occurred in 74% (regimen A) and 65% (regimen B) of patients; 4% of patients overall discontinued for adverse events. Mean change in body weight was 3.2 kg (s.d.=3.49) for regimen A and 1.7 kg (s.d.=3.29) for regimen B. CONCLUSIONS: Adolescent patients in the regimen A group showed greater improvement in total PANSS compared with the regimen B group. Treatment was well tolerated.

Risperidone for the treatment of acute mania in children and adolescents with bipolar disorder: a randomized, double-blind, placebo-controlled study.

OBJECTIVES: To evaluate the efficacy, safety, and tolerability of risperidone monotherapy for the treatment of an acute mixed or manic episode in children and adolescents with bipolar I disorder. METHODS: This randomized, placebo-controlled, double-blind, 3-arm study (N = 169) included children and adolescents (ages 10-17 years) with a DSM-IV diagnosis of bipolar I disorder, experiencing a manic or mixed episode. Study participants were randomized to placebo (n = 58), risperidone 0.5-2.5 mg/day (n = 50), or risperidone 3-6 mg/day (n = 61) for 3 weeks. The primary efficacy measure was change in Young Mania Rating Scale (YMRS) total score from baseline to end point. Safety assessments included adverse event (AE) monitoring and scores on extrapyramidal symptom rating scales. RESULTS: Improvement in mean YMRS total score was significantly greater in risperidone-treated subjects than in placebo-treated subjects [mean change (SD) -9.1 (11.0) for placebo; -18.5 (9.7) for risperidone 0.5-2.5 mg (p < 0.001); -16.5 (10.3) for risperidone 3-6 mg (p < 0.001)]. The most common risperidone-associated AEs were somnolence, headache, and fatigue. Mean (SD) weight gain was 0.7 (1.9) kg, 1.9 (1.7) kg, and 1.4 (2.4) kg in the placebo, risperidone 0.5-2.5 mg, and risperidone 3-6 mg groups, respectively, during this 3-week study. CONCLUSIONS: At daily doses of 0.5-2.5 mg and 3-6 mg, risperidone was effective and well tolerated in children and adolescents experiencing acute manic or mixed episodes of bipolar I disorder. Results indicate that risperidone 0.5-2.5 mg has a better benefit-risk profile than risperidone 3-6 mg.

Evaluation of carisbamate for the treatment of migraine in a randomized, double-blind trial.

OBJECTIVE: This study explored the dose-response relationship of carisbamate administered at doses of 100 mg per day, 300 mg per day, or 600 mg per day, in the prevention of migraine. BACKGROUND: Carisbamate ([S]-2-O-carbamoyl-1-o-chlorophenyl-ethanol; RWJ 333369) is a new chemical entity being studied for efficacy as adjunctive therapy in partial onset epilepsy. Because some antiepileptic drugs are also efficacious in migraine, for example, topiramate and valproate sodium, we tested carisbamate in migraine prophylaxis. DESIGN/METHODS: This was a double-blind, placebo-controlled trial, approximately 22-week duration. The primary efficacy variable was the percent reduction from baseline through the double-blind phase in average monthly migraine frequency using a 48-hour rule. Patients were randomized 1 : 1 : 1 : 1 to treatment with carisbamate 100, 300, or 600 mg per day, or placebo. Migraine attacks were counted during a prospective 4-week baseline period, which was followed by a 2-week titration period, a 12-week maintenance period, a 1-week medication reduction period, and a 3-week observation period. Patients had an established history of migraine, with or without aura, for at least 1 year and a 3-month history of 3-12 migraine attacks per month. RESULTS: Patients (n = 323) were predominantly women (85%) and white (89%); mean age was 41 years. There were no statistically significant differences between any of the carisbamate groups and placebo (P > or = .6) for the median (range) percentage reduction from baseline to end point in average monthly migraine frequency (P value vs placebo): 37% (-250%, 100%) for placebo; 33% (-210%, 100%; P = .7) CRS 100 mg/day; 27% (-100%, 100%; P = .8) CRS 300 mg/day; and 35% (-87%, 100%; P = .6) CRS 600 mg/day. Results for secondary efficacy measures (responder rate, percent reduction in average monthly migraine frequency using the 24-hour rule, and percent reduction in average monthly migraine days) were consistent (P > or = .075). The proportion of patients discontinuing because of adverse events was similar for placebo and carisbamate-treated patients (13% each). The most common (occurring in > or =5% of patients) treatment-emergent adverse events in patients treated with carisbamate were fatigue (17%) and nasopharyngitis (13%). Fatigue appeared to be dose related. CONCLUSIONS: Carisbamate was not more efficacious in migraine prophylaxis than placebo in this well-controlled study that included a suitable population. However, carisbamate monotherapy was well tolerated at doses up to 600 mg per day.

PTSD Biomarker Database: deep dive metadatabase for PTSD biomarkers, visualizations and analysis tools.

The PTSD Biomarker Database (PTSDDB) is a database that provides a landscape view of physiological markers being studied as putative biomarkers in the current post-traumatic stress disorder (PTSD) literature to enable researchers to explore and compare findings quickly. The PTSDDB currently contains over 900 biomarkers and their relevant information from 109 original articles published from 1997 to 2017. Further, the curated content stored in this database is complemented by a web application consisting of multiple interactive visualizations that enable the investigation of biomarker knowledge in PTSD (e.g. clinical study metadata, biomarker findings, experimental methods, etc.) by compiling results from biomarker studies to visualize the level of evidence for single biomarkers and across functional categories. This resource is the first attempt, to the best of our knowledge, to capture and organize biomarker and metadata in the area of PTSD for storage in a comprehensive database that may, in turn, facilitate future analysis and research in the field.